Pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</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>1888</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>63.145652</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>65.841013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>69.176246</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70.314904</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1893 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0        k403     Ramicane  Male          21          16          0   \n",
       "1        k403     Ramicane  Male          21          16          5   \n",
       "2        k403     Ramicane  Male          21          16         10   \n",
       "3        k403     Ramicane  Male          21          16         15   \n",
       "4        k403     Ramicane  Male          21          16         20   \n",
       "...       ...          ...   ...         ...         ...        ...   \n",
       "1888     z969     Naftisol  Male           9          30         25   \n",
       "1889     z969     Naftisol  Male           9          30         30   \n",
       "1890     z969     Naftisol  Male           9          30         35   \n",
       "1891     z969     Naftisol  Male           9          30         40   \n",
       "1892     z969     Naftisol  Male           9          30         45   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "0              45.000000                 0  \n",
       "1              38.825898                 0  \n",
       "2              35.014271                 1  \n",
       "3              34.223992                 1  \n",
       "4              32.997729                 1  \n",
       "...                  ...               ...  \n",
       "1888           63.145652                 2  \n",
       "1889           65.841013                 3  \n",
       "1890           69.176246                 4  \n",
       "1891           70.314904                 4  \n",
       "1892           73.867845                 4  \n",
       "\n",
       "[1893 rows x 8 columns]"
      ]
     },
     "execution_count": 237,
     "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",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = os.path.join(\"data/Mouse_metadata.csv\")\n",
    "study_results_path = os.path.join(\"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 dataset\n",
    "merged_df = pd.merge(mouse_metadata, study_results, on = (\"Mouse ID\" , \"Mouse ID\"))\n",
    "\n",
    "# Display the data table for preview\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "mice_num = merged_df[\"Mouse ID\"].nunique()\n",
    "\n",
    "mice_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "dup_mice_ids = merged_df.loc[merged_df.duplicated(['Timepoint', 'Mouse ID']),'Mouse ID'].unique()\n",
    "\n",
    "dup_mice_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
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       "      <th>910</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "908     g989     Propriva  Female          21          26          0   \n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "910     g989     Propriva  Female          21          26          5   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "912     g989     Propriva  Female          21          26         10   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "914     g989     Propriva  Female          21          26         15   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "916     g989     Propriva  Female          21          26         20   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "918     g989     Propriva  Female          21          26         25   \n",
       "919     g989     Propriva  Female          21          26         30   \n",
       "920     g989     Propriva  Female          21          26         35   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "908           45.000000                 0  \n",
       "909           45.000000                 0  \n",
       "910           48.786801                 0  \n",
       "911           47.570392                 0  \n",
       "912           51.745156                 0  \n",
       "913           49.880528                 0  \n",
       "914           51.325852                 1  \n",
