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 629b4fe..a704b86 100644
--- a/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb
+++ b/content/01-python_v_stata/.ipynb_checkpoints/syntax-checkpoint.ipynb
@@ -506,105 +506,6 @@
     "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",
@@ -658,7 +559,7 @@
    "id": "9357b734-d25a-4008-b0c9-86240daecff2",
    "metadata": {},
    "source": [
-    "Copy-pasting the output into Python to verify it works."
+    "Assuming you have all the correct packages installed, you can directly copy paste this code to see the output. For example, "
    ]
   },
   {
@@ -1049,6 +950,14 @@
     "stata2python(\"describe\",\"nba\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6b4f8ca6-2d2f-4cdd-8bb4-89cef1d52631",
+   "metadata": {},
+   "source": [
+    "Verifying that the output works:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 30,
@@ -1326,7 +1235,7 @@
     }
    ],
    "source": [
-    "import pandas as pd # Verifying that the output works\n",
+    "import pandas as pd \n",
     "nba.describe()"
    ]
   },
@@ -1541,6 +1450,105 @@
     "pollution.hist(column='co2pc',bins=80);"
    ]
   },
+  {
+   "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": [
+    "Verifying that the output works:"
+   ]
+  },
+  {
+   "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": "14245a39-b06f-406b-a597-fc4ffcffead0",
diff --git a/content/01-python_v_stata/syntax.ipynb b/content/01-python_v_stata/syntax.ipynb
index 629b4fe..a704b86 100644
--- a/content/01-python_v_stata/syntax.ipynb
+++ b/content/01-python_v_stata/syntax.ipynb
@@ -506,105 +506,6 @@
     "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",
@@ -658,7 +559,7 @@
    "id": "9357b734-d25a-4008-b0c9-86240daecff2",
    "metadata": {},
    "source": [
-    "Copy-pasting the output into Python to verify it works."
+    "Assuming you have all the correct packages installed, you can directly copy paste this code to see the output. For example, "
    ]
   },
   {
@@ -1049,6 +950,14 @@
     "stata2python(\"describe\",\"nba\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6b4f8ca6-2d2f-4cdd-8bb4-89cef1d52631",
+   "metadata": {},
+   "source": [
+    "Verifying that the output works:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 30,
@@ -1326,7 +1235,7 @@
     }
    ],
    "source": [
-    "import pandas as pd # Verifying that the output works\n",
+    "import pandas as pd \n",
     "nba.describe()"
    ]
   },
@@ -1541,6 +1450,105 @@
     "pollution.hist(column='co2pc',bins=80);"
    ]
   },
+  {
+   "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": [
+    "Verifying that the output works:"
+   ]
+  },
+  {
+   "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": "14245a39-b06f-406b-a597-fc4ffcffead0",