diff --git a/Starter_Code/Pymaceuticals/data/Mouse_metadata.csv b/Starter_Code/Pymaceuticals/data/Mouse_metadata.csv
new file mode 100644
index 0000000..1ce7a42
--- /dev/null
+++ b/Starter_Code/Pymaceuticals/data/Mouse_metadata.csv
@@ -0,0 +1,250 @@
+"Mouse ID","Drug Regimen","Sex","Age_months","Weight (g)"
+"k403","Ramicane","Male",21,16
+"s185","Capomulin","Female",3,17
+"x401","Capomulin","Female",16,15
+"m601","Capomulin","Male",22,17
+"g791","Ramicane","Male",11,16
+"s508","Ramicane","Male",1,17
+"f966","Capomulin","Male",16,17
+"m546","Ramicane","Male",18,16
+"z578","Ramicane","Male",11,16
+"j913","Ramicane","Female",4,17
+"u364","Capomulin","Male",18,17
+"n364","Ramicane","Male",4,17
+"y793","Capomulin","Male",17,17
+"r554","Capomulin","Female",8,17
+"m957","Capomulin","Female",3,19
+"c758","Ramicane","Male",9,17
+"t565","Capomulin","Female",20,17
+"a644","Ramicane","Female",7,17
+"i177","Ramicane","Male",10,18
+"j989","Ramicane","Male",8,19
+"i738","Capomulin","Female",23,20
+"a520","Ramicane","Male",13,21
+"w914","Capomulin","Male",24,21
+"r811","Ramicane","Male",9,19
+"g288","Capomulin","Male",3,19
+"i334","Ramicane","Female",8,20
+"q610","Ramicane","Female",18,21
+"d251","Ramicane","Female",8,19
+"l897","Capomulin","Male",17,19
+"c458","Ramicane","Female",23,20
+"b742","Capomulin","Male",7,21
+"b128","Capomulin","Female",9,22
+"j246","Capomulin","Female",21,21
+"a411","Ramicane","Male",3,22
+"j119","Capomulin","Female",7,23
+"w150","Capomulin","Male",23,23
+"v923","Capomulin","Female",19,21
+"g316","Capomulin","Female",22,22
+"s710","Capomulin","Female",1,23
+"l509","Capomulin","Male",17,21
+"r944","Capomulin","Male",12,25
+"e662","Ramicane","Male",8,24
+"u196","Ramicane","Male",18,25
+"q597","Ramicane","Male",20,25
+"a444","Ramicane","Female",10,25
+"i557","Capomulin","Female",1,24
+"r921","Ramicane","Female",5,25
+"w678","Ramicane","Female",5,24
+"y449","Ramicane","Male",19,24
+"a203","Infubinol","Female",20,23
+"a251","Infubinol","Female",21,25
+"a262","Placebo","Female",17,29
+"a275","Ceftamin","Female",20,28
+"a366","Stelasyn","Female",16,29
+"a401","Zoniferol","Female",8,25
+"a457","Ketapril","Female",11,30
+"a492","Stelasyn","Male",20,25
+"a577","Infubinol","Female",6,25
+"a685","Infubinol","Male",8,30
+"a699","Propriva","Female",5,28
+"a788","Zoniferol","Male",5,30
+"a818","Naftisol","Female",12,28
+"a897","Placebo","Male",7,28
+"a963","Stelasyn","Female",23,27
+"b313","Zoniferol","Male",12,27
+"b447","Ceftamin","Male",2,30
+"b487","Ceftamin","Female",6,28
+"b559","Naftisol","Male",20,26
+"b759","Ceftamin","Female",12,25
+"b879","Stelasyn","Female",4,26
+"c139","Infubinol","Male",11,28
+"c264","Zoniferol","Female",11,27
+"c282","Placebo","Male",12,27
+"c302","Zoniferol","Female",10,29
+"c326","Infubinol","Female",18,25
+"c402","Stelasyn","Female",1,27
+"c559","Zoniferol","Female",19,28
+"c580","Ketapril","Male",22,25
+"c757","Placebo","Male",9,27
+"c766","Placebo","Female",13,26
+"c819","Ketapril","Male",21,25
+"c832","Ketapril","Male",18,29
+"c895","Infubinol","Female",7,29
+"c927","Propriva","Female",4,26
+"d133","Zoniferol","Male",5,30
+"d164","Zoniferol","Male",21,28
+"d474","Ketapril","Male",18,27
+"e213","Naftisol","Male",8,27
+"e227","Placebo","Male",1,30
+"e291","Naftisol","Female",14,29
+"e476","Infubinol","Male",23,26
+"e584","Naftisol","Male",9,27
+"f129","Zoniferol","Female",11,29
+"f234","Stelasyn","Male",14,28
+"f278","Ketapril","Male",12,30
+"f345","Infubinol","Male",23,26
+"f394","Zoniferol","Male",19,30
+"f436","Ceftamin","Female",3,25
+"f545","Zoniferol","Female",20,26
+"f932","Ketapril","Male",15,29
+"f993","Naftisol","Male",21,28
+"g107","Ketapril","Female",2,29
+"g296","Zoniferol","Female",14,29
+"g497","Ketapril","Male",19,28
+"g558","Propriva","Male",8,29
+"g570","Propriva","Male",16,29
+"g867","Stelasyn","Female",9,25
+"g989","Propriva","Female",21,26
+"h246","Ketapril","Male",13,30
+"h333","Stelasyn","Male",21,27
+"h428","Ketapril","Female",1,27
+"h531","Ceftamin","Male",5,27
+"i386","Infubinol","Female",23,29
+"i477","Placebo","Female",3,30
+"i635","Propriva","Male",21,26
+"i669","Placebo","Female",18,27
+"i901","Stelasyn","Male",23,29
+"j235","Placebo","Male",6,30
+"j296","Ceftamin","Female",24,30
+"j365","Zoniferol","Male",24,28
+"j755","Naftisol","Male",23,27
+"j984","Stelasyn","Female",2,30
+"k210","Ceftamin","Male",15,28
+"k382","Ketapril","Male",22,29
+"k483","Infubinol","Female",20,30
+"k510","Stelasyn","Female",8,27
+"k603","Propriva","Male",2,27
+"k754","Zoniferol","Female",8,26
+"k804","Infubinol","Female",23,29
+"k862","Stelasyn","Female",13,25
+"k894","Zoniferol","Female",13,29
+"l215","Propriva","Male",10,29
+"l264","Ketapril","Female",15,30
+"l471","Ceftamin","Female",7,28
+"l490","Ceftamin","Male",24,26
+"l558","Ceftamin","Female",13,30
+"l661","Ceftamin","Male",18,26
+"l700","Naftisol","Female",18,27
+"l725","Naftisol","Female",8,26
+"l733","Ceftamin","Female",4,30
+"l872","Placebo","Male",19,30
+"m133","Naftisol","Female",2,26
+"m269","Stelasyn","Female",22,28
+"m331","Zoniferol","Female",19,26
+"m550","Ketapril","Male",18,28
+"m650","Ketapril","Male",15,27
+"m756","Infubinol","Male",19,30
+"n304","Naftisol","Male",9,26
+"n482","Propriva","Female",4,29
+"n630","Propriva","Female",15,29
+"n671","Infubinol","Male",18,25
+"n678","Propriva","Male",5,29
+"n763","Placebo","Female",16,25
+"n923","Ketapril","Male",19,30
+"n967","Zoniferol","Male",11,27
+"o287","Ceftamin","Male",2,28
+"o302","Placebo","Female",2,29
+"o331","Ketapril","Male",24,30
+"o523","Propriva","Female",6,25
+"o562","Propriva","Female",4,25
+"o725","Naftisol","Male",4,26
+"o795","Placebo","Female",20,26
+"o809","Infubinol","Male",3,25
+"o813","Infubinol","Male",24,28
+"o848","Stelasyn","Female",14,27
+"o926","Zoniferol","Male",15,29
+"o973","Ketapril","Female",11,29
+"p136","Zoniferol","Female",5,28
+"p189","Ketapril","Male",8,28
+"p310","Propriva","Male",6,26
+"p387","Stelasyn","Male",3,30
+"p438","Ceftamin","Female",11,26
+"p981","Stelasyn","Male",20,29
+"q119","Ketapril","Male",17,30
+"q132","Infubinol","Female",1,30
+"q483","Ceftamin","Male",6,26
+"q511","Zoniferol","Female",2,28
+"q582","Placebo","Male",5,30
+"q633","Zoniferol","Male",12,25
+"q787","Placebo","Male",17,27
+"r107","Propriva","Female",2,28
+"r157","Capomulin","Male",22,25
+"r604","Naftisol","Male",7,30
+"r701","Naftisol","Male",21,25
+"r850","Placebo","Male",5,30
+"s121","Infubinol","Male",23,26
+"s141","Propriva","Male",8,25
+"s152","Placebo","Female",4,30
+"s166","Placebo","Male",19,27
+"s187","Propriva","Male",22,25
