Pymaceuticals/pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations and Insights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>h246</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>p189</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>n923</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>q119</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>f993</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>z234</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b559</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>y260</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>x930</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>o725</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "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",
       "5      h246          0                45.0                 0     Ketapril   \n",
       "6      p189          0                45.0                 0     Ketapril   \n",
       "7      n923          0                45.0                 0     Ketapril   \n",
       "8      q119          0                45.0                 0     Ketapril   \n",
       "9      f993          0                45.0                 0     Naftisol   \n",
       "10     z234          0                45.0                 0     Naftisol   \n",
       "11     b559          0                45.0                 0     Naftisol   \n",
       "12     y260          0                45.0                 0     Ketapril   \n",
       "13     x930          0                45.0                 0     Naftisol   \n",
       "14     o725          0                45.0                 0     Naftisol   \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  \n",
       "5     Male          13          30  \n",
       "6     Male           8          28  \n",
       "7     Male          19          30  \n",
       "8     Male          17          30  \n",
       "9     Male          21          28  \n",
       "10  Female          19          27  \n",
       "11    Male          20          26  \n",
       "12  Female           7          25  \n",
       "13    Male          13          26  \n",
       "14    Male           4          26  "
      ]
     },
     "execution_count": 1,
     "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",
    "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 dataset\n",
    "research_combined = pd.merge(study_results,mouse_metadata,how =\"left\", on = \"Mouse ID\")\n",
    "\n",
    "# Display the data table for preview\n",
    "research_combined.head(15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "249"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Checking the initial number of mice.\n",
    "research_combined['Mouse ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
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       "      <td>47.570392</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "137      g989          0           45.000000                 0     Propriva   \n",
       "360      g989          5           47.570392                 0     Propriva   \n",
       "681      g989         10           49.880528                 0     Propriva   \n",
       "869      g989         15           53.442020                 0     Propriva   \n",
       "1111     g989         20           54.657650                 1     Propriva   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "137   Female          21          26  \n",
       "360   Female          21          26  \n",
       "681   Female          21          26  \n",
       "869   Female          21          26  \n",
       "1111  Female          21          26  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "dup_mice = research_combined.loc[research_combined.duplicated(['Mouse ID', 'Timepoint'])]\n",
    "dup_mice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <th>107</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>21</td>\n",
       "      <td>26</td>\n",
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       "      <th>137</th>\n",
       "      <td>g989</td>\n",
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       "      <td>45.000000</td>\n",
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       "      <td>21</td>\n",
       "      <td>26</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>g989</td>\n",
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       "      <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",
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       "      <td>21</td>\n",
       "      <td>26</td>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>g989</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
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       "      <td>21</td>\n",
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       "      <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",
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       "      <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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "research_combined.loc[research_combined['Mouse ID'] == 'g989']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "mice_clean = research_combined.loc[research_combined['Mouse ID'] != 'g989']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the new number of mice\n",