       "915           53.442020                 0  \n",
       "916           55.326122                 1  \n",
       "917           54.657650                 1  \n",
       "918           56.045564                 1  \n",
       "919           59.082294                 1  \n",
       "920           62.570880                 2  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "duplicate_mice_data = merged_df.loc[merged_df[\"Mouse ID\"] == \"g989\"]\n",
    "\n",
    "duplicate_mice_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "clean_df = merged_df[merged_df[\"Mouse ID\"]!= 'g989']\n",
    "\n",
    "clean_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "mice_num = clean_df[\"Mouse ID\"].nunique()\n",
    "\n",
    "mice_num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</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>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</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     Median   Variance       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      52.320930  50.446266  43.852013  6.622085  0.544332\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": 137,
     "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",
    "\n",
    "# This method is the most straighforward, creating multiple series and putting them all together at the end.\n",
    "vol_df = df1.groupby('Drug Regimen', as_index=True)\n",
    "mean_vol = vol_df['Tumor Volume (mm3)'].mean()\n",
    "#mean_vol\n",
    "\n",
    "median_vol = vol_df['Tumor Volume (mm3)'].median()\n",
    "#median_vol\n",
    "\n",
    "variance_vol = vol_df['Tumor Volume (mm3)'].var()\n",
    "#variance_vol\n",
    "\n",
    "std_vol = vol_df['Tumor Volume (mm3)'].std()\n",
    "#std_vol\n",
    "\n",
    "sem_vol = vol_df['Tumor Volume (mm3)'].sem()\n",
    "#sem_vol\n",
    "\n",
    "summary_df = pd.DataFrame({\"Mean\" : mean_vol, \"Median\": median_vol, \"Variance\" : variance_vol, \"Std\" : std_vol, \"SEM\": sem_vol})\n",
    "\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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</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>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</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     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      52.320930  50.446266  43.852013  6.622085  0.544332\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": 138,
     "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",
    "\n",
    "# This method produces everything in a single groupby function\n",
    "\n",
    "summary_df1 = vol_df.agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "summary_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Dataframe Plot Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Drug Regimen\n",
       "Capomulin    230\n",
       "Ceftamin     178\n",
       "Infubinol    178\n",
       "Ketapril     188\n",
       "Naftisol     186\n",
       "Placebo      181\n",
       "Propriva     148\n",
       "Ramicane     228\n",
       "Stelasyn     181\n",
       "Zoniferol    182\n",
       "Name: Drug Regimen, dtype: int64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pandas. \n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "mice_per_dr = vol_df[\"Drug Regimen\"].count()\n",
    "mice_per_dr.plot(kind = 'bar',rot = 0, title = 'Number of Mice per Drug Regimen')\n",
    "plt.ylabel(\"Number Of Mice\",fontsize = 12)\n",
    "plt.show()\n",
    "\n",
    "fig.savefig(\"graphs/mice_per_drug_barpandas.png\")\n",
    "mice_per_dr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Pyplot Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Drug Regimen\n",
       "Capomulin    230\n",
       "Ceftamin     178\n",
       "Infubinol    178\n",
       "Ketapril     188\n",
       "Naftisol     186\n",
       "Placebo      181\n",
       "Propriva     148\n",
       "Ramicane     228\n",
       "Stelasyn     181\n",
       "Zoniferol    182\n",
       "Name: Drug Regimen, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pyplot.\n",
    "fig1, ax1 = plt.subplots(figsize=(14, 7))\n",
    "drugs_name = list(vol_df[\"Drug Regimen\"].unique().str[0])\n",
    "num_of_mice = vol_df[\"Drug Regimen\"].count()\n",
    "plt.bar(drugs_name, mice_per_DR, width = 0.5, align = \"center\", color = \"blue\")\n",
    "plt.title('Number of Mice per Drug Regimen')\n",