+"s337","Zoniferol","Male",14,27
+"s565","Stelasyn","Female",3,29
+"s619","Stelasyn","Male",22,30
+"t198","Propriva","Male",22,26
+"t451","Stelasyn","Male",8,29
+"t573","Ceftamin","Female",15,27
+"t718","Placebo","Female",10,30
+"t724","Naftisol","Female",2,25
+"t994","Placebo","Male",14,30
+"u149","Ceftamin","Male",24,29
+"u153","Ceftamin","Female",11,25
+"u327","Ketapril","Male",17,25
+"u946","Propriva","Male",5,30
+"v199","Naftisol","Female",17,29
+"v289","Ketapril","Female",3,26
+"v295","Naftisol","Female",2,27
+"v339","Infubinol","Male",20,26
+"v409","Placebo","Female",16,25
+"v603","Ketapril","Female",22,30
+"v719","Infubinol","Female",17,30
+"v764","Stelasyn","Female",5,30
+"v766","Infubinol","Male",16,27
+"v835","Naftisol","Male",7,29
+"v989","Placebo","Male",4,25
+"v991","Propriva","Female",10,30
+"w140","Zoniferol","Female",19,30
+"w151","Ceftamin","Male",24,25
+"w167","Placebo","Female",6,28
+"w193","Infubinol","Male",22,30
+"w350","Propriva","Male",7,26
+"w422","Ketapril","Female",18,26
+"w540","Stelasyn","Female",8,26
+"w575","Zoniferol","Female",16,28
+"w584","Infubinol","Male",3,29
+"w697","Stelasyn","Female",14,30
+"w746","Propriva","Male",1,26
+"x111","Propriva","Female",24,27
+"x209","Propriva","Female",7,29
+"x226","Ceftamin","Male",23,28
+"x264","Naftisol","Female",21,27
+"x336","Naftisol","Female",4,29
+"x402","Stelasyn","Male",21,28
+"x581","Ceftamin","Female",19,28
+"x613","Zoniferol","Female",2,29
+"x773","Placebo","Female",21,30
+"x822","Ceftamin","Male",3,29
+"x930","Naftisol","Male",13,26
+"y163","Infubinol","Female",17,27
+"y260","Ketapril","Female",7,25
+"y478","Placebo","Female",3,25
+"y601","Naftisol","Female",23,25
+"y769","Ceftamin","Female",6,27
+"y865","Ceftamin","Male",23,26
+"z234","Naftisol","Female",19,27
+"z314","Stelasyn","Female",21,28
+"z435","Propriva","Female",12,26
+"z581","Infubinol","Female",24,25
+"z795","Naftisol","Female",13,29
+"z969","Naftisol","Male",9,30
diff --git a/Starter_Code/Pymaceuticals/data/Study_results.csv b/Starter_Code/Pymaceuticals/data/Study_results.csv
new file mode 100644
index 0000000..faf6d03
--- /dev/null
+++ b/Starter_Code/Pymaceuticals/data/Study_results.csv
@@ -0,0 +1,1894 @@
+Mouse ID,Timepoint,Tumor Volume (mm3),Metastatic Sites
+b128,0,45,0
+f932,0,45,0
+g107,0,45,0
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\ No newline at end of file
diff --git a/Starter_Code/Pymaceuticals/pymaceuticals_starter.ipynb b/Starter_Code/Pymaceuticals/pymaceuticals_starter.ipynb
new file mode 100644
index 0000000..d239e0e
--- /dev/null
+++ b/Starter_Code/Pymaceuticals/pymaceuticals_starter.ipynb
@@ -0,0 +1,1476 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pymaceuticals Inc.\n",
+    "---\n",
+    "\n",
+    "### Analysis\n",
+    "\n",
+    "- The study's sample of mice included a balanced gender distribution, with females accounting for 49.4% and males 50.6%\n",
+    "- Among the drug regimens tested, Capomulin was administered to the highest number of mice (230), closely followed by Ramicane (228). Notably, these two treatments demonstrated the most significant effectiveness in reducing overall tumor volume when compared to all other drug regimens examined.\n",
+    "- In the case of mouse ID 1509, which recieved the Capomulin treatment, there was a notable reduction in total tumor volume over the 45-day study period, indicating the treatment's efficacy.\n",
+    "- Furthermore, the observed strong positive correlation between mouse weight and tumor size (with a p-value of 0.84) suggests that as mouse weight increases, so does tumor volume.\n",
+    "- The study results exhibit a high level of reliability and consistency, as there was only one outlier (Infubinol) identified within the dataset for the drugs of interest. This strengthens the overall validity and robustness of the findings. \n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "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>Mouse ID</th>\n",
+       "      <th>Timepoint</th>\n",
+       "      <th>Tumor Volume (mm3)</th>\n",
+       "      <th>Metastatic Sites</th>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age_months</th>\n",
+       "      <th>Weight (g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>b128</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Capomulin</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>9</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>f932</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>15</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>g107</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>a457</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>11</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>c819</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
+       "0     b128          0                45.0                 0    Capomulin   \n",
+       "1     f932          0                45.0                 0     Ketapril   \n",
+       "2     g107          0                45.0                 0     Ketapril   \n",
+       "3     a457          0                45.0                 0     Ketapril   \n",
+       "4     c819          0                45.0                 0     Ketapril   \n",
+       "\n",
+       "      Sex  Age_months  Weight (g)  \n",
+       "0  Female           9          22  \n",
+       "1    Male          15          29  \n",
+       "2  Female           2          29  \n",
+       "3  Female          11          30  \n",
+       "4    Male          21          25  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Dependencies and Setup\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import scipy.stats as st\n",
+    "from scipy.stats import linregress\n",
+    "\n",
+    "# Study data files\n",
+    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
+    "study_results_path = \"data/Study_results.csv\"\n",
+    "\n",
+    "# Read the mouse data and the study results\n",
+    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
+    "study_results = pd.read_csv(study_results_path)\n",
+    "\n",
+    "# Combine the data into a single DataFrame\n",
+    "mice_df = pd.merge(mouse_metadata, study_results, on=[\"Mouse ID\"], how=\"right\")\n",
+    "\n",
+    "# Display the data table for preview\n",
+    "mice_df = mice_df[['Mouse ID', 'Timepoint', 'Tumor Volume (mm3)', 'Metastatic Sites', 'Drug Regimen', 'Sex', 'Age_months', 'Weight (g)']]\n",
+    "\n",
+    "mice_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "249"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the number of mice.\n",
