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+<li class="toctree-l2"><a class="reference internal" href="../../../content/01-python_v_stata/install.html">Installation and General Usage</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../content/01-python_v_stata/install.html">Installation and General Usage</a></li>
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diff --git a/_build/html/_sources/content/01-python_v_stata/differences.md b/_build/html/_sources/content/01-python_v_stata/differences.md
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@@ -8,7 +8,7 @@ Python is a versatile, general-purpose programming language that can be used for
 
 ## Syntax
 
-Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results.
+Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results. See the [Syntax subchapter](syntax.ipynb) for more information.
 
 ## Packages/Libraries
 
diff --git a/_build/html/_sources/content/01-python_v_stata/history.md b/_build/html/_sources/content/01-python_v_stata/history.md
index 11a6ad0..61bb507 100644
--- a/_build/html/_sources/content/01-python_v_stata/history.md
+++ b/_build/html/_sources/content/01-python_v_stata/history.md
@@ -1,25 +1,15 @@
 # History of the Languages
 
-This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be.
+Both Python and Stata have become essential tools in various domains. Python's general-purpose nature makes it versatile across many fields, and Stata's specialization in statistical analysis makes it a go-to choice for researchers and analysts in specific disciplines. This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be. 
 
-###### <span style="color:red"> To be updated </span>
+## History of Python
 
-<!-- From ChatGPT
+Python is a high-level, interpreted programming language that was created by Guido van Rossum in 1991 ([GeeksforGeeks](https://www.geeksforgeeks.org/history-of-python/)). Guido van Rossum aimed to create a language that emphasized code readability and ease of use, while still being a general purpose programming language that can be used for a wide variety of tasks.
 
-## History of Python:
+Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers building packages contributed to its success. Today, Python is one of the most widely used programming languages, being used for a wide variety of tasks.
 
-Python is a high-level, interpreted programming language that was created by Guido van Rossum. The language's development began in the late 1980s, and the first official Python release, version 0.9.0, came out in February 1991. Guido van Rossum aimed to create a language that emphasized code readability and ease of use, inspired by the ABC language.
+## History of Stata
 
-The name "Python" was chosen as a tribute to the British comedy group Monty Python. Over the years, Python evolved, with significant releases such as Python 2.0 in 2000 and Python 3.0 in 2008. Python 3 introduced backward-incompatible changes to enhance the language's consistency and eliminate redundancy.
+Stata is a statistical program designed for data analysis, manipulation, and visualization. It was written and first released in 1985 by William Gould and Sean Becketti. Stata then grew over time to include more and more forms of statistical analyses. A key feature was introduced in Stata 1.3, which allowed users to write their own programs for statistical analyses.
 
-Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers contributed to its success.
-
-## History of Stata:
-
-Stata is a statistical software package designed for data analysis, manipulation, and visualization. It was developed by StataCorp, founded by William Gould, James Hardin, and Cleve Moler. Stata's development began in the early 1980s, with the first version, Stata 1.0, released in 1985.
-
-Stata was initially focused on providing tools for econometric and statistical analysis. It gained popularity among researchers, economists, and social scientists for its powerful data management capabilities and robust statistical features.
-
-Over the years, StataCorp released several versions of the software, adding new features and improving existing ones. Stata's scripting language allows users to automate tasks and create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
-
-Both Python and Stata have become essential tools in their respective domains, with Python's general-purpose nature making it versatile across many fields, and Stata's specialization in statistical analysis making it a go-to choice for researchers and analysts in specific disciplines. -->
\ No newline at end of file
+Today, Stata's scripting language allows users to automate tasks and easily create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
diff --git a/_build/html/_sources/content/01-python_v_stata/index.md b/_build/html/_sources/content/01-python_v_stata/index.md
index be74467..96c18ea 100644
--- a/_build/html/_sources/content/01-python_v_stata/index.md
+++ b/_build/html/_sources/content/01-python_v_stata/index.md
@@ -1,5 +1,7 @@
 # Python vs Stata
 
-This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. 
+Python and Stata are two of the most popular languages used for economics research ([source](https://cepr.org/voxeu/columns/which-programming-language-best-economic-research-julia-matlab-python-or-r)). However, there are several key differences between them. Stata is a highly specialized language intended for mainly statistical analysis, while Python is a general-purpose programming language.
+
+This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. We hope that this chapter will help you determine which programming language is right for you. However, as the remainder of this textbook is written entirely in Python, the final subchapter (`Syntax`) will aim to help experienced Stata users become more familiar with Python.
 
 <!-- For more information, please look at [Python's official page](https://www.python.org/) and [Stata's official page](https://www.stata.com/) respectively. -->
diff --git a/_build/html/_sources/content/01-python_v_stata/pedagogy.ipynb b/_build/html/_sources/content/01-python_v_stata/pedagogy.ipynb
new file mode 100644
index 0000000..46995e5
--- /dev/null
+++ b/_build/html/_sources/content/01-python_v_stata/pedagogy.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c41e3963-887e-423e-8e3a-49d49122a0cd",
+   "metadata": {},
+   "source": [
+    "# Economics Pedagogy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3069aff8-c35a-42b7-a08c-8f8e0a45ea25",
+   "metadata": {},
+   "source": [
+    "Both Python and Stata have been used to teach economics to students of all levels, from the beginner to the graduate students. They both have their advantages and disadvantages, some of which are discussed below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2017f83-5353-401d-8da3-2d6bae086953",
+   "metadata": {},
+   "source": [
+    "## Learning Curve\n",
+    "\n",
+    "Students who have little to no experience in coding often find Stata's command-line interface easier to install and use. However, students with more experience in coding may be more familiar with Python and thus find that easier to use. At the end of the day, different students could find either of the softwares easier to learn, and the software you choose to use should consider the level of the students in your class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5590927b-1191-4a2a-9c6a-c2fe15c85e11",
+   "metadata": {},
+   "source": [
+    "## Accessibility\n",
+    "\n",
+    "Both Python and Jupyter notebooks are free and easily accessible, while Stata must be purchased. If your students do not have an instituional license for Stata, it may be more inclusive to use Python to avoid placing an undue financial burden on students. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03140e78-8b1a-4e9e-bdd7-2bc2d1ff07d0",
+   "metadata": {},
+   "source": [
+    "## Cloud Computing\n",
+    "\n",
+    "With the great integration between Jupyter Notebooks and Cloud Computing provided by tools such as [Google Colab](https://colab.research.google.com/), it is possible to easily share your Jupyter notebooks with all your students and have them run your code in real time. To share your Stata `.do` files with students, it may be best to upload them to a Google Drive folder and have your students download and run them locally. Students may then run into issues if they haven't locally installed all the packages/updated all computer paths in your code. These issues are avoided if you use a virtual environment where everyone has access to the same resources, such as Google Colab (more details can be found on Colab's [welcome page](https://colab.research.google.com/#scrollTo=GJBs_flRovLc))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ca554f-7f28-407b-81c9-62ed88a8488a",
+   "metadata": {},
+   "source": [
+    "## Autograding\n",
+    "\n",
+    "In both languages, you can view and grade the final output of student code. However, Stata `.do` files do not have any features to automatically grade the intermediate steps student take towards a solution. Python has a package called [Otter Grader](https://otter-grader.readthedocs.io/en/latest/), designed to help you grade the output of various code cells in Jupyter notebooks. Using Otter Grader, you can create various test cells that have their corresponding solutions and relevant tests. The package provides tests that are both visible to and hidden from students, enabling you to both guide students on the right track and check for correctness as necessary. \n",
+    "\n",
+    "Several UC Berkeley classes, including [Econ 148: Data Science for Economists](https://www.econ148.org), use Otter Grader along with [Gradescope](https://www.gradescope.com/) to automatically grade thousands of student submissions at once. However, you do not need a Gradescope license to use Otter Grader, Otter is a free package that can be used by anyone without any cost. Please look at the [Otter tutorial](https://otter-grader.readthedocs.io/en/latest/tutorial.html) for more information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b4a72a-aeef-42dd-b8c8-12af667ea0fe",
+   "metadata": {},
+   "source": [
+    "## Textbooks\n",
+    "\n",
+    "While both Python and Stata have economics-specific textbooks, their styles and formats may differ. For Stata, the books are often traditional print books, such as [_An Introduction to Modern Econometrics Using Stata_](https://www.stata.com/bookstore/modern-econometrics-stata/). Students may be required to purchase a physical or digital copy of these textbooks in order to use them.\n",
+    "\n",
+    "Meanwhile, Python textbooks may be found online for free. For example, consider the online textbooks [_Coding for Economists_](https://aeturrell.github.io/coding-for-economists/intro.html), [_Causal Inference for The Brave and True_](https://matheusfacure.github.io/python-causality-handbook/landing-page.html), or [_Economic Networks_](https://networks.quantecon.org/). These textbooks may often include interactive code snippets that the students can try running on their own, and corresponding githubs where the students can see how the textbook was written. Such open access to the material offers greater reproducibility, an advantage over Stata."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_build/html/_sources/content/01-python_v_stata/syntax.ipynb b/_build/html/_sources/content/01-python_v_stata/syntax.ipynb
new file mode 100644
index 0000000..629b4fe
--- /dev/null
+++ b/_build/html/_sources/content/01-python_v_stata/syntax.ipynb
@@ -0,0 +1,2020 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3dfa8749-c9af-4218-8367-96918f529dc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "474395b2-eedd-4588-aabf-10cf6a314ad2",
+   "metadata": {},
+   "source": [
+    "# Syntax\n",
+    "\n",
+    "The online textbook [Coding for Economists](https://aeturrell.github.io/coding-for-economists/intro.html) has a great [section summarizing the differences in syntax](https://aeturrell.github.io/coding-for-economists/coming-from-stata.html) between Stata and Python. This chapter does not aim to replace the key material included there. Instead, I hope it can serve as a supplement for those transitioning from Stata to Python.\n",
+    "\n",
+    "This chapter will aim to describe [`stata2python`](https://pypi.org/project/stata2python/), a Python package where you can write Stata commands and recieve the equivalent code for them in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f31b176c-1936-43d8-9a56-ebbf2884e7ca",
+   "metadata": {},
+   "source": [
+    "## stata2python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a5f7c3-ac2d-4560-9d7e-323870c8efeb",
+   "metadata": {},
+   "source": [
+    "### Installation and Import\n",
+    "\n",
+    "To install `stata2python`, you can simply use the package installer [`pip`](https://pypi.org/project/pip/). An example is shown below. To do so, simply run the following command in your terminal.\n",
+    "\n",
+    "    pip install --upgrade stata2python\n",
+    "    \n",
+    "After you have installed the package, you can simply import it into your Jupyter notebook. Alternatively, you can also access a [Python shell](https://www.tutorialsteacher.com/python/python-interective-shell) ( by typing `python3` in your terminal) and begin using it. To import the package in your notebook/shell, the following syntax is encouraged.\n",
+    "\n",
+    "    from stata2python import stata2python\n",
+    "   \n",
+    "We import the package below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9845b073-c30b-41d9-95bd-53ba694474d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stata2python import stata2python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "653ded29-8942-4b92-bb9c-996aca1f077c",
+   "metadata": {},
+   "source": [
+    "We also import in sample NBA, (log) wage and pollution datasets that will be helpful for demonstrating the features of our package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2929442c-5eb6-4c4c-aab1-3c029ea70a4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   married    wage  exper  age  coll  games  minutes  guard  forward  center  \\\n",
+       "0        1  1002.5      4   27     4     77     2867      1        0       0   \n",
+       "1        1  2030.0      5   28     4     78     2789      1        0       0   \n",
+       "2        0   650.0      1   25     4     74     1149      0        0       1   \n",
+       "3        0  2030.0      5   28     4     47     1178      0        1       0   \n",
+       "4        0   755.0      3   24     4     82     2096      1        0       0   \n",
+       "\n",
+       "   points  rebounds  assists  draft  allstar  avgmin  black  children  \n",
+       "0      16         4        5   19.0        0   37.23      1         0  \n",
+       "1      13         3        9   28.0        0   35.76      1         1  \n",
+       "2       6         3        0   19.0        0   15.53      1         0  \n",
+       "3       7         5        2    1.0        0   25.06      1         0  \n",
+       "4      11         4        3   24.0        0   25.56      1         0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba = pd.read_csv(\"data/nba.csv\") # NBA dataset\n",
+    "nba.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10eac6fb-6d2c-4f0f-b172-cfacb74eb6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hispanic</th>\n",
+       "      <th>citizen</th>\n",
+       "      <th>black</th>\n",
+       "      <th>exp</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>female</th>\n",
+       "      <th>education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>5.288462</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>8.461538</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>10.416667</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>21.634615</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.365385</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   hispanic  citizen  black   exp       wage  female  education\n",
+       "0         1        1      0  14.0   5.288462       1          9\n",
+       "1         0        1      0  14.7   8.461538       1         13\n",
+       "2         0        1      0  14.7  10.416667       1         13\n",
+       "3         0        1      0  14.0  21.634615       1         14\n",
+       "4         1        0      0  12.0   3.365385       1         12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wages = pd.read_csv(\"data/la.csv\") # (Log) Wages dataset\n",
+    "wages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69beac78-18de-4389-965c-1f1fbfe17c25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>countryname</th>\n",
+       "      <th>countrycode</th>\n",
+       "      <th>gdp</th>\n",
+       "      <th>gdppc</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>co2pc</th>\n",
+       "      <th>population</th>\n",
+       "      <th>oecd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>ZMB</td>\n",
+       "      <td>9.799629e+09</td>\n",
+       "      <td>741.4421</td>\n",
+       "      <td>2427.554</td>\n",
+       "      <td>0.183669</td>\n",
+       "      <td>13216985</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>French Polynesia</td>\n",
+       "      <td>PYF</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>883.747</td>\n",
+       "      <td>3.296764</td>\n",
+       "      <td>268065</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Monaco</td>\n",
+       "      <td>MCO</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36845</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>UKR</td>\n",
+       "      <td>9.057726e+10</td>\n",
+       "      <td>1974.6212</td>\n",
+       "      <td>304804.720</td>\n",
+       "      <td>6.644867</td>\n",
+       "      <td>45870700</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Venezuela, RB</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1.750000e+11</td>\n",
+       "      <td>6010.0270</td>\n",
+       "      <td>201747.340</td>\n",
+       "      <td>6.946437</td>\n",
+       "      <td>29043283</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year       countryname countrycode           gdp      gdppc         co2  \\\n",
+       "0  2010            Zambia         ZMB  9.799629e+09   741.4421    2427.554   \n",
+       "1  2010  French Polynesia         PYF           NaN        NaN     883.747   \n",
+       "2  2010            Monaco         MCO           NaN        NaN         NaN   \n",
+       "3  2010           Ukraine         UKR  9.057726e+10  1974.6212  304804.720   \n",
+       "4  2010     Venezuela, RB         VEN  1.750000e+11  6010.0270  201747.340   \n",
+       "\n",
+       "      co2pc  population  oecd  \n",
+       "0  0.183669    13216985   0.0  \n",
+       "1  3.296764      268065   0.0  \n",
+       "2       NaN       36845   0.0  \n",
+       "3  6.644867    45870700   0.0  \n",
+       "4  6.946437    29043283   0.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pollution = pd.read_csv(\"data/pollution.csv\") # Pollution dataset\n",
+    "pollution.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3edebbb-363c-489d-b560-09620fe27466",
+   "metadata": {},
+   "source": [
+    "### Usage\n",
+    "\n",
+    "Currently, `stata2python` only supports commands necessary to teach an introductory course in econometrics. If you would like to contribute, feel free to create pull requests [here](https://github.com/rohanjha123/data-h195/tree/main/creating_package). Below, we discuss the commands currently supported by the package.\n",
+    "\n",
+    "By importing `stata2python` via the command `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command. \n",
+    "\n",
+    "Optionally, you may also specify the name of the DataFrame you're working with via the `df_name` parameter in `stata2python`. The default value for `df_name` is simply `df`. \n",
+    "\n",
+    "Note that the provided Python code will (naturally) require you to have `pandas` installed, and may also require you to have the Python packages `numpy`, `scipy`, `matplotlib`, and `statsmodels` installed. The provided code will include all imports necessary (including `import pandas as pd`).\n",
+    "\n",
+    "Below, we discuss all the features currently supported by `Stata2Python`, along with providing example usages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9ace3e5-b5e7-473e-8bdb-cf6559c2fcc1",
+   "metadata": {},
+   "source": [
+    "#### T-tests\n",
+    "\n",
+    "This function helps users determine the code for running [t-tests](https://www.jmp.com/en_us/statistics-knowledge-portal/t-test.html) in Python. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b48ee104-aa0a-4461-a1bc-9f40c6c90996",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
+      "catvar_vals = np.unique(df['guard'])\n",
+      "df_1 = df[df['guard'] == catvar_vals[0]]\n",
+      "df_2 = df[df['guard'] == catvar_vals[1]]\n",
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"ttest wage, by(guard)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87876a2e-0b2c-40d5-a64a-c8ec25941aa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
+      "catvar_vals = np.unique(nba['guard'])\n",
+      "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
+      "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"ttest wage, by(guard) unequal\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89b477e9-82df-43d2-bc97-dc466c1bcb92",
+   "metadata": {},
+   "source": [
+    "Assuming you have all the correct packages installed, you can directly copy paste this code to see the output. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a485ec9f-25f6-4c24-9ee8-380b751ef6a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=2.1432820571177977, pvalue=0.03299634994484977, df=266.3682612357414)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "### First, we must filter the DataFrame to obtain the right values\n",
+    "catvar_vals = np.unique(nba['guard'])\n",
+    "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
+    "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
+    "### Then, we can run our t-test\n",
+    "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcac0225-7131-4a7d-9997-47958c10c172",
+   "metadata": {},
+   "source": [
+    "#### Generating new columns\n",
+    "\n",
+    "Used for generating new columns. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b0fd3d6a-0a9e-459c-896c-d7a53e12cba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "df['degree'] = df['coll'] >= 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"gen degree = (coll >= 4)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9ba0e4f5-d33e-48ed-a5ad-f3afb0a65abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"gen productivity = points/(minutes/games)\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9357b734-d25a-4008-b0c9-86240daecff2",
+   "metadata": {},
+   "source": [
+    "Copy-pasting the output into Python to verify it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b8fd2dcf-621b-40dc-95bf-f1deacf15a09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7b99236a-651a-47dc-8be9-b5c8090f4f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.363571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.386423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.279287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.430344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3210.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.598939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>1</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>4</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.345941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.87</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.559733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.590629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>282</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212766</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>269 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     married    wage  exper  age  coll  games  minutes  guard  forward  \\\n",
+       "0          1  1002.5      4   27     4     77     2867      1        0   \n",
+       "1          1  2030.0      5   28     4     78     2789      1        0   \n",
+       "2          0   650.0      1   25     4     74     1149      0        0   \n",
+       "3          0  2030.0      5   28     4     47     1178      0        1   \n",
+       "4          0   755.0      3   24     4     82     2096      1        0   \n",
+       "..       ...     ...    ...  ...   ...    ...      ...    ...      ...   \n",
+       "264        1  3210.0      7   29     4     79     2638      1        0   \n",
+       "265        1   715.0      5   31     4     75     1084      0        1   \n",
+       "266        1   600.0     11   33     3     67     1197      1        0   \n",
+       "267        0  2500.0      6   28     4     78     2113      0        0   \n",
+       "268        0  2000.0     12   33     3     30      282      0        1   \n",
+       "\n",
+       "     center  points  rebounds  assists  draft  allstar  avgmin  black  \\\n",
+       "0         0      16         4        5   19.0        0   37.23      1   \n",
+       "1         0      13         3        9   28.0        0   35.76      1   \n",
+       "2         1       6         3        0   19.0        0   15.53      1   \n",
+       "3         0       7         5        2    1.0        0   25.06      1   \n",
+       "4         0      11         4        3   24.0        0   25.56      1   \n",
+       "..      ...     ...       ...      ...    ...      ...     ...    ...   \n",
+       "264       0      20         3        3   11.0        1   33.39      1   \n",
+       "265       0       5         3        1   54.0        0   14.45      1   \n",
+       "266       0      10         2        2    4.0        0   17.87      1   \n",
+       "267       1      16         6        2    2.0        0   27.09      0   \n",
+       "268       0       2         3        1    5.0        0    9.40      1   \n",
+       "\n",
+       "     children  productivity  \n",
+       "0           0      0.429717  \n",
+       "1           1      0.363571  \n",
+       "2           0      0.386423  \n",
+       "3           0      0.279287  \n",
+       "4           0      0.430344  \n",
+       "..        ...           ...  \n",
+       "264         0      0.598939  \n",
+       "265         1      0.345941  \n",
+       "266         1      0.559733  \n",
+       "267         0      0.590629  \n",
+       "268         0      0.212766  \n",
+       "\n",
+       "[269 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba # You can see that the productivity column was successfully generated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ee162dd-ad45-4af0-aaa4-062cc4e6e40b",
+   "metadata": {},
+   "source": [
+    "#### Describing the Data\n",
+    "\n",
+    "Used for summarizing the dataframe. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f44496e-8337-4ec4-9677-7d9e770e6b16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba.describe()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"describe\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "da57f17a-214a-4932-a6c8-354a7aeb87e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>240.00000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.442379</td>\n",
+       "      <td>1423.827509</td>\n",
+       "      <td>5.118959</td>\n",
+       "      <td>27.394052</td>\n",
+       "      <td>3.717472</td>\n",
+       "      <td>65.724907</td>\n",
+       "      <td>1682.193309</td>\n",
+       "      <td>0.420074</td>\n",
+       "      <td>0.408922</td>\n",
+       "      <td>0.171004</td>\n",
+       "      <td>10.260223</td>\n",
+       "      <td>4.468401</td>\n",
+       "      <td>2.453532</td>\n",
+       "      <td>20.20000</td>\n",
+       "      <td>0.115242</td>\n",
+       "      <td>23.979257</td>\n",
+       "      <td>0.806691</td>\n",
+       "      <td>0.345725</td>\n",
+       "      <td>0.409677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.497595</td>\n",
+       "      <td>999.774074</td>\n",
+       "      <td>3.400062</td>\n",
+       "      <td>3.391292</td>\n",
+       "      <td>0.754410</td>\n",
+       "      <td>18.851110</td>\n",
+       "      <td>893.327771</td>\n",
+       "      <td>0.494491</td>\n",
+       "      <td>0.492551</td>\n",
+       "      <td>0.377214</td>\n",
+       "      <td>5.882489</td>\n",
+       "      <td>2.892980</td>\n",
+       "      <td>2.148124</td>\n",
+       "      <td>18.73582</td>\n",
+       "      <td>0.319909</td>\n",
+       "      <td>9.731086</td>\n",
+       "      <td>0.395629</td>\n",
+       "      <td>0.476491</td>\n",
+       "      <td>0.114920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.890000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>650.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>983.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.730000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.338710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1186.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1690.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.50000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24.820000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2014.500000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>2438.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.25000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>33.260000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.471168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5740.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>3533.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>139.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>43.090000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.748532</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          married         wage       exper         age        coll  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean     0.442379  1423.827509    5.118959   27.394052    3.717472   \n",
+       "std      0.497595   999.774074    3.400062    3.391292    0.754410   \n",
+       "min      0.000000   150.000000    1.000000   21.000000    0.000000   \n",
+       "25%      0.000000   650.000000    2.000000   25.000000    4.000000   \n",
+       "50%      0.000000  1186.000000    4.000000   27.000000    4.000000   \n",
+       "75%      1.000000  2014.500000    7.000000   30.000000    4.000000   \n",
+       "max      1.000000  5740.000000   18.000000   41.000000    4.000000   \n",
+       "\n",
+       "            games      minutes       guard     forward      center  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean    65.724907  1682.193309    0.420074    0.408922    0.171004   \n",
+       "std     18.851110   893.327771    0.494491    0.492551    0.377214   \n",
+       "min      3.000000    33.000000    0.000000    0.000000    0.000000   \n",
+       "25%     57.000000   983.000000    0.000000    0.000000    0.000000   \n",
+       "50%     74.000000  1690.000000    0.000000    0.000000    0.000000   \n",
+       "75%     79.000000  2438.000000    1.000000    1.000000    0.000000   \n",
+       "max     82.000000  3533.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "           points    rebounds     assists      draft     allstar      avgmin  \\\n",
+       "count  269.000000  269.000000  269.000000  240.00000  269.000000  269.000000   \n",
+       "mean    10.260223    4.468401    2.453532   20.20000    0.115242   23.979257   \n",
+       "std      5.882489    2.892980    2.148124   18.73582    0.319909    9.731086   \n",
+       "min      1.000000    1.000000    0.000000    1.00000    0.000000    2.890000   \n",
+       "25%      5.000000    2.000000    1.000000    7.00000    0.000000   16.730000   \n",
+       "50%      9.000000    4.000000    2.000000   14.50000    0.000000   24.820000   \n",
+       "75%     14.000000    6.000000    3.000000   28.25000    0.000000   33.260000   \n",
+       "max     30.000000   17.000000   13.000000  139.00000    1.000000   43.090000   \n",
+       "\n",
+       "            black    children  productivity  \n",
+       "count  269.000000  269.000000    269.000000  \n",
+       "mean     0.806691    0.345725      0.409677  \n",
+       "std      0.395629    0.476491      0.114920  \n",
+       "min      0.000000    0.000000      0.093851  \n",
+       "25%      1.000000    0.000000      0.338710  \n",
+       "50%      1.000000    0.000000      0.408560  \n",
+       "75%      1.000000    1.000000      0.471168  \n",
+       "max      1.000000    1.000000      0.748532  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48facefc-8cdf-49e8-a071-7bc836552fd9",
+   "metadata": {},
+   "source": [
+    "#### Correlation Matrix\n",
+    "\n",
+    "Used for generating a correlation matrix between relevant variables. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6cd38a22-ea9a-41cb-bfda-e2d439b4d40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba[['points', 'assists', 'rebounds']].corr()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"corr points assists rebounds\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "969d8093-ff69-4224-ba83-a8df7ead7617",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>points</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>rebounds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>points</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>0.563324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assists</th>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rebounds</th>\n",
+       "      <td>0.563324</td>\n",
+       "      <td>0.059956</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            points   assists  rebounds\n",
+       "points    1.000000  0.539269  0.563324\n",
+       "assists   0.539269  1.000000  0.059956\n",
+       "rebounds  0.563324  0.059956  1.000000"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba[['points', 'assists', 'rebounds']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ab3e395-5236-407d-bbef-1a6f7a2104c4",
+   "metadata": {},
+   "source": [
+    "#### Scatter Plots\n",
+    "\n",
+    "Used for making scatter (or twoway) plots. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17411597-3519-41c0-a455-796118431768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+      "plt.xlabel('co2pc');\n",
+      "plt.ylabel('population');\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"twoway (scatter co2pc population, xtitle('co2pc') ytitle('population'))\",\"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ae6cf21d-24ed-453e-b757-7403d7d41113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+    "plt.xlabel('co2pc');\n",
+    "plt.ylabel('population');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33122b3d-c34b-424c-a246-8778b2a872ab",
+   "metadata": {},
+   "source": [
+    "#### Histograms\n",
+    "\n",
+    "Used for generating histograms. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "54d05f02-364d-4e82-b594-b5ba0c550357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "pollution.hist(column='co2pc',bins=80);\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"histogram co2pc, bin(80)\", \"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "77289483-1aee-435e-b78f-909044de0bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "pollution.hist(column='co2pc',bins=80);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14245a39-b06f-406b-a597-fc4ffcffead0",
+   "metadata": {},
+   "source": [
+    "#### Regressions\n",
+    "\n",
+    "Used for running regressions, including Python comments to help explain relevant portions of the code. Note that not all features from Stata's `reg` command are supported. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1bd4cdaa-deb1-417f-80d1-da4e0ca5350e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### Dropping NaN/missing values\n",
+      "wages_no_na = wages[['exp','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_no_na['exp']\n",
+      "y_df = wages_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit()\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "07fc3173-8ec2-4f34-bf8f-ddbb8dd6ccde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4714</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.493</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:39</td>     <th>  Log-Likelihood:    </th> <td> -3541.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7086.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   861</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>   16.0816</td> <td>    3.917</td> <td>    4.105</td> <td> 0.000</td> <td>    8.393</td> <td>   23.771</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>   <td>   -0.2135</td> <td>    0.311</td> <td>   -0.687</td> <td> 0.493</td> <td>   -0.824</td> <td>    0.397</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1263.062</td> <th>  Durbin-Watson:     </th>  <td>   1.830</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>413853.334</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.176</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>109.028</td> <th>  Cond. No.          </th>  <td>    99.5</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.001   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &    -0.001   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &    0.4714   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.493    \\\\\n",
+       "\\textbf{Time:}             &     21:26:39     & \\textbf{  Log-Likelihood:    } &   -3541.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7086.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         861      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &      16.0816  &        3.917     &     4.105  &         0.000        &        8.393    &       23.771     \\\\\n",
+       "\\textbf{exp}   &      -0.2135  &        0.311     &    -0.687  &         0.493        &       -0.824    &        0.397     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1263.062 & \\textbf{  Durbin-Watson:     } &     1.830   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 413853.334  \\\\\n",
+       "\\textbf{Skew:}          &   8.176  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 109.028  & \\textbf{  Cond. No.          } &      99.5   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.001\n",
+       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
+       "Method:                 Least Squares   F-statistic:                    0.4714\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.493\n",
+       "Time:                        21:26:39   Log-Likelihood:                -3541.0\n",
+       "No. Observations:                 863   AIC:                             7086.\n",
+       "Df Residuals:                     861   BIC:                             7096.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         16.0816      3.917      4.105      0.000       8.393      23.771\n",
+       "exp           -0.2135      0.311     -0.687      0.493      -0.824       0.397\n",
+       "==============================================================================\n",
+       "Omnibus:                     1263.062   Durbin-Watson:                   1.830\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           413853.334\n",
+       "Skew:                           8.176   Prob(JB):                         0.00\n",
+       "Kurtosis:                     109.028   Cond. No.                         99.5\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### Dropping NaN/missing values\n",
+    "wages_no_na = wages[['exp','wage']].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_no_na['exp']\n",
+    "y_df = wages_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit()\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3fc841e3-b7a8-40ca-a338-e4595c3e1d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "### Dropping NaN/missing values\n",
+      "df_no_na = df[['exp', 'education','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = df_no_na['exp']\n",
+      "y_df = df_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': df_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp, vce(cluster education)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c86447cd-732b-47b9-a192-70cb8c0e2684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "wages_with_dummies = wages.copy()\n",
+      "x_var = \"'exp', 'i.female'\"\n",
+      "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+      "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+      "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+      "for indicator in indicator_list:\n",
+      "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+      "    dummies = dummies.iloc[:,1:]\n",
+      "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+      "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+      "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+      "x_var = x_var_new\n",
+      "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+      "if clustering_vars:\n",
+      "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+      "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+      "### Dropping NaN/missing values\n",
+      "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+      "y_df = wages_with_dummies_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp i.female, vce(cluster education)\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4f73adae-9b6b-4d5d-9db8-2567fb9935fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.007</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.993</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.183</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:54</td>     <th>  Log-Likelihood:    </th> <td> -3538.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7082.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   860</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   17.4218</td> <td>    3.698</td> <td>    4.712</td> <td> 0.000</td> <td>   10.175</td> <td>   24.669</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>      <td>   -0.2373</td> <td>    0.206</td> <td>   -1.153</td> <td> 0.249</td> <td>   -0.640</td> <td>    0.166</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>1_female</th> <td>   -2.4604</td> <td>    1.261</td> <td>   -1.951</td> <td> 0.051</td> <td>   -4.932</td> <td>    0.011</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1260.198</td> <th>  Durbin-Watson:     </th>  <td>   1.842</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>409372.994</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.142</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>108.449</td> <th>  Cond. No.          </th>  <td>    101.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.007   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.005   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1.993   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.183    \\\\\n",
+       "\\textbf{Time:}             &     21:26:54     & \\textbf{  Log-Likelihood:    } &   -3538.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7082.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         860      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}     &      17.4218  &        3.698     &     4.712  &         0.000        &       10.175    &       24.669     \\\\\n",
+       "\\textbf{exp}       &      -0.2373  &        0.206     &    -1.153  &         0.249        &       -0.640    &        0.166     \\\\\n",
+       "\\textbf{1\\_female} &      -2.4604  &        1.261     &    -1.951  &         0.051        &       -4.932    &        0.011     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1260.198 & \\textbf{  Durbin-Watson:     } &     1.842   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 409372.994  \\\\\n",
+       "\\textbf{Skew:}          &   8.142  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 108.449  & \\textbf{  Cond. No.          } &      101.   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.007\n",
+       "Model:                            OLS   Adj. R-squared:                  0.005\n",
+       "Method:                 Least Squares   F-statistic:                     1.993\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.183\n",
+       "Time:                        21:26:54   Log-Likelihood:                -3538.0\n",
+       "No. Observations:                 863   AIC:                             7082.\n",
+       "Df Residuals:                     860   BIC:                             7096.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:              cluster                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         17.4218      3.698      4.712      0.000      10.175      24.669\n",
+       "exp           -0.2373      0.206     -1.153      0.249      -0.640       0.166\n",
+       "1_female      -2.4604      1.261     -1.951      0.051      -4.932       0.011\n",
+       "==============================================================================\n",
+       "Omnibus:                     1260.198   Durbin-Watson:                   1.842\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           409372.994\n",
+       "Skew:                           8.142   Prob(JB):                         0.00\n",
+       "Kurtosis:                     108.449   Cond. No.                         101.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+    "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+    "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+    "y_var = \"wage\"\n",
+    "clustering_vars = ['education']\n",
+    "wages_with_dummies = wages.copy()\n",
+    "x_var = \"'exp', 'i.female'\"\n",
+    "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+    "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+    "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+    "for indicator in indicator_list:\n",
+    "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+    "    dummies = dummies.iloc[:,1:]\n",
+    "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+    "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+    "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+    "x_var = x_var_new\n",
+    "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+    "if clustering_vars:\n",
+    "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+    "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+    "### Dropping NaN/missing values\n",
+    "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+    "y_df = wages_with_dummies_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21279b0b-e6c2-467e-a028-429da3fac865",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_build/html/content/01-python_v_stata/differences.html b/_build/html/content/01-python_v_stata/differences.html
index 4189ba3..6908f17 100644
--- a/_build/html/content/01-python_v_stata/differences.html
+++ b/_build/html/content/01-python_v_stata/differences.html
@@ -68,6 +68,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'content/01-python_v_stata/differences';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
+    <link rel="next" title="Installation and General Usage" href="install.html" />
+    <link rel="prev" title="History of the Languages" href="history.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -162,8 +164,16 @@
                 </a>
             </li>
         </ul>
-        <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+        <ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Python vs Stata</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="history.html">History of the Languages</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pedagogy.html">Economics Pedagogy</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
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 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/jupyter.html">Jupyter Notebooks</a></li>
 
