session-two/session-two-project-maths-stats-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f66d9df",
   "metadata": {},
   "source": [
    "# Python for Data Science Practice Session 2 : Mathematics and Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735121cd",
   "metadata": {},
   "source": [
    "## Simpson's Paradox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9237d4a",
   "metadata": {},
   "source": [
    "Simpson's Paradox is a phenomenon in Probability and Statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. You can read more about it and find a couple of examples here: https://en.wikipedia.org/wiki/Simpson%27s_paradox\n",
    "\n",
    "In this section, we are going to investigate a dataset which will turn out to be a great example of this paradox. This dataset shows the expenditures on 1000 people from different ethnic groups, age groups and genders. An allegation of discrimination was made and supported by a univariate analysis that examined the average annual expenditures on consumers by ethnicity. In specific, the analysis concluded that the average annual expenditures on Hispanic consumers was approximately one-third of the average annual expenditures on White non-Hispanic consumers. \n",
    "\n",
    "In this section, we are going to carry out our own analysis and investigate whether the data supports the allegation or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "665658ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas and matplotlib.pyplot\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "258d6d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dataset 'californiaDDSDataV2.csv' into a dataframe\n",
    "df = pd.read_csv('californiaDDSDataV2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4aee42c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age Cohort</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Expenditures</th>\n",
       "      <th>Ethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10210</td>\n",
       "      <td>13 to 17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>2113</td>\n",
       "      <td>White not Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10409</td>\n",
       "      <td>22 to 50</td>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>41924</td>\n",
       "      <td>White not Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10486</td>\n",
       "      <td>0 to 5</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>1454</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10538</td>\n",
       "      <td>18 to 21</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>6400</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10568</td>\n",
       "      <td>13 to 17</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>4412</td>\n",
       "      <td>White not Hispanic</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>99622</td>\n",
       "      <td>51+</td>\n",
       "      <td>86</td>\n",
       "      <td>Female</td>\n",
       "      <td>57055</td>\n",
       "      <td>White not Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>99715</td>\n",
       "      <td>18 to 21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>7494</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>99718</td>\n",
       "      <td>13 to 17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>3673</td>\n",
       "      <td>Multi Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>99791</td>\n",
       "      <td>6 to 12</td>\n",
       "      <td>10</td>\n",
       "      <td>Male</td>\n",
       "      <td>3638</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>99898</td>\n",
       "      <td>22 to 50</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>26702</td>\n",
       "      <td>White not Hispanic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id Age Cohort  Age  Gender  Expenditures           Ethnicity\n",
       "0    10210   13 to 17   17  Female          2113  White not Hispanic\n",
       "1    10409   22 to 50   37    Male         41924  White not Hispanic\n",
       "2    10486     0 to 5    3    Male          1454            Hispanic\n",
       "3    10538   18 to 21   19  Female          6400            Hispanic\n",
       "4    10568   13 to 17   13    Male          4412  White not Hispanic\n",
       "..     ...        ...  ...     ...           ...                 ...\n",
       "995  99622        51+   86  Female         57055  White not Hispanic\n",
       "996  99715   18 to 21   20    Male          7494            Hispanic\n",
       "997  99718   13 to 17   17  Female          3673          Multi Race\n",
       "998  99791    6 to 12   10    Male          3638            Hispanic\n",
       "999  99898   22 to 50   23    Male         26702  White not Hispanic\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb993cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>54662.846</td>\n",
       "      <td>25643.673401</td>\n",
       "      <td>10210.0</td>\n",
       "      <td>31808.75</td>\n",
       "      <td>55384.5</td>\n",
       "      <td>76134.75</td>\n",
       "      <td>99898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>22.800</td>\n",
       "      <td>18.462038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expenditures</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>18065.786</td>\n",
       "      <td>19542.830884</td>\n",
       "      <td>222.0</td>\n",
       "      <td>2898.75</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>37712.75</td>\n",
       "      <td>75098.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean           std      min       25%      50%  \\\n",
       "Id            1000.0  54662.846  25643.673401  10210.0  31808.75  55384.5   \n",
       "Age           1000.0     22.800     18.462038      0.0     12.00     18.0   \n",
       "Expenditures  1000.0  18065.786  19542.830884    222.0   2898.75   7026.0   \n",
       "\n",
       "                   75%      max  \n",
       "Id            76134.75  99898.0  \n",
       "Age              26.00     95.0  \n",