+    "mouse_count = mice_df['Mouse ID'].nunique()\n",
+    "mouse_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['g989'], dtype=object)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Our data should be uniquely identified by Mouse ID and Timepoint\n",
+    "# Get the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
+    "duplicate_mice = mice_df.loc[mice_df.duplicated(subset=['Mouse ID', 'Timepoint']), 'Mouse ID'].unique()\n",
+    "duplicate_mice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "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>Mouse ID</th>\n",
+       "      <th>Timepoint</th>\n",
+       "      <th>Tumor Volume (mm3)</th>\n",
+       "      <th>Metastatic Sites</th>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age_months</th>\n",
+       "      <th>Weight (g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>5</td>\n",
+       "      <td>48.786801</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>5</td>\n",
+       "      <td>47.570392</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>10</td>\n",
+       "      <td>51.745156</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>10</td>\n",
+       "      <td>49.880528</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>15</td>\n",
+       "      <td>51.325852</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>869</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>15</td>\n",
+       "      <td>53.442020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>20</td>\n",
+       "      <td>55.326122</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1111</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>20</td>\n",
+       "      <td>54.657650</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1195</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>25</td>\n",
+       "      <td>56.045564</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1380</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>30</td>\n",
+       "      <td>59.082294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1592</th>\n",
+       "      <td>g989</td>\n",
+       "      <td>35</td>\n",
+       "      <td>62.570880</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Propriva</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
+       "107      g989          0           45.000000                 0     Propriva   \n",
+       "137      g989          0           45.000000                 0     Propriva   \n",
+       "329      g989          5           48.786801                 0     Propriva   \n",
+       "360      g989          5           47.570392                 0     Propriva   \n",
+       "620      g989         10           51.745156                 0     Propriva   \n",
+       "681      g989         10           49.880528                 0     Propriva   \n",
+       "815      g989         15           51.325852                 1     Propriva   \n",
+       "869      g989         15           53.442020                 0     Propriva   \n",
+       "950      g989         20           55.326122                 1     Propriva   \n",
+       "1111     g989         20           54.657650                 1     Propriva   \n",
+       "1195     g989         25           56.045564                 1     Propriva   \n",
+       "1380     g989         30           59.082294                 1     Propriva   \n",
+       "1592     g989         35           62.570880                 2     Propriva   \n",
+       "\n",
+       "         Sex  Age_months  Weight (g)  \n",
+       "107   Female          21          26  \n",
+       "137   Female          21          26  \n",
+       "329   Female          21          26  \n",
+       "360   Female          21          26  \n",
+       "620   Female          21          26  \n",
+       "681   Female          21          26  \n",
+       "815   Female          21          26  \n",
+       "869   Female          21          26  \n",
+       "950   Female          21          26  \n",
+       "1111  Female          21          26  \n",
+       "1195  Female          21          26  \n",
+       "1380  Female          21          26  \n",
+       "1592  Female          21          26  "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Optional: Get all the data for the duplicate mouse ID. \n",
+    "duplicate_mice = mice_df[mice_df['Mouse ID'] == \"g989\"]\n",
+    "duplicate_mice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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>Mouse ID</th>\n",
+       "      <th>Timepoint</th>\n",
+       "      <th>Tumor Volume (mm3)</th>\n",
+       "      <th>Metastatic Sites</th>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age_months</th>\n",
+       "      <th>Weight (g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>b128</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Capomulin</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>9</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>f932</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>15</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>g107</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>a457</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>11</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>c819</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ketapril</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
+       "0     b128          0                45.0                 0    Capomulin   \n",
+       "1     f932          0                45.0                 0     Ketapril   \n",
+       "2     g107          0                45.0                 0     Ketapril   \n",
+       "3     a457          0                45.0                 0     Ketapril   \n",
+       "4     c819          0                45.0                 0     Ketapril   \n",
+       "\n",
+       "      Sex  Age_months  Weight (g)  \n",
+       "0  Female           9          22  \n",
+       "1    Male          15          29  \n",
+       "2  Female           2          29  \n",
+       "3  Female          11          30  \n",
+       "4    Male          21          25  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
+    "clean_df = mice_df[mice_df['Mouse ID'].isin(duplicate_mice) == False]\n",
+    "clean_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "248"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the number of mice in the clean DataFrame.\n",