@@ -171,7 +181,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/numpy.html">Numpy</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/loading-looking.html">Loading in and Looking at the Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/select_modify.html">Selecting and Modifying Rows/Columns</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/misc_funcs.html">Helpful Pandas Methods</a></li>
@@ -179,7 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/merge.html">Joins</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/choosing.html">Choosing the Right Plot</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/types.html">Basic Visualizations</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/advanced.html">More Advanced Visualizations</a></li>
@@ -362,7 +372,7 @@ <h2>General Purpose vs Specialized<a class="headerlink" href="#general-purpose-v
 </section>
 <section id="syntax">
 <h2>Syntax<a class="headerlink" href="#syntax" title="Permalink to this heading">#</a></h2>
-<p>Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it’s users to focus moreso on the results.</p>
+<p>Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it’s users to focus moreso on the results. See the <a class="reference internal" href="syntax.html"><span class="doc std std-doc">Syntax subchapter</span></a> for more information.</p>
 </section>
 <section id="packages-libraries">
 <h2>Packages/Libraries<a class="headerlink" href="#packages-libraries" title="Permalink to this heading">#</a></h2>
@@ -416,6 +426,24 @@ <h2>Community<a class="headerlink" href="#community" title="Permalink to this he
                 <footer class="prev-next-footer">
                   <!-- Previous / next buttons -->
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+    <a class="left-prev"
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+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">History of the Languages</p>
+      </div>
+    </a>
+    <a class="right-next"
+       href="install.html"
+       title="next page">
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Installation and General Usage</p>
+      </div>
+      <i class="fa-solid fa-angle-right"></i>
+    </a>
 </div>
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diff --git a/_build/html/content/01-python_v_stata/history.html b/_build/html/content/01-python_v_stata/history.html
index db7241a..e1739c2 100644
--- a/_build/html/content/01-python_v_stata/history.html
+++ b/_build/html/content/01-python_v_stata/history.html
@@ -68,6 +68,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'content/01-python_v_stata/history';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
+    <link rel="next" title="Summary of Differences" href="differences.html" />
+    <link rel="prev" title="Python vs Stata" href="index.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -162,8 +164,16 @@
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-        <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+        <ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Python vs Stata</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">History of the Languages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="differences.html">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pedagogy.html">Economics Pedagogy</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
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 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/jupyter.html">Jupyter Notebooks</a></li>
 
@@ -171,7 +181,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/numpy.html">Numpy</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/loading-looking.html">Loading in and Looking at the Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/select_modify.html">Selecting and Modifying Rows/Columns</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/misc_funcs.html">Helpful Pandas Methods</a></li>
@@ -179,7 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/merge.html">Joins</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/choosing.html">Choosing the Right Plot</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/types.html">Basic Visualizations</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/advanced.html">More Advanced Visualizations</a></li>
@@ -335,7 +345,8 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#span-style-color-red-to-be-updated-span"><span style="color:red"> To be updated </span></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#history-of-python">History of Python</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#history-of-stata">History of Stata</a></li>
 </ul>
             </nav>
         </div>
@@ -349,28 +360,17 @@ <h2> Contents </h2>
                   
   <section class="tex2jax_ignore mathjax_ignore" id="history-of-the-languages">
 <h1>History of the Languages<a class="headerlink" href="#history-of-the-languages" title="Permalink to this heading">#</a></h1>
-<p>This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be.</p>
-<section id="span-style-color-red-to-be-updated-span">
-<h2><span style="color:red"> To be updated </span><a class="headerlink" href="#span-style-color-red-to-be-updated-span" title="Permalink to this heading">#</a></h2>
-<!-- From ChatGPT
-
-## History of Python:
-
-Python is a high-level, interpreted programming language that was created by Guido van Rossum. The language's development began in the late 1980s, and the first official Python release, version 0.9.0, came out in February 1991. Guido van Rossum aimed to create a language that emphasized code readability and ease of use, inspired by the ABC language.
-
-The name "Python" was chosen as a tribute to the British comedy group Monty Python. Over the years, Python evolved, with significant releases such as Python 2.0 in 2000 and Python 3.0 in 2008. Python 3 introduced backward-incompatible changes to enhance the language's consistency and eliminate redundancy.
-
-Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers contributed to its success.
-
-## History of Stata:
-
-Stata is a statistical software package designed for data analysis, manipulation, and visualization. It was developed by StataCorp, founded by William Gould, James Hardin, and Cleve Moler. Stata's development began in the early 1980s, with the first version, Stata 1.0, released in 1985.
-
-Stata was initially focused on providing tools for econometric and statistical analysis. It gained popularity among researchers, economists, and social scientists for its powerful data management capabilities and robust statistical features.
-
-Over the years, StataCorp released several versions of the software, adding new features and improving existing ones. Stata's scripting language allows users to automate tasks and create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
-
-Both Python and Stata have become essential tools in their respective domains, with Python's general-purpose nature making it versatile across many fields, and Stata's specialization in statistical analysis making it a go-to choice for researchers and analysts in specific disciplines. --></section>
+<p>Both Python and Stata have become essential tools in various domains. Python’s general-purpose nature makes it versatile across many fields, and Stata’s specialization in statistical analysis makes it a go-to choice for researchers and analysts in specific disciplines. This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be.</p>
+<section id="history-of-python">
+<h2>History of Python<a class="headerlink" href="#history-of-python" title="Permalink to this heading">#</a></h2>
+<p>Python is a high-level, interpreted programming language that was created by Guido van Rossum in 1991 (<a class="reference external" href="https://www.geeksforgeeks.org/history-of-python/">GeeksforGeeks</a>). Guido van Rossum aimed to create a language that emphasized code readability and ease of use, while still being a general purpose programming language that can be used for a wide variety of tasks.</p>
+<p>Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python’s extensive standard library and a vibrant community of developers building packages contributed to its success. Today, Python is one of the most widely used programming languages, being used for a wide variety of tasks.</p>
+</section>
+<section id="history-of-stata">
+<h2>History of Stata<a class="headerlink" href="#history-of-stata" title="Permalink to this heading">#</a></h2>
+<p>Stata is a statistical program designed for data analysis, manipulation, and visualization. It was written and first released in 1985 by William Gould and Sean Becketti. Stata then grew over time to include more and more forms of statistical analyses. A key feature was introduced in Stata 1.3, which allowed users to write their own programs for statistical analyses.</p>
+<p>Today, Stata’s scripting language allows users to automate tasks and easily create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.</p>
+</section>
 </section>
 
     <script type="text/x-thebe-config">
@@ -403,6 +403,24 @@ <h2><span style="color:red"> To be updated </span><a class="headerlink" href="#s
                 <footer class="prev-next-footer">
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+    <a class="left-prev"
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+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Python vs Stata</p>
+      </div>
+    </a>
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+       href="differences.html"
+       title="next page">
+      <div class="prev-next-info">
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@@ -418,7 +436,8 @@ <h2><span style="color:red"> To be updated </span><a class="headerlink" href="#s
   </div>
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#span-style-color-red-to-be-updated-span"><span style="color:red"> To be updated </span></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#history-of-python">History of Python</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#history-of-stata">History of Stata</a></li>
 </ul>
   </nav></div>
 
diff --git a/_build/html/content/01-python_v_stata/index.html b/_build/html/content/01-python_v_stata/index.html
index f5b5da7..6a2c24c 100644
--- a/_build/html/content/01-python_v_stata/index.html
+++ b/_build/html/content/01-python_v_stata/index.html
@@ -68,6 +68,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'content/01-python_v_stata/index';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
+    <link rel="next" title="History of the Languages" href="history.html" />
+    <link rel="prev" title="Data Science for Economists" href="../intro.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -162,8 +164,16 @@
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-        <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+        <ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">Python vs Stata</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="history.html">History of the Languages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="differences.html">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pedagogy.html">Economics Pedagogy</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
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@@ -171,7 +181,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/numpy.html">Numpy</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/loading-looking.html">Loading in and Looking at the Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/select_modify.html">Selecting and Modifying Rows/Columns</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/misc_funcs.html">Helpful Pandas Methods</a></li>
@@ -179,7 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/merge.html">Joins</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/choosing.html">Choosing the Right Plot</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/types.html">Basic Visualizations</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04-visualizations/advanced.html">More Advanced Visualizations</a></li>
@@ -339,8 +349,11 @@ <h1>Python vs Stata</h1>
                   
   <section class="tex2jax_ignore mathjax_ignore" id="python-vs-stata">
 <h1>Python vs Stata<a class="headerlink" href="#python-vs-stata" title="Permalink to this heading">#</a></h1>
-<p>This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data.</p>
+<p>Python and Stata are two of the most popular languages used for economics research (<a class="reference external" href="https://cepr.org/voxeu/columns/which-programming-language-best-economic-research-julia-matlab-python-or-r">source</a>). However, there are several key differences between them. Stata is a highly specialized language intended for mainly statistical analysis, while Python is a general-purpose programming language.</p>
+<p>This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. We hope that this chapter will help you determine which programming language is right for you. However, as the remainder of this textbook is written entirely in Python, the final subchapter (<code class="docutils literal notranslate"><span class="pre">Syntax</span></code>) will aim to help experienced Stata users become more familiar with Python.</p>
 <!-- For more information, please look at [Python's official page](https://www.python.org/) and [Stata's official page](https://www.stata.com/) respectively. -->
+<div class="toctree-wrapper compound">
+</div>
 </section>
 
     <script type="text/x-thebe-config">
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                 <footer class="prev-next-footer">
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+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title"><a> Data Science for Economists </a></p>
+      </div>
+    </a>
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+       href="history.html"
+       title="next page">
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">History of the Languages</p>
+      </div>
+      <i class="fa-solid fa-angle-right"></i>
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diff --git a/_build/html/content/01-python_v_stata/install.html b/_build/html/content/01-python_v_stata/install.html
index 68b8f4e..59a94e4 100644
--- a/_build/html/content/01-python_v_stata/install.html
+++ b/_build/html/content/01-python_v_stata/install.html
@@ -68,6 +68,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'content/01-python_v_stata/install';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
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+    <link rel="next" title="Syntax" href="syntax.html" />
+    <link rel="prev" title="Summary of Differences" href="differences.html" />
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@@ -162,8 +164,16 @@
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-        <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+        <ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Python vs Stata</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="history.html">History of the Languages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="differences.html">Summary of Differences</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pedagogy.html">Economics Pedagogy</a></li>
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@@ -179,7 +189,7 @@
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+    <h1>Economics Pedagogy</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-curve">Learning Curve</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#accessibility">Accessibility</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cloud-computing">Cloud Computing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#autograding">Autograding</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#textbooks">Textbooks</a></li>
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+
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+<div id="searchbox"></div>
+                <article class="bd-article" role="main">
+                  
+  <section class="tex2jax_ignore mathjax_ignore" id="economics-pedagogy">
+<h1>Economics Pedagogy<a class="headerlink" href="#economics-pedagogy" title="Permalink to this heading">#</a></h1>
+<p>Both Python and Stata have been used to teach economics to students of all levels, from the beginner to the graduate students. They both have their advantages and disadvantages, some of which are discussed below.</p>
+<section id="learning-curve">
+<h2>Learning Curve<a class="headerlink" href="#learning-curve" title="Permalink to this heading">#</a></h2>
+<p>Students who have little to no experience in coding often find Stata’s command-line interface easier to install and use. However, students with more experience in coding may be more familiar with Python and thus find that easier to use. At the end of the day, different students could find either of the softwares easier to learn, and the software you choose to use should consider the level of the students in your class.</p>
+</section>
+<section id="accessibility">
+<h2>Accessibility<a class="headerlink" href="#accessibility" title="Permalink to this heading">#</a></h2>
+<p>Both Python and Jupyter notebooks are free and easily accessible, while Stata must be purchased. If your students do not have an instituional license for Stata, it may be more inclusive to use Python to avoid placing an undue financial burden on students.</p>
+</section>
+<section id="cloud-computing">
+<h2>Cloud Computing<a class="headerlink" href="#cloud-computing" title="Permalink to this heading">#</a></h2>
+<p>With the great integration between Jupyter Notebooks and Cloud Computing provided by tools such as <a class="reference external" href="https://colab.research.google.com/">Google Colab</a>, it is possible to easily share your Jupyter notebooks with all your students and have them run your code in real time. To share your Stata <code class="docutils literal notranslate"><span class="pre">.do</span></code> files with students, it may be best to upload them to a Google Drive folder and have your students download and run them locally. Students may then run into issues if they haven’t locally installed all the packages/updated all computer paths in your code. These issues are avoided if you use a virtual environment where everyone has access to the same resources, such as Google Colab (more details can be found on Colab’s <a class="reference external" href="https://colab.research.google.com/#scrollTo=GJBs_flRovLc">welcome page</a>).</p>
+</section>
+<section id="autograding">
+<h2>Autograding<a class="headerlink" href="#autograding" title="Permalink to this heading">#</a></h2>
+<p>In both languages, you can view and grade the final output of student code. However, Stata <code class="docutils literal notranslate"><span class="pre">.do</span></code> files do not have any features to automatically grade the intermediate steps student take towards a solution. Python has a package called <a class="reference external" href="https://otter-grader.readthedocs.io/en/latest/">Otter Grader</a>, designed to help you grade the output of various code cells in Jupyter notebooks. Using Otter Grader, you can create various test cells that have their corresponding solutions and relevant tests. The package provides tests that are both visible to and hidden from students, enabling you to both guide students on the right track and check for correctness as necessary.</p>
+<p>Several UC Berkeley classes, including <a class="reference external" href="https://www.econ148.org">Econ 148: Data Science for Economists</a>, use Otter Grader along with <a class="reference external" href="https://www.gradescope.com/">Gradescope</a> to automatically grade thousands of student submissions at once. However, you do not need a Gradescope license to use Otter Grader, Otter is a free package that can be used by anyone without any cost. Please look at the <a class="reference external" href="https://otter-grader.readthedocs.io/en/latest/tutorial.html">Otter tutorial</a> for more information.</p>
+</section>
+<section id="textbooks">
+<h2>Textbooks<a class="headerlink" href="#textbooks" title="Permalink to this heading">#</a></h2>
+<p>While both Python and Stata have economics-specific textbooks, their styles and formats may differ. For Stata, the books are often traditional print books, such as <a class="reference external" href="https://www.stata.com/bookstore/modern-econometrics-stata/"><em>An Introduction to Modern Econometrics Using Stata</em></a>. Students may be required to purchase a physical or digital copy of these textbooks in order to use them.</p>
+<p>Meanwhile, Python textbooks may be found online for free. For example, consider the online textbooks <a class="reference external" href="https://aeturrell.github.io/coding-for-economists/intro.html"><em>Coding for Economists</em></a>, <a class="reference external" href="https://matheusfacure.github.io/python-causality-handbook/landing-page.html"><em>Causal Inference for The Brave and True</em></a>, or <a class="reference external" href="https://networks.quantecon.org/"><em>Economic Networks</em></a>. These textbooks may often include interactive code snippets that the students can try running on their own, and corresponding githubs where the students can see how the textbook was written. Such open access to the material offers greater reproducibility, an advantage over Stata.</p>
+</section>
+</section>
+
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+    <i class="fa-solid fa-list"></i> Contents
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-curve">Learning Curve</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#accessibility">Accessibility</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cloud-computing">Cloud Computing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#autograding">Autograding</a></li>
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index 0a9c999..8bdfd8e 100644
--- a/_build/html/content/01-python_v_stata/syntax.html
+++ b/_build/html/content/01-python_v_stata/syntax.html
@@ -65,9 +65,13 @@
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     <script async="async" src="../../_static/sphinx-thebe.js"></script>
+    <script>window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+    <link rel="next" title="Economics Pedagogy" href="pedagogy.html" />
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@@ -162,8 +166,16 @@
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-        <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+        <ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Python vs Stata</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="history.html">History of the Languages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="differences.html">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html">Installation and General Usage</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pedagogy.html">Economics Pedagogy</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
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@@ -171,7 +183,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../02-prereqs/numpy.html">Numpy</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
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 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/select_modify.html">Selecting and Modifying Rows/Columns</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/misc_funcs.html">Helpful Pandas Methods</a></li>
@@ -179,7 +191,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../03-pandas/merge.html">Joins</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
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@@ -243,7 +255,7 @@
       
       
       
-      <li><a href="../../_sources/content/01-python_v_stata/syntax.md" target="_blank"
+      <li><a href="../../_sources/content/01-python_v_stata/syntax.ipynb" target="_blank"
    class="btn btn-sm btn-download-source-button dropdown-item"
    title="Download source file"
    data-bs-placement="left" data-bs-toggle="tooltip"
@@ -253,7 +265,7 @@
 <span class="btn__icon-container">
   <i class="fas fa-file"></i>
   </span>
-<span class="btn__text-container">.md</span>
+<span class="btn__text-container">.ipynb</span>
 </a>
 </li>
       
@@ -335,7 +347,20 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#span-style-color-red-to-be-updated-span"><span style="color:red"> To be updated</span></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#stata2python">stata2python</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#installation-and-import">Installation and Import</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#usage">Usage</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#t-tests">T-tests</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#generating-new-columns">Generating new columns</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#describing-the-data">Describing the Data</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#correlation-matrix">Correlation Matrix</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#scatter-plots">Scatter Plots</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#histograms">Histograms</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#regressions">Regressions</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
             </nav>
         </div>
@@ -347,11 +372,1439 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article" role="main">
                   