    "mice_clean['Mouse ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>45.000000</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>45.000000</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>45.000000</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>45.000000</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>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</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>1888</th>\n",
       "      <td>r944</td>\n",
       "      <td>45</td>\n",
       "      <td>41.581521</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>u364</td>\n",
       "      <td>45</td>\n",
       "      <td>31.023923</td>\n",
       "      <td>3</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>p438</td>\n",
       "      <td>45</td>\n",
       "      <td>61.433892</td>\n",
       "      <td>1</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>x773</td>\n",
       "      <td>45</td>\n",
       "      <td>58.634971</td>\n",
       "      <td>4</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>b879</td>\n",
       "      <td>45</td>\n",
       "      <td>72.555239</td>\n",
       "      <td>2</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0        b128          0           45.000000                 0    Capomulin   \n",
       "1        f932          0           45.000000                 0     Ketapril   \n",
       "2        g107          0           45.000000                 0     Ketapril   \n",
       "3        a457          0           45.000000                 0     Ketapril   \n",
       "4        c819          0           45.000000                 0     Ketapril   \n",
       "...       ...        ...                 ...               ...          ...   \n",
       "1888     r944         45           41.581521                 2    Capomulin   \n",
       "1889     u364         45           31.023923                 3    Capomulin   \n",
       "1890     p438         45           61.433892                 1     Ceftamin   \n",
       "1891     x773         45           58.634971                 4      Placebo   \n",
       "1892     b879         45           72.555239                 2     Stelasyn   \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  \n",
       "...      ...         ...         ...  \n",
       "1888    Male          12          25  \n",
       "1889    Male          18          17  \n",
       "1890  Female          11          26  \n",
       "1891  Female          21          30  \n",
       "1892  Female           4          26  \n",
       "\n",
       "[1880 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mice_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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 Tumor Volume</th>\n",
       "      <th>Median Tumor Volume</th>\n",
       "      <th>Tumor Volume Variance</th>\n",
       "      <th>Tumor Standard Diviation</th>\n",
       "      <th>Tumor Standard Error</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 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              52.320930            50.446266              43.852013   \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 Standard Diviation  Tumor Standard Error  \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                      6.622085              0.544332  \n",
       "Ramicane                      4.846308              0.320955  \n",
       "Stelasyn                      7.710419              0.573111  \n",
       "Zoniferol                     6.966589              0.516398  "
      ]
     },
     "execution_count": 8,
     "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",
    "# This method is the most straighforward, creating multiple series and putting them all together at the end.\n",
    "\n",
    "grouped_df = mice_clean.groupby([\"Drug Regimen\"])\n",
    "\n",
    "MeanTumorVolume = grouped_df[\"Tumor Volume (mm3)\"].mean()\n",
    "MeanTumorVolume_df = pd.DataFrame(MeanTumorVolume)\n",
    "MeanTumorVolume_df = MeanTumorVolume_df.rename(columns={\"Tumor Volume (mm3)\": \"Mean Tumor Volume\"})\n",
    "\n",
    "MedianTumorVolume = grouped_df[\"Tumor Volume (mm3)\"].median()\n",
    "MedianTumorVolume_df = pd.DataFrame(MedianTumorVolume)\n",
    "MedianTumorVolume_df = MedianTumorVolume_df.rename(columns={\"Tumor Volume (mm3)\": \"Median Tumor Volume\"})\n",
    "\n",
    "TumorVolumeVariance = grouped_df[\"Tumor Volume (mm3)\"].var()\n",
    "TumorVolumeVariance_df = pd.DataFrame(TumorVolumeVariance)\n",
    "TumorVolumeVariance_df = TumorVolumeVariance_df.rename(columns={\"Tumor Volume (mm3)\": \"Tumor Volume Variance\"})\n",
    "\n",
    "TumorStandardDiviation = grouped_df[\"Tumor Volume (mm3)\"].std()\n",
    "TumorStandardDiviation_df = pd.DataFrame(TumorStandardDiviation)\n",
    "TumorStandardDiviation_df = TumorStandardDiviation_df.rename(columns={\"Tumor Volume (mm3)\": \"Tumor Standard Diviation\"})\n",
    "\n",
    "TumorStandardError = grouped_df[\"Tumor Volume (mm3)\"].sem()\n",
    "TumorStandardError_df = pd.DataFrame(TumorStandardError)\n",
    "TumorStandardError_df = TumorStandardError_df.rename(columns={\"Tumor Volume (mm3)\": \"Tumor Standard Error\"})\n",
    "\n",