+    "clean_mice = clean_df[mice_df['Mouse ID'] != \"g989\"]\n",
+    "clean_count = clean_mice['Mouse ID'].nunique()\n",
+    "clean_count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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>Mean Tumor Volume</th>\n",
+       "      <th>Median Tumor Volume</th>\n",
+       "      <th>Tumor Volume Variance</th>\n",
+       "      <th>Tumor Volume Std. Dev.</th>\n",
+       "      <th>Tumor Volume Std. Err.</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Capomulin</th>\n",
+       "      <td>40.675741</td>\n",
+       "      <td>41.557809</td>\n",
+       "      <td>24.947764</td>\n",
+       "      <td>4.994774</td>\n",
+       "      <td>0.329346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ceftamin</th>\n",
+       "      <td>52.591172</td>\n",
+       "      <td>51.776157</td>\n",
+       "      <td>39.290177</td>\n",
+       "      <td>6.268188</td>\n",
+       "      <td>0.469821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Infubinol</th>\n",
+       "      <td>52.884795</td>\n",
+       "      <td>51.820584</td>\n",
+       "      <td>43.128684</td>\n",
+       "      <td>6.567243</td>\n",
+       "      <td>0.492236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ketapril</th>\n",
+       "      <td>55.235638</td>\n",
+       "      <td>53.698743</td>\n",
+       "      <td>68.553577</td>\n",
+       "      <td>8.279709</td>\n",
+       "      <td>0.603860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Naftisol</th>\n",
+       "      <td>54.331565</td>\n",
+       "      <td>52.509285</td>\n",
+       "      <td>66.173479</td>\n",
+       "      <td>8.134708</td>\n",
+       "      <td>0.596466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placebo</th>\n",
+       "      <td>54.033581</td>\n",
+       "      <td>52.288934</td>\n",
+       "      <td>61.168083</td>\n",
+       "      <td>7.821003</td>\n",
+       "      <td>0.581331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Propriva</th>\n",
+       "      <td>52.322552</td>\n",
+       "      <td>50.854632</td>\n",
+       "      <td>42.351070</td>\n",
+       "      <td>6.507770</td>\n",
+       "      <td>0.512884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ramicane</th>\n",
+       "      <td>40.216745</td>\n",
+       "      <td>40.673236</td>\n",
+       "      <td>23.486704</td>\n",
+       "      <td>4.846308</td>\n",
+       "      <td>0.320955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stelasyn</th>\n",
+       "      <td>54.233149</td>\n",
+       "      <td>52.431737</td>\n",
+       "      <td>59.450562</td>\n",
+       "      <td>7.710419</td>\n",
+       "      <td>0.573111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zoniferol</th>\n",
+       "      <td>53.236507</td>\n",
+       "      <td>51.818479</td>\n",
+       "      <td>48.533355</td>\n",
+       "      <td>6.966589</td>\n",
+       "      <td>0.516398</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Mean Tumor Volume  Median Tumor Volume  Tumor Volume Variance  \\\n",
+       "Drug Regimen                                                                  \n",
+       "Capomulin             40.675741            41.557809              24.947764   \n",
+       "Ceftamin              52.591172            51.776157              39.290177   \n",
+       "Infubinol             52.884795            51.820584              43.128684   \n",
+       "Ketapril              55.235638            53.698743              68.553577   \n",
+       "Naftisol              54.331565            52.509285              66.173479   \n",
+       "Placebo               54.033581            52.288934              61.168083   \n",
+       "Propriva              52.322552            50.854632              42.351070   \n",
+       "Ramicane              40.216745            40.673236              23.486704   \n",
+       "Stelasyn              54.233149            52.431737              59.450562   \n",
+       "Zoniferol             53.236507            51.818479              48.533355   \n",
+       "\n",
+       "              Tumor Volume Std. Dev.  Tumor Volume Std. Err.  \n",
+       "Drug Regimen                                                  \n",
+       "Capomulin                   4.994774                0.329346  \n",
+       "Ceftamin                    6.268188                0.469821  \n",
+       "Infubinol                   6.567243                0.492236  \n",
+       "Ketapril                    8.279709                0.603860  \n",
+       "Naftisol                    8.134708                0.596466  \n",
+       "Placebo                     7.821003                0.581331  \n",
+       "Propriva                    6.507770                0.512884  \n",
+       "Ramicane                    4.846308                0.320955  \n",
+       "Stelasyn                    7.710419                0.573111  \n",
+       "Zoniferol                   6.966589                0.516398  "
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
+    "drug_mean = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].mean()\n",
+    "drug_median = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].median()\n",
+    "drug_var = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].var()\n",
+    "drug_std = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].std()\n",
+    "drug_SEM = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].sem()\n",
+    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
+    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
+    "# Assemble the resulting series into a single summary DataFrame.\n",
+    "summary_df = pd.DataFrame({'Mean Tumor Volume':drug_mean, 'Median Tumor Volume':drug_median,\n",
+    "                         'Tumor Volume Variance':drug_var, 'Tumor Volume Std. Dev.':drug_std,\n",
+    "                         'Tumor Volume Std. Err.':drug_SEM})\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">Tumor Volume (mm3)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>var</th>\n",
+       "      <th>std</th>\n",
+       "      <th>sem</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Capomulin</th>\n",
+       "      <td>40.675741</td>\n",
+       "      <td>41.557809</td>\n",
+       "      <td>24.947764</td>\n",
+       "      <td>4.994774</td>\n",
+       "      <td>0.329346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ceftamin</th>\n",
+       "      <td>52.591172</td>\n",