-  <section class="tex2jax_ignore mathjax_ignore" id="syntax">
+  <div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="syntax">
 <h1>Syntax<a class="headerlink" href="#syntax" title="Permalink to this heading">#</a></h1>
-<p>For key syntatical differences between</p>
-<section id="span-style-color-red-to-be-updated-span">
-<h2><span style="color:red"> To be updated</span><a class="headerlink" href="#span-style-color-red-to-be-updated-span" title="Permalink to this heading">#</a></h2>
+<p>The online textbook <a class="reference external" href="https://aeturrell.github.io/coding-for-economists/intro.html">Coding for Economists</a> has a great <a class="reference external" href="https://aeturrell.github.io/coding-for-economists/coming-from-stata.html">section summarizing the differences in syntax</a> between Stata and Python. This chapter does not aim to replace the key material included there. Instead, I hope it can serve as a supplement for those transitioning from Stata to Python.</p>
+<p>This chapter will aim to describe <a class="reference external" href="https://pypi.org/project/stata2python/"><code class="docutils literal notranslate"><span class="pre">stata2python</span></code></a>, a Python package where you can write Stata commands and recieve the equivalent code for them in Python.</p>
+<section id="stata2python">
+<h2>stata2python<a class="headerlink" href="#stata2python" title="Permalink to this heading">#</a></h2>
+<section id="installation-and-import">
+<h3>Installation and Import<a class="headerlink" href="#installation-and-import" title="Permalink to this heading">#</a></h3>
+<p>To install <code class="docutils literal notranslate"><span class="pre">stata2python</span></code>, you can simply use the package installer <a class="reference external" href="https://pypi.org/project/pip/"><code class="docutils literal notranslate"><span class="pre">pip</span></code></a>. An example is shown below. To do so, simply run the following command in your terminal.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>pip install --upgrade stata2python
+</pre></div>
+</div>
+<p>After you have installed the package, you can simply import it into your Jupyter notebook. Alternatively, you can also access a <a class="reference external" href="https://www.tutorialsteacher.com/python/python-interective-shell">Python shell</a> ( by typing <code class="docutils literal notranslate"><span class="pre">python3</span></code> in your terminal) and begin using it. To import the package in your notebook/shell, the following syntax is encouraged.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from stata2python import stata2python
+</pre></div>
+</div>
+<p>We import the package below.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">stata2python</span> <span class="kn">import</span> <span class="n">stata2python</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We also import in sample NBA, (log) wage and pollution datasets that will be helpful for demonstrating the features of our package.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nba</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/nba.csv&quot;</span><span class="p">)</span> <span class="c1"># NBA dataset</span>
+<span class="n">nba</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>married</th>
+      <th>wage</th>
+      <th>exper</th>
+      <th>age</th>
+      <th>coll</th>
+      <th>games</th>
+      <th>minutes</th>
+      <th>guard</th>
+      <th>forward</th>
+      <th>center</th>
+      <th>points</th>
+      <th>rebounds</th>
+      <th>assists</th>
+      <th>draft</th>
+      <th>allstar</th>
+      <th>avgmin</th>
+      <th>black</th>
+      <th>children</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1002.5</td>
+      <td>4</td>
+      <td>27</td>
+      <td>4</td>
+      <td>77</td>
+      <td>2867</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>16</td>
+      <td>4</td>
+      <td>5</td>
+      <td>19.0</td>
+      <td>0</td>
+      <td>37.23</td>
+      <td>1</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>2030.0</td>
+      <td>5</td>
+      <td>28</td>
+      <td>4</td>
+      <td>78</td>
+      <td>2789</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>13</td>
+      <td>3</td>
+      <td>9</td>
+      <td>28.0</td>
+      <td>0</td>
+      <td>35.76</td>
+      <td>1</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0</td>
+      <td>650.0</td>
+      <td>1</td>
+      <td>25</td>
+      <td>4</td>
+      <td>74</td>
+      <td>1149</td>
+      <td>0</td>
+      <td>0</td>
+      <td>1</td>
+      <td>6</td>
+      <td>3</td>
+      <td>0</td>
+      <td>19.0</td>
+      <td>0</td>
+      <td>15.53</td>
+      <td>1</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0</td>
+      <td>2030.0</td>
+      <td>5</td>
+      <td>28</td>
+      <td>4</td>
+      <td>47</td>
+      <td>1178</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>7</td>
+      <td>5</td>
+      <td>2</td>
+      <td>1.0</td>
+      <td>0</td>
+      <td>25.06</td>
+      <td>1</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0</td>
+      <td>755.0</td>
+      <td>3</td>
+      <td>24</td>
+      <td>4</td>
+      <td>82</td>
+      <td>2096</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>11</td>
+      <td>4</td>
+      <td>3</td>
+      <td>24.0</td>
+      <td>0</td>
+      <td>25.56</td>
+      <td>1</td>
+      <td>0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wages</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/la.csv&quot;</span><span class="p">)</span> <span class="c1"># (Log) Wages dataset</span>
+<span class="n">wages</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>hispanic</th>
+      <th>citizen</th>
+      <th>black</th>
+      <th>exp</th>
+      <th>wage</th>
+      <th>female</th>
+      <th>education</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>14.0</td>
+      <td>5.288462</td>
+      <td>1</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>14.7</td>
+      <td>8.461538</td>
+      <td>1</td>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>14.7</td>
+      <td>10.416667</td>
+      <td>1</td>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>14.0</td>
+      <td>21.634615</td>
+      <td>1</td>
+      <td>14</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>12.0</td>
+      <td>3.365385</td>
+      <td>1</td>
+      <td>12</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pollution</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/pollution.csv&quot;</span><span class="p">)</span> <span class="c1"># Pollution dataset</span>
+<span class="n">pollution</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>year</th>
+      <th>countryname</th>
+      <th>countrycode</th>
+      <th>gdp</th>
+      <th>gdppc</th>
+      <th>co2</th>
+      <th>co2pc</th>
+      <th>population</th>
+      <th>oecd</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>2010</td>
+      <td>Zambia</td>
+      <td>ZMB</td>
+      <td>9.799629e+09</td>
+      <td>741.4421</td>
+      <td>2427.554</td>
+      <td>0.183669</td>
+      <td>13216985</td>
+      <td>0.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2010</td>
+      <td>French Polynesia</td>
+      <td>PYF</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>883.747</td>
+      <td>3.296764</td>
+      <td>268065</td>
+      <td>0.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2010</td>
+      <td>Monaco</td>
+      <td>MCO</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>36845</td>
+      <td>0.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2010</td>
+      <td>Ukraine</td>
+      <td>UKR</td>
+      <td>9.057726e+10</td>
+      <td>1974.6212</td>
+      <td>304804.720</td>
+      <td>6.644867</td>
+      <td>45870700</td>
+      <td>0.0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2010</td>
+      <td>Venezuela, RB</td>
+      <td>VEN</td>
+      <td>1.750000e+11</td>
+      <td>6010.0270</td>
+      <td>201747.340</td>
+      <td>6.946437</td>
+      <td>29043283</td>
+      <td>0.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="usage">
+<h3>Usage<a class="headerlink" href="#usage" title="Permalink to this heading">#</a></h3>
+<p>Currently, <code class="docutils literal notranslate"><span class="pre">stata2python</span></code> only supports commands necessary to teach an introductory course in econometrics. If you would like to contribute, feel free to create pull requests <a class="reference external" href="https://github.com/rohanjha123/data-h195/tree/main/creating_package">here</a>. Below, we discuss the commands currently supported by the package.</p>
+<p>By importing <code class="docutils literal notranslate"><span class="pre">stata2python</span></code> via the command <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">stata2python</span> <span class="pre">import</span> <span class="pre">stata2python</span></code>, you can access a function named <code class="docutils literal notranslate"><span class="pre">stata2python</span></code>. You can enter in any Stata command as a string to this function. If the command is supported by <code class="docutils literal notranslate"><span class="pre">stata2python</span></code>, the function will output Python code equivalent to the Stata command.</p>
+<p>Optionally, you may also specify the name of the DataFrame you’re working with via the <code class="docutils literal notranslate"><span class="pre">df_name</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">stata2python</span></code>. The default value for <code class="docutils literal notranslate"><span class="pre">df_name</span></code> is simply <code class="docutils literal notranslate"><span class="pre">df</span></code>.</p>
+<p>Note that the provided Python code will (naturally) require you to have <code class="docutils literal notranslate"><span class="pre">pandas</span></code> installed, and may also require you to have the Python packages <code class="docutils literal notranslate"><span class="pre">numpy</span></code>, <code class="docutils literal notranslate"><span class="pre">scipy</span></code>, <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, and <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> installed. The provided code will include all imports necessary (including <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">pandas</span> <span class="pre">as</span> <span class="pre">pd</span></code>).</p>
+<p>Below, we discuss all the features currently supported by <code class="docutils literal notranslate"><span class="pre">Stata2Python</span></code>, along with providing example usages.</p>
+<section id="t-tests">
+<h4>T-tests<a class="headerlink" href="#t-tests" title="Permalink to this heading">#</a></h4>
+<p>This function helps users determine the code for running <a class="reference external" href="https://www.jmp.com/en_us/statistics-knowledge-portal/t-test.html">t-tests</a> in Python. Examples include:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;ttest wage, by(guard)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import numpy as np
+from scipy import stats
+### First, we must filter the DataFrame to obtain the right values
+catvar_vals = np.unique(df[&#39;guard&#39;])
+df_1 = df[df[&#39;guard&#39;] == catvar_vals[0]]
+df_2 = df[df[&#39;guard&#39;] == catvar_vals[1]]
+### Then, we can run our t-test
+stats.ttest_ind(df_1[&#39;wage&#39;], df_2[&#39;wage&#39;], equal_var=True, nan_policy=&#39;propagate&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;ttest wage, by(guard) unequal&quot;</span><span class="p">,</span> <span class="s2">&quot;nba&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import numpy as np
+from scipy import stats
+### First, we must filter the DataFrame to obtain the right values
+catvar_vals = np.unique(nba[&#39;guard&#39;])
+df_1 = nba[nba[&#39;guard&#39;] == catvar_vals[0]]
+df_2 = nba[nba[&#39;guard&#39;] == catvar_vals[1]]
+### Then, we can run our t-test
+stats.ttest_ind(df_1[&#39;wage&#39;], df_2[&#39;wage&#39;], equal_var=False, nan_policy=&#39;propagate&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Assuming you have all the correct packages installed, you can directly copy paste this code to see the output. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+<span class="c1">### First, we must filter the DataFrame to obtain the right values</span>
+<span class="n">catvar_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">nba</span><span class="p">[</span><span class="s1">&#39;guard&#39;</span><span class="p">])</span>
+<span class="n">df_1</span> <span class="o">=</span> <span class="n">nba</span><span class="p">[</span><span class="n">nba</span><span class="p">[</span><span class="s1">&#39;guard&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">catvar_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+<span class="n">df_2</span> <span class="o">=</span> <span class="n">nba</span><span class="p">[</span><span class="n">nba</span><span class="p">[</span><span class="s1">&#39;guard&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">catvar_vals</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+<span class="c1">### Then, we can run our t-test</span>
+<span class="n">stats</span><span class="o">.</span><span class="n">ttest_ind</span><span class="p">(</span><span class="n">df_1</span><span class="p">[</span><span class="s1">&#39;wage&#39;</span><span class="p">],</span> <span class="n">df_2</span><span class="p">[</span><span class="s1">&#39;wage&#39;</span><span class="p">],</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">nan_policy</span><span class="o">=</span><span class="s1">&#39;propagate&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>TtestResult(statistic=2.1432820571177977, pvalue=0.03299634994484977, df=266.3682612357414)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="generating-new-columns">
+<h4>Generating new columns<a class="headerlink" href="#generating-new-columns" title="Permalink to this heading">#</a></h4>
+<p>Used for generating new columns. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;gen degree = (coll &gt;= 4)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+df[&#39;degree&#39;] = df[&#39;coll&#39;] &gt;= 4
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;gen productivity = points/(minutes/games)&quot;</span><span class="p">,</span><span class="s2">&quot;nba&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+nba[&#39;productivity&#39;] = nba[&#39;points&#39;]/(nba[&#39;minutes&#39;]/nba[&#39;games&#39;])
+</pre></div>
+</div>
+</div>
+</div>
+<p>Copy-pasting the output into Python to verify it works.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="n">nba</span><span class="p">[</span><span class="s1">&#39;productivity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">nba</span><span class="p">[</span><span class="s1">&#39;points&#39;</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">nba</span><span class="p">[</span><span class="s1">&#39;minutes&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">nba</span><span class="p">[</span><span class="s1">&#39;games&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nba</span> <span class="c1"># You can see that the productivity column was successfully generated</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>married</th>
+      <th>wage</th>
+      <th>exper</th>
+      <th>age</th>
+      <th>coll</th>
+      <th>games</th>
+      <th>minutes</th>
+      <th>guard</th>
+      <th>forward</th>
+      <th>center</th>
+      <th>points</th>
+      <th>rebounds</th>
+      <th>assists</th>
+      <th>draft</th>
+      <th>allstar</th>
+      <th>avgmin</th>
+      <th>black</th>
+      <th>children</th>
+      <th>productivity</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1002.5</td>
+      <td>4</td>
+      <td>27</td>
+      <td>4</td>
+      <td>77</td>
+      <td>2867</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>16</td>
+      <td>4</td>
+      <td>5</td>
+      <td>19.0</td>
+      <td>0</td>
+      <td>37.23</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.429717</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>2030.0</td>
+      <td>5</td>
+      <td>28</td>
+      <td>4</td>
+      <td>78</td>
+      <td>2789</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>13</td>
+      <td>3</td>
+      <td>9</td>
+      <td>28.0</td>
+      <td>0</td>
+      <td>35.76</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0.363571</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0</td>
+      <td>650.0</td>
+      <td>1</td>
+      <td>25</td>
+      <td>4</td>
+      <td>74</td>
+      <td>1149</td>
+      <td>0</td>
+      <td>0</td>
+      <td>1</td>
+      <td>6</td>
+      <td>3</td>
+      <td>0</td>
+      <td>19.0</td>
+      <td>0</td>
+      <td>15.53</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.386423</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0</td>
+      <td>2030.0</td>
+      <td>5</td>
+      <td>28</td>
+      <td>4</td>
+      <td>47</td>
+      <td>1178</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>7</td>
+      <td>5</td>
+      <td>2</td>
+      <td>1.0</td>
+      <td>0</td>
+      <td>25.06</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.279287</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0</td>
+      <td>755.0</td>
+      <td>3</td>
+      <td>24</td>
+      <td>4</td>
+      <td>82</td>
+      <td>2096</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>11</td>
+      <td>4</td>
+      <td>3</td>
+      <td>24.0</td>
+      <td>0</td>
+      <td>25.56</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.430344</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>264</th>
+      <td>1</td>
+      <td>3210.0</td>
+      <td>7</td>
+      <td>29</td>
+      <td>4</td>
+      <td>79</td>
+      <td>2638</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>20</td>
+      <td>3</td>
+      <td>3</td>
+      <td>11.0</td>
+      <td>1</td>
+      <td>33.39</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.598939</td>
+    </tr>
+    <tr>
+      <th>265</th>
+      <td>1</td>
+      <td>715.0</td>
+      <td>5</td>
+      <td>31</td>
+      <td>4</td>
+      <td>75</td>
+      <td>1084</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>5</td>
+      <td>3</td>
+      <td>1</td>
+      <td>54.0</td>
+      <td>0</td>
+      <td>14.45</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0.345941</td>
+    </tr>
+    <tr>
+      <th>266</th>
+      <td>1</td>
+      <td>600.0</td>
+      <td>11</td>
+      <td>33</td>
+      <td>3</td>
+      <td>67</td>
+      <td>1197</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>10</td>
+      <td>2</td>
+      <td>2</td>
+      <td>4.0</td>
+      <td>0</td>
+      <td>17.87</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0.559733</td>
+    </tr>
+    <tr>
+      <th>267</th>
+      <td>0</td>
+      <td>2500.0</td>
+      <td>6</td>
+      <td>28</td>
+      <td>4</td>
+      <td>78</td>
+      <td>2113</td>
+      <td>0</td>
+      <td>0</td>
+      <td>1</td>
+      <td>16</td>
+      <td>6</td>
+      <td>2</td>
+      <td>2.0</td>
+      <td>0</td>
+      <td>27.09</td>
+      <td>0</td>
+      <td>0</td>
+      <td>0.590629</td>
+    </tr>
+    <tr>
+      <th>268</th>
+      <td>0</td>
+      <td>2000.0</td>
+      <td>12</td>
+      <td>33</td>
+      <td>3</td>
+      <td>30</td>
+      <td>282</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>2</td>
+      <td>3</td>
+      <td>1</td>
+      <td>5.0</td>
+      <td>0</td>
+      <td>9.40</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0.212766</td>
+    </tr>
+  </tbody>
+</table>
+<p>269 rows × 19 columns</p>
+</div></div></div>
+</div>
+</section>
+<section id="describing-the-data">
+<h4>Describing the Data<a class="headerlink" href="#describing-the-data" title="Permalink to this heading">#</a></h4>
+<p>Used for summarizing the dataframe. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;describe&quot;</span><span class="p">,</span><span class="s2">&quot;nba&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+nba.describe()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="n">nba</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>married</th>
+      <th>wage</th>
+      <th>exper</th>
+      <th>age</th>
+      <th>coll</th>
+      <th>games</th>
+      <th>minutes</th>
+      <th>guard</th>
+      <th>forward</th>
+      <th>center</th>
+      <th>points</th>
+      <th>rebounds</th>
+      <th>assists</th>
+      <th>draft</th>
+      <th>allstar</th>
+      <th>avgmin</th>
+      <th>black</th>
+      <th>children</th>
+      <th>productivity</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>240.00000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+      <td>269.000000</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>0.442379</td>
+      <td>1423.827509</td>
+      <td>5.118959</td>
+      <td>27.394052</td>
+      <td>3.717472</td>
+      <td>65.724907</td>
+      <td>1682.193309</td>
+      <td>0.420074</td>
+      <td>0.408922</td>
+      <td>0.171004</td>
+      <td>10.260223</td>
+      <td>4.468401</td>
+      <td>2.453532</td>
+      <td>20.20000</td>
+      <td>0.115242</td>
+      <td>23.979257</td>
+      <td>0.806691</td>
+      <td>0.345725</td>
+      <td>0.409677</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>0.497595</td>
+      <td>999.774074</td>
+      <td>3.400062</td>
+      <td>3.391292</td>
+      <td>0.754410</td>
+      <td>18.851110</td>
+      <td>893.327771</td>
+      <td>0.494491</td>
+      <td>0.492551</td>
+      <td>0.377214</td>
+      <td>5.882489</td>
+      <td>2.892980</td>
+      <td>2.148124</td>
+      <td>18.73582</td>
+      <td>0.319909</td>
+      <td>9.731086</td>
+      <td>0.395629</td>
+      <td>0.476491</td>
+      <td>0.114920</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>0.000000</td>
+      <td>150.000000</td>
+      <td>1.000000</td>
+      <td>21.000000</td>
+      <td>0.000000</td>
+      <td>3.000000</td>
+      <td>33.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>0.000000</td>
+      <td>1.00000</td>
+      <td>0.000000</td>
+      <td>2.890000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.093851</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>0.000000</td>
+      <td>650.000000</td>
+      <td>2.000000</td>
+      <td>25.000000</td>
+      <td>4.000000</td>
+      <td>57.000000</td>
+      <td>983.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>5.000000</td>
+      <td>2.000000</td>
+      <td>1.000000</td>
+      <td>7.00000</td>
+      <td>0.000000</td>
+      <td>16.730000</td>
+      <td>1.000000</td>
+      <td>0.000000</td>
+      <td>0.338710</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>0.000000</td>
+      <td>1186.000000</td>
+      <td>4.000000</td>
+      <td>27.000000</td>
+      <td>4.000000</td>
+      <td>74.000000</td>
+      <td>1690.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>9.000000</td>
+      <td>4.000000</td>
+      <td>2.000000</td>
+      <td>14.50000</td>
+      <td>0.000000</td>
+      <td>24.820000</td>
+      <td>1.000000</td>
+      <td>0.000000</td>
+      <td>0.408560</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>1.000000</td>
+      <td>2014.500000</td>
+      <td>7.000000</td>
+      <td>30.000000</td>
+      <td>4.000000</td>
+      <td>79.000000</td>
+      <td>2438.000000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>0.000000</td>
+      <td>14.000000</td>
+      <td>6.000000</td>
+      <td>3.000000</td>
+      <td>28.25000</td>
+      <td>0.000000</td>
+      <td>33.260000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>0.471168</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>1.000000</td>
+      <td>5740.000000</td>
+      <td>18.000000</td>
+      <td>41.000000</td>
+      <td>4.000000</td>
+      <td>82.000000</td>
+      <td>3533.000000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>30.000000</td>
+      <td>17.000000</td>
+      <td>13.000000</td>
+      <td>139.00000</td>
+      <td>1.000000</td>
+      <td>43.090000</td>
+      <td>1.000000</td>
+      <td>1.000000</td>
+      <td>0.748532</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="correlation-matrix">
+<h4>Correlation Matrix<a class="headerlink" href="#correlation-matrix" title="Permalink to this heading">#</a></h4>
+<p>Used for generating a correlation matrix between relevant variables. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;corr points assists rebounds&quot;</span><span class="p">,</span> <span class="s2">&quot;nba&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+nba[[&#39;points&#39;, &#39;assists&#39;, &#39;rebounds&#39;]].corr()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="n">nba</span><span class="p">[[</span><span class="s1">&#39;points&#39;</span><span class="p">,</span> <span class="s1">&#39;assists&#39;</span><span class="p">,</span> <span class="s1">&#39;rebounds&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>points</th>
+      <th>assists</th>
+      <th>rebounds</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>points</th>
+      <td>1.000000</td>
+      <td>0.539269</td>
+      <td>0.563324</td>
+    </tr>
+    <tr>
+      <th>assists</th>
+      <td>0.539269</td>
+      <td>1.000000</td>
+      <td>0.059956</td>
+    </tr>
+    <tr>
+      <th>rebounds</th>
+      <td>0.563324</td>
+      <td>0.059956</td>
+      <td>1.000000</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="scatter-plots">
+<h4>Scatter Plots<a class="headerlink" href="#scatter-plots" title="Permalink to this heading">#</a></h4>
+<p>Used for making scatter (or twoway) plots. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;twoway (scatter co2pc population, xtitle(&#39;co2pc&#39;) ytitle(&#39;population&#39;))&quot;</span><span class="p">,</span><span class="s2">&quot;pollution&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import matplotlib.pyplot as plt
+plt.scatter(pollution[&#39;co2pc&#39;], pollution[&#39;population&#39;]);
+plt.xlabel(&#39;co2pc&#39;);
+plt.ylabel(&#39;population&#39;);
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pollution</span><span class="p">[</span><span class="s1">&#39;co2pc&#39;</span><span class="p">],</span> <span class="n">pollution</span><span class="p">[</span><span class="s1">&#39;population&#39;</span><span class="p">]);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;co2pc&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;population&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/88b0fc555936a7304c79a3d58ab0a1beaf83542d60919c97ffd9a9118f839a04.png" src="../../_images/88b0fc555936a7304c79a3d58ab0a1beaf83542d60919c97ffd9a9118f839a04.png" />
+</div>
+</div>
+</section>
+<section id="histograms">
+<h4>Histograms<a class="headerlink" href="#histograms" title="Permalink to this heading">#</a></h4>
+<p>Used for generating histograms. For example,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;histogram co2pc, bin(80)&quot;</span><span class="p">,</span> <span class="s2">&quot;pollution&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import matplotlib.pyplot as plt
+pollution.hist(column=&#39;co2pc&#39;,bins=80);
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">pollution</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">&#39;co2pc&#39;</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">80</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/50ce7783af1232b9383f1246d7c0283ae0c867938d2d8153ba8ae6843793b8c5.png" src="../../_images/50ce7783af1232b9383f1246d7c0283ae0c867938d2d8153ba8ae6843793b8c5.png" />
+</div>
+</div>
+</section>
+<section id="regressions">
+<h4>Regressions<a class="headerlink" href="#regressions" title="Permalink to this heading">#</a></h4>
+<p>Used for running regressions, including Python comments to help explain relevant portions of the code. Note that not all features from Stata’s <code class="docutils literal notranslate"><span class="pre">reg</span></code> command are supported. Examples include:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;reg wage exp&quot;</span><span class="p">,</span> <span class="s2">&quot;wages&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import statsmodels.api as sm
+### Dropping NaN/missing values
+wages_no_na = wages[[&#39;exp&#39;,&#39;wage&#39;]].dropna()
+### Below, we extract the relevant variables from the DataFrame
+x_df = wages_no_na[&#39;exp&#39;]
+y_df = wages_no_na[&#39;wage&#39;]
+### We now define the model, fit it to the data and then view a summary of the results
+model = sm.OLS(y_df, sm.add_constant(x_df))
+result = model.fit()
+result.summary()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
+<span class="c1">### Dropping NaN/missing values</span>
+<span class="n">wages_no_na</span> <span class="o">=</span> <span class="n">wages</span><span class="p">[[</span><span class="s1">&#39;exp&#39;</span><span class="p">,</span><span class="s1">&#39;wage&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
+<span class="c1">### Below, we extract the relevant variables from the DataFrame</span>
+<span class="n">x_df</span> <span class="o">=</span> <span class="n">wages_no_na</span><span class="p">[</span><span class="s1">&#39;exp&#39;</span><span class="p">]</span>
+<span class="n">y_df</span> <span class="o">=</span> <span class="n">wages_no_na</span><span class="p">[</span><span class="s1">&#39;wage&#39;</span><span class="p">]</span>
+<span class="c1">### We now define the model, fit it to the data and then view a summary of the results</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y_df</span><span class="p">,</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x_df</span><span class="p">))</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">result</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><table class="simpletable">
+<caption>OLS Regression Results</caption>
+<tr>
+  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.001</td>
+</tr>
+<tr>
+  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>
+</tr>
+<tr>
+  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4714</td>
+</tr>
+<tr>
+  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.493</td> 
+</tr>
+<tr>
+  <th>Time:</th>                 <td>21:26:39</td>     <th>  Log-Likelihood:    </th> <td> -3541.0</td>
+</tr>
+<tr>
+  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7086.</td>
+</tr>
+<tr>
+  <th>Df Residuals:</th>          <td>   861</td>      <th>  BIC:               </th> <td>   7096.</td>
+</tr>
+<tr>
+  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   
+</tr>
+<tr>
+  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   
+</tr>
+</table>
+<table class="simpletable">
+<tr>
+    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  
+</tr>
+<tr>
+  <th>const</th> <td>   16.0816</td> <td>    3.917</td> <td>    4.105</td> <td> 0.000</td> <td>    8.393</td> <td>   23.771</td>
+</tr>
+<tr>
+  <th>exp</th>   <td>   -0.2135</td> <td>    0.311</td> <td>   -0.687</td> <td> 0.493</td> <td>   -0.824</td> <td>    0.397</td>
+</tr>
+</table>
+<table class="simpletable">
+<tr>
+  <th>Omnibus:</th>       <td>1263.062</td> <th>  Durbin-Watson:     </th>  <td>   1.830</td> 
+</tr>
+<tr>
+  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>413853.334</td>
+</tr>
+<tr>
+  <th>Skew:</th>           <td> 8.176</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> 
+</tr>
+<tr>
+  <th>Kurtosis:</th>       <td>109.028</td> <th>  Cond. No.          </th>  <td>    99.5</td> 
+</tr>
+</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;reg wage exp, vce(cluster education)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import statsmodels.api as sm
+### We first perform some string manipulations to ensure we are appropripately accounting for strings. 
+### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.
+### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.
+y_var = &quot;wage&quot;
+clustering_vars = [&#39;education&#39;]
+### Dropping NaN/missing values
+df_no_na = df[[&#39;exp&#39;, &#39;education&#39;,&#39;wage&#39;]].dropna()
+### Below, we extract the relevant variables from the DataFrame
+x_df = df_no_na[&#39;exp&#39;]
+y_df = df_no_na[&#39;wage&#39;]
+### We now define the model, fit it to the data and then view a summary of the results
+model = sm.OLS(y_df, sm.add_constant(x_df))
+result = model.fit(cov_type=&#39;cluster&#39;, cov_kwds={&#39;groups&#39;: df_no_na[&#39;education&#39;]})
+result.summary()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">stata2python</span><span class="p">(</span><span class="s2">&quot;reg wage exp i.female, vce(cluster education)&quot;</span><span class="p">,</span> <span class="s2">&quot;wages&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>import pandas as pd
+import statsmodels.api as sm
+### We first perform some string manipulations to ensure we are appropripately accounting for strings. 
+### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.
+### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.
+y_var = &quot;wage&quot;
+clustering_vars = [&#39;education&#39;]
+wages_with_dummies = wages.copy()
+x_var = &quot;&#39;exp&#39;, &#39;i.female&#39;&quot;
+x_var_new = &quot;&#39;&quot; + &quot;&#39;, &#39;&quot;.join([i.strip(&quot;&#39;&quot;) for i in x_var.split(&quot;, &quot;) if not i.strip(&quot;&#39;&quot;).startswith(&quot;i.&quot;)]) + &quot;&#39;&quot;
+### The below section adds in relevant indicator variables, ensuring they have interpretable names
+indicator_list = [m.strip(&quot;&#39;&quot;) for m in x_var.split(&#39;i.&#39;)[1:]]
+for indicator in indicator_list:
+    dummies = pd.get_dummies(wages_with_dummies[indicator])
+    dummies = dummies.iloc[:,1:]
+    dummies.columns = [str(x) + &#39;_&#39; + str(indicator) for x in dummies.columns]
+    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)
+    x_var_new = x_var_new + &quot;, &#39;&quot; + &quot;&#39;, &#39;&quot;.join(dummies.columns) + &quot;&#39;&quot;
+x_var = x_var_new
+### This helps ensure the clustered variables are extracted from the DataFrame
+if clustering_vars:
+    x_var_temp = x_var + &quot;, &#39;&quot; + &quot;&#39;, &#39;&quot;.join(clustering_vars) + &quot;&#39;&quot;
+var_list = x_var_temp +&quot;, &#39;&quot;+ str(y_var) + &quot;&#39;&quot;
+### Dropping NaN/missing values
+wages_with_dummies_no_na = wages_with_dummies[[i.strip(&quot;&#39;&quot;) for i in var_list.split(&quot;, &quot;)]].dropna()
+### Below, we extract the relevant variables from the DataFrame
+x_df = wages_with_dummies_no_na[[i.strip(&quot;&#39;&quot;) for i in x_var.split(&quot;, &quot;)]]
+y_df = wages_with_dummies_no_na[&#39;wage&#39;]
+### We now define the model, fit it to the data and then view a summary of the results
+model = sm.OLS(y_df, sm.add_constant(x_df))
+result = model.fit(cov_type=&#39;cluster&#39;, cov_kwds={&#39;groups&#39;: wages_with_dummies_no_na[&#39;education&#39;]})
+result.summary()
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># Verifying that the output works</span>
+<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
+<span class="c1">### We first perform some string manipulations to ensure we are appropripately accounting for strings. </span>
+<span class="c1">### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.</span>
+<span class="c1">### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.</span>
+<span class="n">y_var</span> <span class="o">=</span> <span class="s2">&quot;wage&quot;</span>
+<span class="n">clustering_vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;education&#39;</span><span class="p">]</span>
+<span class="n">wages_with_dummies</span> <span class="o">=</span> <span class="n">wages</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">x_var</span> <span class="o">=</span> <span class="s2">&quot;&#39;exp&#39;, &#39;i.female&#39;&quot;</span>
+<span class="n">x_var_new</span> <span class="o">=</span> <span class="s2">&quot;&#39;&quot;</span> <span class="o">+</span> <span class="s2">&quot;&#39;, &#39;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">i</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_var</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;, &quot;</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;i.&quot;</span><span class="p">)])</span> <span class="o">+</span> <span class="s2">&quot;&#39;&quot;</span>
+<span class="c1">### The below section adds in relevant indicator variables, ensuring they have interpretable names</span>
+<span class="n">indicator_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">x_var</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;i.&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]]</span>
+<span class="k">for</span> <span class="n">indicator</span> <span class="ow">in</span> <span class="n">indicator_list</span><span class="p">:</span>
+    <span class="n">dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">wages_with_dummies</span><span class="p">[</span><span class="n">indicator</span><span class="p">])</span>
+    <span class="n">dummies</span> <span class="o">=</span> <span class="n">dummies</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="n">dummies</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">indicator</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">dummies</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
+    <span class="n">wages_with_dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">wages_with_dummies</span><span class="p">,</span><span class="n">dummies</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">x_var_new</span> <span class="o">=</span> <span class="n">x_var_new</span> <span class="o">+</span> <span class="s2">&quot;, &#39;&quot;</span> <span class="o">+</span> <span class="s2">&quot;&#39;, &#39;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dummies</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;&#39;&quot;</span>
+<span class="n">x_var</span> <span class="o">=</span> <span class="n">x_var_new</span>
+<span class="c1">### This helps ensure the clustered variables are extracted from the DataFrame</span>
+<span class="k">if</span> <span class="n">clustering_vars</span><span class="p">:</span>
+    <span class="n">x_var_temp</span> <span class="o">=</span> <span class="n">x_var</span> <span class="o">+</span> <span class="s2">&quot;, &#39;&quot;</span> <span class="o">+</span> <span class="s2">&quot;&#39;, &#39;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">clustering_vars</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;&#39;&quot;</span>
+<span class="n">var_list</span> <span class="o">=</span> <span class="n">x_var_temp</span> <span class="o">+</span><span class="s2">&quot;, &#39;&quot;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">y_var</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;&#39;&quot;</span>
+<span class="c1">### Dropping NaN/missing values</span>
+<span class="n">wages_with_dummies_no_na</span> <span class="o">=</span> <span class="n">wages_with_dummies</span><span class="p">[[</span><span class="n">i</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">var_list</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;, &quot;</span><span class="p">)]]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
+<span class="c1">### Below, we extract the relevant variables from the DataFrame</span>
+<span class="n">x_df</span> <span class="o">=</span> <span class="n">wages_with_dummies_no_na</span><span class="p">[[</span><span class="n">i</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_var</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;, &quot;</span><span class="p">)]]</span>
+<span class="n">y_df</span> <span class="o">=</span> <span class="n">wages_with_dummies_no_na</span><span class="p">[</span><span class="s1">&#39;wage&#39;</span><span class="p">]</span>
+<span class="c1">### We now define the model, fit it to the data and then view a summary of the results</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y_df</span><span class="p">,</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x_df</span><span class="p">))</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;groups&#39;</span><span class="p">:</span> <span class="n">wages_with_dummies_no_na</span><span class="p">[</span><span class="s1">&#39;education&#39;</span><span class="p">]})</span>
+<span class="n">result</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><table class="simpletable">
+<caption>OLS Regression Results</caption>
+<tr>
+  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.007</td>
+</tr>
+<tr>
+  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>
+</tr>
+<tr>
+  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.993</td>
+</tr>
+<tr>
+  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.183</td> 
+</tr>
+<tr>
+  <th>Time:</th>                 <td>21:26:54</td>     <th>  Log-Likelihood:    </th> <td> -3538.0</td>
+</tr>
+<tr>
+  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7082.</td>
+</tr>
+<tr>
+  <th>Df Residuals:</th>          <td>   860</td>      <th>  BIC:               </th> <td>   7096.</td>
+</tr>
+<tr>
+  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   
+</tr>
+<tr>
+  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>     <td> </td>   
+</tr>
+</table>
+<table class="simpletable">
+<tr>
+      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  
+</tr>
+<tr>
+  <th>const</th>    <td>   17.4218</td> <td>    3.698</td> <td>    4.712</td> <td> 0.000</td> <td>   10.175</td> <td>   24.669</td>
+</tr>
+<tr>
+  <th>exp</th>      <td>   -0.2373</td> <td>    0.206</td> <td>   -1.153</td> <td> 0.249</td> <td>   -0.640</td> <td>    0.166</td>
+</tr>
+<tr>
+  <th>1_female</th> <td>   -2.4604</td> <td>    1.261</td> <td>   -1.951</td> <td> 0.051</td> <td>   -4.932</td> <td>    0.011</td>
+</tr>
+</table>
+<table class="simpletable">
+<tr>
+  <th>Omnibus:</th>       <td>1260.198</td> <th>  Durbin-Watson:     </th>  <td>   1.842</td> 
+</tr>
+<tr>
+  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>409372.994</td>
+</tr>
+<tr>
+  <th>Skew:</th>           <td> 8.142</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> 
+</tr>
+<tr>
+  <th>Kurtosis:</th>       <td>108.449</td> <th>  Cond. No.          </th>  <td>    101.</td> 
+</tr>
+</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)</div></div>
+</div>
+</section>
+</section>
 </section>
 </section>
 
@@ -364,16 +1817,16 @@ <h2><span style="color:red"> To be updated</span><a class="headerlink" href="#sp
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         codeMirrorConfig: {
             theme: "abcdef",
-            mode: "python"
+            mode: "conda-env-sklearn-env-py"
         },
         kernelOptions: {
-            name: "python3",
+            name: "conda-env-sklearn-env-py",
             path: "./content/01-python_v_stata"
         },
         predefinedOutput: true
     }
     </script>
-    <script>kernelName = 'python3'</script>
+    <script>kernelName = 'conda-env-sklearn-env-py'</script>
 
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#installation-and-import">Installation and Import</a></li>
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+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#t-tests">T-tests</a></li>
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+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#describing-the-data">Describing the Data</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#correlation-matrix">Correlation Matrix</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#scatter-plots">Scatter Plots</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#histograms">Histograms</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#regressions">Regressions</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
   </nav></div>
 
diff --git a/_build/html/content/02-prereqs/index.html b/_build/html/content/02-prereqs/index.html
index d463362..5ad2fb7 100644
--- a/_build/html/content/02-prereqs/index.html
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-    <link rel="prev" title="Data Science for Economists" href="../intro.html" />
+    <link rel="prev" title="Economics Pedagogy" href="../01-python_v_stata/pedagogy.html" />
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+<li class="toctree-l2"><a class="reference internal" href="../01-python_v_stata/differences.html">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01-python_v_stata/install.html">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01-python_v_stata/syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../01-python_v_stata/pedagogy.html">Economics Pedagogy</a></li>
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                   <!-- Previous / next buttons -->
 <div class="prev-next-area">
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         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><a> Data Science for Economists </a></p>
+        <p class="prev-next-title">Economics Pedagogy</p>
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diff --git a/_build/html/content/02-prereqs/jupyter.html b/_build/html/content/02-prereqs/jupyter.html
index 725335a..1dd2c6a 100644
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diff --git a/_build/html/update_reqs.html b/_build/html/update_reqs.html
index 0a9190d..6064902 100644
--- a/_build/html/update_reqs.html
+++ b/_build/html/update_reqs.html
@@ -163,7 +163,15 @@
             </li>
         </ul>
         <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="content/02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="content/01-python_v_stata/index.html">Python vs Stata</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="content/01-python_v_stata/history.html">History of the Languages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="content/01-python_v_stata/differences.html">Summary of Differences</a></li>
+<li class="toctree-l2"><a class="reference internal" href="content/01-python_v_stata/install.html">Installation and General Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="content/01-python_v_stata/syntax.html">Syntax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="content/01-python_v_stata/pedagogy.html">Economics Pedagogy</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="content/02-prereqs/index.html">Prerequisites</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="content/02-prereqs/python.html">Python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="content/02-prereqs/jupyter.html">Jupyter Notebooks</a></li>
 