       "Expenditures  37712.75  75098.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Produce a summary for the dataframe\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d90a332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian',\n",
       "       'American Indian', 'Other', 'Native Hawaiian'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the unique ethnic groups in the dataframe\n",
    "df['Ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0f465e",
   "metadata": {},
   "source": [
    "- - - - -\n",
    "Let us now check the average annual expenditures of each ethnic group to check whether the result supports the allegation made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f763d551",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expenditures</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethnicity</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3316.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multi Race</th>\n",
       "      <td>4456.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>11065.569149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>18392.372093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>20884.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White not Hispanic</th>\n",
       "      <td>24697.548628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Indian</th>\n",
       "      <td>36438.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native Hawaiian</th>\n",
       "      <td>42782.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Expenditures\n",
       "Ethnicity                       \n",
       "Other                3316.500000\n",
       "Multi Race           4456.730769\n",
       "Hispanic            11065.569149\n",
       "Asian               18392.372093\n",
       "Black               20884.593220\n",
       "White not Hispanic  24697.548628\n",
       "American Indian     36438.250000\n",
       "Native Hawaiian     42782.333333"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by the mean of the expenditures of each ethnic group and arrange them ascendingly  \n",
    "df[['Ethnicity', 'Expenditures']].groupby('Ethnicity').mean().sort_values('Expenditures')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9092ff",
   "metadata": {},
   "source": [
    "The 'White not Hispanic' group has an average expenditure of slightly more than double of that of the 'Hispanic' class, which is not too far off from what the analysis mentioned above stated. Furthermore, Other, Multi Race and Hispanic have much smaller averages than the rest of the ethnic groups. This result goes well with the allegation. \n",
    "\n",
    "Let us plot a box plot of the expenditures of the different ethnic groups to visualise the data and have a clearer view. \n",
    "\n",
    "You can find the documenation of plotting a box plot here: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.boxplot.html\n",
    "- - - - -\n",
    "To start off, create variables that contain the expenditures of each ethnic group separately. Name them `White_not_Hispanic`, `Hispanic`, `Black`, `Multi_Race`, `Asian`, `American_Indian`, `Native_Hawaiian` and `Other`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e74c13cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic = df.loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic = df.loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black = df.loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race = df.loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian = df.loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian = df.loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian = df.loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other = df.loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ae428f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data = [White_not_Hispanic, Hispanic, Black, Multi_Race, Asian, American_Indian, Native_Hawaiian, Other]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37735b82",
   "metadata": {},
   "source": [
    "Now, plot the box plot of the annual expenditures of each ethnic groups. \n",
    "\n",
    "For better visualisation, I would like for the boxes to be represented horizontally on the graph, and for the mean of every box to be visible on the plot. Read the documentation and figure out the parameters that you need to adjust in order to achieve this this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed0add0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ethnicity')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_ylabel('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59a7806",
   "metadata": {},
   "source": [
    "Looking at the above boxplot, there are signs that might support the presence of discrimination towards some of the ethnic groups:\n",
    "1) Other, Asian, Multi Race, Black and Hispanic have significantly smaller median values than White not Hispanic, Native Hawaiian and American Indian.\n",
    "\n",
    "2) Other, Multi Race and Hispanic generally have significantly smaller mean, Q3 and maximum values (i.e. the edge of the right whisker of the box) than the rest of the ethnic groups.\n",
    "\n",
    "3) Comparing Hispanic with White not Hispanic, there is a big difference between the size of the box of both ethnic groups. The Hispanic group's box has a much smaller median and mean, as well as a much smaller maximum value.\n",
    "\n",
    "But, is this enough to conclude that the allegation is in fact true?\n",
    "\n",
    "Should we use the above box plot as evidence, or is there more to it that we cannot observe solely from this analysis? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22ff26b",
   "metadata": {},
   "source": [