+       "      <td>51.776157</td>\n",
+       "      <td>39.290177</td>\n",
+       "      <td>6.268188</td>\n",
+       "      <td>0.469821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Infubinol</th>\n",
+       "      <td>52.884795</td>\n",
+       "      <td>51.820584</td>\n",
+       "      <td>43.128684</td>\n",
+       "      <td>6.567243</td>\n",
+       "      <td>0.492236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ketapril</th>\n",
+       "      <td>55.235638</td>\n",
+       "      <td>53.698743</td>\n",
+       "      <td>68.553577</td>\n",
+       "      <td>8.279709</td>\n",
+       "      <td>0.603860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Naftisol</th>\n",
+       "      <td>54.331565</td>\n",
+       "      <td>52.509285</td>\n",
+       "      <td>66.173479</td>\n",
+       "      <td>8.134708</td>\n",
+       "      <td>0.596466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placebo</th>\n",
+       "      <td>54.033581</td>\n",
+       "      <td>52.288934</td>\n",
+       "      <td>61.168083</td>\n",
+       "      <td>7.821003</td>\n",
+       "      <td>0.581331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Propriva</th>\n",
+       "      <td>52.322552</td>\n",
+       "      <td>50.854632</td>\n",
+       "      <td>42.351070</td>\n",
+       "      <td>6.507770</td>\n",
+       "      <td>0.512884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ramicane</th>\n",
+       "      <td>40.216745</td>\n",
+       "      <td>40.673236</td>\n",
+       "      <td>23.486704</td>\n",
+       "      <td>4.846308</td>\n",
+       "      <td>0.320955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stelasyn</th>\n",
+       "      <td>54.233149</td>\n",
+       "      <td>52.431737</td>\n",
+       "      <td>59.450562</td>\n",
+       "      <td>7.710419</td>\n",
+       "      <td>0.573111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zoniferol</th>\n",
+       "      <td>53.236507</td>\n",
+       "      <td>51.818479</td>\n",
+       "      <td>48.533355</td>\n",
+       "      <td>6.966589</td>\n",
+       "      <td>0.516398</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Tumor Volume (mm3)                                          \n",
+       "                           mean     median        var       std       sem\n",
+       "Drug Regimen                                                             \n",
+       "Capomulin             40.675741  41.557809  24.947764  4.994774  0.329346\n",
+       "Ceftamin              52.591172  51.776157  39.290177  6.268188  0.469821\n",
+       "Infubinol             52.884795  51.820584  43.128684  6.567243  0.492236\n",
+       "Ketapril              55.235638  53.698743  68.553577  8.279709  0.603860\n",
+       "Naftisol              54.331565  52.509285  66.173479  8.134708  0.596466\n",
+       "Placebo               54.033581  52.288934  61.168083  7.821003  0.581331\n",
+       "Propriva              52.322552  50.854632  42.351070  6.507770  0.512884\n",
+       "Ramicane              40.216745  40.673236  23.486704  4.846308  0.320955\n",
+       "Stelasyn              54.233149  52.431737  59.450562  7.710419  0.573111\n",
+       "Zoniferol             53.236507  51.818479  48.533355  6.966589  0.516398"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A more advanced method to generate a summary statistics table of mean, median, variance, standard deviation,\n",
+    "# and SEM of the tumor volume for each regimen (only one method is required in the solution)\n",
+    "\n",
+    "# Using the aggregation method, produce the same summary statistics in a single line\n",
+    "summary_agg = clean_df.groupby(['Drug Regimen'])[['Tumor Volume (mm3)']].agg(['mean', 'median', 'var', 'std', 'sem'])\n",
+    "summary_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar and Pie Charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using Pandas.\n",
+    "drug_regimen = clean_df['Drug Regimen'].value_counts()\n",
+    "drug_regimen.plot(kind='bar', color='steelblue')\n",
+    "\n",
+    "plt.xlabel(\"Drug Regimen\")\n",
+    "plt.ylabel(\"# of Observed Mouse Timepoints\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using pyplot.\n",
+    "x_axis = drug_regimen.index\n",
+    "y_axis = drug_regimen.values\n",
+    "\n",
+    "plt.bar(x_axis, y_axis, color='steelblue')\n",
+    "plt.xlabel(\"Drug Regimen\")\n",
+    "plt.ylabel(\"# of Observed Mouse Timepoints\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a pie plot showing the distribution of female versus male mice using Pandas\n",
+    "gender_data = clean_df['Sex'].value_counts()\n",
+    "gender_data.plot(kind=\"pie\", autopct=\"%1.1f%%\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0THXfL511uV5Zbn5fk8dd0AdHIHrsLEg6PQI7JMNZU4aq35Yj4qpbAQDBKUMQnHJ6l5g1Pwv2U3mIGn0fTr7zJ8RkPAq9ORIFH86GKTENenOEh94xtdXh4ho8+sUuLLyt/8WfTI24u0ygZ1ftx9a8ctEx6Ay6ABMCYrrCXn4S+pBIAICrtum/kbOu8oIf+vqQKBijOkLS6RvvM0YnwllbDtnZ/NwL2WFH2dq3EDXmATjKCyC7nDAlXQZjdGcYozqhvuCAZ94ctduavUX4asdx0TH8CktGkG+zCrB401HRMegsssMOe+kx6EOiYAiPh94cCUvujtOPO+2wHtuDwE69zvsagZ16wV5eAFk+fUEse/kJ6EOiIOmb72qp+GUZTN36IbBDMiC7gIa5GwCQXQ7AxQtr+ZK/f7MXhbxoYIuxZATIOVWDv32ZJToGASj//j1Y83fDXlGI+pMHcOrrZ+Gy1SEkbSQkSUJo/xtQufkL1B38BbZTuSj59jXojIEw9xrW+BolK19B+U8fNH4d2nc8XNZqlK97B/ayE6jLyUTl5i8Q2ndCs/++7VQe6vb/jIirpwMADFGdAUmH6l1rUZeTCXvpcQQk9FD850AtV2V18Pe3FTgn42VWuxN//ngbauodoqMQAEd1CUpWvARnXRX0wWEI7JiKDre9AkN4HAAgbNBUyI56lK19C05rDQI79kTczU82OYfFUXUKkE5vrxnCYhF/85MoW/8uqhfPgiE0GmH9r0fYoKlN/tuyLKNszRuIvPYe6AJMAACdMRDR4x9G2XdvQXbaETX6PhhCY7zwk6DW+OngKSz9LR+38vyZi+IZ/1723H/3YeFPR0THIKJ2Cgk04L8PDUViFE+avRDuLvOi/YVVeG8D52GI1KCm3oFH/7ML3E6/MJaMl8iyjLnLd8PB82GIVGPLkTJ88Euu6Bg+jSXjJZ/8mo8d+RWiYxCRh72wej+OnKoRHcNnsWS8oLjaihdX7xcdg4gUYLW78NcvdnHVjvNgyXjBkyuyUWXl0WREarU9vwLv/MwDes6FJaOwHw8UY2VWgegYRKSwV9cdxIHCatExfA5LRkFWuxPzvuFVLom0wOZw4X94tFkzLBkF/Wv9IRwrs4iOQUResut4JfdcnIUlo5ADhdV4dwP30RJpzctrD8Du5Hpzv2PJKECWZfy/r3bD7uSwmUhr8krr8MmWPNExfAZLRgGf/paPbVzCn0izFnx/mOsTNmDJeFi11Y6X1vD6H0RaVlprw8KfckTH8AksGQ9b8ksuKuqaX5iKiLTl3Q1HUVzN686wZDyott6B9zZyAUwiAix2J15bd0h0DOFYMh60ZHMuyjmKIaIGn2ceQ47G1zVjyXhInc2Bd7mMPxGdweGSNb9uIUvGQz7anIeyWpvoGETkY9bsLcK2vDLRMYRhyXiA1e7EIp54SUTn8dwq7Y5mWDIe8PGWPJTUcBRDROe2Na8ca/cWio4hBEumnax2JxZyiW8iuoh/fndQdAQhWDLttPS3fJyqrhcdg4h83P7Camw6XCI6htexZNqh3uHE2zyrl4haSIvn0bFk2uGzzGMoquIohoha5ocDxTiisfNmWDJtZHO48NaPHMUQUcvJMvD+plzRMbyKJdNGX+84gYJKrktERK3z5fbjqNTQyiAsmTb65FdeL4KIWq/O5sTSzHzRMbyGJdMG2SersOt4pegYROSnPt6SB5dLGxc1ZMm0wTINbYUQkecdL7fg50OnRMfwCpZMK1ntTny944ToGETk