@@ -171,7 +179,7 @@
 <li class="toctree-l2"><a class="reference internal" href="content/02-prereqs/numpy.html">Numpy</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="content/03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="content/03-pandas/index.html">Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="content/03-pandas/loading-looking.html">Loading in and Looking at the Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="content/03-pandas/select_modify.html">Selecting and Modifying Rows/Columns</a></li>
 <li class="toctree-l2"><a class="reference internal" href="content/03-pandas/misc_funcs.html">Helpful Pandas Methods</a></li>
@@ -179,7 +187,7 @@
 <li class="toctree-l2"><a class="reference internal" href="content/03-pandas/merge.html">Joins</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="content/04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="content/04-visualizations/index.html">Visualizations</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="content/04-visualizations/choosing.html">Choosing the Right Plot</a></li>
 <li class="toctree-l2"><a class="reference internal" href="content/04-visualizations/types.html">Basic Visualizations</a></li>
 <li class="toctree-l2"><a class="reference internal" href="content/04-visualizations/advanced.html">More Advanced Visualizations</a></li>
diff --git a/_build/jupyter_execute/50ce7783af1232b9383f1246d7c0283ae0c867938d2d8153ba8ae6843793b8c5.png b/_build/jupyter_execute/50ce7783af1232b9383f1246d7c0283ae0c867938d2d8153ba8ae6843793b8c5.png
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diff --git a/_build/jupyter_execute/_site/content/05-regex/regex.ipynb b/_build/jupyter_execute/_site/content/05-regex/regex.ipynb
index 567c7c7..47ff88b 100644
--- a/_build/jupyter_execute/_site/content/05-regex/regex.ipynb
+++ b/_build/jupyter_execute/_site/content/05-regex/regex.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "976e500b",
+   "id": "2a0b76e3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e418791d",
+   "id": "d55f26d3",
    "metadata": {},
    "source": [
     "# RegEx\n",
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9dcd7052",
+   "id": "1b1c3ba4",
    "metadata": {},
    "source": [
     "`RegEx`, standing for regular expressions, are a powerful tool used to search through large bodies of text. Using RegEx, you can essentially specify patterns you are looking for and get all the substrings which match that pattern. This subchapter will go over how to use RegEx in Python.\n",
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc52aaca",
+   "id": "aeeb0ba6",
    "metadata": {},
    "source": [
     "## RegEx Syntax\n",
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10cda8e7",
+   "id": "dcbca7aa",
    "metadata": {},
    "source": [
     "RegEx patterns are made up of literal characters and special characters. Literal characters are literally the characters they represent. For example, in the code below, the characters `Economics` match the string `Economics` literally and return a match.\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8fe04d4a",
+   "id": "e0df4d17",
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c4de2c8",
+   "id": "1228857e",
    "metadata": {},
    "source": [
     "As the characters are literal, the characters `Economics` do not match the string `Economica`. You can see nothing is returned, meaning no match was found."
@@ -92,7 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e52cf98d",
+   "id": "1d0afe8b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c963189",
+   "id": "d60ce0c6",
    "metadata": {},
    "source": [
     "If we wanted to also match `Economica`, we could modify our pattern to be `Economic(s|a)`. `|` is the 'or' operator here, indicating that either of the options will be acceptable. Since the parenthesis surround `s|a`, this means that the pattern will only match `Economics` or `Economica`, it will not match other patterns like just `a` or just `Economic`."
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "82d5f8a7",
+   "id": "be18a86d",
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "614a58cb",
+   "id": "f44797c3",
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c7a76bac",
+   "id": "a02a73dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "b30bfa00",
+   "id": "f52eff0c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d5d861a",
+   "id": "3a0c1434",
    "metadata": {},
    "source": [
     "### Quantifiers"
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f61940e",
+   "id": "c0a71a22",
    "metadata": {},
    "source": [
     "There are several ways of telling RegEx how many of a character we want. They are summarized below:\n",
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fa9e113",
+   "id": "8848127f",
    "metadata": {},
    "source": [
     "#### Greediness"
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05fbcf3f",
+   "id": "8e51e1ef",
    "metadata": {},
    "source": [
     "RegEx operations are greedy, meaning they look for the longest possible match in a string. For example, given the string _ab_, the RegEx pattern `.*` could match _a_, _b_, _ab_, or nothing at all. However, because it wants the longest match possible, it will choose to match _ab_."
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "37820613",
+   "id": "980178b9",
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1a475cf",
+   "id": "b589d431",
    "metadata": {},
    "source": [
     "If we instead wanted to match as little as possible when using quantifiers, we can simply add `?` after the quantifier to make it instead match as little as possible. For example, given the string _ab_, the RegEx pattern `.*` matches nothing at all."
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "05ed575e",
+   "id": "7aadf098",
    "metadata": {},
    "outputs": [
     {
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab11b123",
+   "id": "6b44ccf0",
    "metadata": {},
    "source": [
     "Here's another example of greedy vs non-greedy matching:"
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "d7f67a8a",
+   "id": "c403ad8f",
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12627f63",
+   "id": "2f407a97",
    "metadata": {},
    "source": [
     "### Character Classes"
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26d62359",
+   "id": "726c489e",
    "metadata": {},
    "source": [
     "RegEx character classes help us match any one character from a list of characters. They are often denoted by a list of characters surrounded by hard brackets (`[]`). For example, _er[ic]*_ will match _eri_, _ericc_, _eriicc_ and _erii_."
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "da4bc87a",
+   "id": "8313c3e5",
    "metadata": {},
    "outputs": [
     {
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8896d393",
+   "id": "1169cdab",
    "metadata": {},
    "source": [
     "You can also use character classes to denote characters you do not wish to match. If you put `^` at the front of a character class, it becomes a character class negation, and matches anything except the characters in the character class. For example, the pattern `consonant: [^aeiou]` will match any string of the pattern `consonant: ` followed by a consonant, as shown below."
@@ -335,7 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "f8573321",
+   "id": "3a1ad6c4",
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3511b78e",
+   "id": "aa6501ad",
    "metadata": {},
    "source": [
     "| Character Class | Description | Matches | Doesn't Match |\n",
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4aa5b2",
+   "id": "f0c333d8",
    "metadata": {},
    "source": [
     "### Positions"
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9f19d7a",
+   "id": "9360a191",
    "metadata": {},
    "source": [
     "When discussing character classes, we'd mentioned how `^` can be used to negate a character class. However, `^` actually has two uses. If the `^` is not in the middle of a bracket and preceded by `[`, it instead represents a position at the beginning of a string. For example, the pattern `^a` will contain a match in `all` but not in `ball`."
@@ -394,7 +394,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "1d365a4b",
+   "id": "c4f137a3",
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55bb1f48",
+   "id": "107cc2a3",
    "metadata": {},
    "source": [
     "Similarly, `$` denotes the end of a string. For example, the pattern `s$` will contain a match in _prawns_ but not in _prawn_."
@@ -422,7 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "7454c4f1",
+   "id": "a2b48acc",
    "metadata": {},
    "outputs": [
     {
@@ -441,7 +441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac25cf3c",
+   "id": "101f56fe",
    "metadata": {},
    "source": [
     "### Escape"
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "887d6026",
+   "id": "22c2f351",
    "metadata": {},
    "source": [
     "The escape character `\\` is used to take special characters and turn them into literal ones. For example, the character `.` will match anything except a newline, but `\\.` will only match `.` and nothing else."
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "b3686f23",
+   "id": "e5eb40c9",
    "metadata": {},
    "outputs": [
     {
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6677c11a",
+   "id": "652592db",
    "metadata": {},
    "source": [
     "## Using Regex in Python"
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f1d4945",
+   "id": "d1f07649",
    "metadata": {},
    "source": [
     "### Regex Library Functions"
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a995610b",
+   "id": "e1803ba5",
    "metadata": {},
    "source": [
     "The `regex` library of Python has several functions that are helpful for working with Python strings. Until now, we've been working with `re.search()`. `re.search()` takes in a RegEx pattern and a Python string, and returns the first match of the pattern within the string. It returns the match as a `regex.Match object`. `regex.Match object's` have 4 helpful methods:\n",
@@ -510,7 +510,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "b3695fa0",
+   "id": "a517badb",
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +532,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "0d0eb5d8",
+   "id": "d0d475f2",
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +553,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "0d67568d",
+   "id": "5c14aee7",
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +574,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "00e12e37",
+   "id": "e9662160",
    "metadata": {},
    "outputs": [
     {
@@ -595,7 +595,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "8341e5b9",
+   "id": "abba6af6",
    "metadata": {},
    "outputs": [
     {
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aa9a6d6",
+   "id": "b48a2b49",
    "metadata": {},
    "source": [
     "The other python RegEx functions we will discuss return either lists or strings. They are described below."
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d86d5f9",
+   "id": "477a776e",
    "metadata": {},
    "source": [
     "| Function        | Description                                | Syntax Example         | Returns         | Example Usage                                    | Example Output                                  |\n",
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18014b63",
+   "id": "4c398a5b",
    "metadata": {},
    "source": [
     "### Capturing Groups"
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e92b0d1f",
+   "id": "72f868ae",
    "metadata": {},
    "source": [
     "Using `re.findall()` allows us to easily extract information from our string. Capturing groups can help us extract information further.\n",
@@ -657,7 +657,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "7df3c89d",
+   "id": "cba80169",
    "metadata": {},
    "outputs": [
     {
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa8f08f8",
+   "id": "d15f0401",
    "metadata": {},
    "source": [
     "Well, if we surround a portion of the pattern with parenthesis, we can capture that portion and extract only that portion into our final list."
@@ -686,7 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "d144611f",
+   "id": "a3f6a772",
    "metadata": {},
    "outputs": [
     {
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24da63d8",
+   "id": "52bca623",
    "metadata": {},
    "source": [
     "This is tricky as earlier, we'd introduced parenthesis as forms of specifying which letters we want to choose between."
@@ -715,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "49ed8c9e",
+   "id": "6c67dd57",
    "metadata": {},
    "outputs": [
     {
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de6fdabf",
+   "id": "c7679354",
    "metadata": {},
    "source": [
     "This brings us to a **key point**: when extracting information with `re.findall()`, parentheses serve as capturing groups. However, when simply matching the first occurrence with `re.search()` without extracting anything, parentheses instead just help group characters together. This is something to be careful of; it is possible to rework any RegEx expression to not use parenthesis where they are not necessary. For example, you could transform the pattern `Economic(s|a)` to `Economic[sa]` and then use `re.findall()` to accomplish the same result as the `re.search()` call above."
@@ -744,7 +744,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "81163d96",
+   "id": "e624ada7",
    "metadata": {},
    "outputs": [
     {
@@ -765,7 +765,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "1370fb38",
+   "id": "3517f378",
    "metadata": {},
    "outputs": [
     {
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c42dc21",
+   "id": "b77256e8",
    "metadata": {},
    "source": [
     "### Integration with `Pandas` String Methods"
@@ -795,7 +795,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60016853",
+   "id": "04d5906c",
    "metadata": {},
    "source": [
     "RegEx patterns work very well with `pandas` [string methods](../03-pandas/misc_funcs.ipynb). For example, the RegEx pattern `ssn_pattern` below matches all strings that follow the pattern of an American social security number (namely: 3 digits, dash, 2 digits, dash, 3 digits). Let us imagine you've surveyed a bunch of (American) people asking them their social security number. You've stored all their responses in the DataFrame `ssn_data` below. You can use `str.findall()` to extract all potential the social security numbers. "
@@ -804,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "d3f618d4",
+   "id": "5f06571b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -815,7 +815,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "db19d79a",
+   "id": "6c5fcdfc",
    "metadata": {},
    "outputs": [
     {
@@ -943,7 +943,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35986af7",
+   "id": "52359f86",
    "metadata": {},
    "source": [
     "Applying the `findall` function;"
@@ -952,7 +952,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "86c97169",
+   "id": "5ebce378",
    "metadata": {},
    "outputs": [
     {
@@ -983,7 +983,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43b88e76",
+   "id": "e697b666",
    "metadata": {},
    "source": [
     "If you were just interested in the digits of the social security numbers, you could use capturing groups combined with `.str.extract()`, as shown below."
@@ -992,7 +992,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "d1f5b04b",
+   "id": "1ee2d208",
    "metadata": {},
    "outputs": [
     {
diff --git a/_build/jupyter_execute/content/01-python_v_stata/pedagogy.ipynb b/_build/jupyter_execute/content/01-python_v_stata/pedagogy.ipynb
new file mode 100644
index 0000000..aae9a7e
--- /dev/null
+++ b/_build/jupyter_execute/content/01-python_v_stata/pedagogy.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c41e3963-887e-423e-8e3a-49d49122a0cd",
+   "metadata": {},
+   "source": [
+    "# Economics Pedagogy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3069aff8-c35a-42b7-a08c-8f8e0a45ea25",
+   "metadata": {},
+   "source": [
+    "Both Python and Stata have been used to teach economics to students of all levels, from the beginner to the graduate students. They both have their advantages and disadvantages, some of which are discussed below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2017f83-5353-401d-8da3-2d6bae086953",
+   "metadata": {},
+   "source": [
+    "## Learning Curve\n",
+    "\n",
+    "Students who have little to no experience in coding often find Stata's command-line interface easier to install and use. However, students with more experience in coding may be more familiar with Python and thus find that easier to use. At the end of the day, different students could find either of the softwares easier to learn, and the software you choose to use should consider the level of the students in your class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5590927b-1191-4a2a-9c6a-c2fe15c85e11",
+   "metadata": {},
+   "source": [
+    "## Accessibility\n",
+    "\n",
+    "Both Python and Jupyter notebooks are free and easily accessible, while Stata must be purchased. If your students do not have an instituional license for Stata, it may be more inclusive to use Python to avoid placing an undue financial burden on students. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03140e78-8b1a-4e9e-bdd7-2bc2d1ff07d0",
+   "metadata": {},
+   "source": [
+    "## Cloud Computing\n",
+    "\n",
+    "With the great integration between Jupyter Notebooks and Cloud Computing provided by tools such as [Google Colab](https://colab.research.google.com/), it is possible to easily share your Jupyter notebooks with all your students and have them run your code in real time. To share your Stata `.do` files with students, it may be best to upload them to a Google Drive folder and have your students download and run them locally. Students may then run into issues if they haven't locally installed all the packages/updated all computer paths in your code. These issues are avoided if you use a virtual environment where everyone has access to the same resources, such as Google Colab (more details can be found on Colab's [welcome page](https://colab.research.google.com/#scrollTo=GJBs_flRovLc))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ca554f-7f28-407b-81c9-62ed88a8488a",
+   "metadata": {},
+   "source": [
+    "## Autograding\n",
+    "\n",
+    "In both languages, you can view and grade the final output of student code. However, Stata `.do` files do not have any features to automatically grade the intermediate steps student take towards a solution. Python has a package called [Otter Grader](https://otter-grader.readthedocs.io/en/latest/), designed to help you grade the output of various code cells in Jupyter notebooks. Using Otter Grader, you can create various test cells that have their corresponding solutions and relevant tests. The package provides tests that are both visible to and hidden from students, enabling you to both guide students on the right track and check for correctness as necessary. \n",
+    "\n",
+    "Several UC Berkeley classes, including [Econ 148: Data Science for Economists](https://www.econ148.org), use Otter Grader along with [Gradescope](https://www.gradescope.com/) to automatically grade thousands of student submissions at once. However, you do not need a Gradescope license to use Otter Grader, Otter is a free package that can be used by anyone without any cost. Please look at the [Otter tutorial](https://otter-grader.readthedocs.io/en/latest/tutorial.html) for more information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b4a72a-aeef-42dd-b8c8-12af667ea0fe",
+   "metadata": {},
+   "source": [
+    "## Textbooks\n",
+    "\n",
+    "While both Python and Stata have economics-specific textbooks, their styles and formats may differ. For Stata, the books are often traditional print books, such as [_An Introduction to Modern Econometrics Using Stata_](https://www.stata.com/bookstore/modern-econometrics-stata/). Students may be required to purchase a physical or digital copy of these textbooks in order to use them.\n",
+    "\n",
+    "Meanwhile, Python textbooks may be found online for free. For example, consider the online textbooks [_Coding for Economists_](https://aeturrell.github.io/coding-for-economists/intro.html), [_Causal Inference for The Brave and True_](https://matheusfacure.github.io/python-causality-handbook/landing-page.html), or [_Economic Networks_](https://networks.quantecon.org/). These textbooks may often include interactive code snippets that the students can try running on their own, and corresponding githubs where the students can see how the textbook was written. Such open access to the material offers greater reproducibility, an advantage over Stata."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/content/01-python_v_stata/syntax.ipynb b/_build/jupyter_execute/content/01-python_v_stata/syntax.ipynb
new file mode 100644
index 0000000..c8f51b8
--- /dev/null
+++ b/_build/jupyter_execute/content/01-python_v_stata/syntax.ipynb
@@ -0,0 +1,2020 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3dfa8749-c9af-4218-8367-96918f529dc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "474395b2-eedd-4588-aabf-10cf6a314ad2",
+   "metadata": {},
+   "source": [
+    "# Syntax\n",
+    "\n",
+    "The online textbook [Coding for Economists](https://aeturrell.github.io/coding-for-economists/intro.html) has a great [section summarizing the differences in syntax](https://aeturrell.github.io/coding-for-economists/coming-from-stata.html) between Stata and Python. This chapter does not aim to replace the key material included there. Instead, I hope it can serve as a supplement for those transitioning from Stata to Python.\n",
+    "\n",
+    "This chapter will aim to describe [`stata2python`](https://pypi.org/project/stata2python/), a Python package where you can write Stata commands and recieve the equivalent code for them in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f31b176c-1936-43d8-9a56-ebbf2884e7ca",
+   "metadata": {},
+   "source": [
+    "## stata2python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a5f7c3-ac2d-4560-9d7e-323870c8efeb",
+   "metadata": {},
+   "source": [
+    "### Installation and Import\n",
+    "\n",
+    "To install `stata2python`, you can simply use the package installer [`pip`](https://pypi.org/project/pip/). An example is shown below. To do so, simply run the following command in your terminal.\n",
+    "\n",
+    "    pip install --upgrade stata2python\n",
+    "    \n",
+    "After you have installed the package, you can simply import it into your Jupyter notebook. Alternatively, you can also access a [Python shell](https://www.tutorialsteacher.com/python/python-interective-shell) ( by typing `python3` in your terminal) and begin using it. To import the package in your notebook/shell, the following syntax is encouraged.\n",
+    "\n",
+    "    from stata2python import stata2python\n",
+    "   \n",
+    "We import the package below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9845b073-c30b-41d9-95bd-53ba694474d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stata2python import stata2python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "653ded29-8942-4b92-bb9c-996aca1f077c",
+   "metadata": {},
+   "source": [
+    "We also import in sample NBA, (log) wage and pollution datasets that will be helpful for demonstrating the features of our package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2929442c-5eb6-4c4c-aab1-3c029ea70a4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   married    wage  exper  age  coll  games  minutes  guard  forward  center  \\\n",
+       "0        1  1002.5      4   27     4     77     2867      1        0       0   \n",
+       "1        1  2030.0      5   28     4     78     2789      1        0       0   \n",
+       "2        0   650.0      1   25     4     74     1149      0        0       1   \n",
+       "3        0  2030.0      5   28     4     47     1178      0        1       0   \n",
+       "4        0   755.0      3   24     4     82     2096      1        0       0   \n",
+       "\n",
+       "   points  rebounds  assists  draft  allstar  avgmin  black  children  \n",
+       "0      16         4        5   19.0        0   37.23      1         0  \n",
+       "1      13         3        9   28.0        0   35.76      1         1  \n",
+       "2       6         3        0   19.0        0   15.53      1         0  \n",
+       "3       7         5        2    1.0        0   25.06      1         0  \n",
+       "4      11         4        3   24.0        0   25.56      1         0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba = pd.read_csv(\"data/nba.csv\") # NBA dataset\n",
+    "nba.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10eac6fb-6d2c-4f0f-b172-cfacb74eb6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hispanic</th>\n",
+       "      <th>citizen</th>\n",
+       "      <th>black</th>\n",
+       "      <th>exp</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>female</th>\n",
+       "      <th>education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>5.288462</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>8.461538</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>10.416667</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>21.634615</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.365385</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   hispanic  citizen  black   exp       wage  female  education\n",
+       "0         1        1      0  14.0   5.288462       1          9\n",
+       "1         0        1      0  14.7   8.461538       1         13\n",
+       "2         0        1      0  14.7  10.416667       1         13\n",
+       "3         0        1      0  14.0  21.634615       1         14\n",
+       "4         1        0      0  12.0   3.365385       1         12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wages = pd.read_csv(\"data/la.csv\") # (Log) Wages dataset\n",
+    "wages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69beac78-18de-4389-965c-1f1fbfe17c25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>countryname</th>\n",
+       "      <th>countrycode</th>\n",
+       "      <th>gdp</th>\n",
+       "      <th>gdppc</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>co2pc</th>\n",
+       "      <th>population</th>\n",
+       "      <th>oecd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>ZMB</td>\n",
+       "      <td>9.799629e+09</td>\n",
+       "      <td>741.4421</td>\n",
+       "      <td>2427.554</td>\n",
+       "      <td>0.183669</td>\n",
+       "      <td>13216985</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>French Polynesia</td>\n",
+       "      <td>PYF</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>883.747</td>\n",
+       "      <td>3.296764</td>\n",
+       "      <td>268065</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Monaco</td>\n",
+       "      <td>MCO</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36845</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>UKR</td>\n",
+       "      <td>9.057726e+10</td>\n",
+       "      <td>1974.6212</td>\n",
+       "      <td>304804.720</td>\n",
+       "      <td>6.644867</td>\n",
+       "      <td>45870700</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Venezuela, RB</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1.750000e+11</td>\n",
+       "      <td>6010.0270</td>\n",
+       "      <td>201747.340</td>\n",
+       "      <td>6.946437</td>\n",
+       "      <td>29043283</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year       countryname countrycode           gdp      gdppc         co2  \\\n",
+       "0  2010            Zambia         ZMB  9.799629e+09   741.4421    2427.554   \n",
+       "1  2010  French Polynesia         PYF           NaN        NaN     883.747   \n",
+       "2  2010            Monaco         MCO           NaN        NaN         NaN   \n",
+       "3  2010           Ukraine         UKR  9.057726e+10  1974.6212  304804.720   \n",
+       "4  2010     Venezuela, RB         VEN  1.750000e+11  6010.0270  201747.340   \n",
+       "\n",
+       "      co2pc  population  oecd  \n",
+       "0  0.183669    13216985   0.0  \n",
+       "1  3.296764      268065   0.0  \n",
+       "2       NaN       36845   0.0  \n",
+       "3  6.644867    45870700   0.0  \n",
+       "4  6.946437    29043283   0.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pollution = pd.read_csv(\"data/pollution.csv\") # Pollution dataset\n",
+    "pollution.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3edebbb-363c-489d-b560-09620fe27466",
+   "metadata": {},
+   "source": [
+    "### Usage\n",
+    "\n",
+    "Currently, `stata2python` only supports commands necessary to teach an introductory course in econometrics. If you would like to contribute, feel free to create pull requests [here](https://github.com/rohanjha123/data-h195/tree/main/creating_package). Below, we discuss the commands currently supported by the package.\n",
+    "\n",
+    "By importing `stata2python` via the command `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command. \n",
+    "\n",
+    "Optionally, you may also specify the name of the DataFrame you're working with via the `df_name` parameter in `stata2python`. The default value for `df_name` is simply `df`. \n",
+    "\n",
+    "Note that the provided Python code will (naturally) require you to have `pandas` installed, and may also require you to have the Python packages `numpy`, `scipy`, `matplotlib`, and `statsmodels` installed. The provided code will include all imports necessary (including `import pandas as pd`).\n",
+    "\n",
+    "Below, we discuss all the features currently supported by `Stata2Python`, along with providing example usages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9ace3e5-b5e7-473e-8bdb-cf6559c2fcc1",
+   "metadata": {},
+   "source": [
+    "#### T-tests\n",
+    "\n",
+    "This function helps users determine the code for running [t-tests](https://www.jmp.com/en_us/statistics-knowledge-portal/t-test.html) in Python. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b48ee104-aa0a-4461-a1bc-9f40c6c90996",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
+      "catvar_vals = np.unique(df['guard'])\n",
+      "df_1 = df[df['guard'] == catvar_vals[0]]\n",
+      "df_2 = df[df['guard'] == catvar_vals[1]]\n",
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"ttest wage, by(guard)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87876a2e-0b2c-40d5-a64a-c8ec25941aa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
+      "catvar_vals = np.unique(nba['guard'])\n",
+      "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
+      "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"ttest wage, by(guard) unequal\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89b477e9-82df-43d2-bc97-dc466c1bcb92",
+   "metadata": {},
+   "source": [
+    "Assuming you have all the correct packages installed, you can directly copy paste this code to see the output. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a485ec9f-25f6-4c24-9ee8-380b751ef6a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=2.1432820571177977, pvalue=0.03299634994484977, df=266.3682612357414)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "### First, we must filter the DataFrame to obtain the right values\n",
+    "catvar_vals = np.unique(nba['guard'])\n",
+    "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
+    "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
+    "### Then, we can run our t-test\n",
+    "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcac0225-7131-4a7d-9997-47958c10c172",
+   "metadata": {},
+   "source": [
+    "#### Generating new columns\n",
+    "\n",
+    "Used for generating new columns. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b0fd3d6a-0a9e-459c-896c-d7a53e12cba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "df['degree'] = df['coll'] >= 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"gen degree = (coll >= 4)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9ba0e4f5-d33e-48ed-a5ad-f3afb0a65abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"gen productivity = points/(minutes/games)\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9357b734-d25a-4008-b0c9-86240daecff2",
+   "metadata": {},
+   "source": [
+    "Copy-pasting the output into Python to verify it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b8fd2dcf-621b-40dc-95bf-f1deacf15a09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7b99236a-651a-47dc-8be9-b5c8090f4f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.363571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.386423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.279287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.430344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3210.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.598939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>1</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>4</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.345941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.87</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.559733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.590629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>282</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212766</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>269 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     married    wage  exper  age  coll  games  minutes  guard  forward  \\\n",
+       "0          1  1002.5      4   27     4     77     2867      1        0   \n",
+       "1          1  2030.0      5   28     4     78     2789      1        0   \n",
+       "2          0   650.0      1   25     4     74     1149      0        0   \n",
+       "3          0  2030.0      5   28     4     47     1178      0        1   \n",
+       "4          0   755.0      3   24     4     82     2096      1        0   \n",
+       "..       ...     ...    ...  ...   ...    ...      ...    ...      ...   \n",
+       "264        1  3210.0      7   29     4     79     2638      1        0   \n",
+       "265        1   715.0      5   31     4     75     1084      0        1   \n",
+       "266        1   600.0     11   33     3     67     1197      1        0   \n",
+       "267        0  2500.0      6   28     4     78     2113      0        0   \n",
+       "268        0  2000.0     12   33     3     30      282      0        1   \n",
+       "\n",
+       "     center  points  rebounds  assists  draft  allstar  avgmin  black  \\\n",
+       "0         0      16         4        5   19.0        0   37.23      1   \n",
+       "1         0      13         3        9   28.0        0   35.76      1   \n",
+       "2         1       6         3        0   19.0        0   15.53      1   \n",
+       "3         0       7         5        2    1.0        0   25.06      1   \n",
+       "4         0      11         4        3   24.0        0   25.56      1   \n",
+       "..      ...     ...       ...      ...    ...      ...     ...    ...   \n",
+       "264       0      20         3        3   11.0        1   33.39      1   \n",
+       "265       0       5         3        1   54.0        0   14.45      1   \n",
+       "266       0      10         2        2    4.0        0   17.87      1   \n",
+       "267       1      16         6        2    2.0        0   27.09      0   \n",
+       "268       0       2         3        1    5.0        0    9.40      1   \n",
+       "\n",
+       "     children  productivity  \n",
+       "0           0      0.429717  \n",
+       "1           1      0.363571  \n",
+       "2           0      0.386423  \n",
+       "3           0      0.279287  \n",
+       "4           0      0.430344  \n",
+       "..        ...           ...  \n",
+       "264         0      0.598939  \n",
+       "265         1      0.345941  \n",
+       "266         1      0.559733  \n",
+       "267         0      0.590629  \n",
+       "268         0      0.212766  \n",
+       "\n",
+       "[269 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba # You can see that the productivity column was successfully generated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ee162dd-ad45-4af0-aaa4-062cc4e6e40b",
+   "metadata": {},
+   "source": [
+    "#### Describing the Data\n",
+    "\n",
+    "Used for summarizing the dataframe. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f44496e-8337-4ec4-9677-7d9e770e6b16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba.describe()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"describe\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "da57f17a-214a-4932-a6c8-354a7aeb87e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>240.00000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.442379</td>\n",
+       "      <td>1423.827509</td>\n",
+       "      <td>5.118959</td>\n",
+       "      <td>27.394052</td>\n",
+       "      <td>3.717472</td>\n",
+       "      <td>65.724907</td>\n",
+       "      <td>1682.193309</td>\n",
+       "      <td>0.420074</td>\n",
+       "      <td>0.408922</td>\n",
+       "      <td>0.171004</td>\n",
+       "      <td>10.260223</td>\n",
+       "      <td>4.468401</td>\n",
+       "      <td>2.453532</td>\n",
+       "      <td>20.20000</td>\n",
+       "      <td>0.115242</td>\n",
+       "      <td>23.979257</td>\n",
+       "      <td>0.806691</td>\n",
+       "      <td>0.345725</td>\n",
+       "      <td>0.409677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.497595</td>\n",
+       "      <td>999.774074</td>\n",
+       "      <td>3.400062</td>\n",
+       "      <td>3.391292</td>\n",
+       "      <td>0.754410</td>\n",
+       "      <td>18.851110</td>\n",
+       "      <td>893.327771</td>\n",
+       "      <td>0.494491</td>\n",
+       "      <td>0.492551</td>\n",
+       "      <td>0.377214</td>\n",
+       "      <td>5.882489</td>\n",
+       "      <td>2.892980</td>\n",
+       "      <td>2.148124</td>\n",
+       "      <td>18.73582</td>\n",
+       "      <td>0.319909</td>\n",
+       "      <td>9.731086</td>\n",
+       "      <td>0.395629</td>\n",
+       "      <td>0.476491</td>\n",
+       "      <td>0.114920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.890000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>650.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>983.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.730000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.338710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1186.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1690.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.50000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24.820000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2014.500000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>2438.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.25000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>33.260000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.471168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5740.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>3533.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>139.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>43.090000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.748532</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          married         wage       exper         age        coll  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean     0.442379  1423.827509    5.118959   27.394052    3.717472   \n",
+       "std      0.497595   999.774074    3.400062    3.391292    0.754410   \n",
+       "min      0.000000   150.000000    1.000000   21.000000    0.000000   \n",
+       "25%      0.000000   650.000000    2.000000   25.000000    4.000000   \n",
+       "50%      0.000000  1186.000000    4.000000   27.000000    4.000000   \n",
+       "75%      1.000000  2014.500000    7.000000   30.000000    4.000000   \n",
+       "max      1.000000  5740.000000   18.000000   41.000000    4.000000   \n",
+       "\n",
+       "            games      minutes       guard     forward      center  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean    65.724907  1682.193309    0.420074    0.408922    0.171004   \n",
+       "std     18.851110   893.327771    0.494491    0.492551    0.377214   \n",
+       "min      3.000000    33.000000    0.000000    0.000000    0.000000   \n",
+       "25%     57.000000   983.000000    0.000000    0.000000    0.000000   \n",
+       "50%     74.000000  1690.000000    0.000000    0.000000    0.000000   \n",
+       "75%     79.000000  2438.000000    1.000000    1.000000    0.000000   \n",
+       "max     82.000000  3533.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "           points    rebounds     assists      draft     allstar      avgmin  \\\n",
+       "count  269.000000  269.000000  269.000000  240.00000  269.000000  269.000000   \n",
+       "mean    10.260223    4.468401    2.453532   20.20000    0.115242   23.979257   \n",
+       "std      5.882489    2.892980    2.148124   18.73582    0.319909    9.731086   \n",
+       "min      1.000000    1.000000    0.000000    1.00000    0.000000    2.890000   \n",
+       "25%      5.000000    2.000000    1.000000    7.00000    0.000000   16.730000   \n",
+       "50%      9.000000    4.000000    2.000000   14.50000    0.000000   24.820000   \n",
+       "75%     14.000000    6.000000    3.000000   28.25000    0.000000   33.260000   \n",
+       "max     30.000000   17.000000   13.000000  139.00000    1.000000   43.090000   \n",
+       "\n",
+       "            black    children  productivity  \n",
+       "count  269.000000  269.000000    269.000000  \n",
+       "mean     0.806691    0.345725      0.409677  \n",
+       "std      0.395629    0.476491      0.114920  \n",
+       "min      0.000000    0.000000      0.093851  \n",
+       "25%      1.000000    0.000000      0.338710  \n",
+       "50%      1.000000    0.000000      0.408560  \n",
+       "75%      1.000000    1.000000      0.471168  \n",
+       "max      1.000000    1.000000      0.748532  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48facefc-8cdf-49e8-a071-7bc836552fd9",
+   "metadata": {},
+   "source": [
+    "#### Correlation Matrix\n",
+    "\n",
+    "Used for generating a correlation matrix between relevant variables. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6cd38a22-ea9a-41cb-bfda-e2d439b4d40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba[['points', 'assists', 'rebounds']].corr()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"corr points assists rebounds\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "969d8093-ff69-4224-ba83-a8df7ead7617",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>points</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>rebounds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>points</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>0.563324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assists</th>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rebounds</th>\n",
+       "      <td>0.563324</td>\n",
+       "      <td>0.059956</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            points   assists  rebounds\n",
+       "points    1.000000  0.539269  0.563324\n",
+       "assists   0.539269  1.000000  0.059956\n",
+       "rebounds  0.563324  0.059956  1.000000"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba[['points', 'assists', 'rebounds']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ab3e395-5236-407d-bbef-1a6f7a2104c4",
+   "metadata": {},
+   "source": [
+    "#### Scatter Plots\n",
+    "\n",
+    "Used for making scatter (or twoway) plots. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17411597-3519-41c0-a455-796118431768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+      "plt.xlabel('co2pc');\n",
+      "plt.ylabel('population');\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"twoway (scatter co2pc population, xtitle('co2pc') ytitle('population'))\",\"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ae6cf21d-24ed-453e-b757-7403d7d41113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+    "plt.xlabel('co2pc');\n",
+    "plt.ylabel('population');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33122b3d-c34b-424c-a246-8778b2a872ab",
+   "metadata": {},
+   "source": [
+    "#### Histograms\n",
+    "\n",
+    "Used for generating histograms. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "54d05f02-364d-4e82-b594-b5ba0c550357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "pollution.hist(column='co2pc',bins=80);\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"histogram co2pc, bin(80)\", \"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "77289483-1aee-435e-b78f-909044de0bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "pollution.hist(column='co2pc',bins=80);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14245a39-b06f-406b-a597-fc4ffcffead0",
+   "metadata": {},
+   "source": [
+    "#### Regressions\n",
+    "\n",
+    "Used for running regressions, including Python comments to help explain relevant portions of the code. Note that not all features from Stata's `reg` command are supported. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1bd4cdaa-deb1-417f-80d1-da4e0ca5350e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### Dropping NaN/missing values\n",
+      "wages_no_na = wages[['exp','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_no_na['exp']\n",
+      "y_df = wages_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit()\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "07fc3173-8ec2-4f34-bf8f-ddbb8dd6ccde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4714</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.493</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:39</td>     <th>  Log-Likelihood:    </th> <td> -3541.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7086.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   861</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>   16.0816</td> <td>    3.917</td> <td>    4.105</td> <td> 0.000</td> <td>    8.393</td> <td>   23.771</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>   <td>   -0.2135</td> <td>    0.311</td> <td>   -0.687</td> <td> 0.493</td> <td>   -0.824</td> <td>    0.397</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1263.062</td> <th>  Durbin-Watson:     </th>  <td>   1.830</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>413853.334</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.176</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>109.028</td> <th>  Cond. No.          </th>  <td>    99.5</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.001   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &    -0.001   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &    0.4714   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.493    \\\\\n",
+       "\\textbf{Time:}             &     21:26:39     & \\textbf{  Log-Likelihood:    } &   -3541.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7086.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         861      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &      16.0816  &        3.917     &     4.105  &         0.000        &        8.393    &       23.771     \\\\\n",
+       "\\textbf{exp}   &      -0.2135  &        0.311     &    -0.687  &         0.493        &       -0.824    &        0.397     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1263.062 & \\textbf{  Durbin-Watson:     } &     1.830   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 413853.334  \\\\\n",
+       "\\textbf{Skew:}          &   8.176  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 109.028  & \\textbf{  Cond. No.          } &      99.5   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.001\n",
+       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
+       "Method:                 Least Squares   F-statistic:                    0.4714\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.493\n",
+       "Time:                        21:26:39   Log-Likelihood:                -3541.0\n",
+       "No. Observations:                 863   AIC:                             7086.\n",
+       "Df Residuals:                     861   BIC:                             7096.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         16.0816      3.917      4.105      0.000       8.393      23.771\n",
+       "exp           -0.2135      0.311     -0.687      0.493      -0.824       0.397\n",
+       "==============================================================================\n",
+       "Omnibus:                     1263.062   Durbin-Watson:                   1.830\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           413853.334\n",
+       "Skew:                           8.176   Prob(JB):                         0.00\n",
+       "Kurtosis:                     109.028   Cond. No.                         99.5\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### Dropping NaN/missing values\n",
+    "wages_no_na = wages[['exp','wage']].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_no_na['exp']\n",
+    "y_df = wages_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit()\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3fc841e3-b7a8-40ca-a338-e4595c3e1d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "### Dropping NaN/missing values\n",
+      "df_no_na = df[['exp', 'education','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = df_no_na['exp']\n",
+      "y_df = df_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': df_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp, vce(cluster education)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c86447cd-732b-47b9-a192-70cb8c0e2684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "wages_with_dummies = wages.copy()\n",
+      "x_var = \"'exp', 'i.female'\"\n",
+      "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+      "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+      "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+      "for indicator in indicator_list:\n",
+      "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+      "    dummies = dummies.iloc[:,1:]\n",
+      "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+      "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+      "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+      "x_var = x_var_new\n",
+      "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+      "if clustering_vars:\n",
+      "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+      "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+      "### Dropping NaN/missing values\n",
+      "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+      "y_df = wages_with_dummies_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp i.female, vce(cluster education)\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4f73adae-9b6b-4d5d-9db8-2567fb9935fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.007</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.993</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.183</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:54</td>     <th>  Log-Likelihood:    </th> <td> -3538.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7082.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   860</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   17.4218</td> <td>    3.698</td> <td>    4.712</td> <td> 0.000</td> <td>   10.175</td> <td>   24.669</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>      <td>   -0.2373</td> <td>    0.206</td> <td>   -1.153</td> <td> 0.249</td> <td>   -0.640</td> <td>    0.166</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>1_female</th> <td>   -2.4604</td> <td>    1.261</td> <td>   -1.951</td> <td> 0.051</td> <td>   -4.932</td> <td>    0.011</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1260.198</td> <th>  Durbin-Watson:     </th>  <td>   1.842</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>409372.994</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.142</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>108.449</td> <th>  Cond. No.          </th>  <td>    101.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.007   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.005   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1.993   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.183    \\\\\n",
+       "\\textbf{Time:}             &     21:26:54     & \\textbf{  Log-Likelihood:    } &   -3538.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7082.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         860      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}     &      17.4218  &        3.698     &     4.712  &         0.000        &       10.175    &       24.669     \\\\\n",
+       "\\textbf{exp}       &      -0.2373  &        0.206     &    -1.153  &         0.249        &       -0.640    &        0.166     \\\\\n",
+       "\\textbf{1\\_female} &      -2.4604  &        1.261     &    -1.951  &         0.051        &       -4.932    &        0.011     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1260.198 & \\textbf{  Durbin-Watson:     } &     1.842   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 409372.994  \\\\\n",
+       "\\textbf{Skew:}          &   8.142  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 108.449  & \\textbf{  Cond. No.          } &      101.   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.007\n",
+       "Model:                            OLS   Adj. R-squared:                  0.005\n",
+       "Method:                 Least Squares   F-statistic:                     1.993\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.183\n",
+       "Time:                        21:26:54   Log-Likelihood:                -3538.0\n",
+       "No. Observations:                 863   AIC:                             7082.\n",
+       "Df Residuals:                     860   BIC:                             7096.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:              cluster                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         17.4218      3.698      4.712      0.000      10.175      24.669\n",
+       "exp           -0.2373      0.206     -1.153      0.249      -0.640       0.166\n",
+       "1_female      -2.4604      1.261     -1.951      0.051      -4.932       0.011\n",