    "plt.xlabel(\"Drug Regimen\",fontsize = 12)\n",
    "plt.ylabel(\"Number Of Mice\",fontsize = 12)\n",
    "plt.show()\n",
    "\n",
    "fig1.savefig(\"graphs/mice_per_drug_barpylot.png\")\n",
    "num_of_mice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Dataframe Plot Pie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Male      958\n",
       "Female    922\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "fig2, ax2 = plt.subplots(figsize=(7, 7))\n",
    "sex_df = clean_df[\"Sex\"].value_counts()\n",
    "sex_df.plot(kind = \"pie\",autopct='%1.2f%%',\n",
    "            title = \"Distribution of Female versus Male Mice\", fontsize = 14, startangle=90)\n",
    "plt.ylabel(\"\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n",
    "\n",
    "fig2.savefig(\"graphs/female_vs_male_piepandas.png\")\n",
    "sex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Pyplot Pie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Male      958\n",
       "Female    922\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "fig3, ax3 = plt.subplots(figsize =(7,7))\n",
    "labels = clean_df[\"Sex\"].unique()\n",
    "plt.pie(sex_df,autopct='%1.2f%%', labels=labels,colors = (\"r\", \"g\"),textprops={'fontsize': 14}, startangle=90)\n",
    "plt.title(\"Distribution of Female versus Male Mice\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n",
    "\n",
    "fig3.savefig(\"graphs/female_vs_male_piepyplot.png\")\n",
    "sex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a411</td>\n",
       "      <td>45</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>38.407618</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a444</td>\n",
       "      <td>45</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>43.047543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint Drug Regimen     Sex  Age_months  Weight (g)  \\\n",
       "0     a203         45    Infubinol  Female          20          23   \n",
       "1     a251         45    Infubinol  Female          21          25   \n",
       "2     a275         45     Ceftamin  Female          20          28   \n",
       "3     a411         45     Ramicane    Male           3          22   \n",
       "4     a444         45     Ramicane  Female          10          25   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           67.973419                 2  \n",
       "1           65.525743                 1  \n",
       "2           62.999356                 3  \n",
       "3           38.407618                 1  \n",
       "4           43.047543                 0  "
      ]
     },
     "execution_count": 183,
     "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",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "four_drug_regimen = clean_df.loc[df1[\"Drug Regimen\"].isin([\"Capomulin\",\"Ramicane\", \"Infubinol\", \"Ceftamin\"]),[\"Mouse ID\", \"Timepoint\"]]\n",
    "time_point = four_drug_regimen.groupby(['Mouse ID'])[\"Timepoint\"].max()\n",
    "time_point = time_point.reset_index()\n",
    "\n",
    "merged_df2 = pd.merge(time_point, clean_df, on = (\"Mouse ID\",\"Timepoint\"), how = 'left')\n",
    "\n",
    "merged_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Q1 of tumor vol of mice on Infubinol is 54.04860769.\n",
      "The Q2 of tumor vol of mice on Infubinol is 60.16518046.\n",
      "The Q3 of tumor vol of mice on Infubinol is 65.52574285.\n",
      "The IQR of tumor vol of mice on Infubinol is 11.477135160000003.\n",
      "Values below 36.83290494999999 on Infubinol could be outliers.\n",
      "Values above 48.310040109999996 on Infubinol could be outliers.\n",
      "The Q1 of tumor vol of mice on Ceftamin is 48.72207785.\n",
      "The Q2 of tumor vol of mice on Ceftamin is 59.85195552.\n",
      "The Q3 of tumor vol of mice on Ceftamin is 64.29983003.\n",
      "The IQR of tumor vol of mice on Ceftamin is 15.577752179999997.\n",
      "Values below 25.355449580000002 on Ceftamin could be outliers.\n",
      "Values above 40.93320176 on Ceftamin could be outliers.\n",
      "The Q1 of tumor vol of mice on Ramicane is 31.56046955.\n",
      "The Q2 of tumor vol of mice on Ramicane is 36.56165229.\n",
      "The Q3 of tumor vol of mice on Ramicane is 40.65900627.\n",
      "The IQR of tumor vol of mice on Ramicane is 9.098536719999998.\n",