55b+po2NVZZMK32bVYAqKy+rSkTts35fMYqr1H/wEEumlbirjIg8weGS8fnWY6JjKI4l0wpHS2qRmVsuOgYRqcSyzGOqPwCAJdMKX3Euhog86Hi5BRtUvp4ZS6YVvtnJkiEiz1q566ToCIpiybTQjvxy5JXWiY5BRCrzw4FiVe8yY8m0EA9bJiIllNTYsD1fvXO9LJkWcDhdWJlVIDoGEanUd/uKREdQDEumBTYcKkFpLS+vTETK+C6bJaNpq/do89rcROQdR07VIkel15lhybTARpUfYkhE4q1T6WiGJXMRR0tqcaLCIjoGEamcWneZsWQugqMYIvKG7fnlKK1R3+XcWTIXsekQS4aIlOeSgfX7i0XH8DiWzAW4XDI2HykVHYOINEKNu8xYMhew+0QlKi3auRY3EYm18VAJrHan6BgexZK5AM7HEJE3WexObFTZLnqWzAVsYskQkZepbZcZS+Y8rHYntuapdz0hIvJNmblloiN4FEvmPDJzy2BzuETHICKNOVpai2qreuaCWTLnwfkYIhJBloE9J6pEx/AYlsx5cD6GiETZc6JSdASPYcmcQ3mtDdkn1bMlQUT+JYslo26ZuWVQ8YXqiMjH7T5eITqCx7BkzuFQsTqX3CYi/5BXVocqlUz+s2TO4VBRtegIRKRhsgzsOa6OXWYsmXM4rNKLBxGR/9itknkZlsxZZFlGTnGt6BhEpHFqmfxnyZzleLkFFpUtUEdE/kcthzGzZM5ymJP+ROQD8krrVLEKPEvmLCwZIvIVahjNsGTOcqiYR5YRkW/IUsERZiyZs3AkQ0S+Qg2nU7BkzsKSISJfUVhlFR2h3VgyZyiusqLK6hAdg4gIAFDEklEXLidDRL6kuLpedIR2Y8mcgbvKiMiXVFsdsNj8+7w9lswZWDJE5GuKq/17lxlL5gz+/o9JROpTVOXfu8xYMmeosnDSn4h8i79P/rNkzlBd7/9LOBCRuvj75D9L5gwcyRCRrynmSEY91HIlOiJSD45kVKSGJ2ISkY/R5JzMunXrzvvYwoUL2xxGpNp6BxwuWXQMIqImNFkyEyZMwJw5c2Cz2RrvO3XqFDIyMjB37lyPhfMm7iojIl+kyd1lP//8M1asWIEBAwZg7969+Pbbb5GWloaamhrs2rXL0xm9opq7yojIB1VbHbD68dV621QygwYNwo4dO9CnTx/069cPkydPxpw5c/D9998jMTHR0xm9okoFV6AjInWqrfffjeA2T/wfOHAAmZmZ6Ny5MwwGA/bv34+6ujpPZvMq7i4jIl/l9OP54jaVzPPPP4/Bgwdj9OjR2LNnDzIzMxtHNps3b/Z0Rq/gOTJE5KvsWiuZf/3rX/j666+xYMECmEwmXHrppfjtt98wZcoUDB8+3MMRvaOaIxki8lFOp/+WjKEt37R7927ExMQ0uc9oNOKll17CxIkTPRLM23ixMiLyVQ6XS3SENmvTSCYmJgYVFRV49913MXfuXJSVlQEAtm/fjuTkZI8G9BZ/nlgjInXz5zmZNo1ksrKyMGrUKISHhyM3Nxf33HMPoqKi8NVXXyEvLw8ffvihp3MqTidJoiMQEZ2TP58o3qaSmT17NmbMmIEXX3wRoaGhjfePGzcO06ZN81g4bzLqucKOVuklFyKMToQbHIgwOhCutyPM6ECozo5Qgx0hOvfNLNkRrKtHkGRDEGwwwQYT6hEo1yNArodRtsHossLoskLnskGS/feDgXyLTloMIEx0jDZpU8lkZmaec/mYTp06obCwsN2hRDAaOJLxNUadjAiDHZFGB8IN7luYwYFQvR0hejtCdTaYdXYE62wIltw3E+wwof6sD/96GF1WGJzum95ZD53TAslhgWS3QHI2nFHtAuDfJ1eTWkn+ezJmm0rGZDKhqqqq2f0HDhxAbGxsu0OJYNRxJNNSJp0TkUYnwo0ORBjsCDM4Eaa3IVT/+5a/A+YzPvjdH/4NW/2wnf7wb9jqNzit0LsaPvwdFugcFsBugeRqOOLP2XBjAZBW6dr0Ue0T2pT8hhtuwJNPPonPP/8cACBJEvLz8/HYY49h6tSpHg3oLUa9/49kzHonIoy/b/k7EWawI1TvQJjejlC9vXGr39zwwf/7bp/Ahi3/gMYP/9Nb/npnPfQNW/06hwVwWCG5Gg6ScDTciEhZOr3oBG3WppJ5+eWXMX78eMTFxcFisWDYsGEoKCjA4MGD8cwzz3g6o1cYFJyTCTM4EGls2N9vcLg/+A0NW/56B4J1NoRIdvdWv+73/f31MP2+1Y96BLhOb/nrXfXQO63QOyzQOayQHBbAYYEkNxzmaG+4EZE6aG0kExYWho0bN+L777/H9u3b4XK50K9fP4wcOdLT+bymQ6AN18cVu7f6DQ6YJfd+f7NUD7POBpNkRxBsCGrc6rc1bPlbG7f8f9/q1zmt0DkskBxW924fyIAMwNZwIyJqDb1RdII2a1XJ/PrrrygrK8O4ceMAANdeey2OHTuGv//976irq8OkSZOwYMECBAYGKhJWSaOMWRhV9bDoGEREzfnxSKZV+4j+8Y9/ICsrq/Hr3bt345577sHo0aPx2GOPYcWKFXjuuec8HtIrAv3z8EAi0gB9gOgEbdaqktm5c2eTXWLLli3DwIEDsWjRIsyePRuvv/5648EAficw9OLPISLyNn0gEBQhOkWbtapkysvLER8f3/j1Tz/9hLFjxzZ+PWDAABw7dsxz6byJJUNEvig0/uLP8WGtKpn4+HgcPXoUAGCz2bB9+3YMHjy48fHq6moYjX46QcWSISJfFNJBdIJ2aVXJjB07Fo899hg2bNiAuXPnIjg4GEOHDm18PCsrC927d/d4SK9gyRCRL/LzkUyrDll4+umnMWXKFAwbNgwhISFYsmQJAgJOT0gtXrwY1113ncdDekUAS4aIfJCfj2RaVTKxsbHYsGEDKisrERISAr2+6VmoX3zxBUJCQjwa0Gv0BiAoCrCUiU5CRHSan49k2nSae3h4eLOCAYCoqKgmIxu/E3WJ6ARERE35+UiGq0KeKZIlQ0Q+JpQlox4cyRCRrwnR4O4y1eJIhoh8TWiC6ATtwpI5E0cyRORLdAbAHCM6RbuwZM7EkQwR+RJzHCD597WuWDJnCu0AGIJEpyAicvPzw5cBlkxTkgREdhWdgojIzc/nYwCWTHOclyEiXxGbKjpBu7FkzsZ5GSLyFQmXi07QbiyZs3EkQ0S+ouMVohO0G0vmbBzJEJEvMEWoYo6YJXM2jmSIyBeoYFcZwJJpLiIJMJhEpyAirWPJqJTeCHRMF52CiLROBfMxAEvm3LoMvvhziIiUlHCF6AQewZI5l6QhohMQkZYFhgFR3USn8AiWzLkkDgQk/miISJCEy/1+zbLf8ZP0XExhQHya6BREpFUqmfQHWDLnl8R5GSISRCXzMQBL5vw4+U9EonAkowGc/CciEQJCgOhk0Sk8hiVzPqHxqjm6g4j8yCXXADr1fDSr550ogaMZIvK2lLGiE3gUS+ZCOC9DRF4lsWQ0hUeYEZE3deyriksun4klcyHR3QFznOgURKQVPceJTuBxLJmL6T5CdAIi0gqV7SoDWDIXd+lk0QmISAvCOgMJfUSn8DiWzMV0HwmYwkWnICK1S7lOdAJFsGQuxhAApGaITkFEapeivvkYgCXTMmncZUZECjIGu0/CVCGWTEtcMhwIjhGdgojUqtsIwKjOy76zZFpCbwB6Xy86BRGpVU/1HVX2O5ZMS106RXQCIlIlCegxRnQIxbBkWqrLVUBogugURKQ2nQeo7iz/M7FkWkqnA3pPEp2CiNSm3x2iEyiKJdMaadxlRkQeZIoA0qaKTqEolkxrJA4EIpJEpyAitbhiGmAMEp1CUSyZ1uIyM0TkKf1nik6gOJZMa6XdKDoBEalB16FATA/RKRTHkmmthD5A4iDRKYjI32lgFAOwZNpmyF9EJyAifxYSD/TSxpqILJm26DkBiOouOgUR+au+0wG9UXQKr2DJtIVOBwy+X3QKIvJHkg7oN0N0Cq9hybTVFX/koplE1Ho9rtPUqRAsmbYyBgED7hadgoj8jUYm/H/HkmmPgfcABnWfSEVEHhSRBCSPFp3Cq1gy7WGOAS6/RXQKIvIX/e50z+lqiLberRIGz3JP5BERXUhwtHvvh8bw07G9YpJVe21uIvKgoXOAwFDRKbyOJeMJVz0oOgER+bKwzpo9UIgl4wlJVwKdB4pOQUS+atj/AIZA0SmEYMl4CkczRHQu0cnuM/w1iiXjKb0yOJohouZG/D9ApxedQhiWjCeNex6AJDoFEfmKDn2AS7V9RV2WjCd16gdcfqvoFETkK0Y+AUja3vBkyXjaqL8DASGiUxCRaElDgB7aOrv/XFgynhbaAbj6EdEpiEi0kU+ITuATWDJKGPIXIKKL6BREJEqP64Aug0Wn8AksGSUYAoHrnhKdgoiEkIBr54kO4TNYMkrpfQPQdajoFETkbZffCiT0EZ3CZ7BklDT2OS6eSaQlIfHA2GdFp/Ap/ARUUofLgPTbRacgIm+Z8AoQFCk6hU9hySjt2nlAYLjoFESktEsnu1f+oCZYMkozx7gXxyMi9QqOBsa/LDqFT2LJeMOge4GOfUWnICKljHvRvUFJzbBkvEFvBKa8CxjNopMQkaf1nABcdqPoFD6LJeMtMcnuo82ISD1M4cDEf4pO4dNYMt7U7w5ODPqR5zbUQ5pfhYdXWxvvK6pxYcbXFnR8pRrBz1Rh7Me1OFTqbPFrLttjhzS/CpOW1TW5/5MsOxJfrUbUC1V4dK21yWO5FS6kLKhBVb3cvjdEnjfmWfdSUnReLBlvy3gdCO0oOgVdROYJJ97ZbkOf+NO/IrIsY9JnFhwpd+GbW4Kx414zuoTrMOqjOtTaLl4AeRUu/HWtFUOTml5bpKTOhbtXWPDyaBPWTDdjyS47vj1ob3z8z99a8PyoQIQFans1X5/TfaSmL0bWUiwZbwuOAia/DV53xnfV2GT8cbkFizKCEGk6/e90qMyFLcedeGuCCQM66dEzRo83J5hQYwOW7rFf4BUBp8v9mvOHB6JbZNNfuyPlMsIDJfwhzYgBnfQYcYke2adcAIBPd9sRoJcwpZfR82+U2i4gFMj4l+gUfoElI0K3YcCQWaJT0Hk8sMqKCT0MGNXN0OT+eof7T5PhdPHodRIC9MDG/AvvMnvyp3rEmiXclR7Q7LEeUTrU2WXsKHCizCIj84QTfeL1KLPIeOIHK94YZ2r/myLPGv0PICJRdAq/wJIR5don3FfNI5+ybI8d2wuceG5UYLPHUmN06BIuYe56K8otMmxOGc9vrEdhjYyCGtd5X3NTvgPv7bBjUca5yyIySMKSSUG4/WsLBi6qwe2XGzEm2YC/rrXiLwMDcLTChb4La5D2Zg3+k33hERN5QY/rgP53iU7hNwwXfwopwhAATH0PeGcYYK+7+PNJcccqXXhotRVrpwc3Ga38zqiX8OXNwbjr/yyIerEaegkY1U2Pccnn/zWqrpcx/SsLFmWYEBN8/m26yb2MmHzGLrEfcx3YXezEG+NNSH69BkunBqFDiISB79bimi56xJm5fShEdDIw9V3NX+2yNSRZlnnIikhbFwMreZEzX/D1fjsmf2aB/ozPD6fsnj3TSUD946HQ69wPVlrdI5lYsw6D3q1B/wQ9/j0hqNlr7ix0ou/C2iav6Wr4jdNJwIFZIege1bQw6h0y+i6sxcdTgmDQAaM+rEPxo6EAgAGLavDENYHI6Mk5Gq8LCAXuWQ/E9hSdxK9wJCNa/5nAoXXAgW9FJ9G8kZcYsPvPTU+YvfMbC1Jj9PjbVQGNBQMA4SYJgIRDpU5sPenCUyPOvSssNUbX7DUf/74e1TYZ/xprQmJ48y3ip36ux7hkA9IT9NhR4ITDdXo70O50Fx95mwRMeYcF0wYsGV9w/QLg7e1AdYHoJJoWGighLa7p4cVmo4TooNP3f7HXjlizhKRwHXYXOfHQaismpRpwXffTv0q3f2VBp1AJz40ywWRo/poRDUesnX0/AOwtduKzvQ7svNddTKkxOugkCe9tt6FDiIT9JS4M6Nj8+0hhwx8DUseLTuGXWDK+wBwN3LoMeH88YK8VnYYuoKDGhdlrbSiqkZEQKuH2PkbMG9b0IIH8Shd0bbiOkCzL+NNKK14dEwhzgLuIgowSPphkwgOrrKh3AG+MN6FTGOdjvCp1IjDsb6JT+C3OyfiSg2uApbcCcsvPICciBcWmAnevAwJDRSfxW9wk8iUpY4DxL4pOQUSAe12yWz5lwbQTS8bXDLgbGPKg6BRE2ibpgKmLgejuopP4PZaMLxr9pPsqe0QkxrXzgB6jRKdQBZaML5IkYNLbQOKVopMQac+lk4Ghs0WnUA2WjK8ymoBblwJRHK4TeU3HvsANb4pOoSosGV8WHAVM/4/7+uFEpKzYXsD05UBAsOgkqsKS8XVR3dzn0Bi4Ei+RYqK6Abd/496wI49iyfiDxIHuJS14DRoizwvr7C6Y0HjRSVSJJeMvet/gvtQrEXmOOc5dMBFJopOoFkvGnwy+H7