+       "==============================================================================\n",
+       "Omnibus:                     1260.198   Durbin-Watson:                   1.842\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           409372.994\n",
+       "Skew:                           8.142   Prob(JB):                         0.00\n",
+       "Kurtosis:                     108.449   Cond. No.                         101.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+    "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+    "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+    "y_var = \"wage\"\n",
+    "clustering_vars = ['education']\n",
+    "wages_with_dummies = wages.copy()\n",
+    "x_var = \"'exp', 'i.female'\"\n",
+    "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+    "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+    "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+    "for indicator in indicator_list:\n",
+    "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+    "    dummies = dummies.iloc[:,1:]\n",
+    "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+    "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+    "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+    "x_var = x_var_new\n",
+    "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+    "if clustering_vars:\n",
+    "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+    "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+    "### Dropping NaN/missing values\n",
+    "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+    "y_df = wages_with_dummies_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21279b0b-e6c2-467e-a028-429da3fac865",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/content/03-pandas/loading-looking.ipynb b/_build/jupyter_execute/content/03-pandas/loading-looking.ipynb
index 0fe26d7..f3d08cf 100644
--- a/_build/jupyter_execute/content/03-pandas/loading-looking.ipynb
+++ b/_build/jupyter_execute/content/03-pandas/loading-looking.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "22f74f98",
+   "id": "43215cba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "213500b1",
+   "id": "386b5256",
    "metadata": {},
    "source": [
     "# Loading in and Looking at the Data"
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca75da66",
+   "id": "b39a490b",
    "metadata": {},
    "source": [
     "Throughout this chapter, we will use `pandas` to explore a dataset on beer sales across 50 continental states in the US. \n",
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cdd4a0e",
+   "id": "8f3aaaa6",
    "metadata": {},
    "source": [
     "## Loading in the dataset\n",
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e4bb194f",
+   "id": "a91aeac6",
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e0496f8",
+   "id": "43fcada0",
    "metadata": {},
    "source": [
     "Whenever we use a dataset, we ought to think about where it came from, how it was generated and what it represents. Doing so is absolutely crucial for good data science. Doing so will help us keep our eyes open for potential sources of bias in the data, avoiding unfortunate mistakes. Additionally, if we do not think about where we source our data from, we cannot truly consider the ethical implications of our analysis.\n",
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a51d286b",
+   "id": "3c44cfd2",
    "metadata": {},
    "source": [
     "## DataFrames, Series and Indices"
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56143c4f",
+   "id": "a13a8745",
    "metadata": {},
    "source": [
     "In `pandas`, all tables are stored as data structures called as `DataFrames`. Each DataFrame has an index (the bolded column on the left) and columns (called as `Series`). \n",
@@ -263,7 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "7b4ef35f",
+   "id": "b00f8572",
    "metadata": {},
    "outputs": [
     {
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65fc90e6",
+   "id": "74295ccb",
    "metadata": {},
    "source": [
     "\n",
@@ -452,7 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "89f16aae",
+   "id": "fe4e4049",
    "metadata": {},
    "outputs": [
     {
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "515ee9ae",
+   "id": "5d56fc01",
    "metadata": {},
    "source": [
     "As you can see above, to pull out a singular Series from a DataFrame, we simply need to follow the notation `DataFrame_Name[Series_Name]`.\n",
@@ -494,7 +494,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a188b993",
+   "id": "aa803d01",
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "765d3086",
+   "id": "a135e5c3",
    "metadata": {},
    "source": [
     "### Series Attributes"
@@ -631,7 +631,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba607433",
+   "id": "c1c6eb62",
    "metadata": {},
    "source": [
     "When we look at a Series, we get the index on the left and the corresponding values on the right. You can view the index via the attribute `index` and the values via the attribute `values`. Examples are shown below."
@@ -640,7 +640,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "b88ea62b",
+   "id": "9ceb42fa",
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +662,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bc2e814f",
+   "id": "e111ebd3",
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20c1912a",
+   "id": "5448a090",
    "metadata": {},
    "source": [
     "### DataFrame Attributes"
@@ -690,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f542870",
+   "id": "0145a529",
    "metadata": {},
    "source": [
     "Three important DataFrame attributes are `index`, `columns`, and `shape`. `index` simply returns the index of the dataset, as shown below."
@@ -699,7 +699,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ae036ed1",
+   "id": "00e1a8d7",
    "metadata": {},
    "outputs": [
     {
@@ -719,7 +719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d9c5d65",
+   "id": "707517d6",
    "metadata": {},
    "source": [
     "`columns` returns the name of the columns/Series that make up the dataset. Note that each column/Series name must be unique."
@@ -728,7 +728,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "bf9b8802",
+   "id": "cc0bdb84",
    "metadata": {},
    "outputs": [
     {
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d31975dd",
+   "id": "41e63965",
    "metadata": {},
    "source": [
     "`shape` returns the dimensions, or the number of rows and the number of columns (in that order), of the dataset."
@@ -759,7 +759,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "8b88121a",
+   "id": "cfe390a8",
    "metadata": {},
    "outputs": [
     {
@@ -779,7 +779,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06434ceb",
+   "id": "69655c6c",
    "metadata": {},
    "source": [
     "Moreover, `size` is another attributes that returns the number of rows times the number of columns, or the total number of datapoints, in the dataset."
@@ -788,7 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "43488d9f",
+   "id": "e585c50d",
    "metadata": {},
    "outputs": [
     {
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fce2af10",
+   "id": "e08fc3cf",
    "metadata": {},
    "source": [
     "Finally, `dtypes` returns the data type of each column in the DataFrame."
@@ -817,7 +817,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "b2f5d024",
+   "id": "2a71a85d",
    "metadata": {},
    "outputs": [
     {
@@ -844,7 +844,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8ddcb56",
+   "id": "20f47d24",
    "metadata": {},
    "source": [
     "Note: the data type for the `st_name` column is `object` even though the column contains strings. Click [here](https://stackoverflow.com/a/21020411) to learn why this happens."
@@ -852,7 +852,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "262bc58e",
+   "id": "34f18da4",
    "metadata": {},
    "source": [
     "## Extracting Data using Labels and Indices"
@@ -860,7 +860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa355edf",
+   "id": "845559d7",
    "metadata": {},
    "source": [
     "There are four primary methods of looking at extracting specific rows/columns from DataFrames. They are :-\n",
@@ -872,7 +872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c365d99",
+   "id": "0f31b1c8",
    "metadata": {},
    "source": [
     "### 1. `.head` and `.tail`"
@@ -880,7 +880,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "158432dd",
+   "id": "3a2479f1",
    "metadata": {},
    "source": [
     "If we just want to select the first or last n rows from a dataset, we can use `DataFrame_Name.head(n)` (for the first n rows) or `DataFrame_Name.tail(n)` (for the last n rows)."
@@ -889,7 +889,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "32724118",
+   "id": "7d05d383",
    "metadata": {},
    "outputs": [
     {
@@ -998,7 +998,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4a26c79a",
+   "id": "b65aab5b",
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fb7b3df",
+   "id": "9bfaa6b4",
    "metadata": {},
    "source": [
     "### 2. `.loc`"
@@ -1114,7 +1114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1877a09",
+   "id": "cb2e1d7a",
    "metadata": {},
    "source": [
     "To better illustrate the difference between `.loc` and `iloc`, we will use the DataFrame `df2`, as created earlier. We've reposted it here for ease of access."
@@ -1123,7 +1123,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "7b9b442f",
+   "id": "1620238a",
    "metadata": {},
    "outputs": [
     {
@@ -1300,7 +1300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c40a5abf",
+   "id": "1cb33786",
    "metadata": {},
    "source": [
     "`.loc` helps us get values based on the index/column labels associated with them. For example, let's say we're interested in all the `btax_dollars` values associated with an index label of `a`. We can use `.loc` to get all such values, as shown below."
@@ -1309,7 +1309,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "762fb1e5",
+   "id": "be38a429",
    "metadata": {},
    "outputs": [
     {
@@ -1340,7 +1340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b6a0cce",
+   "id": "c0d5bb16",
    "metadata": {},
    "source": [
     "Above, we first passed in the row labels (or index labels) we're interested in, followed by the column labels we're interested in. If we're interested in multiple row/column labels, we must pass in a list of the labels we're interested in. An example is shown below."
@@ -1349,7 +1349,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "38505b8b",
+   "id": "aec06661",
    "metadata": {},
    "outputs": [
     {
@@ -1478,7 +1478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38bbf31e",
+   "id": "bc83a362",
    "metadata": {},
    "source": [
     "We can also use a slice (`:`) to specify that we're interested in all the labels."
@@ -1487,7 +1487,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "7652a6c8",
+   "id": "32c501cf",
    "metadata": {},
    "outputs": [
     {
@@ -1616,7 +1616,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a37a404",
+   "id": "3f38fa73",
    "metadata": {},
    "source": [
     "### 3. `.iloc`"
@@ -1624,7 +1624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67ec0f62",
+   "id": "1d9120c0",
    "metadata": {},
    "source": [
     "`.iloc` works similarly to `.loc`, but it looks at the integers associated with each label rather than the label itself. For example, let's extract all the `btax_dollars` values associated with an index label of `a` using `.iloc`."
@@ -1633,7 +1633,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "a68ea6f3",
+   "id": "339ce4ba",
    "metadata": {},
    "outputs": [
     {
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f43df5fc",
+   "id": "70842baa",
    "metadata": {},
    "source": [
     "Here, we extracted the same Series as with `.loc`, but needed to specify the index positions of the rows/columns we were interested in rather than their labels. We passed in `0:854` to indicate that we were interested in the first 854 rows, and `4` to indicate that we were interested in the fifth column.\n",
@@ -1675,7 +1675,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "e1af816b",
+   "id": "efd4072a",
    "metadata": {},
    "outputs": [
     {
@@ -1804,7 +1804,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21fbb77b",
+   "id": "ecd4e337",
    "metadata": {},
    "source": [
     "### 4. `[]`"
@@ -1812,7 +1812,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd9a4cf2",
+   "id": "6905acb5",
    "metadata": {},
    "source": [
     "The `[]` is the hardest to describe, but the easiest to write and for many, the easiest to use. The `[]` simply follows the notation `DataFrame_Name[...]`, where `...` can be a single column label (similar to `.loc`), a list of column labels (similar to `.loc`) or a slice of row index positions (similar to `.iloc`). We've included examples for all three below. Do not worry if this seems confusing at first, feel free to use one of the first three methods instead."
@@ -1820,7 +1820,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53df9f26",
+   "id": "a1b5d328",
    "metadata": {},
    "source": [
     "#### Single Column Label"
@@ -1829,7 +1829,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "c5c04b1c",
+   "id": "9e66ba08",
    "metadata": {},
    "outputs": [
     {
@@ -1860,7 +1860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1296ec3f",
+   "id": "156bba2b",
    "metadata": {},
    "source": [
     "#### List of Column Labels"
@@ -1869,7 +1869,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "49d6ee66",
+   "id": "aecb64f9",
    "metadata": {},
    "outputs": [
     {
@@ -1998,7 +1998,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e8ec46a",
+   "id": "2c1d93d8",
    "metadata": {},
    "source": [
     "#### Slice of Row Index Positions"
@@ -2007,7 +2007,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "f0703abb",
+   "id": "a5aa8c28",
    "metadata": {},
    "outputs": [
     {
@@ -2115,7 +2115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63731b7a",
+   "id": "43f790d6",
    "metadata": {},
    "source": [
     "In the next subchapter, we will talk about conditional selection, which allows us to view all rows that satisfy a certain condition."
diff --git a/_build/jupyter_execute/content/04-visualizations/types.ipynb b/_build/jupyter_execute/content/04-visualizations/types.ipynb
index 1af7f03..988b1a1 100644
--- a/_build/jupyter_execute/content/04-visualizations/types.ipynb
+++ b/_build/jupyter_execute/content/04-visualizations/types.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "66e3dbe8",
+   "id": "96dda6b9",
    "metadata": {
     "tags": []
    },
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0abb75f",
+   "id": "d5815884",
    "metadata": {},
    "source": [
     "# Basic Visualizations"
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b09546a",
+   "id": "4a596313",
    "metadata": {},
    "source": [
     "In thus subchapter, we will go over some key types of plots, as well as how to create them using two of the most common plotting libraries in Python: `matplotlib` and `seaborn`. However, first we will need to load in the dataset we will be using for this analysis. \n",
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "46a502b7",
+   "id": "56965890",
    "metadata": {
     "tags": []
    },
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee09023a",
+   "id": "b88e15a9",
    "metadata": {},
    "source": [
     "## Matplotlib and Seaborn\n",
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9df6ebfb",
+   "id": "68faa633",
    "metadata": {
     "tags": []
    },
@@ -492,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ee2af15",
+   "id": "aaf56530",
    "metadata": {},
    "source": [
     "## Bar Charts"
@@ -500,7 +500,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8138a0ef",
+   "id": "34fb14e2",
    "metadata": {},
    "source": [
     "Although our dataset has complete information for several metrics for each of the 25 regions, it doesn't have data on the exact same metrics for each region. We have a lot more data from some regions than from others, as can be seen from the `.value_counts()` below."
@@ -509,7 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f31a0931",
+   "id": "fde4a71e",
    "metadata": {
     "tags": []
    },
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "366090f8",
+   "id": "23a6caac",
    "metadata": {},
    "source": [
     "Let us attempt to visualize this. As we have a qualitative nominal and a quantitative discrete variable here, a bar chart would be helpful. Bar charts display a bar for each category, and the length of each bar is proportional to the frequency of the corresponding category. While not necessary, most bar charts have equally spaced and equally wide columns. \n",
@@ -567,7 +567,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7a8f9801",
+   "id": "a315819d",
    "metadata": {
     "tags": []
    },
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad641c67",
+   "id": "d5f2e3a3",
    "metadata": {},
    "source": [
     "It is impossible to read any of the labels on the x-axis of the above bar chart as they all overlap. This is an example of **overplotting**, which is defined as data/labels on a plot overlapping, making it difficult to see what's going to. To fix this, we could rotate the x-axis labels so they're vertical, as done below."
@@ -601,7 +601,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0dd74729",
+   "id": "f6520e7a",
    "metadata": {
     "tags": []
    },
@@ -627,7 +627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39cde091",
+   "id": "74caa3d1",
    "metadata": {},
    "source": [
     "Alternatively, you can easily make this plot in `seaborn` using the [`.countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) command. As you can see, `seaborn` automatically gave the plot a color scheme and x and y-axis labels (`matplotlib` would not do this without us specifying the labels). However, we still have to use `plt.xticks()` to avoid overplotting. "
@@ -636,7 +636,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ce45fde2",
+   "id": "6583d4a2",
    "metadata": {
     "tags": []
    },
@@ -659,7 +659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91e48f30",
+   "id": "018dad60",
    "metadata": {},
    "source": [
     "Another way to avoid overplotting would be to make a horizontal bar chart instead. We have done so using `seaborn` below."
@@ -668,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "64435700",
+   "id": "6e4bdf56",
    "metadata": {
     "tags": []
    },
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "269cb4d0",
+   "id": "161f2db3",
    "metadata": {},
    "source": [
     "Finally, if you would like to also sort these values from highest to lowest (similar to how the `.value_counts()` command sorted the `matplotlib` bar chart in descending order), you can use the `order` parameter."
@@ -702,7 +702,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "501b15db",
+   "id": "00cb006e",
    "metadata": {
     "tags": []
    },
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54fe7e0a",
+   "id": "d74d0165",
    "metadata": {},
    "source": [
     "## Scatter Plots\n",
@@ -744,7 +744,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "d6ab73e2",
+   "id": "c03ae8c2",
    "metadata": {
     "tags": []
    },
@@ -773,7 +773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d937529a",
+   "id": "d0f3dd93",
    "metadata": {},
    "source": [
     "We could've also made this plot with `seaborn`."
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "aecf3875",
+   "id": "b5a39cf3",
    "metadata": {
     "tags": []
    },
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0082681",
+   "id": "2713622c",
    "metadata": {},
    "source": [
     "Finally, to add some context to our visualization, let us also plot a `y = x` line, to represent where the data should be if there was gender quality in primary school enrollment. Notice how we have also added a legend here."
@@ -820,7 +820,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a5a42d89",
+   "id": "955c315f",
    "metadata": {
     "tags": []
    },
@@ -853,7 +853,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7848271",
+   "id": "f73ae5e5",
    "metadata": {},
    "source": [
     "Based on the scatter plots above, it appears that there is a positive association between the percent of males and the percent of females enrolled in primary school. As the association roughly follows a straight-ish line, we can say that the association appears to be strong.\n",
@@ -864,7 +864,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2fe5aac2",
+   "id": "605c901e",
    "metadata": {
     "tags": []
    },
@@ -2054,7 +2054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a82f4f74",
+   "id": "4b0cfc5c",
    "metadata": {},
    "source": [
     "With the help of these hover points, we can see that we have trended closer to gender equality in primary school enrollment over time. Great news!"
@@ -2062,7 +2062,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d011010",
+   "id": "efa030f1",
    "metadata": {},
    "source": [
     "## Line Plots"
@@ -2070,7 +2070,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fcf00f2",
+   "id": "71a76158",
    "metadata": {},
    "source": [
     "A line plot is used to display data as a series of points connected by a line. It's generally used to visualize how a variable changes over time (also known as [time series data](https://www.investopedia.com/terms/t/timeseries.asp)), often with a time-related variable on the x-axis (minutes, days, months, years, etc.) and a numerical variable on the y-axis.\n",
@@ -2081,7 +2081,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "0aece6e2",
+   "id": "e56b2518",
    "metadata": {
     "tags": []
    },
@@ -2109,7 +2109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42b974b6",
+   "id": "7051db5d",
    "metadata": {},
    "source": [
     "We can also do this with `seaborn`."
@@ -2118,7 +2118,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "a164f708",
+   "id": "b2065529",
    "metadata": {
     "tags": []
    },
@@ -2146,7 +2146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f281b974",
+   "id": "ae55fdd1",
    "metadata": {},
    "source": [
     "It appears that more people are being enrolled in primary school over time, great news!"
@@ -2154,7 +2154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "110014bd",
+   "id": "fc5e7be1",
    "metadata": {},
    "source": [
     "## Histograms, Box Plots and Violin Plots"
@@ -2162,7 +2162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00859134",
+   "id": "081f3da6",
    "metadata": {},
    "source": [
     "Histograms, box plots and violin plots are all ways to visualize the distribution of a single quantitative variable. They help you understand the spread of values in the data. To help explain these, we will import in another dataset on education from the World Bank. We have obtained this dataset from [Data 100's Course Notes Visualization I](https://ds100.org/fa23-course-notes/visualization_1/visualization_1.html).\n"
@@ -2171,7 +2171,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "05028d28",
+   "id": "496f7d93",
    "metadata": {
     "tags": []
    },
@@ -2753,7 +2753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93de4827",
+   "id": "95d6fd97",
    "metadata": {},
    "source": [
     "### Histograms\n",
@@ -2769,7 +2769,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "7eb09043",
+   "id": "77a54a0f",
    "metadata": {},
    "outputs": [
     {
@@ -2794,7 +2794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b834716",
+   "id": "77d159b2",
    "metadata": {},
    "source": [
     "### Box Plots and Violin Plots\n",
@@ -2807,7 +2807,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "ae52b615",
+   "id": "74dbf86d",
    "metadata": {},
    "outputs": [
     {
@@ -2829,7 +2829,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91f432eb",
+   "id": "cfa50c32",
    "metadata": {},
    "source": [
     "Both the whiskers always have the same length (to be precise, the length is 1.5 times the inter quartile range). The existence of dots beyond the whiskers indicate outliers in the data.\n",
@@ -2840,7 +2840,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "83dbde25",
+   "id": "b1c7d51d",
    "metadata": {},
    "outputs": [
     {
@@ -2862,7 +2862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd4f14b4",
+   "id": "94a0a498",
    "metadata": {},
    "source": [
     "### Overlaid Histograms and Side-by-Side Box and Violin Plots."
@@ -2870,7 +2870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac8218ca",
+   "id": "7f58ea4c",
    "metadata": {},
    "source": [
     "To compare a quantitative distribution across the different categories of a qualitative categorical variable, we can overlay histograms or plot side-by-side box and violin plots. Examples of all of those have been done below to visualize the difference in primary school completion rate for females vs males."
@@ -2878,7 +2878,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b320064",
+   "id": "c19c2bc7",
    "metadata": {},
    "source": [
     "#### Overlaid Histograms"
@@ -2887,7 +2887,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "2ada7a01",
+   "id": "3e67f24f",
    "metadata": {},
    "outputs": [
     {
@@ -2918,7 +2918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a0a327c",
+   "id": "4e31fcf7",
    "metadata": {},
    "source": [
     "#### Side-by-Side Box Plots"
@@ -2927,7 +2927,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "696930e1",
+   "id": "3c9678be",
    "metadata": {},
    "outputs": [
     {
@@ -2952,7 +2952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b7b4e16",
+   "id": "0e7f41a0",
    "metadata": {},
    "source": [
     "#### Side-by-Side Violin Plots"
@@ -2961,7 +2961,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "948b723e",
+   "id": "5f4fc46e",
    "metadata": {
     "tags": []
    },
diff --git a/_build/jupyter_execute/content/05-regex/regex.ipynb b/_build/jupyter_execute/content/05-regex/regex.ipynb
index e19f355..9d41870 100644
--- a/_build/jupyter_execute/content/05-regex/regex.ipynb
+++ b/_build/jupyter_execute/content/05-regex/regex.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "ce0ecbec",
+   "id": "5355510a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0339060f",
+   "id": "93297677",
    "metadata": {},
    "source": [
     "# RegEx\n",
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f3f6051",
+   "id": "27aea2e3",
    "metadata": {},
    "source": [
     "`RegEx`, standing for regular expressions, are a powerful tool used to search through large bodies of text. Using RegEx, you can essentially specify patterns you are looking for and get all the substrings which match that pattern. This subchapter will go over how to use RegEx in Python.\n",
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a3d538e",
+   "id": "94fbe46e",
    "metadata": {},
    "source": [
     "## RegEx Syntax\n",
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd2d2ae4",
+   "id": "53568a88",
    "metadata": {},
    "source": [
     "RegEx patterns are made up of literal characters and special characters. Literal characters are literally the characters they represent. For example, in the code below, the characters `Economics` match the string `Economics` literally and return a match.\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1bbc02ae",
+   "id": "6c7296c2",
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "647c9dd6",
+   "id": "8de122c4",
    "metadata": {},
    "source": [
     "As the characters are literal, the characters `Economics` do not match the string `Economica`. You can see nothing is returned, meaning no match was found."
@@ -92,7 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "337e3175",
+   "id": "051f2915",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e57daf1",
+   "id": "54714b2a",
    "metadata": {},
    "source": [
     "If we wanted to also match `Economica`, we could modify our pattern to be `Economic(s|a)`. `|` is the 'or' operator here, indicating that either of the options will be acceptable. Since the parenthesis surround `s|a`, this means that the pattern will only match `Economics` or `Economica`, it will not match other patterns like just `a` or just `Economic`."
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "bb41b570",
+   "id": "5e36fdc4",
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "d86b8431",
+   "id": "42471fb2",
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "625e0535",
+   "id": "eada7989",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "8075dda1",
+   "id": "a570f554",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1439f116",
+   "id": "777a1cf9",
    "metadata": {},
    "source": [
     "### Quantifiers"
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e75865de",
+   "id": "12813019",
    "metadata": {},
    "source": [
     "There are several ways of telling RegEx how many of a character we want. They are summarized below:\n",
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d512ad34",
+   "id": "6d92a9df",
    "metadata": {},
    "source": [
     "#### Greediness"
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "963dfab4",
+   "id": "c620520e",
    "metadata": {},
    "source": [
     "RegEx operations are greedy, meaning they look for the longest possible match in a string. For example, given the string _ab_, the RegEx pattern `.*` could match _a_, _b_, _ab_, or nothing at all. However, because it wants the longest match possible, it will choose to match _ab_."
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "013c1995",
+   "id": "4f74955b",
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd95eebe",
+   "id": "dd654f6a",
    "metadata": {},
    "source": [
     "If we instead wanted to match as little as possible when using quantifiers, we can simply add `?` after the quantifier to make it instead match as little as possible. For example, given the string _ab_, the RegEx pattern `.*` matches nothing at all."
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "915b4e37",
+   "id": "9d778990",
    "metadata": {},
    "outputs": [
     {
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f53aead1",
+   "id": "ea83e438",
    "metadata": {},
    "source": [
     "Here's another example of greedy vs non-greedy matching:"
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "cbfa0d74",
+   "id": "29d13dea",
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67954271",
+   "id": "32d39880",
    "metadata": {},
    "source": [
     "### Character Classes"
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0df70ebd",
+   "id": "fa82bba7",
    "metadata": {},
    "source": [
     "RegEx character classes help us match any one character from a list of characters. They are often denoted by a list of characters surrounded by hard brackets (`[]`). For example, _er[ic]*_ will match _eri_, _ericc_, _eriicc_ and _erii_."
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "c3a8581c",
+   "id": "b25f5d25",
    "metadata": {},
    "outputs": [
     {
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f3348e3",
+   "id": "f8ac782b",
    "metadata": {},
    "source": [
     "You can also use character classes to denote characters you do not wish to match. If you put `^` at the front of a character class, it becomes a character class negation, and matches anything except the characters in the character class. For example, the pattern `consonant: [^aeiou]` will match any string of the pattern `consonant: ` followed by a consonant, as shown below."
@@ -335,7 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "547df4af",
+   "id": "53e569ae",
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3134dda",
+   "id": "bed32d78",
    "metadata": {},
    "source": [
     "| Character Class | Description | Matches | Doesn't Match |\n",
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9567ceef",
+   "id": "fee44f1d",
    "metadata": {},
    "source": [
     "### Positions"
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1434043e",
+   "id": "8f640d92",
    "metadata": {},
    "source": [
     "When discussing character classes, we'd mentioned how `^` can be used to negate a character class. However, `^` actually has two uses. If the `^` is not in the middle of a bracket and preceded by `[`, it instead represents a position at the beginning of a string. For example, the pattern `^a` will contain a match in `all` but not in `ball`."
@@ -394,7 +394,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "fd9c02f6",
+   "id": "0375b452",
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72c95a99",
+   "id": "bc1947ab",
    "metadata": {},
    "source": [
     "Similarly, `$` denotes the end of a string. For example, the pattern `s$` will contain a match in _prawns_ but not in _prawn_."
@@ -422,7 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "42c47ed1",
+   "id": "f144ef2a",
    "metadata": {},
    "outputs": [
     {
@@ -441,7 +441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ca4ec03",
+   "id": "fd524511",
    "metadata": {},
    "source": [
     "### Escape"
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01618c6e",
+   "id": "659dfd89",
    "metadata": {},
    "source": [
     "The escape character `\\` is used to take special characters and turn them into literal ones. For example, the character `.` will match anything except a newline, but `\\.` will only match `.` and nothing else."
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "3855efcc",
+   "id": "a16d37ea",
    "metadata": {},
    "outputs": [
     {
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fbcd91c",
+   "id": "ab0bbb79",
    "metadata": {},
    "source": [
     "## Using Regex in Python"
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c34e747",
+   "id": "5196eaa7",
    "metadata": {},
    "source": [
     "### Regex Library Functions"
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a68de665",
+   "id": "e654bc58",
    "metadata": {},
    "source": [
     "The `regex` library of Python has several functions that are helpful for working with Python strings. Until now, we've been working with `re.search()`. `re.search()` takes in a RegEx pattern and a Python string, and returns the first match of the pattern within the string. It returns the match as a `regex.Match object`. `regex.Match object's` have 4 helpful methods:\n",
@@ -510,7 +510,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "1662bca4",
+   "id": "0eab065d",
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +532,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "efa1c8e4",
+   "id": "1b2ca135",
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +553,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "095ffffd",
+   "id": "89349b46",
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +574,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "b07e7c6d",
+   "id": "c2811abb",
    "metadata": {},
    "outputs": [
     {
@@ -595,7 +595,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "4d6306e2",
+   "id": "28ec1e3b",
    "metadata": {},
    "outputs": [
     {
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44f76d30",
+   "id": "c68871ec",
    "metadata": {},
    "source": [
     "The other python RegEx functions we will discuss return either lists or strings. They are described below."
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a46086e",
+   "id": "0d2fbbf6",
    "metadata": {},
    "source": [
     "| Function        | Description                                | Syntax Example         | Returns         | Example Usage                                    | Example Output                                  |\n",
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14967ca4",
+   "id": "3a999e34",
    "metadata": {},
    "source": [
     "### Capturing Groups"
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "091871fd",
+   "id": "1c7ba12d",
    "metadata": {},
    "source": [
     "Using `re.findall()` allows us to easily extract information from our string. Capturing groups can help us extract information further.\n",
@@ -657,7 +657,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "33b4b714",
+   "id": "0ef97808",
    "metadata": {},
    "outputs": [
     {
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7954e0b",
+   "id": "0452fe0b",
    "metadata": {},
    "source": [
     "Well, if we surround a portion of the pattern with parenthesis, we can capture that portion and extract only that portion into our final list."
@@ -686,7 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "48ade279",
+   "id": "c809c4cd",
    "metadata": {},
    "outputs": [
     {
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5726ae32",
+   "id": "6f9dac02",
    "metadata": {},
    "source": [
     "This is tricky as earlier, we'd introduced parenthesis as forms of specifying which letters we want to choose between."
@@ -715,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "416f9d89",
+   "id": "b283760a",
    "metadata": {},
    "outputs": [
     {
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55ae7986",
+   "id": "50b727d1",
    "metadata": {},
    "source": [
     "This brings us to a **key point**: when extracting information with `re.findall()`, parentheses serve as capturing groups. However, when simply matching the first occurrence with `re.search()` without extracting anything, parentheses instead just help group characters together. This is something to be careful of; it is possible to rework any RegEx expression to not use parenthesis where they are not necessary. For example, you could transform the pattern `Economic(s|a)` to `Economic[sa]` and then use `re.findall()` to accomplish the same result as the `re.search()` call above."
@@ -744,7 +744,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "4476869c",
+   "id": "f6ea3620",
    "metadata": {},
    "outputs": [
     {
@@ -765,7 +765,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "50741586",
+   "id": "9171f39c",
    "metadata": {},
    "outputs": [
     {
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52c3190d",
+   "id": "d21ff84d",
    "metadata": {},
    "source": [
     "### Integration with `Pandas` String Methods"
@@ -795,7 +795,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6c00552",
+   "id": "57486599",
    "metadata": {},
    "source": [
     "RegEx patterns work very well with `pandas` [string methods](../03-pandas/misc_funcs.ipynb). For example, the RegEx pattern `ssn_pattern` below matches all strings that follow the pattern of an American social security number (namely: 3 digits, dash, 2 digits, dash, 3 digits). Let us imagine you've surveyed a bunch of (American) people asking them their social security number. You've stored all their responses in the DataFrame `ssn_data` below. You can use `str.findall()` to extract all potential the social security numbers. "
@@ -804,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "aff5afd7",
+   "id": "c60dff4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -815,7 +815,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "e6c0ece5",
+   "id": "cdd5a08f",
    "metadata": {},
    "outputs": [
     {
@@ -943,7 +943,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f364590",
+   "id": "437f30ea",
    "metadata": {},
    "source": [
     "Applying the `findall` function;"
@@ -952,7 +952,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "5e0cd044",
+   "id": "2dab69c9",
    "metadata": {},
    "outputs": [
     {
@@ -983,7 +983,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89daeb41",
+   "id": "57e7aed9",
    "metadata": {},
    "source": [
     "If you were just interested in the digits of the social security numbers, you could use capturing groups combined with `.str.extract()`, as shown below."
@@ -992,7 +992,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "d5455376",
+   "id": "53f56ff4",
    "metadata": {},
    "outputs": [
     {
diff --git a/_build/jupyter_execute/content/07-sql/sql.ipynb b/_build/jupyter_execute/content/07-sql/sql.ipynb
index 6151874..cbcb245 100644
--- a/_build/jupyter_execute/content/07-sql/sql.ipynb
+++ b/_build/jupyter_execute/content/07-sql/sql.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4d43c3b8",
+   "id": "d27946bd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f073dc5",
+   "id": "f932242c",
    "metadata": {},
    "source": [
     "# SQL"
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4e026ba",
+   "id": "70366f7d",
    "metadata": {},
    "source": [
     "This chapter will introduce Structured Query Language, commonly abbreviated as SQL (commonly pronounced \"sequel,\" though this is the subject of [fierce debate](https://patorjk.com/blog/2012/01/26/pronouncing-sql-s-q-l-or-sequel/)), and go over some basic commands. SQL is a special programming language designed to communicate with databases.\n",
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d8d9bd9",
+   "id": "37d37bc7",
    "metadata": {},
    "source": [
     "## Connecting to a SQL Database in Jupyter\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "47225a6e",
+   "id": "61e3d5f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e81cfc29",
+   "id": "8dec81e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "fcfcb289",
+   "id": "0df3b07d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8ed80b0d",
+   "id": "8342bdbd",
    "metadata": {},
    "outputs": [
     {
@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbadd610",
+   "id": "4e82f4ad",
    "metadata": {},
    "source": [
     "As you can see from the summary above, our database contains 3 tables `schools`, `frpm` (short for free or reduced price meals), and `satscores`. Each of these tables contains some important information for our analysis. \n",
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "b7d4b867",
+   "id": "a026f32d",
    "metadata": {},
    "outputs": [
     {
@@ -632,7 +632,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "7f59d6f4",
+   "id": "2a5837d4",
    "metadata": {},
    "outputs": [
     {
@@ -865,7 +865,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "fa8e77c4",
+   "id": "82f84f03",
    "metadata": {},
    "outputs": [
     {
@@ -1001,7 +1001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dea8c6d",
+   "id": "a76ac0b2",
    "metadata": {},
    "source": [
     "Without further ado, let's begin discussing SQL's syntax!"
@@ -1009,7 +1009,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7da695a2",
+   "id": "b10fd4e7",
    "metadata": {},
    "source": [
     "## Syntax"
@@ -1017,7 +1017,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57477faf",
+   "id": "67f67ac7",
    "metadata": {},
    "source": [
     "It is common practice to capitalize all keywords in SQL queries. We shall stick to this common practice throughout this section. \n",
@@ -1040,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42dbb536",
+   "id": "e9650eca",
    "metadata": {},
    "source": [
     "### `SELECT`"
@@ -1048,7 +1048,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cac76b77",
+   "id": "02e05367",
    "metadata": {},
    "source": [
     "Every single SQL query must include a `SELECT` statement with a `FROM` command. The `SELECT` statement tells SQL which columns you are interested in, and the `FROM` command tells SQL which tables those columns are in. To select all the columns from a table, we can use `*` in the select statement. For example, let us select all the columns from the table `schools`."
@@ -1057,7 +1057,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "61562896",
+   "id": "a6538ee4",
    "metadata": {},
    "outputs": [
     {
@@ -1676,7 +1676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b09d7e9",
+   "id": "b0777f41",
    "metadata": {},
    "source": [
     "#### `AS`\n",
@@ -1687,7 +1687,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "60d1ff38",
+   "id": "58fa2146",
    "metadata": {},
    "outputs": [
     {
@@ -1778,7 +1778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4699f74f",
+   "id": "d45cfbfa",
    "metadata": {},
    "source": [
     "#### `CAST`\n",
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "6db246bf",
+   "id": "b32a930d",
    "metadata": {},
    "outputs": [
     {
@@ -1880,7 +1880,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c2dee7b",
+   "id": "bfc28e2f",
    "metadata": {},
    "source": [
     "If you like, you can rename this new column to be something easier to interpret."
@@ -1888,7 +1888,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e5dfa54",
+   "id": "faf0c6e6",
    "metadata": {},
    "source": [
     "#### `CASE`\n",
@@ -1908,7 +1908,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1a9765f7",
+   "id": "63c5fa25",
    "metadata": {},
    "outputs": [
     {
@@ -2025,7 +2025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75568002",
+   "id": "8460577e",
    "metadata": {},
    "source": [
     "You will notice that the first 3 rows have no school name. That is because they correspond to state/county/district-wide data rather than data from a specific school. We can filter the dataset to only include schools using the `WHERE` clause."
@@ -2033,7 +2033,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e71b7666",
+   "id": "6f9842ac",
    "metadata": {},
    "source": [
     "#### `DISTINCT`\n",
@@ -2044,7 +2044,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "b6aa0dc4",
+   "id": "88235908",
    "metadata": {},
    "outputs": [
     {
@@ -2109,7 +2109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a87974fd",
+   "id": "d0d443f7",
    "metadata": {},
    "source": [
     "As an aside, the column `rtype` represents the type of region being covered by the row. `S` represents a single school, `D` represents a school district, `C` represents a county and `X` represents the entire state."
@@ -2117,7 +2117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bf6790b",
+   "id": "83debdab",
    "metadata": {},
    "source": [
     "### `JOIN`\n",
@@ -2135,7 +2135,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "a97e13eb",
+   "id": "36b5b21f",
    "metadata": {},
    "outputs": [
     {
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3ff58e0",
+   "id": "80fc8e7e",
    "metadata": {},
    "source": [
     "As you can see, when specifying columns in the query above, I specified which tables the columns came from. Technically this isn't necessary if both tables don't share the column name, but it is still good practice to do so."
@@ -2303,7 +2303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95abcb3d",
+   "id": "d5ad802c",
    "metadata": {},
    "source": [
     "### `WHERE`"
@@ -2311,7 +2311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80d6b0f2",
+   "id": "fc710f50",
    "metadata": {},
    "source": [
     "The `WHERE` clause helps us filter rows we're interested in. For example, we can filter the `satscores` dataset to include all schools where over 50% of the school got over a 1500 on the SAT (as captured in the `PctGE1500` column).\n",
@@ -2321,7 +2321,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "e2920163",
+   "id": "ea37e738",
    "metadata": {},
    "outputs": [
     {
@@ -2534,7 +2534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99ce4fb3",
+   "id": "5a446ccb",
    "metadata": {},
    "source": [
     "If we're interested in filtering based on string data, it is recommended to use the `LIKE` operator. As a reminder, while `pandas` doesn't differentiate between single quotes ('') and double quotes (\"\"), SQL does. When I find it easiest to just use double quotes when denoting (string) column names in Jupyter SQL cell magic, you can read more about the details [here](https://ibm.github.io/db2-jupyter/linevscell/#python-versus-db2-character-strings). For example, let us filter the dataset to include all the rows corresponding to a school (as indicated by the `rtype` column being `S`)."
@@ -2543,7 +2543,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "cdaa6a74",
+   "id": "85a7d199",
    "metadata": {},
    "outputs": [
     {
@@ -2756,7 +2756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f85e669d",
+   "id": "3cc30696",
    "metadata": {},
    "source": [
     "When using the `LIKE` operator, the character `%` represents a wildcard which matches any string (similar to the `.*` pattern in [RegEx](../05-regex/regex.ipynb)). For example, let us say we're interested in all high school names which end with the word 'High'. As we're only interested in high schools, we want to put filters on both the `rtype` and the `sname` columns. We extract those rows using the code below."
@@ -2765,7 +2765,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "7d45702e",
+   "id": "b86bf4a2",
    "metadata": {},
    "outputs": [
     {
@@ -2979,7 +2979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "033de4e7",
+   "id": "0e6d894b",
    "metadata": {},
    "source": [
     "`%` was just an example, read more about the different wildcards in SQL [here](https://engineering.monstar-lab.com/en/post/2023/04/06/SQL-wildcard-operators-and-how-to-escape-them/)."
@@ -2987,7 +2987,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f06b27d",
+   "id": "60e88dd8",
    "metadata": {},
    "source": [
     "### `GROUP BY`"
@@ -2995,7 +2995,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2d0e08f",
+   "id": "ce0a1d76",
    "metadata": {},
    "source": [
     "The SQL `GROUP BY` statement accomplishes the exact same task as the `.groupby()` in `pandas` (review the [`Pandas` subchapter](../03-pandas/groupby_pivot.ipynb) if necessary). To specify which column you want to group by, you simply add that into the query, following the syntax below:\n",
@@ -3007,7 +3007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e8dac99",
+   "id": "de4d19cf",
    "metadata": {},
    "source": [
     "#### Aggregate"
@@ -3015,7 +3015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e47f6d17",
+   "id": "11c5f8fc",
    "metadata": {},
    "source": [
     "Specifying the aggregate function in SQL is not always intuitive for those who are familar with `pandas`. In SQL, you specify the aggregate function inside the `SELECT` statement. For example, let us say you are interested in the total number of test takers across all high schools in the different countries. In `pandas`, you could do:     \n",
@@ -3028,7 +3028,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "7f2e2c14",
+   "id": "0c69e02e",
    "metadata": {},
    "outputs": [
     {
@@ -3132,7 +3132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c53fa3b",
+   "id": "658b6702",
    "metadata": {},
    "source": [
     "An advantage of the SQL approach is that it's quite easy to compute multiple aggregations at once."
@@ -3141,7 +3141,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "3b6f83ae",
+   "id": "536bbb91",
    "metadata": {},
    "outputs": [
     {
@@ -3256,7 +3256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "096b5277",
+   "id": "bfc59c99",
    "metadata": {},
    "source": [
     "SQL comes with multiple aggregate functions, including `COUNT`, `MIN`, `MAX`, `SUM`, `AVG`, etc. Feel free to google around for more examples of aggregate functions and creative ways to use the functions above!"
@@ -3264,7 +3264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73d8badd",
+   "id": "e806a3d1",
    "metadata": {},
    "source": [
     "#### `HAVING`"
@@ -3272,7 +3272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63278408",
+   "id": "6b7f8b51",
    "metadata": {},
    "source": [
     "The `HAVING` clause in a SQL `GROUP BY` is equivalent to the `filter()` in a `pandas` groupby (review the `pandas` filter [here](../03-pandas/groupby_pivot.ipynb) if necessary). It goes directly after the `GROUP BY` statement. For example, suppose we want to identify school districts where the average percentage of students scoring above 1500 on the SAT is less than 10%. We could write the following SQL code to achieve this."
@@ -3281,7 +3281,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "8c688387",
+   "id": "bc2ae4e5",
    "metadata": {},
    "outputs": [
     {
@@ -3397,7 +3397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "376dd3d0",
+   "id": "a5967e2b",
    "metadata": {},
    "source": [
     "Note how we easily performed both an aggregation and a filter in the code above. The ease of writing this is an advantage of SQL's groupby approach."
@@ -3405,7 +3405,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "168503d8",
+   "id": "865191b1",
    "metadata": {},
    "source": [
     "### `ORDER BY`"
@@ -3413,7 +3413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f9ac351",
+   "id": "b171e8e3",
    "metadata": {},
    "source": [
     "Orders the table by a given column, in ascending (using `ASC`) or descending (using `DESC`) order. For example, let us say we're interested in which schools have the highest proportion of students scoring above a 1500 on the SAT. We could use the code below to accomplish this."
@@ -3422,7 +3422,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "ff386a3d",
+   "id": "f9b81f9f",
    "metadata": {},
    "outputs": [
     {
@@ -3636,7 +3636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46dbd70d",
+   "id": "aad2d590",
    "metadata": {},
    "source": [
     "### `LIMIT`\n",
@@ -3647,7 +3647,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "4b49db57",
+   "id": "239cd05f",
    "metadata": {},
    "outputs": [
     {
@@ -4005,7 +4005,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91178d30",
+   "id": "2e7ed913",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
diff --git a/_toc.yml b/_toc.yml
index 52ab9bf..3bdab4f 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -2,12 +2,13 @@ format: jb-article
 root: content/intro
 title: Home
 sections:
-# - file: content/01-python_v_stata/index.md
-#   sections:
-#     - file: content/01-python_v_stata/history.md
-#     - file: content/01-python_v_stata/differences.md
-#     - file: content/01-python_v_stata/install.md
-#     - file: content/01-python_v_stata/syntax.md
+- file: content/01-python_v_stata/index.md
+  sections:
+    - file: content/01-python_v_stata/history.md
+    - file: content/01-python_v_stata/differences.md
+    - file: content/01-python_v_stata/install.md
+    - file: content/01-python_v_stata/syntax.ipynb
+    - file: content/01-python_v_stata/pedagogy.ipynb
 - file: content/02-prereqs/index.md
   sections:
       - file: content/02-prereqs/python.ipynb
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/differences-checkpoint.md b/content/01-python_v_stata/.ipynb_checkpoints/differences-checkpoint.md
index 8027620..af9ac67 100644
--- a/content/01-python_v_stata/.ipynb_checkpoints/differences-checkpoint.md
+++ b/content/01-python_v_stata/.ipynb_checkpoints/differences-checkpoint.md
@@ -8,7 +8,7 @@ Python is a versatile, general-purpose programming language that can be used for
 