      "Values below 17.912664470000003 on Ramicane could be outliers.\n",
      "Values above 27.01120119 on Ramicane could be outliers.\n",
      "The Q1 of tumor vol of mice on Capomulin is 32.37735684.\n",
      "The Q2 of tumor vol of mice on Capomulin is 38.125164399999996.\n",
      "The Q3 of tumor vol of mice on Capomulin is 40.1592203.\n",
      "The IQR of tumor vol of mice on Capomulin is 7.781863460000004.\n",
      "Values below 20.70456164999999 on Capomulin could be outliers.\n",
      "Values above 28.486425109999995 on Capomulin could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "drug_list = ['Infubinol', 'Ceftamin', 'Ramicane', 'Capomulin']\n",
    "\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumor_vol_data = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "for drug in drug_list:\n",
    "    data = merged_df2.loc[merged_df2[\"Drug Regimen\"] == drug ,\"Tumor Volume (mm3)\"]\n",
    "    # First quartile (Q1) \n",
    "    Q1 = np.percentile(data, 25, interpolation = 'midpoint') \n",
    "    \n",
    "    # Second quartile (Q2)\n",
    "    Q2 = np.percentile(data, 50, interpolation = 'midpoint') \n",
    "    \n",
    "    # Third quartile (Q3) \n",
    "    Q3 = np.percentile(data, 75, interpolation = 'midpoint') \n",
    "  \n",
    "    # Interquaritle range (IQR) \n",
    "    IQR = Q3 - Q1 \n",
    "    print(f\"The Q1 of tumor vol of mice on {drug} is {Q1}.\")\n",
    "    print(f\"The Q2 of tumor vol of mice on {drug} is {Q2}.\")\n",
    "    print(f\"The Q3 of tumor vol of mice on {drug} is {Q3}.\")\n",
    "    print(f\"The IQR of tumor vol of mice on {drug} is {IQR}.\")\n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes\n",
    "    tumor_vol_data.append(data)\n",
    "    #print(tumor_vol_data)\n",
    "    \n",
    "    # Determine outliers using upper and lower bounds\n",
    "    lower_bound = Q1 - (1.5 * IQR)\n",
    "    upper_bound = Q3 - (1.5 * IQR)\n",
    "    print(f\"Values below {lower_bound} on {drug} could be outliers.\")\n",
    "    print(f\"Values above {upper_bound} on {drug} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "blue_square = dict(markerfacecolor = \"b\", marker = \"s\")\n",
    "fig4, ax4 = plt.subplots(figsize =(14, 7))\n",
    "ax4.set_title(\"Tumor Volume of Each Mouse Across Four Regimens\")\n",
    "ax4.set_xlabel(\"Drug Regimen\",fontsize = 12)\n",
    "ax4.set_ylabel(\"Tumor Volume (mm3)\",fontsize = 12)\n",
    "ax4.set_ylim(10,80)\n",
    "ax4.boxplot(tumor_vol_data, flierprops = blue_square, whis = 0.75, labels = drug_list)\n",
    "plt.show()\n",
    "\n",
    "fig4.savefig(\"graphs/tumor_vol_for_four_regimens.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Pyplot Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the mouse id\n",
    "mouse = clean_df.loc[clean_df[\"Drug Regimen\"] == \"Capomulin\", [\"Mouse ID\"]]\n",
    "#mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin\n",
    "time_tumor_capo = clean_df.loc[clean_df[\"Mouse ID\"] == \"i557\", :]\n",
    "#time_tumor_capo\n",
    "xaxis = time_tumor_capo[\"Timepoint\"]\n",
    "yaxis = time_tumor_capo[\"Tumor Volume (mm3)\"]\n",
    "fig5, ax5 = plt.subplots(figsize =(14, 7))\n",
    "plt.plot(xaxis, yaxis, marker = 'o',markersize = 10, linestyle = '-')\n",
    "plt.title(\"Timepoint versus Tumor Volume of Mouse i557 for Capomulin\")\n",
    "plt.xlabel(\"Timepoint\", fontsize = 12)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\",fontsize = 12 )\n",
    "plt.show()\n",
    "\n",
    "fig5.savefig(\"graphs/timepoint_v_tumor_i557_lineplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Pyplot Scatter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vUhwPP6GYkOQzFK02V1G8v2O7VX6cIkDfSPGZP6ccQ3eUYvKB36U4Pv8ReEFm7m+0vjFeQnGMj3QT/NgMH0fSLImc/pT8kgTc1m3kGmBLZu6can9VJyK+AbwrM8ebQliqtYh4DXDfzLzDlxuSFj9bgCRNS0T8TkSsKrvSXEDRp/7rc1xW7UXEY6NYi+SkKNameTDFIHxJkjRKpQGo7P+6JyI+VV7fEBFfj2Jxvl0R8fCpHkPSnHsUxeD6ka5LT59kqmJV59couqgdohhT8/vlWCNJkjRKpV3gIuLPgS6gPTOfEhGfA96cmZ+JiCcDr8jMx1VWkCRJkqRaqawFqJxF5izg3aM2J7fPyrKS46fAlCRJkqRZVeU6QG+hWFBt9LSrLwX+MyLeRBHGHj3eHSPiXIp1S1ixYsWmdevWNblUSZIkSQtVd3f3DZk57mLUlQSgiHgK0JuZ3eV8+iO2Ai/LzH+PiGcB76GYLvY4mXkxcDFAV1dX7tq1q4KqJUmSJC1EEXHVRLdV1QL0GOCp5TifNqA9It5PMYD6JeU+H+H47nGSJEmSNKsqGQOUmedn5umZeQbFomlfKufe/wW3r4h8JsWChJIkSZLUFFWOARrPnwBvjYiTKFZpPneO65EkSZK0iFUegDLzcuDy8uevApuqrkGSJElSPVW6EKokSZIkzSUDkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTaqDQARcTSiNgTEZ8ate3PIuKHEfG9iHhjlfVIkiRJqpeTKn6+lwA/ANoBImIz8DTgwZk5GBEdFdcjSZIkqUYqawGKiNOBs4B3j9q8FXhDZg4CZGZvVfVIkiRJqp8qu8C9BXgFMDxq2/2B34yIb0TElyPiYePdMSLOjYhdEbHr+uuvr6JWSZIkSQ3ITHZffSOf/e517L76RjJzrkuaVCVd4CLiKUBvZnZHxOPGPP8pwCOBhwGXRsS9c8y7lpkXAxcDdHV1ze93VJIkSaqJnft7uWDHPvqPDBERDGeycnkL285ez+Z183N0S1UtQI8BnhoRVwIfAs6MiPcD1wCXZeGbFK1Dp1ZUkyRJkqQZ2rm/l63buzlwaIBbjh7j5sFbOXz0GAcODbB1ezc798/P0S2VBKDMPD8zT8/MM4DnAF/KzOcBHwPOBIiI+wPLgBuqqEmSJEnSzGQm51+2j4Gh4XFvHxga5oId++Zld7i5XgfovcC9I+K7FC1DLxzb/U2SJEnS/LKnp4+bBoYm3af/yBB7e/oqqqhxVU+DTWZeDlxe/nwUeF7VNUiSJEmaud7+QSJi0n0igoP9gxVV1Li5bgGSJEmStMB0tLcyPEXHrcxkdXtrRRU1zgAkSZIkaVo2dq6iva1l0n3al7ewoXNVRRU1zgAkSZIkaVoiggvPWU9by/hxoq1lCdvOXj9lN7m5YACSJEmSNG2b13Vw0ZZNrFnZxoplSzm59SRWLFvKmpVtXLRl07xdB6jySRAkSZIkLQ6b13VwxXlnsrenj4P9g6xub2VD56p52fIzwgAkSZIkacYigo1rT5nrMhpmFzhJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtXHSXBcgSZIkzXeZyZ6ePnr7B+lob2Vj5yoiYq7L0gwYgCRJkqRJ7NzfywU79tF/ZIiIYDiTlctb2Hb2ejav65jr8jRNdoGTJEmSJrBzfy9bt3dz4NAAtxw9xs2Dt3L46DEOHBpg6/Zudu7vnesSNU0GIEmSJGkcmcn5l+1jYGh43NsHhoa5YMc+MrPiynQiDECSJEnSOPb09HHTwNCk+/QfGWJvT19FFWk2OAZIkiTVioPZ1aje/sEpj42I4GD/YEUVaTYYgCRJUm04mF3T0dHeyvAU3dsyk9XtrRVVpNlgFzhJklQLDmbXdG3sXEV7W8uk+7Qvb2FD56qKKtJsMABJkqRFz8HsmomI4MJz1tPWMv4pc1vLEradvd4ulAuMAUiSJC16DmZvTGay++ob+ex3r2P31TcaCIHN6zq4aMsm1qxsY8WypZzcehIrli1lzco2Ltqyya6TC5BjgCRJWoQc6H88B7NPzfFRE9u8roMrzjuTvT19HOwfZHV7Kxtq/playAxAkiQtMp7I3pGD2Sc3Mj5qbBfBw0ePsXV7ty0dFAF549pT5roMzQK7wEmStIg40H98DmafmOOjVDcGIEmSFglPZCfmYPaJOT5KdWMAkiRpkfBEdnIOZh+f46NUN44BkiRpkfBEdmoOZr8jx0epbgxAkiQtEp7INsbB7McbGR91+OixCfep6/goLU52gZMkaZFwoL9mwvFRqhsDkCRJi4Qnspopx0epTmKhzQTT1dWVu3btmusyJEmat8auA5SZtNd8HSA1JjMdH6VFISK6M7Nr3NsMQJIkLT6eyEqqs8kCkJMgSJK0CDnQX5LG5xggSZIkSbVhAJIkSZJUG5UGoIhYGhF7IuJTY7a/PCIyIk6tsh5JkiRJ9VJ1C9BLgB+M3hARncBvA1dXXIskSZKkmqksAEXE6cBZwLvH3PRm4BXAwpqOTpIkSdKCU2UL0Fsogs7wyIaIeCpwbWZ+e7I7RsS5EbErInZdf/31TS5TkiRJ0mJVSQCKiKcAvZnZPWrbnYBXAa+e6v6ZeXFmdmVm12mnndbESiVJkiQtZlWtA/QY4KkR8WSgDWgH3gfcC/h2uTDb6cDuiHh4Zl5XUV2SJEmSaqSSAJSZ5wPnA0TE44CXZ+YzRu8TEVcCXZl5QxU1SZIkSaof1wGSJEmSVBtVdYG7TWZeDlw+zvYzqq5FkiRJUr3YAiRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmqj8mmwJUmSJC0emcmenj56+wfpaG9lY+cqImKuy5qQAUiSJEnSjOzc38sFO/bRf2SIiGA4k5XLW9h29no2r+uY6/LGZRc4SZIkSdO2c38vW7d3c+DQALccPcbNg7dy+OgxDhwaYOv2bnbu753rEsdlAJIkSZI0LZnJ+ZftY2BoeNzbB4aGuWDHPjKz4sqmZgCSJGkRykx2X30jn/3udey++sZ5eRIiaeHa09PHTQNDk+7Tf2SIvT19FVXUOMcASZK0yCzEPvmSFpbe/sEpJzqICA72D1ZUUeNsAZIkaRFZqH3yJS0sHe2tDE/RspyZrG5vraiixk0agCLi1Ij484j4YkTcEBFD5b9fjIiXR8RpVRUqSZImt5D75EtaWDZ2rqK9rWXSfdqXt7Chc1VFFTVuwgAUERcCe4BfA94D/DbwgPLf9wD3A3ZHxBsqqFOSJE1hIffJl7SwRAQXnrOetpbx40RbyxK2nb1+Xq4HNNkYoF8A983M8Tru7QE+EBFtwP9sSmWSJGlaFnKffEkLz+Z1HVy0ZdNxYw4zk/Z5PuZwwgCUmW+f6s6ZOQC8Y1YrkiRJM7KQ++RLWpg2r+vgivPOZG9PHwf7B1nd3sqGzlXzsuVnxJSzwEXEEuAumXlDeX09cG/gG5l5XZPrkyRJDRrpk3/46LEJ95mvffIlLVwRwca1p8x1GQ2bahKERwPXAwcj4l0R8Xzgw8CbgB9GxGMqqFGSJDVgIffJl6SqTDUN9t8Bfww8HfgT4GhmPjAz7wdsA17X5PokSdI0jPTJX7OyjRXLlnJy60msWLaUNSvbuGjLpnnbJ1+SqhKTTYUZEb/KzLtE8VXRAHDnzDxa3tYGXJOZp1ZTaqGrqyt37dpV5VNKkrTgZOaC6pMvSbMpIrozs2u826YaAzQMkJkZEftHwk/pGDD55N+SJGlOLLQ++ZJUlakC0P6IuF9m/jgzHzLmtocDP29SXZIkSapYZrKnp4/e/kE62lvZaMuhFqGpAtDTgJsnuO1W4MWzW44kSZLmws79vcet5zKcycp5vp6LNBOTToKQmb+cYCFUMvMbmfmV5pQlSZKkquzc38vW7d0cODTALUePcfPgrRw+eowDhwbYur2bnft757pEadZMuQ4QQESspGjt2QicPPq2zHxiE+qSJElSBTKT8y/bx8DQ8Li3DwwNc8GOfVxx3pl2h9Oi0FAAAj4CLAV2AEeaV44kSZKqtKenj5sGhibdp//IEHt7+pxYQ4tCowHokcBdM3PyT4ckSZIWlN7+wSlbdiKCg/3jjoqQFpypFkId8VXgAc0sRJIkSdXraG9leJJ1IaHoJre6vbWiiqTmarQF6A+BT0fEN4CDo2/IzL+e7aIkSZJUjY2dq2hva+Hw0WMT7tO+vIUNnasqrEpqnkZbgF4PdAKrgfuNuty3SXVJkkqZye6rb+Sz372O3VffSE7xTa0kTUdEcOE562lrGf+0sK1lCdvOXu8ECFo0Gm0Beg5w/8w80MxiJEnHc10OSVXYvK6Di7ZsOu7vTWbS7t8bLULRyDeJEfFt4PGZeUPzS5pcV1dX7tq1a67LkKSmG1mXY7ypadtalnDRlk2elEiaVZnJ3p4+DvYPsrq9lQ2dq2z50YIUEd2Z2TXebY22AL0P+EREvJ07jgH60gnWJ0kaw3U5JM2FiHCqay16jQag/1P+u23M9gTuPXvlSJLAdTkkSWqWhgJQZt6r2YVIkm7nuhySJDVHo7PASZIq5LockiQ1R0MBKCIeEhFfiohfRcTR8jIUEUebXaAk1dHIuhyTcV0OSZKmr9EWoA8C/w38FvCA8rKu/FeSNMtcl0OSpOZodBKEuwGvTlffk6TKuC6HJEmzr9EAdAnwXGB7E2uRJI2xeV0HV5x3putySJI0SxoNQG8AvhYRF3DHdYDOnPWqJEm3cV0OSZJmT6MB6KPAz4EdwJHmlSNJkiRJzdNoANoA3DUznfVNkiRJ0oLVaAD6L+CBwN4m1iJJGkdmsqenj97+QTraW9noGCDphPiZkuqt0QD0c+BzEbGDO44BenWjTxYRS4FdwLWZ+ZSI+Dvg94CjwE+BP8rMvkYfT5IWu537e4+bBW44k5XOAifNmJ8pSY2uA3Qn4D+AZUDnqMvp03y+lwA/GHX988CDMvPBwI+A86f5eJK0aO3c38vW7d0cODTALUePcfPgrRw+eowDhwbYur2bnft757pEaUHxMyUJGgxAmflHE1z+uNEniojTgbOAd4963M9l5q3l1a8z/UAlSYtSZnL+ZfsYGBoe9/aBoWEu2LEPl2eTGuNnStKICQNQRDTUDhwRqxt8rrcArwDG/8sDfwx8ZoLnODcidkXEruuvv77Bp5OkhWtPTx83DQxNuk//kSH29thrWGqEnylJIyZrAdoZEf8YEY+KiOP2i4glEfHIiPhH4ItTPUlEPAXozczuCW5/FXArEyy0mpkXZ2ZXZnaddtppUz2dJC14vf2DUw7KjggO9g9WVJG0sPmZkjRisgC0Efg+cDFwU0Tsi4grImIfcBPwLmAf8NAGnucxwFMj4krgQ8CZEfF+gIh4IfAUYEva7ixJAHS0tzI8xZ/EzGR1e2tFFUkLm58pSSMmnAWuXPPnHcA7IqITWA+sAm4EvpOZ1zb6JJl5PuUEBxHxOODlmfm8iHgS8ErgsZl5eMavQpIWmY2dq2hva+Hw0WMT7tO+vIUNnasqrEpauPxMSRrR6CQIPZn56cz8QGZ+ZjrhZwrvAO4MfD4i9oOd08IAAB8RSURBVEbEu2bpcSVpQYsILjxnPW0t4/+ZbmtZwraz17t2idQgP1OSRsRC63XW1dWVu3btmusyJKkSY9csyUzaXbNEmjE/U1I9RER3ZnaNe5sBSJLmt8xkb08fB/sHWd3eygZXrZdOiJ8pafGbLABNOAZIkjQ/RAQb154y12VogclM9vT00ds/SEd7Kxs9yb+Nnymp3qYVgMrpsFdn5oEm1SNJkk7Q2G5ew5mstJuXJAENToIQEasi4gPAAPCTcttTI+J1zSxOkiRNz879vWzd3s2BQwPccvQYNw/eyuGjxzhwaICt27vZub93rkuUpDnVUACiWPPnEHBP4Gi57WvAs5tRlCRJmr7M5PzL9jEwNDzu7QNDw1ywYx8LbfyvJM2mRrvAPR64e2YORUQCZOb1EWE7uiRJ88Senj5uGhiadJ/+I0Ps7elzDIyk2mo0AB0CTgVuG/sTEWtHX5ckSXOrt39wyokOIoKD/YMVVaSFxskzVAeNBqB3A/8eEa8ClkTEo4BtFF3jJEnSPNDR3srwFN3bMpPV7a0VVaSFxMkzVBeNjgH6W+BS4J1AC/Be4OPAW5tUlyRJmqaNnatob2uZdJ/25S1s6FxVUUVaKJw8Q3XSUADKwlsy84GZuSIzH1BedxSlJEnzRETwnId3TrrPsx/WaZcmHcfJM1Q3jbYAERFnlFNfP3f0pZnFSZKkxmUmH/pmz6T7fPhbPZ7I6jjTmTxDWgwaGgMUEecDrwa+BxwZdVMCH2hCXZIkaZqcBU4z4eQZqptGJ0H4C2BTZn6/mcVIkqSZ80RWM+HkGaqbRrvA/RK4sol1SJKkE+SJrGbCyTNUN40GoJcCF0dEV0SsHX1pZnGSJKlxnshqJiKCC89ZT1vL+KeFbS1L2Hb2eifP0KLRaABaBjwR+CZFS9DI5efNKEqSJE2fJ7Kaqc3rOrhoyybWrGxjxbKlnNx6EiuWLWXNyjYu2rLJdYC0qEQjM8FExLXAXwEf4vhJEMjMY80pbXxdXV25a9euKp9SkqQFZeyClplJuwtaqgGZyd6ePg72D7K6vZUNnasMzFqQIqI7M7vGva3BAHQQuHvVYWc8BiBJkqbmiaykOpssADU6C9ybgPMiYpuLn0qSNP9FhFNdS9I4Gg1ALwbuBlwQEb8cfUNmOhGCJEmSpAWh0QD0vKZWIUmSJEkVaCgAZeaXm12IJEmSJDVbQwEoIv56otsy89WzV44kSZIkNU+jXeA6x1y/G/BYYMfsliNJkiRJzdNoF7g/GrstIp4E/MGsVyRJkiRJTTL+UtGN+Rzw9NkqRJIkSZKardExQPces+lOwHOBnlmvSJIkSZKapNExQD8BEhhZQvowsAd4YTOKkiRJkqRmaHQM0Il0lZMkSZKkecFgI0mSJKk2JmwBiogeim5vk8rMtbNakSRJkiQ1yWRd4J5XWRWSJEmSVIEJA1BmfrnKQiRJkiSp2RoaAxQRLRHx2oj4WUQMlP++NiKWNbtASZIkSZotjU6D/Ubg4cCLgKuAewL/D2gHXtac0iRJkiRpdjUagJ4JPCQzf1le/2FE7Aa+jQFIkiRJ0gLR6DTYMc3tkiRJkjTvNBqAPgJ8MiJ+JyIeEBFPAj4GXNq80iRJkiRpdk3aBS4ilmTmMPAK4C+BdwJ3B64FPgS8rukVSpIkSdIsmWoM0LUR8T7g3zLz1cCrK6hJkiRJkppiqi5wLwLuBXwrInZHxIsj4tQK6pIkSZKkWTdpAMrMj2fmM4E1wD8BzwKuiYhPRMQzIqKliiIlSdL0ZCa7r76Rz373OnZffSOZOdclSdK80NA02JnZRxGA/iki7gU8H3hzuc0WIUmS5pGd+3u5YMc++o8MEREMZ7JyeQvbzl7P5nUdc12eJM2pRmeBAyAiWoGHAY8AVgP7mlGUJEmamZ37e9m6vZsDhwa45egxbh68lcNHj3Hg0ABbt3ezc3/vXJcoSXOqoQAUEb8RERcDBylmfvs6cP/M3NzM4iRJUuMyk/Mv28fA0PC4tw8MDXPBjn12h5NUa5MGoIh4TUT8FPhkuemszLx/Zv5NZl7V/PIkSVKj9vT0cdPA0KT79B8ZYm9PX0UVSdL8M9UYoEcCrwI+lpkDJ/pkEbEU2AVcm5lPiYi7AB8GzgCuBJ6VmTee6PNIklRHvf2DRMSk+0QEB/sHK6pIkuafqWaBe1Jmfmg2wk/pJcAPRl0/D/hiZt4P+GJ5XVqUnJFJUrN1tLcyPMXflsxkdXtrRRVJ0vzT0CxwsyEiTgfOAl4P/Hm5+WnA48qfLwEuB15ZVU1SVZyRSVIVNnauor2thcNHj024T/vyFjZ0rqqwKkmaX6Y1C9wJegvwCmD0yMzVmXkAoPx33DPBiDg3InZFxK7rr7+++ZVKs8gZmSRVJSK48Jz1tLWM/997W8sStp29fspucpK0mE0ZgKJw73L8zoxExFOA3szsnsn9M/PizOzKzK7TTjttpmVIlXNGJklV27yug4u2bGLNyjZWLFvKya0nsWLZUtasbOOiLZtsdZZUe1N2gcvMjIh9wJ1P4HkeAzw1Ip4MtAHtEfF+4GBErMnMAxGxBvCrcC0q05mRaePaUyqqStJit3ldB1ecdyZ7e/o42D/I6vZWNnSusuVHkmi8C9we4P4zfZLMPD8zT8/MM4DnAF/KzOcBnwBeWO72QuDjM30OaT5yRiZJcyUi2Lj2FJ70oLuxce0phh9JKjU6CcLlwGcj4l+BHuC2/jqZ+d4TeP43AJdGxP8ArgaeeQKPJc07zsgkSZI0vzQagB4D/Bx47JjtCUwrAGXm5RSBisz8JfD46dxfWkickUmSJGl+aSgAZebmZhciLUYjMzJt3d497kQIzsgkSZJUrYanwY6IUyLiBRFxfvmvI7alBjgjkyRJ0vwRjUy/GxGPAv4D2A9cBawFHgCclZlfa2qFY3R1deWuXbuqfEppVmSmMzJJkiRVICK6M7NrvNsaHQP0FuB/Z+aHRj3os4G3AQ878RKlxW9kRiZJkiTNnUa7wN0fuHTMto8C953dciRJkiSpeRoNQD+mWL9ntGcCP53dciRJkiSpeRrtAvdS4FMR8WKKMUBnAPcDntKkuiRJkiRp1jU6DfYVEXEf4Czg7sAngU9n5q+aWZy0mGQme3r66O0fpKO9lY1OgiBJklS5RluAyMwbgfc3sRZp0dq5v5cLduyj/8gQEcFwJiuXt7Dt7PVOgy1JklShhsYARcTaiHhPROyOiB+NvjS7QGmh27m/l63buzlwaIBbjh7j5sFbOXz0GAcODbB1ezc79/fOdYmSJEm10WgL0Eco1gB6NXCkeeVIi0tmcv5l+xgYGh739oGhYS7YsY8rzjvT7nCSJEkVaDQArQMelZnjn8VJGteenj5uGhiadJ/+I0Ps7elzjSBJkqQKNDoN9ieBxzazEGkx6u0fnLJlJyI42D9YUUXS4pKZ7L76Rj773evYffWNZOZclyRJmucabQF6MXBFRPwUODj6hsz841mvSlokOtpbGZ7ihCwzWd3eWlFF0uLh5CKSpJlotAXoX4BjwA+Aa8dcJE1gY+cq2ttaJt2nfXkLGzpXVVSRtDg4uYgkaaYabQE6E7h7Zt7UzGKkxSYiuPCc9Wzd3j3uRAhtLUvYdvZ6J0DQpFxD6nhOLiJJOhGNBqDvAHcFDEDSNG1e18FFWzYd11UnM2m3q44aYDevO3JyEUnSiWg0AH0J+FxE/At3HAP03lmvSlpkNq/r4IrzzmRvTx8H+wdZ3d7Khpp/i6+pjXTzGtvScfjoMbZu7+aiLZtqGYKcXESSdCIaDUC/QTHe54ljtidgAJIaEBF+G62G2c1rYk4uIkk6EQ0FoMzc3OxCJEm3s5vXxEYmFzl89NiE+zi5iCRpIg3NAhcRSya6NLtASaoju3lNbGRykbaW8f8LcnIRSdJkGg0wtwJDE1wkSbPMbl6TG5lcZM3KNlYsW8rJrSexYtlS1qxsq+3YKElSYxodA3SvMdfXAOcBn5zdciRJYDevRji5iCRpJhodA3TVmE1XRcQLgW8B75n1qiSp5lxDqjFOLiJJmq4TGcPTDpw2W4VIko5nNy9JkmbfpC1AEfEHmfnBiHgfxZTXI+4E/Bbw/mYWJ0l1ZzcvSZJm11Rd4P4J+CDwkzHbbwHelZlfaEpVkqTb2M1LkqTZM1UACoDMfG0FtUiSJElSU00VgJZGxGbKIDSezPzS7JYkSZIkSc0xVQBqpZjlbaIAlMC9Z7UiaZHKTPb09NHbP0hHeysbHcchSZJUuakC0C2ZacCRTtDO/b1csGMf/UeGiAiGM1m5vIVtZ693Ji9JkqQKncg02JIasHN/L1u3d3Pg0AC3HD3GzYO3cvjoMQ4cGmDr9m527u+d6xIlSZJqY6oAZP8c6QRkJudftm/chSwBBoaGuWDHPjJz3NslSZI0uyYNQJl556oKkRajPT193DQwNOk+/UeG2NvTV1FFkiRJ9WYXOKmJevsHp5zoICI42D9YUUWSJEn1ZgCSmqijvZXhKbq3ZSar21srqkiSJKneDEBSE23sXEV7W8uk+7Qvb2FD56qKKpIkSao3A5DURBHBheesp61l/I9aW8sStp293vWAJEmSKmIAkpps87oOLtqyiTUr21ixbCknt57EimVLWbOyjYu2bHIdIEmSpApNtRCqpFmweV0HV5x3Jnt7+jjYP8jq9lY2dK6y5UeSJKliBiCpIhHBxrWnzHUZkiRJtWYXOEmSJEm1YQCSJEmSVBsGIEmSJEm1YQCSJEmSVBuVBKCIaIuIb0bEtyPiexHx2nL7hoj4ekTsjYhdEfHwKuqRJEmSVE9VzQI3CJyZmTdHRAvw1Yj4DPDXwGsz8zMR8WTgjcDjKqpJkiRJUs1UEoAyM4Gby6st5SXLS3u5fSXwiyrqkSRJklRPla0DFBFLgW7gvsA7M/MbEfFS4D8j4k0U3fEePcF9zwXOBVi7dm1FFUuSJElabCqbBCEzj2XmBuB04OER8SBgK/CyzOwEXga8Z4L7XpyZXZnZddppp1VVsiRJkqRFpvJZ4DKzD7gceBLwQuCy8qaPAE6CIEmSJKlpqpoF7rSIWFX+vBx4ArCfYszPY8vdzgR+XEU9kiRJkuqpqjFAa4BLynFAS4BLM/NTEdEHvDUiTgIGKMf5SJIkSVIzVDUL3HeAjeNs/yqwqYoaJEmSJKnyMUCSJEmSNFcMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJq46S5LkCqi+HhYT686xquvOEWzjh1Bc/uOp0lS/wOQpIkqUoGIKkCb/nCj3jbF3/McN6+7VU79vHix9+Plz7h/nNXmCRJUs0YgKQme8sXfsRbvvDjO2wfTm7bbgiSJEmqhv1vpCYaHh7mbV+8Y/gZ7W1f/DHDw8MVVSRJklRvBiCpiT6865rjur2NZzjhI93XVFOQJElSzRmApCa68oZbGtrvZ9c3tp8kSZJOjAFIaqIzTl3R0H73Pq2x/SRJknRiDEBSEz2763SWxOT7LAl45qbTqylIkiSp5gxAM5CZ7L76Rj773evYffWNZE4xyEO1tWTJEl78+PtNus+LH38/1wOSJEmqiNNgT9PO/b1csGMf/UeGiAiGM1m5vIVtZ69n87qOuS5P89DIFNdj1wFaErgOkCRJUsViobVedHV15a5du+bkuXfu72Xr9m4Ghu44ZXFbyxIu2rLJEKQJDQ8P85Hua/jZ9bdw79NW8MxNp9vyI0mS1AQR0Z2ZXePdZgtQgzKT8y/bN274ARgYGuaCHfu44rwziZhi0IdqacmSJTz7YWvnugxJkqRa8+vnBu3p6eOmgaFJ9+k/MsTenr6KKpIkSZI0XQagBvX2D07ZshMRHOwfrKgiSZIkSdNlAGpQR3srw1OMl8pMVre3VlSRJEmSpOmqJABFRFtEfDMivh0R34uI14667c8i4ofl9jdWUc9MbOxcRXtby6T7tC9vYUPnqooqkiRJkjRdVU2CMAicmZk3R0QL8NWI+AywHHga8ODMHIyIeTuFWkRw4TnrJ50FbtvZ650AQZIkSZrHKmkBysLN5dWW8pLAVuANmTlY7tdbRT0ztXldBxdt2cSalW2sWLaUk1tPYsWypaxZ2eYU2JIkSdICUNk6QBGxFOgG7gu8MzNfGRF7gY8DTwIGgJdn5rfGue+5wLkAa9eu3XTVVVdVUvNEMpO9PX0c7B9kdXsrGzpX2fIjSZIkzRPzYh2gzDwGbIiIVcCOiHhQ+fynAI8EHgZcGhH3zjGpLDMvBi6GYiHUqmqeSESwce0pc12GJEmSpGmqfBa4zOwDLqdo9bkGuKzsIvdNYBg4teqaJEmSJNVDVbPAnVa2/BARy4EnAPuBjwFnltvvDywDbqiiJkmSJEn1U1UXuDXAJeU4oCXApZn5qYhYBrw3Ir4LHAVeOLb7myRJkiTNlkoCUGZ+B9g4zvajwPOqqEGSJEmSKh8DJEmSJElzxQAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJqwwAkSZIkqTYMQJIkSZJq46S5LkCqi8xkT08fvf2DdLS3srFzFREx12VJkiTVigFIqsDO/b1csGMf/UeGiAiGM1m5vIVtZ69n87qOuS5PkiSpNuwCJzXZzv29bN3ezYFDA9xy9Bg3D97K4aPHOHBogK3bu9m5v3euS5QkSaoNA5DURJnJ+ZftY2BoeNzbB4aGuWDHPjKz4sokSZLqyQAkNdGenj5uGhiadJ/+I0Ps7emrqCJJkqR6MwBJTdTbPzjlRAcRwcH+wYoqkiRJqjcDkNREHe2tDE/RvS0zWd3eWlFFkiRJ9WYAkppoY+cq2ttaJt2nfXkLGzpXVVSRJElSvRmApCaKCC48Zz1tLeN/1NpalrDt7PWuByRJklQRA5DUZJvXdfCix96HJWMyzpKAFz32Pq4DJEmSVCEDkNRkO/f38q4v/5ThMUOBhhPe9eWfug6QJElShQxAUhO5DpAkSdL8YgCSmsh1gCRJkuYXA5DURK4DJEmSNL8YgKQmch0gSZKk+cUAJDWR6wBJkiTNLwYgqYlcB0iSJGl+MQBJTbZ5XQcXbdnEmpVtrFi2lJNbT2LFsqWsWdnGRVs2uQ6QJElShU6a6wKkOti8roMrzjuTvT19HOwfZHV7Kxs6V9nyI0mSVDEDkFSRiGDj2lPmugxJkqRaswucJEmSpNowAEmSJEmqDQOQJEmSpNowAEmSJEmqDQOQJEmSpNqoJABFRFtEfDMivh0R34uI1465/eURkRFxahX1SJIkSaqnqqbBHgTOzMybI6IF+GpEfCYzvx4RncBvA1dXVIskSZKkmqqkBSgLN5dXW8pLltffDLxi1HVJkiRJaorKFkKNiKVAN3Bf4J2Z+Y2IeCpwbWZ+OyImu++5wLnl1Zsj4odNL1gn6lTghrkuQguSx45mwuNGM+Fxo5nwuFkY7jnRDZFZbcNLRKwCdgAvAf4ZeGJmHoqIK4GuzPSAWgQiYldmds11HVp4PHY0Ex43mgmPG82Ex83CV/kscJnZB1wOPA24F/DtMvycDuyOiLtVXZMkSZKkeqhqFrjTypYfImI58ARgT2Z2ZOYZmXkGcA3w0My8roqaJEmSJNVPVWOA1gCXlOOAlgCXZuanKnpuzY2L57oALVgeO5oJjxvNhMeNZsLjZoGrfAyQJEmSJM2VyscASZIkSdJcMQBJkiRJqg0DkE5YRLw3Inoj4rtjtv9ZRPwwIr4XEW+cq/o0P4133ETEhoj4ekTsjYhdEfHwuaxR809EdEbEzoj4Qfm35SXl9rtExOcj4sflv6fMda2aPyY5bv4uIvZHxHciYsfIhE0STHzcjLr95RGREXHqXNWomXEMkE5YRPwWcDPwb5n5oHLbZuBVwFmZORgRHZnZO5d1an6Z4Lj5HPDmzPxMRDwZeEVmPm4Oy9Q8ExFrgDWZuTsi7kyxwPbTgT8EfpWZb4iI84BTMvOVc1iq5pFJjpvTgS9l5q0R8bcAHjcaMdFxk5nfj4hO4N3AOmCT61guLLYA6YRl5leAX43ZvBV4Q2YOlvsYfnScCY6bBNrLn1cCv6i0KM17mXkgM3eXP98E/AC4B8XacpeUu11CcXIrARMfN5n5ucy89f+3d/ehepd1HMffn6WTYD6M1h5Ka2YJPZGzwh5cq0GWUcM/MoRVRkVoGsxKYw0SpPqjVuIYQsSCEauYS0rIwCKdm7hZFsNqjBGznHuIZKXlU8tvf/x+B+/d3mc769zn3Pfpfr/gcM593dfvd33/uDjnfM7vuq7TdttBE4gk4LjfbwBuBm6g+bmlGcYApKlyPrA0yc4kW5O8ddAFaUZYBXwzySPAWmD1gOvREEuyGFgC7AQWVNVBaH5pAeYPrjINs6550+mTwM+nux7NDJ3zJskK4NGq2jXQovQ/MwBpqpwCzAXeBlwPbE6SwZakGeBq4LqqOge4Dtgw4Ho0pJLMAX4MrKqqxwddj2aG8eZNkjXAUWDToGrT8OqcNzTzZA3wlYEWpUkxAGmq7Adur8YDwHOAmwR1IlcCt7df3wZ4CIJeIMmpNL+MbKqqsflyuF2vP7Zu32W3OsY484YkVwIfBFaWG6PVpce8OQ84F9iV5GGaZZO/TbJwcFXqZBmANFV+AiwHSHI+MBtwg6BO5ACwrP16ObB3gLVoCLVPkjcAu6vq2x1v3UEToGk//3S6a9PwGm/eJHk/8CVgRVU9Oaj6NJx6zZuqeqiq5lfV4qpaTPMH3wur6tAAS9VJ8hQ4TVqSHwLvpnnCcxi4Efg+8D3gAuBZ4ItV9atB1ajhM8682QPcQrOE8mngs1X14KBq1PBJcjGwDXiI5skywJdp9nNsBl4B/AW4vKq6D9nQiDrOvFkHnAY81rbtqKqrpr9CDaPx5k1V3dnR52HgLZ4CN7MYgCRJkiSNDJfASZIkSRoZBiBJkiRJI8MAJEmSJGlkGIAkSZIkjQwDkCRJkqSRYQCSJA2VJCuT3DXBvp9Isr0PY96XZMkE+z6Q5PWTHVOSNBgGIEnSpCVZneTOrra947Rdcbx7VdWmqrqkT3Xdk+TTJ+jzIeCJqvrdBG+7Frhp0sVJkgbCACRJ6od7gXcmeRFAkoXAqcCFXW2vbvsOk6to/nnzRN0BvCfJoimqR5I0hQxAkqR++DVN4Lmgff0u4G5gT1fbn6rqQJIzk2xIcjDJo0m+2hGUjlnWluSSJHuS/CPJrUm2dj/VSbI2yZEk+5Jc2rZ9DVgKrE/yzyTru4tOMhtYDmztaHtxko3t/XYnuSHJ/rH3q+pp4EGgL0+pJEnTywAkSZq0qnoW2EkTcmg/bwO2d7WNPf3ZCByleSK0hCZMvGCpWpJ5wBZgNfASmkD1jq5uF7Xt84BvABuSpKrWtDVcW1VzquraHqW/BniuqvZ3tN0ILAZeBbwX+GiP63YDb+rRLkkacgYgSVK/bOX5sLOUJnxs62rbmmQBcCmwqqr+VVV/BW4Geu0N+gDwh6q6vaqOAuuAQ119/lxV362q/9AEq0XAggnWfBbwRFfbR4CvV9WRNhit63HdE+21kqQZ5pRBFyBJ+r9xL3BNkrnAS6tqb5LDwMa27Q1tn1fSLJc7mGTs2lnAIz3u+bLO9qqqzuVorUMd7z/Z3nPOBGs+Apx+vDHHqet04O8THEOSNER8AiRJ6pf7gTOBzwD3AVTV48CBtu1AVe2jCRTPAPOq6qz244yq6nW09EHg7LEXadLN2T36jadO8P7e9rYvH29M4Jwe170W2HUSdUiShoQBSJLUF1X1FPAb4PM0S9/GbG/b7m37HQTuAr6V5Iwks5Kcl2RZj9v+DHhjksuSnAJcAyw8ibIO0+zlGa/mfwO/BDrH3gysTjK3DUbH7B1KchrwZuAXJ1GHJGlIGIAkSf20FZhPE3rGbGvbOo+//jgwG/gjzTK0LTR7d45RVX8DLqc53OAx4HU0IeuZCdZzC/Dh9kS3Xnt5AL4DfKzj9U3AfmAfTTja0jXeCuCeqjowwRokSUMkVSdaHSBJ0nBIMosmnKysqrv7eN/twOd6/TPUJFcDV1TVsvb1TuBTVfX7fo0vSZo+HoIgSRpqSd5Hc8T2U8D1QIAd/Ryjqi7uGG8RzbK5+2mOyf4CsL6j70X9HFuSNL0MQJKkYfd24Ac8v2Tusna/0VSZTbMs7lyak95+BNw6heNJkqaRS+AkSZIkjQwPQZAkSZI0MgxAkiRJkkaGAUiSJEnSyDAASZIkSRoZBiBJkiRJI+O/WGCnWhJ2suAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen\n",
    "weight_tumor_capo = clean_df.loc[clean_df[\"Drug Regimen\"] == (\"Capomulin\"), :].groupby(\"Mouse ID\").mean()\n",
    "weight = weight_tumor_capo[\"Weight (g)\"]\n",
    "tumor = weight_tumor_capo[\"Tumor Volume (mm3)\"]\n",
    "fig6, ax6 = plt.subplots(figsize = (14, 7))\n",
    "plt.scatter(weight, tumor, s = 75)\n",
    "plt.title(\"Weight versus Average Tumor Volume of mouse for Capomulin\")\n",
    "plt.xlabel(\"Weight (g)\", fontsize = 12)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\", fontsize = 12)\n",
    "plt.ylim(34, 48)\n",
    "plt.show()\n",
    "\n",
    "fig6.savefig(\"graphs/weight_v_tumor_scatterplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between mouse weight and the average tumor vol is 0.8419363424694717.\n",
      "The linear equation is y = 0.95x + 21.55.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "corr = weight_tumor_capo[\"Weight (g)\"].corr(weight_tumor_capo[\"Tumor Volume (mm3)\"])\n",
    "print(f\"The correlation between mouse weight and the average tumor vol is {corr}.\")\n",
    "\n",
    "# Perform a linear regression on weight vs. average tumor vol\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(weight, tumor)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = weight * slope +intercept\n",
    "#regress_values\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" +str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "print(f\"The linear equation is {line_eq}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.7088568047708717\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7, ax7 = plt.subplots(figsize = (14, 7))\n",
    "plt.scatter(weight, tumor, s = 75)\n",
    "plt.plot(weight, regress_values, \"r-\")\n",
    "plt.title(\"Linear Regression on Weight vs. Average Tumor Volume\")\n",
    "plt.xlabel(\"Weight (g)\",fontsize = 12)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\", fontsize = 12)\n",
    "plt.ylim(34, 48)\n",
    "plt.annotate(line_eq,(20,40),fontsize = 20, color = \"red\")\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "plt.show()\n",
    "\n",
    "fig7.savefig(\"graphs/liner_regression.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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