juGdEpiNQhKBK47SsgJll0ElVjyfibIbOAcS+Bu86I2iEoCrj9/4AOaaKTqB7XLvNXW99vuA4N//mIWiU4xr2LjAXjFSwZf7bjE+D/ZgHy+S/9S0RnMMcCd6wA4nqJTqIZLBl/l/UF8PV9gMshOgmRbwuJdxcMLzzmVSwZNTiwGvhiBuCwiE5C5JtCE9wFE9NDdBLNYcmoRf4W4NObAWul6CREvqVDH/cJzeGdRCfRJJaMmhTtBT6eyss4E/2uVwYw+R0uFSMQS0ZtyvOAj6cApYdFJyESa+gc95L9Eg/3F4klo0a1JcDSW4DjmaKTEHmfPhC4fgFw+R9EJyGwZNTLYQPWzAUy3xWdhMh7zLHAHz4BkgaJTkINWDJql/U5sOIhwF4nOgmRsuIuBaYt4zpkPoYlowVF2cDnt3GehtQrZSww9V0gMFR0EjoLS0YrrFXAN/cD+1aITkLkWYNnAaOfAnRcitEXsWS0ZtPrwPr5XCGA/J/OCEx8FUi/TXQSugCWjBblbgL+cydQUyQ6CVHbxKQAk98GOvUTnYQugiWjVdWFwBd3Avm/iE5C1AoSMOg+YNTfAWOQ6DDUAiwZLXM6gHV/Bza/IToJ0cWFJwGT3gQuGSo6CbUCS4bcBwN8O4e7z8h39Z0OjH2eR4/5IZYMuVkqgHX/ALZ9AF4IjXxGSDyQ8TrQc6zoJNRGLBlqKn+L++TNU/tFJyGt6z3JffRYcJToJNQOLBlqzmEDNv0L2PAy4LCKTkNaY4oAJrwCXHaj6CTkASwZOr/SHGDlw8DRn0UnIa1IHgVc/wYQliA6CXkIS4YubuenwJr/BSxlopOQWoUnASOfAPrcJDoJeRhLhlqmthRY+7/ArqWik5CamMKBoX8FBt0LGAJFpyEFsGSodY78CKx8BCg7IjoJ+TN9ADDgHuCav3JiX+VYMtR6DhuwfQmw4Z9A9UnRacivSEDaFPeusciuosOQF7BkqO0c9cC2JcDGV1k2dHFJQ4DrngY6c70xLWHJUPs56t0ncW58FaguEJ2GfE10D2D0fCB1gugkJABLhjzHbj1dNjWFotOQaOZYYPhjQPoMQG8QnYYEYcmQ59mtwLb3gY2vsWy0KDYVGPgn4PJbgACz6DQkGEuGlGO3AFvfBza9xsU3VU8CUsa4D0Xufq3oMORDWDKkPLsF2P6Re1da8V7RaciTAsOAK/4IDLwHiO4uOg35IJYMedeJbe7C2fMlUF8lOg21VXSye5fYFdO4/D5dEEuGxLDVAdnfADs+AvI2iU5DLSK5d4Vd+Wf3GmOSJDoQ+QGWDIlXmuMum51LeaCALwqOAS6d7B65xKaITkN+hiVDvsPlBA595y6cg6sBl0N0Iu0K6wT0ynDfkgYDOr3oROSnWDLkm2qKgV3LgP3fAsczAdkpOpH6RXVvKJbrgU7p3B1GHsGSId9nrXQvzHl4PZDzPVB5THQi9Yi/7PSIJb636DSkQiwZ8j+nDgCH17lLJ28Tr97ZKhLQuf/pYonqJjoQqRxLhvyb3eIumsPr3beSA6IT+ZbAcKBTX6DzAPetU3/AHC06FWkIS4bUpeKYe9daYRZQlO0++dNSLjqVd0h6IK6Xe6TSqb+7VGJ7cm6FhGLJkPpVnQSK9ja9lRwEXHbRydonJL6hTBoKpWNfIDBEdCqiJlgypE1Ou7toirKBoj1AcTZQvN99no7TJjqdW1AUEN4ZCE9s+LPz6a8jEoHQDqITEl0US4bobPXVQF2p+1ZbevrvTW5lQF2J+++WckB2XeAFJcBgAowmwBDU9M8As/uclLNLJLwzVzAmVWDJELWXy9Ww6006Y/7jjL9LOs6LkGaxZIiISDE60QGIiEi9WDJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDJGGde3aFa+99proGKRiLBkiL5kxYwYkSWp2O3z4sOhoRIoxiA5ApCVjx47F+++/3+S+2NhYQWmIlMeRDJEXBQYGokOHDk1uer0eK1asQL9+/WAymdCtWzfMnz8fDoej8fskScLChQsxceJEBAcHo1evXti8eTMOHz6M4cOHw2w2Y/DgwcjJyWn8npycHNxwww2Ij49HSEgIBgwYgHXr1l0wX2VlJf70pz8hLi4OYWFhuPbaa7Fr1y7Ffh6kfiwZIsHWrFmD6dOn48EHH0R2djYWLlyIDz74AM8880yT5z311FO4/fbbsXPnTqSmpmLatGm49957MXfuXGzduhUAMGvWrMbn19TUYPz48Vi3bh127NiBMWPGICMjA/n5+efMIcsyJkyYgMLCQqxatQrbtm1Deno6Ro4cibKyMuV+AKRuMhF5xR133CHr9XrZbDY33m688UZ56NCh8rPPPtvkuR999JGckJDQ+DUA+fHHH2/8evPmzTIA+b333mu8b+nSpbLJZLpght69e8sLFixo/LpLly7yq6++KsuyLK9fv14OCwuTrVZrk+/p3r27vHDhwla/XyJZlmXOyRB50YgRI/DWW281fm02m5GcnIzMzMwmIxen0wmr1Yq6ujoEBwcDAPr06dP4eHx8PADgsssua3Kf1WpFVVUVwsLCUFtbi/nz52PlypU4efIkHA4HLBbLeUcy27ZtQ01NDaKjm16e2WKxNNkNR9QaLBkiL/q9VM7kcrkwf/58TJkypdnzTSZT49+NRmPj36WGSwec6z6Xy31tm0cffRRr1qzByy+/jOTkZAQFBeHGG2+EzXbui7K5XC4kJCTgxx9/bPZYREREy94g0VlYMkSCpaen48CBA83Kp702bNiAGTNmYPLkyQDcczS5ubkXzFFYWAiDwYCuXbt6NAtpF0uGSLAnnngCEydORGJiIm666SbodDpkZWVh9+7dePrpp9v8usnJyVi+fDkyMjIgSRLmzZvXOMo5l1GjRmHw4MGYNGkSXnjhBfTs2RMnT57EqlWrMGnSJPTv37/NWUi7eHQZkWBjxozBypUr8d1332HAgAG48sor8c9//hNdunRp1+u++uqriIyMxJAhQ5CRkYExY8YgPT39vM+XJAmrVq3CNddcg5kzZyIlJQW33HILcnNzG+eAiFqLV8YkIiLFcCRDRESKYckQEZFiWDJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDBERKYYlQ0REimHJEBGRYlgyRESkGJYMEREphiVDRESKYckQEZFiWDJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDBERKYYlQ0REimHJEBGRYlgyRESkGJYMEREphiVDRESKYckQEZFiWDJERKSY/w86N9JmkThK2AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
+    "labels = ['Male', 'Female']\n",
+    "sizes = [958,935]\n",
+    "plt.pie(sizes, labels=labels, autopct=\"%1.1f%%\")\n",
+    "plt.ylabel('Sex')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quartiles, Outliers and Boxplots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "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>Mouse ID</th>\n",
+       "      <th>Timepoint</th>\n",
+       "      <th>Tumor Volume (mm3)</th>\n",
+       "      <th>Metastatic Sites</th>\n",
+       "      <th>Drug Regimen</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age_months</th>\n",
+       "      <th>Weight (g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a203</td>\n",
+       "      <td>45</td>\n",
+       "      <td>67.973419</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Infubinol</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a251</td>\n",
+       "      <td>45</td>\n",
+       "      <td>65.525743</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Infubinol</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>a275</td>\n",
+       "      <td>45</td>\n",
+       "      <td>62.999356</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ceftamin</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>20</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>a411</td>\n",
+       "      <td>45</td>\n",
+       "      <td>38.407618</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ramicane</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>a444</td>\n",
+       "      <td>45</td>\n",
+       "      <td>43.047543</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ramicane</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>y769</td>\n",
+       "      <td>45</td>\n",
+       "      <td>68.594745</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Ceftamin</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>6</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>y793</td>\n",
+       "      <td>45</td>\n",
+       "      <td>31.896238</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Capomulin</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>y865</td>\n",
+       "      <td>45</td>\n",
+       "      <td>64.729837</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ceftamin</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>z578</td>\n",
+       "      <td>45</td>\n",
+       "      <td>30.638696</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ramicane</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>11</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>z581</td>\n",
+       "      <td>45</td>\n",
+       "      <td>62.754451</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Infubinol</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
+       "0      a203         45           67.973419                 2    Infubinol   \n",
+       "1      a251         45           65.525743                 1    Infubinol   \n",
+       "2      a275         45           62.999356                 3     Ceftamin   \n",
+       "3      a411         45           38.407618                 1     Ramicane   \n",