 ## Syntax
 
-Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results.
+Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results. See the [Syntax subchapter](syntax.ipynb) for more information.
 
 ## Packages/Libraries
 
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/history-checkpoint.md b/content/01-python_v_stata/.ipynb_checkpoints/history-checkpoint.md
index 11a6ad0..61bb507 100644
--- a/content/01-python_v_stata/.ipynb_checkpoints/history-checkpoint.md
+++ b/content/01-python_v_stata/.ipynb_checkpoints/history-checkpoint.md
@@ -1,25 +1,15 @@
 # History of the Languages
 
-This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be.
+Both Python and Stata have become essential tools in various domains. Python's general-purpose nature makes it versatile across many fields, and Stata's specialization in statistical analysis makes it a go-to choice for researchers and analysts in specific disciplines. This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be. 
 
-###### <span style="color:red"> To be updated </span>
+## History of Python
 
-<!-- From ChatGPT
+Python is a high-level, interpreted programming language that was created by Guido van Rossum in 1991 ([GeeksforGeeks](https://www.geeksforgeeks.org/history-of-python/)). Guido van Rossum aimed to create a language that emphasized code readability and ease of use, while still being a general purpose programming language that can be used for a wide variety of tasks.
 
-## History of Python:
+Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers building packages contributed to its success. Today, Python is one of the most widely used programming languages, being used for a wide variety of tasks.
 
-Python is a high-level, interpreted programming language that was created by Guido van Rossum. The language's development began in the late 1980s, and the first official Python release, version 0.9.0, came out in February 1991. Guido van Rossum aimed to create a language that emphasized code readability and ease of use, inspired by the ABC language.
+## History of Stata
 
-The name "Python" was chosen as a tribute to the British comedy group Monty Python. Over the years, Python evolved, with significant releases such as Python 2.0 in 2000 and Python 3.0 in 2008. Python 3 introduced backward-incompatible changes to enhance the language's consistency and eliminate redundancy.
+Stata is a statistical program designed for data analysis, manipulation, and visualization. It was written and first released in 1985 by William Gould and Sean Becketti. Stata then grew over time to include more and more forms of statistical analyses. A key feature was introduced in Stata 1.3, which allowed users to write their own programs for statistical analyses.
 
-Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers contributed to its success.
-
-## History of Stata:
-
-Stata is a statistical software package designed for data analysis, manipulation, and visualization. It was developed by StataCorp, founded by William Gould, James Hardin, and Cleve Moler. Stata's development began in the early 1980s, with the first version, Stata 1.0, released in 1985.
-
-Stata was initially focused on providing tools for econometric and statistical analysis. It gained popularity among researchers, economists, and social scientists for its powerful data management capabilities and robust statistical features.
-
-Over the years, StataCorp released several versions of the software, adding new features and improving existing ones. Stata's scripting language allows users to automate tasks and create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
-
-Both Python and Stata have become essential tools in their respective domains, with Python's general-purpose nature making it versatile across many fields, and Stata's specialization in statistical analysis making it a go-to choice for researchers and analysts in specific disciplines. -->
\ No newline at end of file
+Today, Stata's scripting language allows users to automate tasks and easily create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/index-checkpoint.md b/content/01-python_v_stata/.ipynb_checkpoints/index-checkpoint.md
index be74467..96c18ea 100644
--- a/content/01-python_v_stata/.ipynb_checkpoints/index-checkpoint.md
+++ b/content/01-python_v_stata/.ipynb_checkpoints/index-checkpoint.md
@@ -1,5 +1,7 @@
 # Python vs Stata
 
-This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. 
+Python and Stata are two of the most popular languages used for economics research ([source](https://cepr.org/voxeu/columns/which-programming-language-best-economic-research-julia-matlab-python-or-r)). However, there are several key differences between them. Stata is a highly specialized language intended for mainly statistical analysis, while Python is a general-purpose programming language.
+
+This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. We hope that this chapter will help you determine which programming language is right for you. However, as the remainder of this textbook is written entirely in Python, the final subchapter (`Syntax`) will aim to help experienced Stata users become more familiar with Python.
 