    "One thing to notice is that we haven't considered all the grouping options that we have. We tried grouping the data by the mean of the expenditures of each ethnic group, but we didn't consider grouping the data by Gender, or Age groups. Let us try doing both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bdd253f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expenditures</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>18001.195171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>18129.606362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Expenditures\n",
       "Gender              \n",
       "Male    18001.195171\n",
       "Female  18129.606362"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by the mean of the expenditures of each Gender group and arrange them ascendingly\n",
    "df[['Gender', 'Expenditures']].groupby('Gender').mean().sort_values('Expenditures')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8264c6",
   "metadata": {},
   "source": [
    "The mean expenditures for Males and Females are very close which isn't very interesting for our Analysis. Keep in mind though that this result came from using all the data, hence results might turn out to be very different when looking at specific ethnic groups. This is something for you to explore on your own to check and see if there is gender discrimination.\n",
    "\n",
    "Let us now group the data by the average annual expenditures of each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9152c599",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expenditures</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Cohort</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 to 5</th>\n",
       "      <td>1415.280488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 to 12</th>\n",
       "      <td>2226.862857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13 to 17</th>\n",
       "      <td>3922.613208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18 to 21</th>\n",
       "      <td>9888.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22 to 50</th>\n",
       "      <td>40209.283186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51+</th>\n",
       "      <td>53521.896226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Expenditures\n",
       "Age Cohort              \n",
       "0 to 5       1415.280488\n",
       "6 to 12      2226.862857\n",
       "13 to 17     3922.613208\n",
       "18 to 21     9888.537688\n",
       "22 to 50    40209.283186\n",
       "51+         53521.896226"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by the mean of the expenditures of each Age group and arrange them ascendingly\n",
    "df[['Age Cohort', 'Expenditures']].groupby('Age Cohort').mean().sort_values('Expenditures')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94434e65",
   "metadata": {},
   "source": [
    "Now this is interesting. It is clear that as we increase the age group, the average of the annual expenditures increases. \n",
    "\n",
    "Also, there is a huge leap from the mean of expenditures of people of ages less than 21 to the mean of expenditures of people of ages more than 21. \n",
    "\n",
    "A thing to keep in mind is that the people in each ethnic group are not divided equally among the 6 age groups that are present, so some of the ethnic groups have more people in a specific age group than the other ethnic groups.\n",
    "\n",
    "I would like for you to take a moment and think about the consequences of this before moving on to the next tasks, because this is where the paradox stems from.\n",
    "- - - - -\n",
    "\n",
    "In the next tasks, I would like for you to create box plots of the expenditures of the different ethnic groups, with respect to a specific age group.\n",
    "\n",
    "So, in total, I would like for you to create six box plots corresponding to the six age groups that are present, following the same exact procedures that we followed in the previous box plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397bed5a",
   "metadata": {},
   "source": [
    "## Ages 0 to 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98046466",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_0to5 = df.loc[df['Age Cohort'] == '0 to 5'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c454c4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_0to5 = [White_not_Hispanic_0to5, Hispanic_0to5, Black_0to5, Multi_Race_0to5, Asian_0to5, American_Indian_0to5, Native_Hawaiian_0to5, Other_0to5 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24f364fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 0 to 5')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_0to5, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 0 to 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd6834d",
   "metadata": {},
   "source": [
    "## Ages 6 to 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87287b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_6to12 = df.loc[df['Age Cohort'] == '6 to 12'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9cd39d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_6to12 = [White_not_Hispanic_6to12, Hispanic_6to12, Black_6to12, Multi_Race_6to12, Asian_6to12, American_Indian_6to12, Native_Hawaiian_6to12, Other_6to12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "48f5d63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 6 to 12')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_6to12, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 6 to 12')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e955ebcd",
   "metadata": {},
   "source": [
    "## Ages 13 to 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97e8cb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_13to17 = df.loc[df['Age Cohort'] == '13 to 17'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3eb06594",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_13to17 = [White_not_Hispanic_13to17, Hispanic_13to17, Black_13to17, Multi_Race_13to17, Asian_13to17, American_Indian_13to17, Native_Hawaiian_13to17, Other_13to17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50649674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 13 to 17')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_13to17, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 13 to 17')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714b4c72",