+       "4      a444         45           43.047543                 0     Ramicane   \n",
+       "..      ...        ...                 ...               ...          ...   \n",
+       "95     y769         45           68.594745                 4     Ceftamin   \n",
+       "96     y793         45           31.896238                 2    Capomulin   \n",
+       "97     y865         45           64.729837                 3     Ceftamin   \n",
+       "98     z578         45           30.638696                 0     Ramicane   \n",
+       "99     z581         45           62.754451                 3    Infubinol   \n",
+       "\n",
+       "       Sex  Age_months  Weight (g)  \n",
+       "0   Female          20          23  \n",
+       "1   Female          21          25  \n",
+       "2   Female          20          28  \n",
+       "3     Male           3          22  \n",
+       "4   Female          10          25  \n",
+       "..     ...         ...         ...  \n",
+       "95  Female           6          27  \n",
+       "96    Male          17          17  \n",
+       "97    Male          23          26  \n",
+       "98    Male          11          16  \n",
+       "99  Female          24          25  \n",
+       "\n",
+       "[100 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
+    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
+    "treatment_regimens = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
+    "filtered_df = mice_df[mice_df['Drug Regimen'].isin(treatment_regimens)]\n",
+    "\n",
+    "# Start by getting the last (greatest) timepoint for each mouse\n",
+    "timepoint_df = filtered_df.groupby(['Mouse ID'])['Timepoint'].max().reset_index()\n",
+    "timepoint_df\n",
+    "\n",
+    "# Merge this group df with the original DataFrame to get the tumor volume at the last timepoint\n",
+    "tumor_df = timepoint_df.merge(filtered_df,on=['Mouse ID', 'Timepoint'], how=\"left\")\n",
+    "tumor_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Capomulin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
+      "Ramicane's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
+      "Infubinol's potential outliers: 15    36.321346\n",
+      "Name: Tumor Volume (mm3), dtype: float64\n",
+      "Ceftamin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Put treatments into a list for for loop (and later for plot labels)\n",
+    "drug_list = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
+    "\n",
+    "# Create empty list to fill with tumor vol data (for plotting)\n",
+    "tumor_data = []\n",
+    "\n",
+    "# Locate the rows which contain mice on each drug and get the tumor volumes\n",
+    "for drug in drug_list:\n",
+    "    tumor_vol = tumor_df.loc[tumor_df['Drug Regimen'] == drug, 'Tumor Volume (mm3)']\n",
+    "\n",
+    "    # Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
+    "    quartiles = tumor_vol.quantile([0.25, 0.5, 0.75])\n",
+    "    lowerq = quartiles[0.25]\n",
+    "    upperq = quartiles[0.75]\n",
+    "    iqr = upperq - lowerq\n",
+    "        \n",
+    "    # add subset \n",
+    "    tumor_data.append(tumor_vol)\n",
+    "    \n",
+    "    # Determine outliers using upper and lower bounds\n",
+    "    lower_bound = lowerq - (1.5*iqr)\n",
+    "    upper_bound = upperq + (1.5*iqr)\n",
+    "\n",
+    "    outliers = tumor_vol.loc[(tumor_vol < lower_bound) | (tumor_vol > upper_bound)]\n",
+    "\n",
+    "    print(f\"{drug}'s potential outliers: {outliers}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a box plot that shows the distrubution of the tumor volume for each treatment group.\n",
+    "red = dict(markerfacecolor = 'red', markersize=12)\n",
+    "plt.ylabel('Final Tumor Volume (mm3)')\n",
+    "plt.boxplot(tumor_data, labels=['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin'],flierprops=red)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line and Scatter Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a line plot of tumor volume vs. time point for a single mouse treated with Capomulin\n",
+    "capomulin_df = clean_df.loc[clean_df['Drug Regimen'] == 'Capomulin']\n",
+    "single_mouse = capomulin_df.loc[capomulin_df['Mouse ID'] == 'l509']\n",
+    "x_axis = single_mouse[\"Timepoint\"]\n",
+    "y_axis = single_mouse[\"Tumor Volume (mm3)\"]\n",
+    "\n",
+    "plt.title('Capomulin treatment of mouse 1509')\n",
+    "plt.xlabel('Timepoint (days)')\n",
+    "plt.ylabel('Tumor Volume (mm3)')\n",
+    "plt.plot(x_axis, y_axis)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a scatter plot of mouse weight vs. the average observed tumor volume for the entire Capomulin regimen\n",
+    "capomulin_df = clean_df.loc[clean_df['Drug Regimen'] == 'Capomulin']\n",
+    "total_mice = capomulin_df.groupby(['Mouse ID']).mean(numeric_only=True)\n",
+    "x_axis = total_mice['Weight (g)']\n",
+    "y_axis = total_mice['Tumor Volume (mm3)']\n",
+    "\n",
+    "plt.xlabel('Weight (g)')\n",
+    "plt.ylabel('Average Tumor Volume (mm3)')\n",
+    "plt.scatter(x_axis, y_axis)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correlation and Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The correlation between mouse weight and the average tumor volume is: 0.84\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the correlation coefficient and a linear regression model \n",
+    "# for mouse weight and average observed tumor volume for the entire Capomulin regimen\n",
+    "capomulin_df = clean_df.loc[clean_df['Drug Regimen'] == 'Capomulin']\n",
+    "\n",
+    "mouse_weight = capomulin_df.groupby('Mouse ID')['Weight (g)'].mean()\n",
+    "avg_tumor_vol = capomulin_df.groupby('Mouse ID')['Tumor Volume (mm3)'].mean()\n",
+    "\n",
+    "correlation_coefficient = st.pearsonr(mouse_weight, avg_tumor_vol)[0]\n",
+    "rounded_coefficient = round(correlation_coefficient, 2)\n",
+    "print(f\"The correlation between mouse weight and the average tumor volume is: {rounded_coefficient}\")\n",
+    "\n",
+    "plt.xlabel('Weight (g)')\n",
+    "plt.ylabel('Average Tumor Volume (mm3)')\n",
+    "\n",
+    "slope, intercept, rvalue, pvalue, stderr = linregress(mouse_weight, avg_tumor_vol)\n",
+    "regress_values = mouse_weight * slope + intercept\n",
+    "\n",
+    "plt.scatter(mouse_weight, avg_tumor_vol)\n",
+    "plt.plot(mouse_weight, regress_values, color='r')\n",
+    "plt.xlabel('Weight (g)')\n",
+    "plt.ylabel('Average Tumor Volume (mm3)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}