 <!-- For more information, please look at [Python's official page](https://www.python.org/) and [Stata's official page](https://www.stata.com/) respectively. -->
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/pedagogy-checkpoint.ipynb b/content/01-python_v_stata/.ipynb_checkpoints/pedagogy-checkpoint.ipynb
new file mode 100644
index 0000000..46995e5
--- /dev/null
+++ b/content/01-python_v_stata/.ipynb_checkpoints/pedagogy-checkpoint.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c41e3963-887e-423e-8e3a-49d49122a0cd",
+   "metadata": {},
+   "source": [
+    "# Economics Pedagogy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3069aff8-c35a-42b7-a08c-8f8e0a45ea25",
+   "metadata": {},
+   "source": [
+    "Both Python and Stata have been used to teach economics to students of all levels, from the beginner to the graduate students. They both have their advantages and disadvantages, some of which are discussed below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2017f83-5353-401d-8da3-2d6bae086953",
+   "metadata": {},
+   "source": [
+    "## Learning Curve\n",
+    "\n",
+    "Students who have little to no experience in coding often find Stata's command-line interface easier to install and use. However, students with more experience in coding may be more familiar with Python and thus find that easier to use. At the end of the day, different students could find either of the softwares easier to learn, and the software you choose to use should consider the level of the students in your class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5590927b-1191-4a2a-9c6a-c2fe15c85e11",
+   "metadata": {},
+   "source": [
+    "## Accessibility\n",
+    "\n",
+    "Both Python and Jupyter notebooks are free and easily accessible, while Stata must be purchased. If your students do not have an instituional license for Stata, it may be more inclusive to use Python to avoid placing an undue financial burden on students. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03140e78-8b1a-4e9e-bdd7-2bc2d1ff07d0",
+   "metadata": {},
+   "source": [
+    "## Cloud Computing\n",
+    "\n",
+    "With the great integration between Jupyter Notebooks and Cloud Computing provided by tools such as [Google Colab](https://colab.research.google.com/), it is possible to easily share your Jupyter notebooks with all your students and have them run your code in real time. To share your Stata `.do` files with students, it may be best to upload them to a Google Drive folder and have your students download and run them locally. Students may then run into issues if they haven't locally installed all the packages/updated all computer paths in your code. These issues are avoided if you use a virtual environment where everyone has access to the same resources, such as Google Colab (more details can be found on Colab's [welcome page](https://colab.research.google.com/#scrollTo=GJBs_flRovLc))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ca554f-7f28-407b-81c9-62ed88a8488a",
+   "metadata": {},
+   "source": [
+    "## Autograding\n",
+    "\n",
+    "In both languages, you can view and grade the final output of student code. However, Stata `.do` files do not have any features to automatically grade the intermediate steps student take towards a solution. Python has a package called [Otter Grader](https://otter-grader.readthedocs.io/en/latest/), designed to help you grade the output of various code cells in Jupyter notebooks. Using Otter Grader, you can create various test cells that have their corresponding solutions and relevant tests. The package provides tests that are both visible to and hidden from students, enabling you to both guide students on the right track and check for correctness as necessary. \n",
+    "\n",
+    "Several UC Berkeley classes, including [Econ 148: Data Science for Economists](https://www.econ148.org), use Otter Grader along with [Gradescope](https://www.gradescope.com/) to automatically grade thousands of student submissions at once. However, you do not need a Gradescope license to use Otter Grader, Otter is a free package that can be used by anyone without any cost. Please look at the [Otter tutorial](https://otter-grader.readthedocs.io/en/latest/tutorial.html) for more information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b4a72a-aeef-42dd-b8c8-12af667ea0fe",
+   "metadata": {},
+   "source": [
+    "## Textbooks\n",
+    "\n",
+    "While both Python and Stata have economics-specific textbooks, their styles and formats may differ. For Stata, the books are often traditional print books, such as [_An Introduction to Modern Econometrics Using Stata_](https://www.stata.com/bookstore/modern-econometrics-stata/). Students may be required to purchase a physical or digital copy of these textbooks in order to use them.\n",
+    "\n",
+    "Meanwhile, Python textbooks may be found online for free. For example, consider the online textbooks [_Coding for Economists_](https://aeturrell.github.io/coding-for-economists/intro.html), [_Causal Inference for The Brave and True_](https://matheusfacure.github.io/python-causality-handbook/landing-page.html), or [_Economic Networks_](https://networks.quantecon.org/). These textbooks may often include interactive code snippets that the students can try running on their own, and corresponding githubs where the students can see how the textbook was written. Such open access to the material offers greater reproducibility, an advantage over Stata."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb b/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb
index ec37972..629b4fe 100644
--- a/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb
+++ b/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb
@@ -63,7 +63,7 @@
    "id": "653ded29-8942-4b92-bb9c-996aca1f077c",
    "metadata": {},
    "source": [
-    "We also import a sample NBA dataset that will be helpful for demonstrating the features of our package."
+    "We also import in sample NBA, (log) wage and pollution datasets that will be helpful for demonstrating the features of our package."
    ]
   },
   {
@@ -245,10 +245,249 @@
     }
    ],
    "source": [
-    "nba = pd.read_csv(\"data/nba.csv\")\n",
+    "nba = pd.read_csv(\"data/nba.csv\") # NBA dataset\n",
     "nba.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10eac6fb-6d2c-4f0f-b172-cfacb74eb6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hispanic</th>\n",
+       "      <th>citizen</th>\n",
+       "      <th>black</th>\n",
+       "      <th>exp</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>female</th>\n",
+       "      <th>education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>5.288462</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>8.461538</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>10.416667</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>21.634615</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.365385</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   hispanic  citizen  black   exp       wage  female  education\n",
+       "0         1        1      0  14.0   5.288462       1          9\n",
+       "1         0        1      0  14.7   8.461538       1         13\n",
+       "2         0        1      0  14.7  10.416667       1         13\n",
+       "3         0        1      0  14.0  21.634615       1         14\n",
+       "4         1        0      0  12.0   3.365385       1         12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wages = pd.read_csv(\"data/la.csv\") # (Log) Wages dataset\n",
+    "wages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69beac78-18de-4389-965c-1f1fbfe17c25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>countryname</th>\n",
+       "      <th>countrycode</th>\n",
+       "      <th>gdp</th>\n",
+       "      <th>gdppc</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>co2pc</th>\n",
+       "      <th>population</th>\n",
+       "      <th>oecd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>ZMB</td>\n",
+       "      <td>9.799629e+09</td>\n",
+       "      <td>741.4421</td>\n",
+       "      <td>2427.554</td>\n",
+       "      <td>0.183669</td>\n",
+       "      <td>13216985</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>French Polynesia</td>\n",
+       "      <td>PYF</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>883.747</td>\n",
+       "      <td>3.296764</td>\n",
+       "      <td>268065</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Monaco</td>\n",
+       "      <td>MCO</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36845</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>UKR</td>\n",
+       "      <td>9.057726e+10</td>\n",
+       "      <td>1974.6212</td>\n",
+       "      <td>304804.720</td>\n",
+       "      <td>6.644867</td>\n",
+       "      <td>45870700</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Venezuela, RB</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1.750000e+11</td>\n",
+       "      <td>6010.0270</td>\n",
+       "      <td>201747.340</td>\n",
+       "      <td>6.946437</td>\n",
+       "      <td>29043283</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year       countryname countrycode           gdp      gdppc         co2  \\\n",
+       "0  2010            Zambia         ZMB  9.799629e+09   741.4421    2427.554   \n",
+       "1  2010  French Polynesia         PYF           NaN        NaN     883.747   \n",
+       "2  2010            Monaco         MCO           NaN        NaN         NaN   \n",
+       "3  2010           Ukraine         UKR  9.057726e+10  1974.6212  304804.720   \n",
+       "4  2010     Venezuela, RB         VEN  1.750000e+11  6010.0270  201747.340   \n",
+       "\n",
+       "      co2pc  population  oecd  \n",
+       "0  0.183669    13216985   0.0  \n",
+       "1  3.296764      268065   0.0  \n",
+       "2       NaN       36845   0.0  \n",
+       "3  6.644867    45870700   0.0  \n",
+       "4  6.946437    29043283   0.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pollution = pd.read_csv(\"data/pollution.csv\") # Pollution dataset\n",
+    "pollution.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f3edebbb-363c-489d-b560-09620fe27466",
@@ -258,10 +497,12 @@
     "\n",
     "Currently, `stata2python` only supports commands necessary to teach an introductory course in econometrics. If you would like to contribute, feel free to create pull requests [here](https://github.com/rohanjha123/data-h195/tree/main/creating_package). Below, we discuss the commands currently supported by the package.\n",
     "\n",
-    "By importing `stata2python` via `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command.  \n",
+    "By importing `stata2python` via the command `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command. \n",
     "\n",
     "Optionally, you may also specify the name of the DataFrame you're working with via the `df_name` parameter in `stata2python`. The default value for `df_name` is simply `df`. \n",
     "\n",
+    "Note that the provided Python code will (naturally) require you to have `pandas` installed, and may also require you to have the Python packages `numpy`, `scipy`, `matplotlib`, and `statsmodels` installed. The provided code will include all imports necessary (including `import pandas as pd`).\n",
+    "\n",
     "Below, we discuss all the features currently supported by `Stata2Python`, along with providing example usages."
    ]
   },
@@ -270,14 +511,14 @@
    "id": "d9ace3e5-b5e7-473e-8bdb-cf6559c2fcc1",
    "metadata": {},
    "source": [
-    "#### T-test\n",
+    "#### T-tests\n",
     "\n",
     "This function helps users determine the code for running [t-tests](https://www.jmp.com/en_us/statistics-knowledge-portal/t-test.html) in Python. Examples include:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "b48ee104-aa0a-4461-a1bc-9f40c6c90996",
    "metadata": {},
    "outputs": [
@@ -285,17 +526,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "import numpy as np\n",
       "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
       "catvar_vals = np.unique(df['guard'])\n",
-      "if len(catvar_vals) != 2:\n",
-      "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
       "df_1 = df[df['guard'] == catvar_vals[0]]\n",
       "df_2 = df[df['guard'] == catvar_vals[1]]\n",
-      "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n",
-      "t_stat = ttest.statistic\n",
-      "p_val = ttest.pvalue\n",
-      "print(f'T-stat: {t_stat}, P-value: {p_val}')\n"
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n"
      ]
     }
    ],
@@ -305,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "87876a2e-0b2c-40d5-a64a-c8ec25941aa1",
    "metadata": {},
    "outputs": [
@@ -313,17 +552,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "import numpy as np\n",
       "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
       "catvar_vals = np.unique(nba['guard'])\n",
-      "if len(catvar_vals) != 2:\n",
-      "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
       "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
       "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
-      "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n",
-      "t_stat = ttest.statistic\n",
-      "p_val = ttest.pvalue\n",
-      "print(f'T-stat: {t_stat}, P-value: {p_val}')\n"
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n"
      ]
     }
    ],
@@ -341,30 +578,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 28,
    "id": "a485ec9f-25f6-4c24-9ee8-380b751ef6a1",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "T-stat: 2.1432820571177977, P-value: 0.03299634994484977\n"
-     ]
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=2.1432820571177977, pvalue=0.03299634994484977, df=266.3682612357414)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "import pandas as pd\n",
     "import numpy as np\n",
     "from scipy import stats\n",
+    "### First, we must filter the DataFrame to obtain the right values\n",
     "catvar_vals = np.unique(nba['guard'])\n",
-    "if len(catvar_vals) != 2:\n",
-    "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
     "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
     "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
-    "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n",
-    "t_stat = ttest.statistic\n",
-    "p_val = ttest.pvalue\n",
-    "print(f'T-stat: {t_stat}, P-value: {p_val}')"
+    "### Then, we can run our t-test\n",
+    "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')"
    ]
   },
   {
@@ -372,7 +610,7 @@
    "id": "bcac0225-7131-4a7d-9997-47958c10c172",
    "metadata": {},
    "source": [
-    "### Gen\n",
+    "#### Generating new columns\n",
     "\n",
     "Used for generating new columns. For example,"
    ]
@@ -387,6 +625,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "df['degree'] = df['coll'] >= 4\n"
      ]
     }
@@ -405,6 +644,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])\n"
      ]
     }
@@ -413,12 +653,1346 @@
     "stata2python(\"gen productivity = points/(minutes/games)\",\"nba\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9357b734-d25a-4008-b0c9-86240daecff2",
+   "metadata": {},
+   "source": [
+    "Copy-pasting the output into Python to verify it works."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "b8fd2dcf-621b-40dc-95bf-f1deacf15a09",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7b99236a-651a-47dc-8be9-b5c8090f4f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.363571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.386423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.279287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.430344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3210.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.598939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>1</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>4</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.345941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.87</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.559733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.590629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>282</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212766</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>269 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     married    wage  exper  age  coll  games  minutes  guard  forward  \\\n",
+       "0          1  1002.5      4   27     4     77     2867      1        0   \n",
+       "1          1  2030.0      5   28     4     78     2789      1        0   \n",
+       "2          0   650.0      1   25     4     74     1149      0        0   \n",
+       "3          0  2030.0      5   28     4     47     1178      0        1   \n",
+       "4          0   755.0      3   24     4     82     2096      1        0   \n",
+       "..       ...     ...    ...  ...   ...    ...      ...    ...      ...   \n",
+       "264        1  3210.0      7   29     4     79     2638      1        0   \n",
+       "265        1   715.0      5   31     4     75     1084      0        1   \n",
+       "266        1   600.0     11   33     3     67     1197      1        0   \n",
+       "267        0  2500.0      6   28     4     78     2113      0        0   \n",
+       "268        0  2000.0     12   33     3     30      282      0        1   \n",
+       "\n",
+       "     center  points  rebounds  assists  draft  allstar  avgmin  black  \\\n",
+       "0         0      16         4        5   19.0        0   37.23      1   \n",
+       "1         0      13         3        9   28.0        0   35.76      1   \n",
+       "2         1       6         3        0   19.0        0   15.53      1   \n",
+       "3         0       7         5        2    1.0        0   25.06      1   \n",
+       "4         0      11         4        3   24.0        0   25.56      1   \n",
+       "..      ...     ...       ...      ...    ...      ...     ...    ...   \n",
+       "264       0      20         3        3   11.0        1   33.39      1   \n",
+       "265       0       5         3        1   54.0        0   14.45      1   \n",
+       "266       0      10         2        2    4.0        0   17.87      1   \n",
+       "267       1      16         6        2    2.0        0   27.09      0   \n",
+       "268       0       2         3        1    5.0        0    9.40      1   \n",
+       "\n",
+       "     children  productivity  \n",
+       "0           0      0.429717  \n",
+       "1           1      0.363571  \n",
+       "2           0      0.386423  \n",
+       "3           0      0.279287  \n",
+       "4           0      0.430344  \n",
+       "..        ...           ...  \n",
+       "264         0      0.598939  \n",
+       "265         1      0.345941  \n",
+       "266         1      0.559733  \n",
+       "267         0      0.590629  \n",
+       "268         0      0.212766  \n",
+       "\n",
+       "[269 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba # You can see that the productivity column was successfully generated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ee162dd-ad45-4af0-aaa4-062cc4e6e40b",
+   "metadata": {},
+   "source": [
+    "#### Describing the Data\n",
+    "\n",
+    "Used for summarizing the dataframe. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f44496e-8337-4ec4-9677-7d9e770e6b16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba.describe()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"describe\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "da57f17a-214a-4932-a6c8-354a7aeb87e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>240.00000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.442379</td>\n",
+       "      <td>1423.827509</td>\n",
+       "      <td>5.118959</td>\n",
+       "      <td>27.394052</td>\n",
+       "      <td>3.717472</td>\n",
+       "      <td>65.724907</td>\n",
+       "      <td>1682.193309</td>\n",
+       "      <td>0.420074</td>\n",
+       "      <td>0.408922</td>\n",
+       "      <td>0.171004</td>\n",
+       "      <td>10.260223</td>\n",
+       "      <td>4.468401</td>\n",
+       "      <td>2.453532</td>\n",
+       "      <td>20.20000</td>\n",
+       "      <td>0.115242</td>\n",
+       "      <td>23.979257</td>\n",
+       "      <td>0.806691</td>\n",
+       "      <td>0.345725</td>\n",
+       "      <td>0.409677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.497595</td>\n",
+       "      <td>999.774074</td>\n",
+       "      <td>3.400062</td>\n",
+       "      <td>3.391292</td>\n",
+       "      <td>0.754410</td>\n",
+       "      <td>18.851110</td>\n",
+       "      <td>893.327771</td>\n",
+       "      <td>0.494491</td>\n",
+       "      <td>0.492551</td>\n",
+       "      <td>0.377214</td>\n",
+       "      <td>5.882489</td>\n",
+       "      <td>2.892980</td>\n",
+       "      <td>2.148124</td>\n",
+       "      <td>18.73582</td>\n",
+       "      <td>0.319909</td>\n",
+       "      <td>9.731086</td>\n",
+       "      <td>0.395629</td>\n",
+       "      <td>0.476491</td>\n",
+       "      <td>0.114920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.890000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>650.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>983.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.730000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.338710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1186.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1690.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.50000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24.820000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2014.500000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>2438.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.25000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>33.260000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.471168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5740.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>3533.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>139.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>43.090000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.748532</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          married         wage       exper         age        coll  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean     0.442379  1423.827509    5.118959   27.394052    3.717472   \n",
+       "std      0.497595   999.774074    3.400062    3.391292    0.754410   \n",
+       "min      0.000000   150.000000    1.000000   21.000000    0.000000   \n",
+       "25%      0.000000   650.000000    2.000000   25.000000    4.000000   \n",
+       "50%      0.000000  1186.000000    4.000000   27.000000    4.000000   \n",
+       "75%      1.000000  2014.500000    7.000000   30.000000    4.000000   \n",
+       "max      1.000000  5740.000000   18.000000   41.000000    4.000000   \n",
+       "\n",
+       "            games      minutes       guard     forward      center  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean    65.724907  1682.193309    0.420074    0.408922    0.171004   \n",
+       "std     18.851110   893.327771    0.494491    0.492551    0.377214   \n",
+       "min      3.000000    33.000000    0.000000    0.000000    0.000000   \n",
+       "25%     57.000000   983.000000    0.000000    0.000000    0.000000   \n",
+       "50%     74.000000  1690.000000    0.000000    0.000000    0.000000   \n",
+       "75%     79.000000  2438.000000    1.000000    1.000000    0.000000   \n",
+       "max     82.000000  3533.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "           points    rebounds     assists      draft     allstar      avgmin  \\\n",
+       "count  269.000000  269.000000  269.000000  240.00000  269.000000  269.000000   \n",
+       "mean    10.260223    4.468401    2.453532   20.20000    0.115242   23.979257   \n",
+       "std      5.882489    2.892980    2.148124   18.73582    0.319909    9.731086   \n",
+       "min      1.000000    1.000000    0.000000    1.00000    0.000000    2.890000   \n",
+       "25%      5.000000    2.000000    1.000000    7.00000    0.000000   16.730000   \n",
+       "50%      9.000000    4.000000    2.000000   14.50000    0.000000   24.820000   \n",
+       "75%     14.000000    6.000000    3.000000   28.25000    0.000000   33.260000   \n",
+       "max     30.000000   17.000000   13.000000  139.00000    1.000000   43.090000   \n",
+       "\n",
+       "            black    children  productivity  \n",
+       "count  269.000000  269.000000    269.000000  \n",
+       "mean     0.806691    0.345725      0.409677  \n",
+       "std      0.395629    0.476491      0.114920  \n",
+       "min      0.000000    0.000000      0.093851  \n",
+       "25%      1.000000    0.000000      0.338710  \n",
+       "50%      1.000000    0.000000      0.408560  \n",
+       "75%      1.000000    1.000000      0.471168  \n",
+       "max      1.000000    1.000000      0.748532  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48facefc-8cdf-49e8-a071-7bc836552fd9",
+   "metadata": {},
+   "source": [
+    "#### Correlation Matrix\n",
+    "\n",
+    "Used for generating a correlation matrix between relevant variables. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6cd38a22-ea9a-41cb-bfda-e2d439b4d40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba[['points', 'assists', 'rebounds']].corr()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"corr points assists rebounds\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "969d8093-ff69-4224-ba83-a8df7ead7617",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>points</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>rebounds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>points</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>0.563324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assists</th>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rebounds</th>\n",
+       "      <td>0.563324</td>\n",
+       "      <td>0.059956</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            points   assists  rebounds\n",
+       "points    1.000000  0.539269  0.563324\n",
+       "assists   0.539269  1.000000  0.059956\n",
+       "rebounds  0.563324  0.059956  1.000000"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba[['points', 'assists', 'rebounds']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ab3e395-5236-407d-bbef-1a6f7a2104c4",
+   "metadata": {},
+   "source": [
+    "#### Scatter Plots\n",
+    "\n",
+    "Used for making scatter (or twoway) plots. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17411597-3519-41c0-a455-796118431768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+      "plt.xlabel('co2pc');\n",
+      "plt.ylabel('population');\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"twoway (scatter co2pc population, xtitle('co2pc') ytitle('population'))\",\"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ae6cf21d-24ed-453e-b757-7403d7d41113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+    "plt.xlabel('co2pc');\n",
+    "plt.ylabel('population');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33122b3d-c34b-424c-a246-8778b2a872ab",
+   "metadata": {},
+   "source": [
+    "#### Histograms\n",
+    "\n",
+    "Used for generating histograms. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "54d05f02-364d-4e82-b594-b5ba0c550357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "pollution.hist(column='co2pc',bins=80);\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"histogram co2pc, bin(80)\", \"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "77289483-1aee-435e-b78f-909044de0bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "pollution.hist(column='co2pc',bins=80);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14245a39-b06f-406b-a597-fc4ffcffead0",
+   "metadata": {},
+   "source": [
+    "#### Regressions\n",
+    "\n",
+    "Used for running regressions, including Python comments to help explain relevant portions of the code. Note that not all features from Stata's `reg` command are supported. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1bd4cdaa-deb1-417f-80d1-da4e0ca5350e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### Dropping NaN/missing values\n",
+      "wages_no_na = wages[['exp','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_no_na['exp']\n",
+      "y_df = wages_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit()\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "07fc3173-8ec2-4f34-bf8f-ddbb8dd6ccde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4714</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.493</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:39</td>     <th>  Log-Likelihood:    </th> <td> -3541.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7086.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   861</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>   16.0816</td> <td>    3.917</td> <td>    4.105</td> <td> 0.000</td> <td>    8.393</td> <td>   23.771</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>   <td>   -0.2135</td> <td>    0.311</td> <td>   -0.687</td> <td> 0.493</td> <td>   -0.824</td> <td>    0.397</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1263.062</td> <th>  Durbin-Watson:     </th>  <td>   1.830</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>413853.334</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.176</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>109.028</td> <th>  Cond. No.          </th>  <td>    99.5</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.001   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &    -0.001   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &    0.4714   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.493    \\\\\n",
+       "\\textbf{Time:}             &     21:26:39     & \\textbf{  Log-Likelihood:    } &   -3541.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7086.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         861      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &      16.0816  &        3.917     &     4.105  &         0.000        &        8.393    &       23.771     \\\\\n",
+       "\\textbf{exp}   &      -0.2135  &        0.311     &    -0.687  &         0.493        &       -0.824    &        0.397     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1263.062 & \\textbf{  Durbin-Watson:     } &     1.830   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 413853.334  \\\\\n",
+       "\\textbf{Skew:}          &   8.176  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 109.028  & \\textbf{  Cond. No.          } &      99.5   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.001\n",
+       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
+       "Method:                 Least Squares   F-statistic:                    0.4714\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.493\n",
+       "Time:                        21:26:39   Log-Likelihood:                -3541.0\n",
+       "No. Observations:                 863   AIC:                             7086.\n",
+       "Df Residuals:                     861   BIC:                             7096.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         16.0816      3.917      4.105      0.000       8.393      23.771\n",
+       "exp           -0.2135      0.311     -0.687      0.493      -0.824       0.397\n",
+       "==============================================================================\n",
+       "Omnibus:                     1263.062   Durbin-Watson:                   1.830\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           413853.334\n",
+       "Skew:                           8.176   Prob(JB):                         0.00\n",
+       "Kurtosis:                     109.028   Cond. No.                         99.5\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### Dropping NaN/missing values\n",
+    "wages_no_na = wages[['exp','wage']].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_no_na['exp']\n",
+    "y_df = wages_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit()\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3fc841e3-b7a8-40ca-a338-e4595c3e1d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "### Dropping NaN/missing values\n",
+      "df_no_na = df[['exp', 'education','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = df_no_na['exp']\n",
+      "y_df = df_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': df_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp, vce(cluster education)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c86447cd-732b-47b9-a192-70cb8c0e2684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "wages_with_dummies = wages.copy()\n",
+      "x_var = \"'exp', 'i.female'\"\n",
+      "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+      "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+      "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+      "for indicator in indicator_list:\n",
+      "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+      "    dummies = dummies.iloc[:,1:]\n",
+      "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+      "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+      "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+      "x_var = x_var_new\n",
+      "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+      "if clustering_vars:\n",
+      "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+      "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+      "### Dropping NaN/missing values\n",
+      "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+      "y_df = wages_with_dummies_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp i.female, vce(cluster education)\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4f73adae-9b6b-4d5d-9db8-2567fb9935fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.007</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.993</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.183</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:54</td>     <th>  Log-Likelihood:    </th> <td> -3538.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7082.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   860</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   17.4218</td> <td>    3.698</td> <td>    4.712</td> <td> 0.000</td> <td>   10.175</td> <td>   24.669</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>      <td>   -0.2373</td> <td>    0.206</td> <td>   -1.153</td> <td> 0.249</td> <td>   -0.640</td> <td>    0.166</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>1_female</th> <td>   -2.4604</td> <td>    1.261</td> <td>   -1.951</td> <td> 0.051</td> <td>   -4.932</td> <td>    0.011</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1260.198</td> <th>  Durbin-Watson:     </th>  <td>   1.842</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>409372.994</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.142</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>108.449</td> <th>  Cond. No.          </th>  <td>    101.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.007   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.005   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1.993   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.183    \\\\\n",
+       "\\textbf{Time:}             &     21:26:54     & \\textbf{  Log-Likelihood:    } &   -3538.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7082.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         860      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}     &      17.4218  &        3.698     &     4.712  &         0.000        &       10.175    &       24.669     \\\\\n",
+       "\\textbf{exp}       &      -0.2373  &        0.206     &    -1.153  &         0.249        &       -0.640    &        0.166     \\\\\n",
+       "\\textbf{1\\_female} &      -2.4604  &        1.261     &    -1.951  &         0.051        &       -4.932    &        0.011     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1260.198 & \\textbf{  Durbin-Watson:     } &     1.842   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 409372.994  \\\\\n",
+       "\\textbf{Skew:}          &   8.142  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 108.449  & \\textbf{  Cond. No.          } &      101.   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.007\n",
+       "Model:                            OLS   Adj. R-squared:                  0.005\n",
+       "Method:                 Least Squares   F-statistic:                     1.993\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.183\n",
+       "Time:                        21:26:54   Log-Likelihood:                -3538.0\n",
+       "No. Observations:                 863   AIC:                             7082.\n",
+       "Df Residuals:                     860   BIC:                             7096.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:              cluster                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         17.4218      3.698      4.712      0.000      10.175      24.669\n",
+       "exp           -0.2373      0.206     -1.153      0.249      -0.640       0.166\n",
+       "1_female      -2.4604      1.261     -1.951      0.051      -4.932       0.011\n",
+       "==============================================================================\n",
+       "Omnibus:                     1260.198   Durbin-Watson:                   1.842\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           409372.994\n",
+       "Skew:                           8.142   Prob(JB):                         0.00\n",
+       "Kurtosis:                     108.449   Cond. No.                         101.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+    "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+    "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+    "y_var = \"wage\"\n",
+    "clustering_vars = ['education']\n",
+    "wages_with_dummies = wages.copy()\n",
+    "x_var = \"'exp', 'i.female'\"\n",
+    "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+    "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+    "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+    "for indicator in indicator_list:\n",
+    "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+    "    dummies = dummies.iloc[:,1:]\n",
+    "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+    "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+    "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+    "x_var = x_var_new\n",
+    "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+    "if clustering_vars:\n",
+    "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+    "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+    "### Dropping NaN/missing values\n",
+    "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+    "y_df = wages_with_dummies_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21279b0b-e6c2-467e-a028-429da3fac865",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.md b/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.md
deleted file mode 100644
index b16eba3..0000000
--- a/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Syntax
-
-For key syntatical differences between 
-
-###### <span style="color:red"> To be updated</span>
\ No newline at end of file
diff --git a/content/01-python_v_stata/data/.ipynb_checkpoints/la-checkpoint.csv b/content/01-python_v_stata/data/.ipynb_checkpoints/la-checkpoint.csv
new file mode 100644
index 0000000..d5400da
--- /dev/null
+++ b/content/01-python_v_stata/data/.ipynb_checkpoints/la-checkpoint.csv
@@ -0,0 +1,864 @@
+hispanic,citizen,black,exp,wage,female,education
+1,1,0,14.0,5.2884615,1,9
+0,1,0,14.7,8.4615385,1,13
+0,1,0,14.7,10.416667,1,13
+0,1,0,14.0,21.634615,1,14
+1,0,0,12.0,3.3653846,1,12
+0,1,0,11.0,10.096154,1,16
+0,1,0,13.0,33.846154,1,18
+0,1,0,13.7,5.7692308,1,13
+0,1,0,10.0,8.1730769,1,12
+0,1,0,14.7,28.25,1,14
+1,0,0,15.0,11.111111,1,10
+0,1,0,13.0,15.52906,1,16
+0,1,0,10.7,9.6153846,1,13
+0,0,0,12.7,6.25,1,13
+0,0,0,15.0,4.0,1,16
+1,1,0,12.0,78.333333,1,12
+0,1,0,13.47,8.2666667,1,11
+1,0,0,10.0,10.833333,1,12
+0,1,0,14.0,23.076923,1,10
+1,1,0,12.7,11.538462,1,13
+0,1,0,12.0,7.2115385,1,11
+1,0,0,13.74,2.8846154,1,7
+1,1,0,11.0,11.749038,1,12
+1,0,0,15.0,3.2307692,1,11
+1,0,0,11.0,10.576923,1,9
+1,0,0,14.0,3.3,1,10
+0,1,1,11.47,4.3645833,1,11
+1,0,0,11.74,4.25,1,7
+1,0,0,10.0,5.952381,1,11
+1,0,0,12.0,3.9772727,1,12
+1,0,0,11.0,3.125,1,9
+1,1,0,13.0,5.7692308,1,12
+1,0,0,13.74,3.6057692,1,7
+1,0,0,10.74,6.75,1,7
+1,0,0,10.0,5.2083333,1,12
+1,1,0,15.0,3.8461538,1,12
+1,0,0,11.47,14.423077,1,11
+1,0,0,15.0,5.5384615,1,11
+1,1,0,12.7,10.576923,1,13
+0,1,0,10.0,9.0277778,1,12
+0,0,1,13.7,21.5,1,13
+0,1,0,14.0,10.384615,1,10
+0,1,0,10.0,6.7307692,1,12
+0,0,0,13.7,8.6538462,1,13
+0,1,1,10.0,16.563147,1,16
+1,0,0,11.0,4.0,1,12
+1,0,0,11.0,4.296875,1,11
+0,1,0,15.0,6.7708333,1,12
+0,1,0,12.0,14.423077,1,20
+0,0,0,10.0,10.0,1,16
+0,0,0,15.0,6.0576923,1,16
+0,1,0,13.0,6.0,1,16
+0,1,0,11.0,11.783961,1,18
+0,1,0,14.0,38.541667,1,11
+0,1,1,10.0,19.964349,1,18
+0,1,0,11.0,16.025641,1,20
+1,1,0,14.0,7.1168269,1,12
+0,1,0,12.0,12.403846,1,16
+0,1,0,12.0,14.74359,1,16
+0,1,0,11.0,10.48951,1,12
+0,1,0,14.7,3.925,1,13
+0,1,0,13.0,8.9067308,1,12
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diff --git a/content/01-python_v_stata/data/la.csv b/content/01-python_v_stata/data/la.csv
new file mode 100644
index 0000000..d5400da
--- /dev/null
+++ b/content/01-python_v_stata/data/la.csv
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diff --git a/content/01-python_v_stata/data/pollution.csv b/content/01-python_v_stata/data/pollution.csv
new file mode 100644
index 0000000..4cae00b
--- /dev/null
+++ b/content/01-python_v_stata/data/pollution.csv
@@ -0,0 +1,215 @@
+year,countryname,countrycode,gdp,gdppc,co2,co2pc,population,oecd
+2010,Zambia,ZMB,9799629000.0,741.4421,2427.554,0.18366927,13216985,0.0
+2010,French Polynesia,PYF,,,883.747,3.296764,268065,0.0
+2010,Monaco,MCO,,,,,36845,0.0
+2010,Ukraine,UKR,90577255848.0,1974.6212,304804.72,6.644867,45870700,0.0
+2010,"Venezuela, RB",VEN,175000000000.0,6010.027,201747.34,6.946437,29043283,0.0
+2010,Romania,ROM,112000000000.0,5233.163,78745.16,3.6731577,21438001,0.0
+2010,Saudi Arabia,SAU,436000000000.0,15994.78,464480.56,17.039913,27258387,0.0
+2010,Malawi,MWI,3290262502.0,219.15076,1239.446,0.08255437,15013694,0.0
+2010,Moldova,MDA,3501430332.0,982.9785,4855.108,1.3630049,3562062,0.0
+2010,West Bank and Gaza,WBG,,,2365.215,0.62061185,3811102,0.0
+2010,Cuba,CUB,55436887631.0,4913.8477,38364.152,3.4005444,11281768,0.0
+2010,Benin,BEN,5230812508.0,550.04456,5188.805,0.54562724,9509798,0.0
+2010,Turks and Caicos Islands,TCA,,,161.348,5.20595,30993,0.0
+2010,Nepal,NPL,10103288981.0,376.34222,3755.008,0.13987207,26846016,0.0
+2010,Latvia,LVA,15502642580.0,6923.8887,7616.359,3.4016666,2239008,0.0
+2010,Eritrea,ERI,1056636372.0,184.04584,513.38,0.08942097,5741159,0.0
+2010,Trinidad and Tobago,TTO,18988955441.0,14297.89,50681.605,38.16113,1328095,0.0
+2010,Tonga,TON,268854243.0,2582.7034,157.681,1.514736,104098,0.0
+2010,Cambodia,KHM,8693197236.0,605.168,4180.38,0.29101288,14364931,0.0
+2010,Belarus,BLR,42939088225.0,4524.667,62221.656,6.5565495,9490000,0.0
+2010,"Macao SAR, China",MAC,20684077559.0,38688.875,1030.427,1.9273791,534626,0.0
+2010,Algeria,DZA,117000000000.0,3146.7188,123475.23,3.3315117,37062820,0.0
+2010,Costa Rica,CRI,25017792423.0,5357.49,7770.373,1.6640037,4669685,0.0
+2010,Swaziland,SWZ,2917342648.0,2445.0803,1023.093,0.8574737,1193148,0.0
+2010,Sudan,SDN,35822262376.0,785.69775,14172.955,0.3108586,35652002,0.0
+2010,Rwanda,RWA,3818993880.0,352.41196,594.054,0.054818556,10836732,0.0
+2010,Syrian Arab Republic,SYR,36613976515.0,1700.3937,61858.62,2.872783,21532647,0.0
+2010,India,IND,1250000000000.0,1034.2408,2008822.9,1.6662092,1205624648,0.0
+2010,St. Vincent and the Grenadines,VCT,591892928.9,5414.513,209.019,1.9120623,109316,0.0
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+2010,Central African Republic,CAF,1895403785.0,435.7329,264.024,0.060696274,4349921,0.0
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+2010,"Egypt, Arab Rep.",EGY,121000000000.0,1550.2406,204776.28,2.6227913,78075705,0.0
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+2010,Fiji,FJI,3032066121.0,3523.3682,1290.784,1.4999367,860559,0.0
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+2010,St. Lucia,LCA,1067166275.0,6015.6953,403.37,2.2738266,177397,0.0
+2010,"Iran, Islamic Rep.",IRN,,,571611.94,7.676527,74462314,0.0
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+2010,United Arab Emirates,ARE,204000000000.0,24219.297,167596.56,19.853798,8441537,0.0
+2010,Comoros,COM,413107576.0,604.771,139.346,0.2039963,683081,0.0
+2010,"Yemen, Rep.",YEM,20710400137.0,909.827,21851.652,0.9599633,22763008,0.0
+2010,New Caledonia,NCL,,,3920.023,15.6805935,249992,0.0
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+2010,Palau,PLW,171792215.1,8392.39,216.353,10.569272,20470,0.0
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+2010,Kazakhstan,KAZ,77245319067.0,4732.216,248728.94,15.237675,16323287,0.0
+2010,Lithuania,LTU,27346902406.0,8320.17,13560.566,4.1257405,3286820,0.0
+2010,South Sudan,SSD,,,,,9940929,0.0
+2010,Guatemala,GTM,32556756309.0,2270.0962,11118.344,0.7752526,14341576,0.0
+2010,Liberia,LBR,960255604.2,242.61194,799.406,0.20197272,3957990,0.0
+2010,Lesotho,LSO,1766292382.0,879.2244,18.335,0.00912679,2008921,0.0
+2010,Cameroon,CMR,19209829593.0,931.41534,7234.991,0.3507986,20624343,0.0
+2010,Lebanon,LBN,29991683920.0,6908.788,20403.188,4.7000127,4341092,0.0
+2010,Nicaragua,NIC,7290785418.0,1252.2369,4547.08,0.7809888,5822209,0.0
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+2010,Antigua and Barbuda,ATG,1009445957.0,11571.836,513.38,5.885158,87233,0.0
+2010,"Congo, Rep.",COG,7852666838.0,1909.8276,2027.851,0.49318862,4111715,0.0
+2010,"Congo, Dem. Rep.",ZAR,9438751813.0,151.76999,3039.943,0.04888063,62191161,0.0
+2010,Turkmenistan,TKM,13272745852.0,2632.4392,53054.156,10.522453,5041995,0.0
+2010,Jordan,JOR,17030688372.0,2816.8523,20821.227,3.4438019,6046000,0.0
+2010,Papua New Guinea,PNG,6553203636.0,955.42444,3135.285,0.4571089,6858945,0.0
+2010,Mauritania,MRT,2835163755.0,785.4901,2214.868,0.6136354,3609420,0.0
+2010,China,CHN,3840000000000.0,2869.0938,8286892.0,6.1948576,1337705000,0.0
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+2010,"Micronesia, Fed. Sts.",FSM,246418335.6,2378.1191,102.676,0.9908993,103619,0.0
+2010,Bosnia and Herzegovina,BIH,12803068421.0,3328.9924,31125.496,8.093102,3845929,0.0
+2010,Tajikistan,TJK,3181403840.0,417.10605,2860.26,0.37500167,7627326,0.0
+2010,Senegal,SEN,10366034228.0,800.4311,7058.975,0.5450708,12950564,0.0
+2010,Belize,BLZ,1277466194.0,4139.6206,421.705,1.3665322,308595,0.0
+2010,Bermuda,BMU,4975071875.0,76393.83,476.71,7.3200355,65124,0.0
+2010,Angola,AGO,50371160616.0,2576.6455,30417.766,1.5559657,19549124,0.0
+2010,Burundi,BDI,1391766915.0,150.74236,308.028,0.03336253,9232753,0.0
+2010,Sierra Leone,SLE,2130751273.0,370.43814,689.396,0.11985377,5751976,0.0
+2010,Uruguay,URY,22990234866.0,6818.018,6644.604,1.9705336,3371982,0.0
+2010,Suriname,SUR,2188868338.0,4169.591,2383.55,4.540441,524960,0.0
+2010,Armenia,ARM,7611465936.0,2568.4077,4220.717,1.4242358,2963496,0.0
+2010,Bangladesh,BGD,81471733197.0,539.0999,56152.77,0.3715639,151125475,0.0
+2010,Bahrain,BHR,17792456287.0,14216.757,24202.2,19.338352,1251513,0.0
+2010,Bolivia,BOL,11954060130.0,1176.9745,15456.405,1.5218089,10156601,0.0
+2010,Cayman Islands,CYM,,,590.387,10.635879,55509,0.0
+2010,Dominican Republic,DOM,47848657632.0,4776.8423,20964.238,2.0929084,10016797,0.0
+2010,Isle of Man,IMY,,,,,83992,0.0
+2010,Chad,TCD,8425171572.0,718.82336,469.376,0.04004648,11720781,0.0
+2010,Kyrgyz Republic,KGZ,3055806771.0,560.9146,6398.915,1.1745654,5447900,0.0
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+2010,Guinea,GIN,3267609255.0,300.44128,1235.779,0.11362406,10876033,0.0
+2010,Niger,NER,4381636015.0,275.683,1411.795,0.08882707,15893746,0.0
+2010,Cote d'Ivoire,CIV,18217368879.0,959.9918,5804.861,0.30589592,18976588,0.0
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+2010,"Macedonia, FYR",MKD,7140170814.0,3396.4973,10872.655,5.171997,2102216,0.0
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+2010,Puerto Rico,PRI,77337254438.0,20782.836,,,3721208,0.0
+2010,St. Martin (French part),MAF,,,,,30235,0.0
+2010,San Marino,SMR,,,,,30861,0.0
+2010,"Bahamas, The",BHS,7575468595.0,21013.898,2464.224,6.835611,360498,0.0
+2010,Serbia,SRB,27879217077.0,3823.5564,45962.18,6.3035836,7291436,0.0
+2010,Andorra,ADO,,,517.047,6.6367207,77907,0.0
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+2010,Greenland,GRL,,,634.391,11.148247,56905,0.0
+2010,Aruba,ABW,,,2321.211,22.84724,101597,0.0
+2010,Ghana,GHA,14804825657.0,610.18365,8998.818,0.37088796,24262901,0.0
+2010,"Korea, Dem. Rep.",PRK,,,71623.84,2.92336,24500520,0.0
+2010,Equatorial Guinea,GNQ,9718622518.0,13960.188,4679.092,6.7212205,696167,0.0
+2010,Mali,MLI,6971653981.0,498.47516,623.39,0.044572555,13985961,0.0
+2010,"Gambia, The",GMB,784275580.7,466.65292,473.043,0.28146598,1680640,0.0
+2010,Indonesia,IDN,378000000000.0,1570.153,433989.44,1.8032067,240676485,0.0
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+2010,Botswana,BWA,12116816309.0,6152.7266,5232.809,2.6571372,1969341,0.0
+2010,Tuvalu,TUV,23813390.63,2423.2615,,,9827,0.0
+2010,Georgia,GEO,8241335083.0,1850.8208,6241.234,1.4016426,4452800,0.0
+2010,Ethiopia,ETH,19946243845.0,229.01636,6494.257,0.07456497,87095281,0.0
+2010,Virgin Islands (U.S.),VIR,,,,,106267,0.0
+2010,Azerbaijan,AZE,28310397707.0,3126.724,45731.156,5.050749,9054332,0.0
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+2010,Belgium,BEL,400000000000.0,36742.01,108946.57,9.999147,10895586,1.0
+2010,Netherlands,NLD,683000000000.0,41110.25,182077.55,10.9583645,16615394,1.0
+2010,Norway,NOR,316000000000.0,64589.977,57186.863,11.6964445,4889252,1.0
+2010,Luxembourg,LUX,40696164752.0,80276.01,10828.651,21.360266,506953,1.0
+2010,Germany,DEU,2950000000000.0,36127.047,745383.75,9.114841,81776930,1.0
+2010,Czech Republic,CZE,148000000000.0,14112.9,111751.83,10.623007,10519792,1.0
+2010,Switzerland,CHE,428000000000.0,54642.664,38756.523,4.952968,7824909,1.0
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+2010,Japan,JPN,4650000000000.0,36472.848,1170715.4,9.185651,127450459,1.0
+2010,"Korea, Rep.",KOR,1020000000000.0,20625.1,567567.25,11.486891,49410000,1.0
+2010,Chile,CHL,148000000000.0,8610.022,72258.234,4.2131214,17150760,1.0
+2010,Australia,AUS,799000000000.0,36202.844,373080.6,16.908022,22065300,1.0
+2010,Spain,ESP,1180000000000.0,25595.99,269674.84,5.8534656,46070971,1.0
+2010,Austria,AUT,326000000000.0,38803.234,66897.08,7.973648,8389771,1.0
+2010,Hungary,HUN,109000000000.0,10926.469,50582.598,5.058248,10000023,1.0
+2010,United Kingdom,GBR,2360000000000.0,37899.32,493504.88,7.9250927,62271177,1.0
+2010,Ireland,IRL,207000000000.0,46214.957,39999.637,8.939753,4474356,1.0
+2010,France,FRA,2200000000000.0,33898.38,361272.84,5.555374,65031235,1.0
+2010,Iceland,ISL,16388130235.0,51528.355,1961.845,6.1685286,318041,1.0
+2010,Turkey,TUR,565000000000.0,7833.529,298002.44,4.1310306,72137546,1.0
+2010,Portugal,PRT,197000000000.0,18535.13,52361.094,4.9223833,10637346,1.0
+2010,Israel,ISR,165000000000.0,21696.613,70655.76,9.26803,7623600,1.0
+2010,Denmark,DNK,257000000000.0,46379.664,46303.21,8.346405,5547683,1.0
+2010,Mexico,MEX,957000000000.0,8117.3516,443674.0,3.7635722,117886404,1.0
+2010,Slovenia,SVN,39034227961.0,19054.258,15328.06,7.4822745,2048583,1.0
+2010,New Zealand,NZL,119000000000.0,27257.096,31550.867,7.223515,4367800,1.0
+2010,Greece,GRC,241000000000.0,21310.186,86717.22,7.6689987,11307502,1.0
+2010,Poland,POL,383000000000.0,10035.851,317254.2,8.308632,38183683,1.0
+2010,Canada,CAN,1200000000000.0,35277.27,499137.38,14.626073,34126547,1.0
+2010,Estonia,EST,13897254467.0,10369.84,18338.668,13.6839285,1340161,1.0
+2010,United States,USA,13600000000000.0,43952.445,5433056.5,17.564163,309326225,1.0
+2010,Slovak Republic,SVK,76899962691.0,14161.798,36094.28,6.647076,5430099,1.0
+2010,Finland,FIN,204000000000.0,38064.652,61843.953,11.53084,5363352,1.0
+2010,Italy,ITA,1760000000000.0,29163.146,406307.28,6.7176676,60483385,1.0
diff --git a/content/01-python_v_stata/differences.md b/content/01-python_v_stata/differences.md
index 8027620..af9ac67 100644
--- a/content/01-python_v_stata/differences.md
+++ b/content/01-python_v_stata/differences.md
@@ -8,7 +8,7 @@ Python is a versatile, general-purpose programming language that can be used for
 
 ## Syntax
 
-Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results.
+Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results. See the [Syntax subchapter](syntax.ipynb) for more information.
 
 ## Packages/Libraries
 
diff --git a/content/01-python_v_stata/history.md b/content/01-python_v_stata/history.md
index 11a6ad0..61bb507 100644
--- a/content/01-python_v_stata/history.md
+++ b/content/01-python_v_stata/history.md
@@ -1,25 +1,15 @@
 # History of the Languages
 
-This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be.
+Both Python and Stata have become essential tools in various domains. Python's general-purpose nature makes it versatile across many fields, and Stata's specialization in statistical analysis makes it a go-to choice for researchers and analysts in specific disciplines. This subchapter will discuss the history of how Python and Stata were created, as well as their intended uses. By doing so, it will shed further light on how and why the differences between Python and Stata came to be. 
 
-###### <span style="color:red"> To be updated </span>
+## History of Python
 
-<!-- From ChatGPT
+Python is a high-level, interpreted programming language that was created by Guido van Rossum in 1991 ([GeeksforGeeks](https://www.geeksforgeeks.org/history-of-python/)). Guido van Rossum aimed to create a language that emphasized code readability and ease of use, while still being a general purpose programming language that can be used for a wide variety of tasks.
 
-## History of Python:
+Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers building packages contributed to its success. Today, Python is one of the most widely used programming languages, being used for a wide variety of tasks.
 
-Python is a high-level, interpreted programming language that was created by Guido van Rossum. The language's development began in the late 1980s, and the first official Python release, version 0.9.0, came out in February 1991. Guido van Rossum aimed to create a language that emphasized code readability and ease of use, inspired by the ABC language.
+## History of Stata
 
-The name "Python" was chosen as a tribute to the British comedy group Monty Python. Over the years, Python evolved, with significant releases such as Python 2.0 in 2000 and Python 3.0 in 2008. Python 3 introduced backward-incompatible changes to enhance the language's consistency and eliminate redundancy.
+Stata is a statistical program designed for data analysis, manipulation, and visualization. It was written and first released in 1985 by William Gould and Sean Becketti. Stata then grew over time to include more and more forms of statistical analyses. A key feature was introduced in Stata 1.3, which allowed users to write their own programs for statistical analyses.
 
-Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers contributed to its success.
-
-## History of Stata:
-
-Stata is a statistical software package designed for data analysis, manipulation, and visualization. It was developed by StataCorp, founded by William Gould, James Hardin, and Cleve Moler. Stata's development began in the early 1980s, with the first version, Stata 1.0, released in 1985.
-
-Stata was initially focused on providing tools for econometric and statistical analysis. It gained popularity among researchers, economists, and social scientists for its powerful data management capabilities and robust statistical features.
-
-Over the years, StataCorp released several versions of the software, adding new features and improving existing ones. Stata's scripting language allows users to automate tasks and create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
-
-Both Python and Stata have become essential tools in their respective domains, with Python's general-purpose nature making it versatile across many fields, and Stata's specialization in statistical analysis making it a go-to choice for researchers and analysts in specific disciplines. -->
\ No newline at end of file
+Today, Stata's scripting language allows users to automate tasks and easily create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
diff --git a/content/01-python_v_stata/index.md b/content/01-python_v_stata/index.md
index be74467..96c18ea 100644
--- a/content/01-python_v_stata/index.md
+++ b/content/01-python_v_stata/index.md
@@ -1,5 +1,7 @@
 # Python vs Stata
 
-This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. 
+Python and Stata are two of the most popular languages used for economics research ([source](https://cepr.org/voxeu/columns/which-programming-language-best-economic-research-julia-matlab-python-or-r)). However, there are several key differences between them. Stata is a highly specialized language intended for mainly statistical analysis, while Python is a general-purpose programming language.
+
+This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. We hope that this chapter will help you determine which programming language is right for you. However, as the remainder of this textbook is written entirely in Python, the final subchapter (`Syntax`) will aim to help experienced Stata users become more familiar with Python.
 