   "metadata": {},
   "source": [
    "## Ages 18 to 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9475b6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_18to21 = df.loc[df['Age Cohort'] == '18 to 21'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36742e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_18to21 = [White_not_Hispanic_18to21, Hispanic_18to21, Black_18to21, Multi_Race_18to21, Asian_18to21, American_Indian_18to21, Native_Hawaiian_18to21, Other_18to21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b00b9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 18 to 21')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_18to21, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 18 to 21')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec1a086",
   "metadata": {},
   "source": [
    "## Ages 22 to 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "895dd8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_22to50 = df.loc[df['Age Cohort'] == '22 to 50'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2744c684",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_22to50 = [White_not_Hispanic_22to50, Hispanic_22to50, Black_22to50, Multi_Race_22to50, Asian_22to50, American_Indian_22to50, Native_Hawaiian_22to50, Other_22to50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aba806af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 22 to 50')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J09cxjrox58mnqx2CmVmHaqkHOgm4LCK2jIjREbEF8CiwVxeOcRPwhdYFSeML7DMcGEqWcFdIGgns14UYynUHcJCkQamX/LES27Qmy+fT9eFJFYjDzMwKqKUEOgW4pk3ZVUDba5YdOR5oSjcDPQAcU8Y+3wCGp5t0lgATI2IJ2dDt/cDFwO+7EENZImIRMBdYTNbOO0ts8yJwIdkw9bXAgu6Oo1ZN3myTaodgZtYhRXQ4imi9SFNTUzQ3N1c7jLKMvXQsy45YVu0wzKyPk7Qw3VD6FrXUAzUzM6sbTqBWk45tWVHtEMzMOuQEajXp81/6a7VDMDPrkBOomZlZAU6gZmZmBTiBmpmZFeAEamZmVoATqJmZWQFOoGZmZgU4gZqZmRXgBGpmZlaAE6iZmVkBTqBmZmYFOIGamZkV4ARqZmZWgBOoWQ9oaGhAUq+fRh40smQ504dVPbaOpoaGhmr/iFgdKiuBSvqCpOGVDsast2ppaSEiev004sARJcuBqsfW0dTS0lLlnxCrR+X2QN8BLJB0paSPSFIlgzKz+tQ4qLHaIZj1mLISaEScCmwNXAQcCTwi6ZuS3lXB2HoVSQdJCknbdLLdbyRt2ENhlc3/M1k55h0yr9ohWJ2p578tZV8DjWwc5pk0rQKGA7+U9J0KxdbbTAHmA5M72igi9o+IF3skIrNuNnPxzGqHYNZjyr0GerykhcB3gN8DYyPiWGBn4OAKxtcrSBoM7AkcRUqgkjaRdIekxZLuk7R3Kn9M0sZp/lpJCyXdL2lq7nivSJohaYmkuySNrEKzzN5i1pJZ1Q7BrMf0L3O7jYFPRMTj+cKIeFPSAd0fVq9zIHBjRDws6QVJOwETgd9GxAxJ/YC3l9jvMxHxgqRBZNegr4qIvwMbAHdFxLQ0AnA08I1SFafEOxVg1KhR69SIeh5qMeuMf76tq8pNoFu1TZ6SfhoRn46IBysQV28zBTgnzc9Jy78GLpY0ALg2IhaX2O94SQel+S3IrkP/HfgncH0qXwh8sL2KI2I2MBugqakp1qURrXdTWtf5j3Pt8893ddTz70a5CXT7/ELqMe3c/eH0PpI2AvYBxkgKoB8QwEnA+4CPAj+V9N2IuCy33wRgX2D3iHhN0m3AwLT6jVjz276a8s+jWUXNOWBOtUMw6zEdXgOVdIqkl4Fxkl5K08vAcuBXPRJh/ZsEXBYRW0bE6IjYAniULHkuj4gLye5u3qnNfsOAlpQ8twF269GozcysQx0m0Ij4VkQMAb4bEUPTNCQiNoqIU3ooxno3BbimTdlVwCXAYkn3kt2I9YM229wI9Je0FPg6cFeF4zRbZ5Ov7/Amc7NepcOhP0nbRMRDwC/SjS9riYhFFYusl4iICSXKzgXObWf70bnF/drZZnBu/pfAL9cpyDL4+pCZVUI9/23p7NrZl8nu4DyrxLogu7ZnZmbW53SYQCNianqd2DPhmPVe9Xy3YblGHDgCHfnWdsbpQ2u6/cOH+6u+revKuntT0n8Bl7d+Q076YvkpEeGvHTErQz0PU3WXmF7tCMy6V7lf5Xd0/uvlIqKF7MP7ZmZmfVK5CXS9/BNY0udA31aZkMzMzGpfuR/A/y1wpaQLyG4eOobsYxZmZmZ9UrkJ9H+AzwHHAgJuAn5cqaDMzMxqXVkJNCLeBGalyczMrM/r7IsUroyIQyQtIxu6XUtEjKtYZGZmZjWssx7oF9OrH1lmZmaW09kXKTydXh/vaDszM7O+pqyPsUj6hKRHJK1ofSKLpJcqHZyZmVmtKvcu3O8AH/PDs83MzDLlfpHCs06eZmZma5TbA22WNBe4FljZWhgRV1ciKDMzs1pXbg90KPAa8CHgY2nynblmfcjMc0ZVOwSzmlLuFyn8Z6UDMbPaNmv4MD5f7SDMaki5jzNrJHv6yuj8PhHxmcqEZWa1pnHVqmqHYFZTyr0G+ivgTuB3wOrKhWNmtWreE09VOwSzmlLuNdC3R8T/RMSVEXFV61TRyHqIpJD009xyf0nPSbq+jH1fSa+jJX0yV94k6dwS209In6W9V9JDkr7XXe0wq7SZGw6rdghmNaXcBHq9pP0rGkn1vAqMkTQoLX8QeLKLxxgN/CuBRkRzRBzfzrZ3RsSOwI7AAZL27GJdZlUxa7gTqFlehwk0941DXyRLoq/30m8i+l/go2l+CnBF6wpJ0yWdmFu+T9LoNvufCewtabGkE1JPs8MebES8DiwGNkvHPVrSAklLJF0l6e2pfKSka1L5Ekl7pPJPSbon1fmj9JBzMzPrIR0m0IgYEhFD0+t6ETEotzy0p4LsAXOAyZIGAuOAu7u4/8lkPcvxEXF2OTtIGg5sDdyRiq6OiF0iYgfgQeCoVH4ucHsq3wm4X9K2wKHAnhExnuy69GHt1DNVUrOk5ueee66LzTIzs/aU+124t5RTVq8iYinZMOwU4DcVrm5vSUuBZ4DrI+KZVD5G0p3p0XGHAdun8n1Iz2GNiNURsQL4ALAzsEDS4rT8zlKVRcTsiGiKiKbGxsaKNcp6vzlPPl3tEMxqSmfPAx0IbABsnHpMSquGAptWOLaedh3wPWACsFGufBVr/6MxcB3ruTMiDpD0bmC+pGsiYjFwCXBgRCyRdGSKoz0CLo2IU9YxFjMzK6izHujngGZgG2ARsDBNvwJ+WNnQetzFwNciYlmb8sfIhk6RtBOwVYl9XwaGdKWyiHgY+BbwP6loCPC0pAGsPRx7C3Bsqr+fpKGpbJKkEam8QdKWXanfrKsmb7ZJtUMwqymdXQP9QURsBZwYEVvlph0i4vweirFHRMTfIuIHJVZdBTSkodJjgYdLbLMUWJVu8jmhC9VeALxP0lbAV8muvd4MPJTb5ovAxDS0uxDYPiIeAE4FbkrDwTcD/utmZtaDFBHtr5ROiojvpPn/iIhf5NZ9MyK+0gMxWjdpamqK5ubmaodhdWrspWNZdkTbARqz3k3SwohoKrWusyHcybn5ttfbPrJOUZlZXTm2ZUW1QzCrKZ0lULUzX2rZzHqxz3/pr9UOwaymdJZAo535UstmZmZ9RmdfJr9D+sYhAYNy3z4k1v3jHGZmZnWrwwQaEf56ODMzsxLK/TJ5MzMzy3ECNTMzK8AJ1MzMrAAnUDMzswKcQM3MzApwAjUzMyvACdTMzKwAJ1AzM7MCnEDNzMwKcAI162MaGhqQVNVp5EEjO92G6cOqHmdDQ0O1T5fVMCdQsz6mpaWFiKjqNOLAEZ1uA1Q9zpaWliqfLatlTqBmZmYFOIFaTZH8mNm+oHFQY7VDsBL8+9c1TqA9QNJqSYslLZG0SNIeqXy0pPsKHvM2SU3dG6lZz5h3yLxqh2C2zpxAe8brETE+InYATgG+Ve2AzKpp5uKZ1Q7BbJ05gfa8ocBb7kxIvdE7Uw/1X73UtO4kSctSD/bMNvutJ+lSSd/ogdjNusWsJbOqHYLZOuvwgdrWbQZJWgwMBDYB9imxzXLggxHxD0lbA1cATZL2Aw4E3hsRr0nK31ffH7gcuC8iZpSqWNJUYCrAqFGjuqk5leXrMFZL/PNo7XEC7RmvR8R4AEm7A5dJGtNmmwHA+ZLGA6uBd6fyfYGfRMRrABHxQm6fHwFXtpc80/azgdkATU1Nse5NqbzWjzBYZTghdE1f+nn0z0bXeAi3h0XEH4GNgba3IZ4APAvsADQBb0vlAtr7Df4DMFHSwAqEalYxcw6YU+0QzNaZE2gPk7QN0A/4e5tVw4CnI+JN4NNpG4CbgM9IenvaPz+EexHwG+AXkjyaYGbWg5xAe8ag9DGWxcBc4IiIWN1mm5nAEZLuIhu+fRUgIm4ErgOa0/4n5neKiO8Di4CfSqr789mXhsv6ssnXT652CFaCf/+6xr2WHhAR/dopfwwYk+YfAcblVp+S2+5M4Mw2+07IzZ/efdGamVk56r7HYmZmVg1OoGZ9ULWfcrL82uWdP42lBuIcPnx4lc+U1TIP4Zr1MfV0nSumVzsCs/a5B2pmZlaAE6iZmVkBTqBmZmYFOIGamZkV4ARqZmZWgBOomZlZAU6gZmZmBTiBmpmZFeAEamZmVoATqJmZWQFOoGZmZgU4gZr1cg0NDVX/UnZJMH1YyfKRB42semyVmBoaGqp96q3CnEDNermWlhYiouoTULJ8xIEjqh5bJaaWlpYqn3mrNCdQMzOzApxAzayqGgc1VjsEs0KcQMsk6ZU2y0dKOj/NHyPp8B6K42uS9u2JurpC6QHIZl0175B51Q7BCvDvvBNot4iICyLish6q67SI+F1P1GXWE2YunlntEMwKcQLtBpKmSzoxzR8v6QFJSyXNya3/qaR5kh6RdHQqHyzpFkmLJC2T9PFUPlrSg5IulHS/pJskDUrrLpE0Kc3vIukPkpZIukfSkOq8A2bFzVoyq9ohmBXSv9oB1JFBkhbnlhuA60psdzKwVUSslLRhrnwcsBuwAXCvpBuA5cBBEfGSpI2BuyS1HnNrYEpEHC3pSuBg4GetB5P0NmAucGhELJA0FHi9bTCSpgJTAUaNGlWg2eXzkI7Z2vw70bs5gZbv9YgY37og6UigqcR2S4HLJV0LXJsr/1VEvA68LulWYFfgBuCbkt4HvAlsBoxM2z8aEYvT/EJgdJt63gM8HRELACLipVJBR8RsYDZAU1NTdN7M4lo/qmC1xX/Eq6c3/07458pDuJXwUeCHwM7AQkmt/6S0/U0K4DCgEdg5JedngYFp/crctqt56z87KnFMs7oz54A51Q7BrBAn0G4kaT1gi4i4FTgJ2BAYnFZ/XNJASRsBE4AFwDBgeUS8IWkisGUXqnsI2FTSLqnuIblkbWZmFeYE2r36AT+TtAy4Fzg7Il5M6+4hG7K9C/h6RDwFXA40SWom640+VG5FEfFP4FDgPElLgJtZ03vtcb15qMoqa/L1k6sdghXg33lfAy1bRAxus3wJcEman55btVc7h3g4Iqa2OcbzwO7tbD8mt933cvNH5uYXkN2YZGZmPcw9UDMzswLcA+0BbXqoZj2uFu6YjNOHloxjxIEj0JHVj6+7DR8+vNohWIU5gZr1crV0rcr/Slpv4iFcMzOzApxAzczMCnACNTMzK8AJ1MzMrAAnUDMzswKcQM3MzApwAjUzMyvACdTMzKwAJ1AzM7MCnEDNzMwKcAI1MzMrwAnUrBdpaGhAUs1NIw8audYy04dVPaaGhoZqny6rc06gZr1IS0sLEVFz04gDR6y1DFQ9ppaWliqfLat3TqBmZmYFOIGaWcU1Dmqsdghm3a4iCVTS2ZK+lFv+raQf55bPkvRlSRMkXd/OMX4sabs0/5VKxNmmvgmS9mhn3ZGSzm9TdpukpjT/G0kbVjrGVNcfeqKe7lALD3G22jDvkHnVDsFqWKX+VlxxxRWMGTOGfv36MWbMGK644opuPX6leqB/APYAkLQesDGwfW79HsDvOzpARHw2Ih5IixVPoMAEUsxdFRH7R8SL3RpN+3UVitGsmmYunlntEKyPueKKK5g2bRrnnXce//jHPzjvvPOYNm1atybRSiXQ37MmGW0P3Ae8LGm4pPWBbYF70/rBkn4p6SFJlyv9K9Law5N0JjBI0mJJl6d1n5J0Tyr7kaR+bQOQ9JikMyQtkrRM0japvEHStZKWSrpL0jhJo4FjgBPSMffuSmNTXRtL2kDSDZKWSLpP0qG59d9OMd8j6d9S+cck3S3pXkm/kzQylU+XdHF6D/4i6fhcXa/k5k9KbVuS3iezmjRryaxqh2B9zIwZM7jooouYOHEiAwYMYOLEiVx00UXMmDGj2+ro321HyomIpyStkjSKLJH+EdgM2B1YASyNiH+mXLkjWZJ9iizx7gnMzx3rZElfiIjxAJK2BQ4F9oyINyTNBA4DLisRyvMRsZOkzwMnAp8FzgDujYgDJe0DXBYR4yVdALwSEd9rp1mHStort/xvJbb5CPBURHw0xTost+6liNhV0uHAOcABqZ27RURI+ixwEvDfafttgInAEOBPkmZFxButB5O0H3Ag8N6IeE1SyXvyJU0FpgKMGjWqnaZVjodxrZb557P3evDBB9lrr73WKttrr7148MEHu62OiiTQpLUXugfwfbIEugdZAs1fx7snIv4GIGkxMJpcAi3hA8DOwIL0wz8IWN7Otlen14XAJ9L8XsDBABExT9JGbRJde+ZGxBdaFyTdVmKbZcD3JH0buD4i7sytuyL3enaa3xyYK2kT4G3Ao7ntb4iIlcBKScuBkcDfcuv3BX4SEa+ltrxQKuiImA3MBmhqaooy2tmtWj+yYD3DCaFr/PNZGyrxc7vtttsyf/58Jk6c+K+y+fPns+2223ZbHZW8C7f1OuhYsiHcu8h6oG2vf67Mza+m86Qu4NKIGJ+m90TE9Ha2bT12/rilzlS3/BZFxMNkyX0Z8C1Jp7VTR+v8ecD5ETEW+BwwMLdNZ++Luitus0qbc8Ccaodgfcy0adM46qijuPXWW3njjTe49dZbOeqoo5g2bVq31VHJBPp7smHKFyJideohbUiWRP/YxWO9IWlAmr8FmCRpBPzrmuaWXTjWHWRDvkiaQDbM+xLwMtlwaWGSNgVei4ifAd8DdsqtPjT32tr+YcCTaf6ILlZ3E/AZSW9PdftrVczMkilTpjBjxgyOO+44Bg4cyHHHHceMGTOYMmVKt9VRySHcZWR33/68TdngiHi+i8eaDSyVtCgiDpN0KnBTusP3DeC/gMfLPNZ04CeSlgKvsSZx/Rr4paSPA8e1GX4t11jgu5LeTHEdm1u3vqS7yf5paT2D04FfSHqSrIe+VbkVRcSNksYDzZL+CfyGnrlb2azLJl8/mWVHLKt2GNbHTJkypVsTZlvyNYDKk/QY0FTgH4du1dTUFM3NzdUMwSpMUk1e1xt76di1E+j0YTB9RfUConbfK6stkhZGRFOpdf4mIjMzswKcQHtARIyudu/T+g7VwNNX2k7Lr12+1nItxDl8+PAqnymrd5W8BmpmPayehiTbvXferE64B2pmZlaAE6iZmVkBTqBmZmYFOIGamZkV4ARqZmZWgL9IoQ+R9Bzlf2PTxkC9f/Sm3ttQ7/FD/beh3uMHt2FdbRkRjaVWOIFaSZKa2/v2jXpR722o9/ih/ttQ7/GD21BJHsI1MzMrwAnUzMysACdQa8/sagfQDeq9DfUeP9R/G+o9fnAbKsbXQM3MzApwD9TMzKwAJ1AzM7MCnEB7MUlbSLpV0oOS7pf0xVQ+XdKTkhanaf/cPqdI+j9Jf5L04Vz5zpKWpXXnKj2TStL6kuam8rslje7mNgyUdI+kJakNZ6TyBkk3S3okvQ7P7VMzbegg/ro5B6mOfpLulXR9Wq6L97+TNtTbOXgs1b1YUnMqq5vz0E78dXUO3iIiPPXSCdgE2CnNDwEeBrYDpgMnlth+O2AJsD6wFfBnoF9adw+wOyDgf4H9UvnngQvS/GRgbje3QcDgND8AuBvYDfgOcHIqPxn4di22oYP46+YcpON+Gfg5cH1arov3v5M21Ns5eAzYuE1Z3ZyHduKvq3PQdnIPtBeLiKcjYlGafxl4ENisg10+DsyJiJUR8Sjwf8CukjYBhkbEHyP76bwMODC3z6Vp/pfAB1r/I+ymNkREvJIWB6Qp2tR7aZt4aqYNHcTfnpqKH0DS5sBHgR+3ibPm3/9O2tCemmxDB7HWzXnogrqI3wm0j0jDGTuS9YAAviBpqaSLc8M+mwFP5Hb7WyrbLM23LV9rn4hYBawANurm2PtJWgwsB26OiLuBkRHxdKr3aWBErbahnfihfs7BOcBJwJu5srp5/5NzeGsboH7OAWT/eN0kaaGkqamsns5Dqfihvs7BWpxA+wBJg4GrgC9FxEvALOBdwHjgaeCs1k1L7B4dlHe0T7eJiNURMR7YnOy/0DEdbF5zbWgn/ro4B5IOAJZHxMJyd2knlqq9/x20oS7OQc6eEbETsB/wX5Le18G2tdiGUvHX2zlYixNoLydpAFnyvDwirgaIiGfTH/U3gQuBXdPmfwO2yO2+OfBUKt+8RPla+0jqDwwDXqhEWyLiReA24CPAs2k4h/S6vNbbkI+/js7BnsC/S3oMmAPsI+ln1Nf7X7INdXQOAIiIp9LrcuCaFG/dnIdS8dfbOWjLCbQXS+P/FwEPRsT3c+Wb5DY7CLgvzV8HTE53s20FbA3ck4aGXpa0Wzrm4cCvcvsckeYnAfPStYnuakOjpA3T/CBgX+ChNvUe0SaemmlDe/HXyzmIiFMiYvOIGE12Y8a8iPgUdfL+d9SGejkHAJI2kDSkdR74UIq3Ls5De/HX0zkoqaM7jDzV9wTsRTaEsRRYnKb9gZ8Cy1L5dcAmuX2mkd3x9ifS3W2pvInsh/vPwPms+RargcAvyC7y3wO8s5vbMA64N8V6H3BaKt8IuAV4JL021GIbOoi/bs5Brv4JrLmDtS7e/07aUDfnAHgn2V2pS4D7gWn1dB46iL9uzkGpyV/lZ2ZmVoCHcM3MzApwAjUzMyvACdTMzKwAJ1AzM7MCnEDNzMwKcAI1MySt1ponYiyWdHKV4rhE0qQ0/2NJ26X5r1QjHrOO+GMsZoakVyJicA3EcQnZ5zR/2aa8y/FJ6h/Zd6KaVYR7oGZWkqRhyp7F+J60fIWko9P8K5LOkrRI0i2SGlP5uyTdmL4w/E5J26TyS5Q9u/EPkv6S62VK0vmSHpB0A2u+DB1Jt0lqknQmMCj1jC+XNFrSfbntTpQ0PbfPNyXdDnxR2bMjb0/x/Db3tXfHpzqXSprTE++n9T5OoGYGaxJU63RoRKwAvgBcImkyMDwiLkzbbwAsiuzLwW8HTk/ls4HjImJn4ERgZq6OTci+HesA4MxUdhDwHmAscDSwR9vAIuJk4PWIGB8Rh5XRlg0j4v3AucB5wKQUz8XAjLTNycCOETEOOKaMY5q9Rf9qB2BmNeH1yJ4Ys5aIuFnSfwA/BHbIrXoTmJvmfwZcreypP3sAv9CaxzCun9vn2si+NPwBSSNT2fuAKyJiNfCUpHnd0JbWuN4DjAFuTvH0I3viB2RfHXe5pGuBa7uhTuuDnEDNrF2S1gO2BV4HGlj7WYx5QTai9WKpRJyszB+6zb5dsYq1R88Gtln/aq6O+yNi9xLH+ChZ8v534KuStvf1UusqD+GaWUdOAB4EpgAXK3s8HmR/Oyal+U8C8yN71uyjqcfaen1zh7YHbOMOsqdu9EvXJye2s90bubqfBUZI2kjS+mRDwqX8CWiUtHuKZ4Ck7dM/BVtExK1kD9neEKj6DVRWf9wDNTNI10BzyzeSXTP8LNlzG1+WdAdwKtn1zleB7SUtBFYAh6b9DgNmSToVGED2/M0lHdR7DbAP2RM5Hia7nlrKbGCppEURcZikrwF3A4+SPd7uLSLin+lmpXMlDSP7e3dOqudnqUzA2ZE9q9WsS/wxFjPrslr52ItZNXkI18zMrAD3QM3MzApwD9TMzKwAJ1AzM7MCnEDNzMwKcAI1MzMrwAnUzMysgP8PUB8TIwWx5g8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot\n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_22to50, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 22 to 50')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3633d7",
   "metadata": {},
   "source": [
    "## Ages 51+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6a4841a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variables that contain the expenditures of each ethnic group separately\n",
    "White_not_Hispanic_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'White not Hispanic']['Expenditures']\n",
    "Hispanic_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Hispanic']['Expenditures']\n",
    "Black_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Black']['Expenditures']\n",
    "Multi_Race_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Multi Race']['Expenditures']\n",
    "Asian_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Asian']['Expenditures']\n",
    "American_Indian_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'American Indian']['Expenditures']\n",
    "Native_Hawaiian_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Native Hawaiian']['Expenditures']\n",
    "Other_51plus = df.loc[df['Age Cohort'] == '51+'].loc[df['Ethnicity'] == 'Other']['Expenditures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a086242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of the variables\n",
    "data_51plus = [White_not_Hispanic_51plus, Hispanic_51plus, Black_51plus, Multi_Race_51plus, Asian_51plus, American_Indian_51plus, Native_Hawaiian_51plus, Other_51plus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b081c4af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ages 51+')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot \n",
    "fig, ax = plt.subplots()\n",
    "plot = ax.boxplot(data_51plus, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')\n",
    "ax.set_title('Ages 51+')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a35e96",
   "metadata": {},
   "source": [
    "Now, I want you to plot the six box plots together as well as the first box plot that included the data of all of the age groups together so that we can compare them easily. Adjust the figure sizes of the box plots in order for all of the plots to fit on your screen for ease of visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7cc4d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Expenditures')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2880x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the six box plots together with the first boxplot\n",
    "fig, axs = plt.subplots(2,3, figsize=(40,15))\n",
    "axs[0,0].boxplot(data_0to5, vert=False, meanline=True, showmeans=True)\n",
    "axs[0,1].boxplot(data_6to12, vert=False, meanline=True, showmeans=True)\n",
    "axs[0,2].boxplot(data_13to17, vert=False, meanline=True, showmeans=True)\n",
    "axs[1,0].boxplot(data_18to21, vert=False, meanline=True, showmeans=True)\n",
    "axs[1,1].boxplot(data_22to50, vert=False, meanline=True, showmeans=True)\n",
    "axs[1,2].boxplot(data_51plus, vert=False, meanline=True, showmeans=True)\n",
    "axs[0,0].set_title('Ages 0 to 5')\n",
    "axs[0,1].set_title('Ages 6 to 12')\n",
    "axs[0,2].set_title('Ages 13 to 17')\n",
    "axs[1,0].set_title('Ages 18 to 21')\n",
    "axs[1,1].set_title('Ages 22 to 50')\n",
    "axs[1,2].set_title('Ages 51+')\n",
    "axs[0,0].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "axs[0,1].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "axs[0,2].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "axs[1,0].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "axs[1,1].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "axs[1,2].set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "fig, ax = plt.subplots(figsize=(40,7.5))\n",
    "plot = ax.boxplot(data, vert=False, meanline=True, showmeans=True)\n",
    "ax.set_yticklabels(['White not Hispanic', 'Hispanic', 'Black', 'Multi Race', 'Asian', 'American Indian', 'Native Hawaiian', 'Other'])\n",
    "ax.set_ylabel('Ethnicity')\n",
    "ax.set_xlabel('Expenditures')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade33be4",
   "metadata": {},
   "source": [
    "Now, take a close look into all of the plots. For each age group, there doesn't seem to be any obvious discrimination towards any of the ethnic groups as we saw in the first boxplot that included all of the age groups at the same time. \n",
    "\n",
    "Also, regarding the analysis that concluded that the average annual expenditures on Hispanic consumers was approximately one-third of the average expenditures on White not Hispanic consumers, It turns out that this is not the case when considering the data of each age group separately. In fact, the average annual expenditures of the Hispanic group is larger than the average annual expenditures of the White non Hispanic group in 5 out of 6 of the age groups, which is the complete opposite of what the analysis stated.\n",
    "\n",
    "But, why is this the case? I will leave this for you to discover. \n",
    "\n",
    "<b>HINT</b>: Remember when I asked you to take a moment and think about the consequences before? Count the number of Hispanic and White non Hispanic people for each age group, and alongside that, keep in mind the annual expenditures for each of the Hispanic and White non Hispanic groups.\n",
    "- - - -\n",
    "\n",
    "This was an example of Simpson's Paradox. A thing to learn here is to make sure that you've considered every aspect of the data that you have before making a strong allegation like the one that was made here. \n",
    "- - - - "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a7bbf5",
   "metadata": {},
   "source": [
    "# The Mandelbrot Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3a9d06",
   "metadata": {},
   "source": [
    "The Mandelbrot Set is the set of complex numbers $c$ for which the function $f_{c}(z) = z^2 + c$ does not diverge when iterated from $z = 0$. When drawn on a complex plane, it shows some very interesting features like the appearance of the Fibonacci sequence in its structure and being able to approximate $\\pi$ in an unexpected way. \n",
    "\n",
    "Have a look at these videos if you'd like to know more: \n",
    "- https://www.youtube.com/watch?v=4LQvjSf6SSw\n",
    "- https://www.youtube.com/watch?v=d0vY0CKYhPY&t=412s\n",
    "- https://www.youtube.com/watch?v=FFftmWSzgmk&t=351s\n",
    "\n",
    "It is famously known for its appealing structure when graphed, which is what we are going to do in the upcoming tasks.\n",
    "\n",
    "The Mandelbrot set image can be drawn in Python by sampling the complex numbers $c$ and testing for each sample point $c$ whether the sequence $f_{c}(0)$, $f_{c}(f_{c}(0)), \\dotsc$  diverges. Then, the real and imaginary parts of the sampled complex numbers $c$ are treated as the co-ordinates of the image (i.e. the location of the pixel on the grid of the image), where the pixels are coloured according to the number of iterations needed by the function $f_{c}$ for its magnitude to pass a specific threshold (we are going to set the threshhold at $2$) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30074d81",
   "metadata": {},
   "source": [
    "Let's go through the process step by step. Firstly, let's import the packages needed: `matplotlib.pyplot` and `numpy`.\n",
    "\n",
    "<b>NOTE:</b> We are going to explore `numpy` properly in the upcoming session, but for now, we are going to need some of its functions for the plotting of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "297835a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import matplotlib.pyplot and numpy (as np)\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbee119",
   "metadata": {},
   "source": [
    "Now, create a function called `mandelbrot` that returns the number of iterations needed for the function $f_{c}$ for some complex number $c$ to pass the threshold value of $2$, starting from $z=0$. It should include the parameters `Re` for the real part of $c$, `Im` for the imaginary part of $c$, and `max_iter` for the maximum number of iterations that the function can go through before terminating and returning `max_iter` as the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc04613b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function mandelbrot\n",
    "def mandelbrot(Re, Im, max_iter):\n",
    "    c = complex(Re,Im)\n",
    "    z = 0 \n",
    "    for i in range(max_iter):\n",
    "        z = z*z + c\n",
    "        if abs(z) >= 2:\n",
    "            return i\n",
    "        \n",
    "    return max_iter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fcf739",
   "metadata": {},
   "source": [
    "In the next steps, we are going to go through the process of creating the image.\n",
    "\n",
    "To start off, create two variables, `rows` and `columns`, which contain the number of pixels we would like to have per each row/column of pixels in the image. We would like to have a 2000x2000 image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "35e0a62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the variables rows and columns\n",
    "rows = 2000\n",
    "columns = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c197d758",
   "metadata": {},
   "source": [
    "Now, use `numpy.zeros` in order to create a 2000x2000 array of zeros. You can find its documentation here: https://numpy.org/doc/stable/reference/generated/numpy.zeros.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "52c9cf84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 2000x2000 array of zeros, name it result\n",
    "result = np.zeros((rows,columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fac9215",
   "metadata": {},
   "source": [
    "For this image, we are going to consider the ranges $[-2,1]$ for the real part of $c$ and $[-1,1]$ for the imaginary part. What we would like to do now is to create a 1D array of 2000 evenly spaced numbers between $[-2,1]$ and $[-1,1]$ which represent the values of the real and imaginary parts of the co-ordinates of the image (i.e. the sampled values of $c$)\n",
    "\n",
    "We do this by using the function `numpy.linspace`. You can find its documentation here: https://numpy.org/doc/stable/reference/generated/numpy.linspace.html\n",
    "\n",
    "Call the first array `Re_vals` and the second array `Im_vals`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7bc03652",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Re_vals and Im_vals\n",
    "Re_vals = np.linspace(-2, 1, num=rows)\n",
    "Im_vals = np.linspace(-1, 1, num=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819632f1",
   "metadata": {},
   "source": [
    "Now, I would like for you to:\n",
    "1) Loop over both the indexes and the values of `Re_vals` and `Im_vals` in order to replace each entry in the 2000x2000 array called `result` with the number of iterations that is associated with each co-ordinate (i.e. each $c$ value).\n",
    "\n",
    "2) Transpose the result array (I will leave it for you to think about why we do this)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7c0bf771",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in the result array \n",
    "for row_index, Re in enumerate(Re_vals):\n",
    "    for column_index, Im in enumerate(Im_vals):\n",
    "        result[row_index, column_index] = mandelbrot(Re, Im, 200)\n",
    "result = result.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1751ef",
   "metadata": {},
   "source": [
    "For the final step, create a plot for the image which has the `dpi` set to $150$, and then use `ax.imshow()` in order to create the image. You can find its documentation here: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html\n",
    "\n",
    "I would like for you to set the following parameters in `ax.imshow()` as follows: \n",
    "1) `cmap`='hot'\n",
    "\n",
    "2)`interpolation`='bilinear'\n",
    "\n",
    "3) `extent`=$[-2,1,-1,1]$ \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd7a0043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcc03e64cd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the image\n",
    "fig, ax = plt.subplots(dpi=150)\n",
    "ax.imshow(result, cmap='hot', interpolation='bilinear', extent=[-2,1,-1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7edf37",
   "metadata": {},
   "source": [
    "There you go!\n",
    "\n",
    "You can go the extra mile and produce an animation of this fractal as well. You can find a guide of how to do this here: https://matplotlib.org/matplotblog/posts/animated-fractals/ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb1b0bb",
   "metadata": {},
   "source": [
    "## Rejection Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f622f6a",
   "metadata": {},
   "source": [
    "Rejection Sampling is a technique that is used to generate observations from distributions. The basic intuition behind it is to uniformly sample points on a 2D square/rectangle that contains the probability density function inside, choose the points that lie underneath the probability density function, and then their x co-ordinates are the simulated samples from this distribution.\n",
    "\n",