 <!-- For more information, please look at [Python's official page](https://www.python.org/) and [Stata's official page](https://www.stata.com/) respectively. -->
diff --git a/content/01-python_v_stata/pedagogy.ipynb b/content/01-python_v_stata/pedagogy.ipynb
new file mode 100644
index 0000000..46995e5
--- /dev/null
+++ b/content/01-python_v_stata/pedagogy.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c41e3963-887e-423e-8e3a-49d49122a0cd",
+   "metadata": {},
+   "source": [
+    "# Economics Pedagogy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3069aff8-c35a-42b7-a08c-8f8e0a45ea25",
+   "metadata": {},
+   "source": [
+    "Both Python and Stata have been used to teach economics to students of all levels, from the beginner to the graduate students. They both have their advantages and disadvantages, some of which are discussed below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2017f83-5353-401d-8da3-2d6bae086953",
+   "metadata": {},
+   "source": [
+    "## Learning Curve\n",
+    "\n",
+    "Students who have little to no experience in coding often find Stata's command-line interface easier to install and use. However, students with more experience in coding may be more familiar with Python and thus find that easier to use. At the end of the day, different students could find either of the softwares easier to learn, and the software you choose to use should consider the level of the students in your class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5590927b-1191-4a2a-9c6a-c2fe15c85e11",
+   "metadata": {},
+   "source": [
+    "## Accessibility\n",
+    "\n",
+    "Both Python and Jupyter notebooks are free and easily accessible, while Stata must be purchased. If your students do not have an instituional license for Stata, it may be more inclusive to use Python to avoid placing an undue financial burden on students. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03140e78-8b1a-4e9e-bdd7-2bc2d1ff07d0",
+   "metadata": {},
+   "source": [
+    "## Cloud Computing\n",
+    "\n",
+    "With the great integration between Jupyter Notebooks and Cloud Computing provided by tools such as [Google Colab](https://colab.research.google.com/), it is possible to easily share your Jupyter notebooks with all your students and have them run your code in real time. To share your Stata `.do` files with students, it may be best to upload them to a Google Drive folder and have your students download and run them locally. Students may then run into issues if they haven't locally installed all the packages/updated all computer paths in your code. These issues are avoided if you use a virtual environment where everyone has access to the same resources, such as Google Colab (more details can be found on Colab's [welcome page](https://colab.research.google.com/#scrollTo=GJBs_flRovLc))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ca554f-7f28-407b-81c9-62ed88a8488a",
+   "metadata": {},
+   "source": [
+    "## Autograding\n",
+    "\n",
+    "In both languages, you can view and grade the final output of student code. However, Stata `.do` files do not have any features to automatically grade the intermediate steps student take towards a solution. Python has a package called [Otter Grader](https://otter-grader.readthedocs.io/en/latest/), designed to help you grade the output of various code cells in Jupyter notebooks. Using Otter Grader, you can create various test cells that have their corresponding solutions and relevant tests. The package provides tests that are both visible to and hidden from students, enabling you to both guide students on the right track and check for correctness as necessary. \n",
+    "\n",
+    "Several UC Berkeley classes, including [Econ 148: Data Science for Economists](https://www.econ148.org), use Otter Grader along with [Gradescope](https://www.gradescope.com/) to automatically grade thousands of student submissions at once. However, you do not need a Gradescope license to use Otter Grader, Otter is a free package that can be used by anyone without any cost. Please look at the [Otter tutorial](https://otter-grader.readthedocs.io/en/latest/tutorial.html) for more information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b4a72a-aeef-42dd-b8c8-12af667ea0fe",
+   "metadata": {},
+   "source": [
+    "## Textbooks\n",
+    "\n",
+    "While both Python and Stata have economics-specific textbooks, their styles and formats may differ. For Stata, the books are often traditional print books, such as [_An Introduction to Modern Econometrics Using Stata_](https://www.stata.com/bookstore/modern-econometrics-stata/). Students may be required to purchase a physical or digital copy of these textbooks in order to use them.\n",
+    "\n",
+    "Meanwhile, Python textbooks may be found online for free. For example, consider the online textbooks [_Coding for Economists_](https://aeturrell.github.io/coding-for-economists/intro.html), [_Causal Inference for The Brave and True_](https://matheusfacure.github.io/python-causality-handbook/landing-page.html), or [_Economic Networks_](https://networks.quantecon.org/). These textbooks may often include interactive code snippets that the students can try running on their own, and corresponding githubs where the students can see how the textbook was written. Such open access to the material offers greater reproducibility, an advantage over Stata."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:sklearn-env]",
+   "language": "python",
+   "name": "conda-env-sklearn-env-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/content/01-python_v_stata/syntax.ipynb b/content/01-python_v_stata/syntax.ipynb
index ec37972..629b4fe 100644
--- a/content/01-python_v_stata/syntax.ipynb
+++ b/content/01-python_v_stata/syntax.ipynb
@@ -63,7 +63,7 @@
    "id": "653ded29-8942-4b92-bb9c-996aca1f077c",
    "metadata": {},
    "source": [
-    "We also import a sample NBA dataset that will be helpful for demonstrating the features of our package."
+    "We also import in sample NBA, (log) wage and pollution datasets that will be helpful for demonstrating the features of our package."
    ]
   },
   {
@@ -245,10 +245,249 @@
     }
    ],
    "source": [
-    "nba = pd.read_csv(\"data/nba.csv\")\n",
+    "nba = pd.read_csv(\"data/nba.csv\") # NBA dataset\n",
     "nba.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10eac6fb-6d2c-4f0f-b172-cfacb74eb6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hispanic</th>\n",
+       "      <th>citizen</th>\n",
+       "      <th>black</th>\n",
+       "      <th>exp</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>female</th>\n",
+       "      <th>education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>5.288462</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>8.461538</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>10.416667</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>21.634615</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.365385</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   hispanic  citizen  black   exp       wage  female  education\n",
+       "0         1        1      0  14.0   5.288462       1          9\n",
+       "1         0        1      0  14.7   8.461538       1         13\n",
+       "2         0        1      0  14.7  10.416667       1         13\n",
+       "3         0        1      0  14.0  21.634615       1         14\n",
+       "4         1        0      0  12.0   3.365385       1         12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wages = pd.read_csv(\"data/la.csv\") # (Log) Wages dataset\n",
+    "wages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69beac78-18de-4389-965c-1f1fbfe17c25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>countryname</th>\n",
+       "      <th>countrycode</th>\n",
+       "      <th>gdp</th>\n",
+       "      <th>gdppc</th>\n",
+       "      <th>co2</th>\n",
+       "      <th>co2pc</th>\n",
+       "      <th>population</th>\n",
+       "      <th>oecd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>ZMB</td>\n",
+       "      <td>9.799629e+09</td>\n",
+       "      <td>741.4421</td>\n",
+       "      <td>2427.554</td>\n",
+       "      <td>0.183669</td>\n",
+       "      <td>13216985</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>French Polynesia</td>\n",
+       "      <td>PYF</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>883.747</td>\n",
+       "      <td>3.296764</td>\n",
+       "      <td>268065</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Monaco</td>\n",
+       "      <td>MCO</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36845</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>UKR</td>\n",
+       "      <td>9.057726e+10</td>\n",
+       "      <td>1974.6212</td>\n",
+       "      <td>304804.720</td>\n",
+       "      <td>6.644867</td>\n",
+       "      <td>45870700</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>Venezuela, RB</td>\n",
+       "      <td>VEN</td>\n",
+       "      <td>1.750000e+11</td>\n",
+       "      <td>6010.0270</td>\n",
+       "      <td>201747.340</td>\n",
+       "      <td>6.946437</td>\n",
+       "      <td>29043283</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year       countryname countrycode           gdp      gdppc         co2  \\\n",
+       "0  2010            Zambia         ZMB  9.799629e+09   741.4421    2427.554   \n",
+       "1  2010  French Polynesia         PYF           NaN        NaN     883.747   \n",
+       "2  2010            Monaco         MCO           NaN        NaN         NaN   \n",
+       "3  2010           Ukraine         UKR  9.057726e+10  1974.6212  304804.720   \n",
+       "4  2010     Venezuela, RB         VEN  1.750000e+11  6010.0270  201747.340   \n",
+       "\n",
+       "      co2pc  population  oecd  \n",
+       "0  0.183669    13216985   0.0  \n",
+       "1  3.296764      268065   0.0  \n",
+       "2       NaN       36845   0.0  \n",
+       "3  6.644867    45870700   0.0  \n",
+       "4  6.946437    29043283   0.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pollution = pd.read_csv(\"data/pollution.csv\") # Pollution dataset\n",
+    "pollution.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f3edebbb-363c-489d-b560-09620fe27466",
@@ -258,10 +497,12 @@
     "\n",
     "Currently, `stata2python` only supports commands necessary to teach an introductory course in econometrics. If you would like to contribute, feel free to create pull requests [here](https://github.com/rohanjha123/data-h195/tree/main/creating_package). Below, we discuss the commands currently supported by the package.\n",
     "\n",
-    "By importing `stata2python` via `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command.  \n",
+    "By importing `stata2python` via the command `from stata2python import stata2python`, you can access a function named `stata2python`. You can enter in any Stata command as a string to this function. If the command is supported by `stata2python`, the function will output Python code equivalent to the Stata command. \n",
     "\n",
     "Optionally, you may also specify the name of the DataFrame you're working with via the `df_name` parameter in `stata2python`. The default value for `df_name` is simply `df`. \n",
     "\n",
+    "Note that the provided Python code will (naturally) require you to have `pandas` installed, and may also require you to have the Python packages `numpy`, `scipy`, `matplotlib`, and `statsmodels` installed. The provided code will include all imports necessary (including `import pandas as pd`).\n",
+    "\n",
     "Below, we discuss all the features currently supported by `Stata2Python`, along with providing example usages."
    ]
   },
@@ -270,14 +511,14 @@
    "id": "d9ace3e5-b5e7-473e-8bdb-cf6559c2fcc1",
    "metadata": {},
    "source": [
-    "#### T-test\n",
+    "#### T-tests\n",
     "\n",
     "This function helps users determine the code for running [t-tests](https://www.jmp.com/en_us/statistics-knowledge-portal/t-test.html) in Python. Examples include:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "b48ee104-aa0a-4461-a1bc-9f40c6c90996",
    "metadata": {},
    "outputs": [
@@ -285,17 +526,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "import numpy as np\n",
       "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
       "catvar_vals = np.unique(df['guard'])\n",
-      "if len(catvar_vals) != 2:\n",
-      "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
       "df_1 = df[df['guard'] == catvar_vals[0]]\n",
       "df_2 = df[df['guard'] == catvar_vals[1]]\n",
-      "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n",
-      "t_stat = ttest.statistic\n",
-      "p_val = ttest.pvalue\n",
-      "print(f'T-stat: {t_stat}, P-value: {p_val}')\n"
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=True, nan_policy='propagate')\n"
      ]
     }
    ],
@@ -305,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "87876a2e-0b2c-40d5-a64a-c8ec25941aa1",
    "metadata": {},
    "outputs": [
@@ -313,17 +552,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "import numpy as np\n",
       "from scipy import stats\n",
+      "### First, we must filter the DataFrame to obtain the right values\n",
       "catvar_vals = np.unique(nba['guard'])\n",
-      "if len(catvar_vals) != 2:\n",
-      "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
       "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
       "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
-      "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n",
-      "t_stat = ttest.statistic\n",
-      "p_val = ttest.pvalue\n",
-      "print(f'T-stat: {t_stat}, P-value: {p_val}')\n"
+      "### Then, we can run our t-test\n",
+      "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n"
      ]
     }
    ],
@@ -341,30 +578,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 28,
    "id": "a485ec9f-25f6-4c24-9ee8-380b751ef6a1",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "T-stat: 2.1432820571177977, P-value: 0.03299634994484977\n"
-     ]
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=2.1432820571177977, pvalue=0.03299634994484977, df=266.3682612357414)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "import pandas as pd\n",
     "import numpy as np\n",
     "from scipy import stats\n",
+    "### First, we must filter the DataFrame to obtain the right values\n",
     "catvar_vals = np.unique(nba['guard'])\n",
-    "if len(catvar_vals) != 2:\n",
-    "    raise ValueError(f'The categorical variable (guard) doesn\\'t have 2 groups')\n",
     "df_1 = nba[nba['guard'] == catvar_vals[0]]\n",
     "df_2 = nba[nba['guard'] == catvar_vals[1]]\n",
-    "ttest = stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')\n",
-    "t_stat = ttest.statistic\n",
-    "p_val = ttest.pvalue\n",
-    "print(f'T-stat: {t_stat}, P-value: {p_val}')"
+    "### Then, we can run our t-test\n",
+    "stats.ttest_ind(df_1['wage'], df_2['wage'], equal_var=False, nan_policy='propagate')"
    ]
   },
   {
@@ -372,7 +610,7 @@
    "id": "bcac0225-7131-4a7d-9997-47958c10c172",
    "metadata": {},
    "source": [
-    "### Gen\n",
+    "#### Generating new columns\n",
     "\n",
     "Used for generating new columns. For example,"
    ]
@@ -387,6 +625,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "df['degree'] = df['coll'] >= 4\n"
      ]
     }
@@ -405,6 +644,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "import pandas as pd\n",
       "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])\n"
      ]
     }
@@ -413,12 +653,1346 @@
     "stata2python(\"gen productivity = points/(minutes/games)\",\"nba\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9357b734-d25a-4008-b0c9-86240daecff2",
+   "metadata": {},
+   "source": [
+    "Copy-pasting the output into Python to verify it works."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "b8fd2dcf-621b-40dc-95bf-f1deacf15a09",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nba['productivity'] = nba['points']/(nba['minutes']/nba['games'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7b99236a-651a-47dc-8be9-b5c8090f4f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1002.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2867</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.363571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>4</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.53</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.386423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2030.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.279287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.430344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3210.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.598939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>1</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>4</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.345941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.87</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.559733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.590629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>282</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212766</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>269 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     married    wage  exper  age  coll  games  minutes  guard  forward  \\\n",
+       "0          1  1002.5      4   27     4     77     2867      1        0   \n",
+       "1          1  2030.0      5   28     4     78     2789      1        0   \n",
+       "2          0   650.0      1   25     4     74     1149      0        0   \n",
+       "3          0  2030.0      5   28     4     47     1178      0        1   \n",
+       "4          0   755.0      3   24     4     82     2096      1        0   \n",
+       "..       ...     ...    ...  ...   ...    ...      ...    ...      ...   \n",
+       "264        1  3210.0      7   29     4     79     2638      1        0   \n",
+       "265        1   715.0      5   31     4     75     1084      0        1   \n",
+       "266        1   600.0     11   33     3     67     1197      1        0   \n",
+       "267        0  2500.0      6   28     4     78     2113      0        0   \n",
+       "268        0  2000.0     12   33     3     30      282      0        1   \n",
+       "\n",
+       "     center  points  rebounds  assists  draft  allstar  avgmin  black  \\\n",
+       "0         0      16         4        5   19.0        0   37.23      1   \n",
+       "1         0      13         3        9   28.0        0   35.76      1   \n",
+       "2         1       6         3        0   19.0        0   15.53      1   \n",
+       "3         0       7         5        2    1.0        0   25.06      1   \n",
+       "4         0      11         4        3   24.0        0   25.56      1   \n",
+       "..      ...     ...       ...      ...    ...      ...     ...    ...   \n",
+       "264       0      20         3        3   11.0        1   33.39      1   \n",
+       "265       0       5         3        1   54.0        0   14.45      1   \n",
+       "266       0      10         2        2    4.0        0   17.87      1   \n",
+       "267       1      16         6        2    2.0        0   27.09      0   \n",
+       "268       0       2         3        1    5.0        0    9.40      1   \n",
+       "\n",
+       "     children  productivity  \n",
+       "0           0      0.429717  \n",
+       "1           1      0.363571  \n",
+       "2           0      0.386423  \n",
+       "3           0      0.279287  \n",
+       "4           0      0.430344  \n",
+       "..        ...           ...  \n",
+       "264         0      0.598939  \n",
+       "265         1      0.345941  \n",
+       "266         1      0.559733  \n",
+       "267         0      0.590629  \n",
+       "268         0      0.212766  \n",
+       "\n",
+       "[269 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nba # You can see that the productivity column was successfully generated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ee162dd-ad45-4af0-aaa4-062cc4e6e40b",
+   "metadata": {},
+   "source": [
+    "#### Describing the Data\n",
+    "\n",
+    "Used for summarizing the dataframe. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f44496e-8337-4ec4-9677-7d9e770e6b16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba.describe()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"describe\",\"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "da57f17a-214a-4932-a6c8-354a7aeb87e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>married</th>\n",
+       "      <th>wage</th>\n",
+       "      <th>exper</th>\n",
+       "      <th>age</th>\n",
+       "      <th>coll</th>\n",
+       "      <th>games</th>\n",
+       "      <th>minutes</th>\n",
+       "      <th>guard</th>\n",
+       "      <th>forward</th>\n",
+       "      <th>center</th>\n",
+       "      <th>points</th>\n",
+       "      <th>rebounds</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>draft</th>\n",
+       "      <th>allstar</th>\n",
+       "      <th>avgmin</th>\n",
+       "      <th>black</th>\n",
+       "      <th>children</th>\n",
+       "      <th>productivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>240.00000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "      <td>269.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.442379</td>\n",
+       "      <td>1423.827509</td>\n",
+       "      <td>5.118959</td>\n",
+       "      <td>27.394052</td>\n",
+       "      <td>3.717472</td>\n",
+       "      <td>65.724907</td>\n",
+       "      <td>1682.193309</td>\n",
+       "      <td>0.420074</td>\n",
+       "      <td>0.408922</td>\n",
+       "      <td>0.171004</td>\n",
+       "      <td>10.260223</td>\n",
+       "      <td>4.468401</td>\n",
+       "      <td>2.453532</td>\n",
+       "      <td>20.20000</td>\n",
+       "      <td>0.115242</td>\n",
+       "      <td>23.979257</td>\n",
+       "      <td>0.806691</td>\n",
+       "      <td>0.345725</td>\n",
+       "      <td>0.409677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.497595</td>\n",
+       "      <td>999.774074</td>\n",
+       "      <td>3.400062</td>\n",
+       "      <td>3.391292</td>\n",
+       "      <td>0.754410</td>\n",
+       "      <td>18.851110</td>\n",
+       "      <td>893.327771</td>\n",
+       "      <td>0.494491</td>\n",
+       "      <td>0.492551</td>\n",
+       "      <td>0.377214</td>\n",
+       "      <td>5.882489</td>\n",
+       "      <td>2.892980</td>\n",
+       "      <td>2.148124</td>\n",
+       "      <td>18.73582</td>\n",
+       "      <td>0.319909</td>\n",
+       "      <td>9.731086</td>\n",
+       "      <td>0.395629</td>\n",
+       "      <td>0.476491</td>\n",
+       "      <td>0.114920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.890000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>650.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>983.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.730000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.338710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1186.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1690.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.50000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24.820000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2014.500000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>2438.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.25000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>33.260000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.471168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5740.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>3533.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>139.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>43.090000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.748532</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          married         wage       exper         age        coll  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean     0.442379  1423.827509    5.118959   27.394052    3.717472   \n",
+       "std      0.497595   999.774074    3.400062    3.391292    0.754410   \n",
+       "min      0.000000   150.000000    1.000000   21.000000    0.000000   \n",
+       "25%      0.000000   650.000000    2.000000   25.000000    4.000000   \n",
+       "50%      0.000000  1186.000000    4.000000   27.000000    4.000000   \n",
+       "75%      1.000000  2014.500000    7.000000   30.000000    4.000000   \n",
+       "max      1.000000  5740.000000   18.000000   41.000000    4.000000   \n",
+       "\n",
+       "            games      minutes       guard     forward      center  \\\n",
+       "count  269.000000   269.000000  269.000000  269.000000  269.000000   \n",
+       "mean    65.724907  1682.193309    0.420074    0.408922    0.171004   \n",
+       "std     18.851110   893.327771    0.494491    0.492551    0.377214   \n",
+       "min      3.000000    33.000000    0.000000    0.000000    0.000000   \n",
+       "25%     57.000000   983.000000    0.000000    0.000000    0.000000   \n",
+       "50%     74.000000  1690.000000    0.000000    0.000000    0.000000   \n",
+       "75%     79.000000  2438.000000    1.000000    1.000000    0.000000   \n",
+       "max     82.000000  3533.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "           points    rebounds     assists      draft     allstar      avgmin  \\\n",
+       "count  269.000000  269.000000  269.000000  240.00000  269.000000  269.000000   \n",
+       "mean    10.260223    4.468401    2.453532   20.20000    0.115242   23.979257   \n",
+       "std      5.882489    2.892980    2.148124   18.73582    0.319909    9.731086   \n",
+       "min      1.000000    1.000000    0.000000    1.00000    0.000000    2.890000   \n",
+       "25%      5.000000    2.000000    1.000000    7.00000    0.000000   16.730000   \n",
+       "50%      9.000000    4.000000    2.000000   14.50000    0.000000   24.820000   \n",
+       "75%     14.000000    6.000000    3.000000   28.25000    0.000000   33.260000   \n",
+       "max     30.000000   17.000000   13.000000  139.00000    1.000000   43.090000   \n",
+       "\n",
+       "            black    children  productivity  \n",
+       "count  269.000000  269.000000    269.000000  \n",
+       "mean     0.806691    0.345725      0.409677  \n",
+       "std      0.395629    0.476491      0.114920  \n",
+       "min      0.000000    0.000000      0.093851  \n",
+       "25%      1.000000    0.000000      0.338710  \n",
+       "50%      1.000000    0.000000      0.408560  \n",
+       "75%      1.000000    1.000000      0.471168  \n",
+       "max      1.000000    1.000000      0.748532  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48facefc-8cdf-49e8-a071-7bc836552fd9",
+   "metadata": {},
+   "source": [
+    "#### Correlation Matrix\n",
+    "\n",
+    "Used for generating a correlation matrix between relevant variables. For example, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6cd38a22-ea9a-41cb-bfda-e2d439b4d40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "nba[['points', 'assists', 'rebounds']].corr()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"corr points assists rebounds\", \"nba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "969d8093-ff69-4224-ba83-a8df7ead7617",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>points</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>rebounds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>points</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>0.563324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assists</th>\n",
+       "      <td>0.539269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.059956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rebounds</th>\n",
+       "      <td>0.563324</td>\n",
+       "      <td>0.059956</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            points   assists  rebounds\n",
+       "points    1.000000  0.539269  0.563324\n",
+       "assists   0.539269  1.000000  0.059956\n",
+       "rebounds  0.563324  0.059956  1.000000"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "nba[['points', 'assists', 'rebounds']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ab3e395-5236-407d-bbef-1a6f7a2104c4",
+   "metadata": {},
+   "source": [
+    "#### Scatter Plots\n",
+    "\n",
+    "Used for making scatter (or twoway) plots. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17411597-3519-41c0-a455-796118431768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+      "plt.xlabel('co2pc');\n",
+      "plt.ylabel('population');\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"twoway (scatter co2pc population, xtitle('co2pc') ytitle('population'))\",\"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ae6cf21d-24ed-453e-b757-7403d7d41113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(pollution['co2pc'], pollution['population']);\n",
+    "plt.xlabel('co2pc');\n",
+    "plt.ylabel('population');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33122b3d-c34b-424c-a246-8778b2a872ab",
+   "metadata": {},
+   "source": [
+    "#### Histograms\n",
+    "\n",
+    "Used for generating histograms. For example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "54d05f02-364d-4e82-b594-b5ba0c550357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "pollution.hist(column='co2pc',bins=80);\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"histogram co2pc, bin(80)\", \"pollution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "77289483-1aee-435e-b78f-909044de0bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import matplotlib.pyplot as plt\n",
+    "pollution.hist(column='co2pc',bins=80);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14245a39-b06f-406b-a597-fc4ffcffead0",
+   "metadata": {},
+   "source": [
+    "#### Regressions\n",
+    "\n",
+    "Used for running regressions, including Python comments to help explain relevant portions of the code. Note that not all features from Stata's `reg` command are supported. Examples include:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1bd4cdaa-deb1-417f-80d1-da4e0ca5350e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### Dropping NaN/missing values\n",
+      "wages_no_na = wages[['exp','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_no_na['exp']\n",
+      "y_df = wages_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit()\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "07fc3173-8ec2-4f34-bf8f-ddbb8dd6ccde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4714</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.493</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:39</td>     <th>  Log-Likelihood:    </th> <td> -3541.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7086.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   861</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>   16.0816</td> <td>    3.917</td> <td>    4.105</td> <td> 0.000</td> <td>    8.393</td> <td>   23.771</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>   <td>   -0.2135</td> <td>    0.311</td> <td>   -0.687</td> <td> 0.493</td> <td>   -0.824</td> <td>    0.397</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1263.062</td> <th>  Durbin-Watson:     </th>  <td>   1.830</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>413853.334</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.176</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>109.028</td> <th>  Cond. No.          </th>  <td>    99.5</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.001   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &    -0.001   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &    0.4714   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.493    \\\\\n",
+       "\\textbf{Time:}             &     21:26:39     & \\textbf{  Log-Likelihood:    } &   -3541.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7086.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         861      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &      16.0816  &        3.917     &     4.105  &         0.000        &        8.393    &       23.771     \\\\\n",
+       "\\textbf{exp}   &      -0.2135  &        0.311     &    -0.687  &         0.493        &       -0.824    &        0.397     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1263.062 & \\textbf{  Durbin-Watson:     } &     1.830   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 413853.334  \\\\\n",
+       "\\textbf{Skew:}          &   8.176  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 109.028  & \\textbf{  Cond. No.          } &      99.5   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.001\n",
+       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
+       "Method:                 Least Squares   F-statistic:                    0.4714\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.493\n",
+       "Time:                        21:26:39   Log-Likelihood:                -3541.0\n",
+       "No. Observations:                 863   AIC:                             7086.\n",
+       "Df Residuals:                     861   BIC:                             7096.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         16.0816      3.917      4.105      0.000       8.393      23.771\n",
+       "exp           -0.2135      0.311     -0.687      0.493      -0.824       0.397\n",
+       "==============================================================================\n",
+       "Omnibus:                     1263.062   Durbin-Watson:                   1.830\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           413853.334\n",
+       "Skew:                           8.176   Prob(JB):                         0.00\n",
+       "Kurtosis:                     109.028   Cond. No.                         99.5\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### Dropping NaN/missing values\n",
+    "wages_no_na = wages[['exp','wage']].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_no_na['exp']\n",
+    "y_df = wages_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit()\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3fc841e3-b7a8-40ca-a338-e4595c3e1d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "### Dropping NaN/missing values\n",
+      "df_no_na = df[['exp', 'education','wage']].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = df_no_na['exp']\n",
+      "y_df = df_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': df_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp, vce(cluster education)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c86447cd-732b-47b9-a192-70cb8c0e2684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import pandas as pd\n",
+      "import statsmodels.api as sm\n",
+      "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+      "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+      "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+      "y_var = \"wage\"\n",
+      "clustering_vars = ['education']\n",
+      "wages_with_dummies = wages.copy()\n",
+      "x_var = \"'exp', 'i.female'\"\n",
+      "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+      "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+      "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+      "for indicator in indicator_list:\n",
+      "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+      "    dummies = dummies.iloc[:,1:]\n",
+      "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+      "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+      "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+      "x_var = x_var_new\n",
+      "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+      "if clustering_vars:\n",
+      "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+      "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+      "### Dropping NaN/missing values\n",
+      "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+      "### Below, we extract the relevant variables from the DataFrame\n",
+      "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+      "y_df = wages_with_dummies_no_na['wage']\n",
+      "### We now define the model, fit it to the data and then view a summary of the results\n",
+      "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+      "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+      "result.summary()\n"
+     ]
+    }
+   ],
+   "source": [
+    "stata2python(\"reg wage exp i.female, vce(cluster education)\", \"wages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4f73adae-9b6b-4d5d-9db8-2567fb9935fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>wage</td>       <th>  R-squared:         </th> <td>   0.007</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.993</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sun, 04 Feb 2024</td> <th>  Prob (F-statistic):</th>  <td> 0.183</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>21:26:54</td>     <th>  Log-Likelihood:    </th> <td> -3538.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   863</td>      <th>  AIC:               </th> <td>   7082.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   860</td>      <th>  BIC:               </th> <td>   7096.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   17.4218</td> <td>    3.698</td> <td>    4.712</td> <td> 0.000</td> <td>   10.175</td> <td>   24.669</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>exp</th>      <td>   -0.2373</td> <td>    0.206</td> <td>   -1.153</td> <td> 0.249</td> <td>   -0.640</td> <td>    0.166</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>1_female</th> <td>   -2.4604</td> <td>    1.261</td> <td>   -1.951</td> <td> 0.051</td> <td>   -4.932</td> <td>    0.011</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>1260.198</td> <th>  Durbin-Watson:     </th>  <td>   1.842</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>409372.994</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 8.142</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>108.449</td> <th>  Cond. No.          </th>  <td>    101.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       wage       & \\textbf{  R-squared:         } &     0.007   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.005   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1.993   \\\\\n",
+       "\\textbf{Date:}             & Sun, 04 Feb 2024 & \\textbf{  Prob (F-statistic):} &    0.183    \\\\\n",
+       "\\textbf{Time:}             &     21:26:54     & \\textbf{  Log-Likelihood:    } &   -3538.0   \\\\\n",
+       "\\textbf{No. Observations:} &         863      & \\textbf{  AIC:               } &     7082.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         860      & \\textbf{  BIC:               } &     7096.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}     &      17.4218  &        3.698     &     4.712  &         0.000        &       10.175    &       24.669     \\\\\n",
+       "\\textbf{exp}       &      -0.2373  &        0.206     &    -1.153  &         0.249        &       -0.640    &        0.166     \\\\\n",
+       "\\textbf{1\\_female} &      -2.4604  &        1.261     &    -1.951  &         0.051        &       -4.932    &        0.011     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 1260.198 & \\textbf{  Durbin-Watson:     } &     1.842   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 409372.994  \\\\\n",
+       "\\textbf{Skew:}          &   8.142  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      & 108.449  & \\textbf{  Cond. No.          } &      101.   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors are robust to cluster correlation (cluster)"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                   wage   R-squared:                       0.007\n",
+       "Model:                            OLS   Adj. R-squared:                  0.005\n",
+       "Method:                 Least Squares   F-statistic:                     1.993\n",
+       "Date:                Sun, 04 Feb 2024   Prob (F-statistic):              0.183\n",
+       "Time:                        21:26:54   Log-Likelihood:                -3538.0\n",
+       "No. Observations:                 863   AIC:                             7082.\n",
+       "Df Residuals:                     860   BIC:                             7096.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:              cluster                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         17.4218      3.698      4.712      0.000      10.175      24.669\n",
+       "exp           -0.2373      0.206     -1.153      0.249      -0.640       0.166\n",
+       "1_female      -2.4604      1.261     -1.951      0.051      -4.932       0.011\n",
+       "==============================================================================\n",
+       "Omnibus:                     1260.198   Durbin-Watson:                   1.842\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           409372.994\n",
+       "Skew:                           8.142   Prob(JB):                         0.00\n",
+       "Kurtosis:                     108.449   Cond. No.                         101.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd # Verifying that the output works\n",
+    "import statsmodels.api as sm\n",
+    "### We first perform some string manipulations to ensure we are appropripately accounting for strings. \n",
+    "### This section seems complicated as this code is meant to be a general-purpose code for converting the Stata commands to Python.\n",
+    "### You can greatly simplify this step if you know exactly which variables you are interested in; simply extract those variables from the DataFrame directly.\n",
+    "y_var = \"wage\"\n",
+    "clustering_vars = ['education']\n",
+    "wages_with_dummies = wages.copy()\n",
+    "x_var = \"'exp', 'i.female'\"\n",
+    "x_var_new = \"'\" + \"', '\".join([i.strip(\"'\") for i in x_var.split(\", \") if not i.strip(\"'\").startswith(\"i.\")]) + \"'\"\n",
+    "### The below section adds in relevant indicator variables, ensuring they have interpretable names\n",
+    "indicator_list = [m.strip(\"'\") for m in x_var.split('i.')[1:]]\n",
+    "for indicator in indicator_list:\n",
+    "    dummies = pd.get_dummies(wages_with_dummies[indicator])\n",
+    "    dummies = dummies.iloc[:,1:]\n",
+    "    dummies.columns = [str(x) + '_' + str(indicator) for x in dummies.columns]\n",
+    "    wages_with_dummies = pd.concat([wages_with_dummies,dummies],axis=1)\n",
+    "    x_var_new = x_var_new + \", '\" + \"', '\".join(dummies.columns) + \"'\"\n",
+    "x_var = x_var_new\n",
+    "### This helps ensure the clustered variables are extracted from the DataFrame\n",
+    "if clustering_vars:\n",
+    "    x_var_temp = x_var + \", '\" + \"', '\".join(clustering_vars) + \"'\"\n",
+    "var_list = x_var_temp +\", '\"+ str(y_var) + \"'\"\n",
+    "### Dropping NaN/missing values\n",
+    "wages_with_dummies_no_na = wages_with_dummies[[i.strip(\"'\") for i in var_list.split(\", \")]].dropna()\n",
+    "### Below, we extract the relevant variables from the DataFrame\n",
+    "x_df = wages_with_dummies_no_na[[i.strip(\"'\") for i in x_var.split(\", \")]]\n",
+    "y_df = wages_with_dummies_no_na['wage']\n",
+    "### We now define the model, fit it to the data and then view a summary of the results\n",
+    "model = sm.OLS(y_df, sm.add_constant(x_df))\n",
+    "result = model.fit(cov_type='cluster', cov_kwds={'groups': wages_with_dummies_no_na['education']})\n",
+    "result.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21279b0b-e6c2-467e-a028-429da3fac865",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/content/01-python_v_stata/syntax.md b/content/01-python_v_stata/syntax.md
deleted file mode 100644
index b16eba3..0000000
--- a/content/01-python_v_stata/syntax.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Syntax
-
-For key syntatical differences between 
-
-###### <span style="color:red"> To be updated</span>
\ No newline at end of file
diff --git a/content/09-teachers-perspective/index.md b/content/09-teachers-perspective/index.md
deleted file mode 100644
index 2444064..0000000
--- a/content/09-teachers-perspective/index.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Teacher's Perspective
-
-## Under Construction
\ No newline at end of file