    "You can read more about it here: https://en.wikipedia.org/wiki/Rejection_sampling#:~:text=Rejection%20sampling%20is%20based%20on,graph%20of%20its%20density%20function.\n",
    "\n",
    "We are going to test out this technique on the Standard Normal Distribution and simulate points manually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca63b460",
   "metadata": {},
   "source": [
    "Firstly, import the libraries `math`, `random` and `numpy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6113f80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import math, random and numpy\n",
    "import random\n",
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52eafe7",
   "metadata": {},
   "source": [
    "Here are the main steps of the process:\n",
    "1) Generate random uniform points on a rectangle that ranges from $[-3,3]$ on the x-axis and from $[0,0.6]$ on the y-axis.\n",
    "\n",
    "2) Plot the probability density function of the Standard Normal Distribution.\n",
    "\n",
    "3) Identify the points that are underneath the density function of the Standard Normal Distribution.\n",
    "\n",
    "4) Extract the x co-ordinates of those points.\n",
    " - - - "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088ae5ad",
   "metadata": {},
   "source": [
    "To start off, create 4 empty sets: \n",
    "1) `xs` - this will contain the x co-ordinates of the uniformly sampled points\n",
    "\n",
    "2) `ys` - this will contain the y co-ordinates of the uniformly sampled points\n",
    "\n",
    "3) `xs_samples` - this will contain the x co-ordinates of the uniformly sampled points that fall underneath the density function\n",
    "\n",
    "4) `ys_samples` - this will contain the y co-ordinates of the uniformly sampled points that fall underneath the density function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3bd83de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the 4 variables mentioned \n",
    "xs = []\n",
    "ys = []\n",
    "xs_samples = []\n",
    "ys_samples = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b754c6ad",
   "metadata": {},
   "source": [
    "Now, let N be the number of points that we will uniformly sample. Set N to 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "898afb0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create N\n",
    "N = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fca5b5",
   "metadata": {},
   "source": [
    "For the following task, I would like for you to:\n",
    "1) Fill in the variables `xs` and `ys` with N uniformly sampled points from $[-3,3]$ and $[0,0.6]$.\n",
    "\n",
    "2) For each point, check if it lies underneath the density function of the Standard Normal Distribution. If it does, add the x and y co-ordinates of this point to `xs_samples` and `ys_samples`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a5336ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(N):\n",
    "        xs.append(random.uniform(-3,3))\n",
    "        ys.append(random.uniform(0,0.6))\n",
    "        if ys[i] <= math.exp(-xs[i]*xs[i]/2) / math.sqrt(2*math.pi):\n",
    "            xs_samples.append(xs[i])\n",
    "            ys_samples.append(ys[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb185f62",
   "metadata": {},
   "source": [
    "Plot a figure which contains all of the uniformly sampled points and a graph of the density function, where the points above the density function are coloured in `blue`, the points below the density function are coloured in `green`, and the density function coloured in `red`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "669bd0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcbed610ca0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the figure as instructed\n",
    "fig, ax = plt.subplots()\n",
    "xs2= np.linspace(-3,3,2000)\n",
    "ys2= [math.exp(-x*x/2) / math.sqrt(2*math.pi) for x in xs2] \n",
    "ax.plot(xs2,ys2, c='red')\n",
    "ax.scatter(xs,ys, c='blue')\n",
    "ax.scatter(xs_samples,ys_samples,c='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b9a017",
   "metadata": {},
   "source": [
    "Now, according to Rejection Sampling, the simulated points in `xs_samples` should follow a Standard Normal Distribution. To check if this is true, we are going to plot a `qqplot` using the library called `statsmodels.api`. You will need to download it on your environment before importing it into Python. \n",
    "\n",
    "Use the following command: `pip install statsmodels` to install it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2bd71af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import statsmodels.api\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814208f9",
   "metadata": {},
   "source": [
    "The `qqplot` function requires the data to be presented in an array instead of a list, so the next line of code does so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d73cdc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This converts xs_samples from a list to a numpy array\n",
    "xs_samples = np.array(xs_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f342cb38",
   "metadata": {},
   "source": [
    "Using the documentation here: https://www.statsmodels.org/stable/generated/statsmodels.graphics.gofplots.qqplot.html\n",
    "Plot a qqplot of `xs_samples`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "457b8f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the qqplot of xs_samples\n",
    "sm.qqplot(xs_samples, line='45')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68c0ec8",
   "metadata": {},
   "source": [
    "This is very strong evidence that the values simulated in `xs_samples` do in fact follow a Standard Normal Distribution, which supports the validity of Rejection Sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda924d5",
   "metadata": {},
   "source": [
    "Bonus: \n",
    "1) Play around with the value N, and check the results.\n",
    "\n",
    "2) Try out this technique on other distributions too."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}