    "merge1 = pd.merge(MeanTumorVolume_df, MedianTumorVolume_df, how='outer', on='Drug Regimen')\n",
    "merge2 = pd.merge(merge1, TumorVolumeVariance_df, how='outer', on='Drug Regimen')\n",
    "merge3 = pd.merge(merge2, TumorStandardDiviation_df, how='outer', on='Drug Regimen')\n",
    "merge_final = pd.merge(merge3, TumorStandardError_df, how='outer', on='Drug Regimen')\n",
    "\n",
    "merge_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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": 9,
     "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",
    "# This method produces everything in a single groupby function\n",
    "\n",
    "grouped_final = grouped_df['Tumor Volume (mm3)'].agg(['mean','median','var','std','sem'])\n",
    "grouped_final\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c999ca048>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pandas. \n",
    "mice_per_drug = grouped_df[\"Tumor Volume (mm3)\"].count()\n",
    "mice_per_drug.plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], <a list of 10 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pyplot.\n",
    "plt.bar(mice_per_drug.index, mice_per_drug)\n",
    "plt.xticks(rotation=45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c9a1bb240>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "sex_df = mice_clean.groupby([\"Sex\"])\n",
    "mice_sex = sex_df[\"Sex\"].count()\n",
    "mice_sex.plot(kind='pie',autopct='%.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x12c9a23afd0>,\n",
       "  <matplotlib.patches.Wedge at 0x12c9a2467b8>],\n",
       " [Text(0.03308201408261561, 1.0995024239828841, 'Female'),\n",
       "  Text(-0.0330820140826155, -1.0995024239828841, 'Male')],\n",
       " [Text(0.018044734954153964, 0.5997285948997549, '49.04%'),\n",
       "  Text(-0.018044734954153905, -0.5997285948997549, '50.96%')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "plt.pie(mice_sex, autopct='%.2f%%',labels=mice_sex.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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",
       "    <tr>\n",
       "      <th>Mouse ID</th>\n",
       "      <th></th>\n",
       "      <th></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>b879</th>\n",
       "      <td>45</td>\n",
       "      <td>72.555239</td>\n",
       "      <td>2</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "Mouse ID                                                                 \n",
       "b879             45           72.555239                 2     Stelasyn   \n",
       "\n",
       "             Sex  Age_months  Weight (g)  \n",
       "Mouse ID                                  \n",
       "b879      Female           4          26  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mice_clean.groupby('Mouse ID').max()\n",
    "df.loc[df.index=='b879']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "1     f932          0                45.0                 0     Ketapril   \n",
       "\n",
       "    Sex  Age_months  Weight (g)  \n",
       "1  Male          15          29  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mice_clean.loc[mice_clean['Mouse ID']== 'f932']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of tumors in mice is: 48.72207785\n",
      "The upper quartile of tumors in mice is: 64.29983003\n",
      "The interquartile range of tumors in mice is: 15.577752179999997\n",
      "Values below 25.355449580000002 could be outliers.\n",
      "Values above 87.66645829999999 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "max_timepoint = mice_clean.groupby('Mouse ID').Timepoint.max()\n",
    "\n",
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "max_timepoint_df = mice_clean.merge(max_timepoint, on=['Mouse ID','Timepoint'])\n",
    "\n",
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:\n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "drugs = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "tumors = []\n",
    "tumor_volume=[]\n",
    "\n",
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "for drug in drugs:\n",
    "    final_tumor = max_timepoint_df.loc[max_timepoint_df['Drug Regimen'] == drug, 'Tumor Volume (mm3)']\n",
    "    tumors.append(final_tumor)\n",
    "    quartiles = final_tumor.quantile([.25, .50, .75])\n",
    "    quartiles = pd.DataFrame(quartiles)\n",
    "    tumors = pd.DataFrame(tumors)\n",
    "    \n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers.\n",
    "# Determine outliers using upper and lower bounds\n",
    "lowerq = final_tumor.quantile([0.25]).mean()\n",
    "upperq = final_tumor.quantile([0.75]).mean()\n",
    "iqr = upperq-lowerq\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "# A high IQR determines that we do have outliers in the data across a calculation of all 4 drug regimens\n",
    "print(f\"The lower quartile of tumors in mice is: {lowerq}\")\n",
    "print(f\"The upper quartile of tumors in mice is: {upperq}\")\n",
    "print(f\"The interquartile range of tumors in mice is: {iqr}\")\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c9db60b38>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "tumors.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tumor Volume (mm3)')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin\n",
    "mice_clean.loc[mice_clean['Mouse ID']== 'b128'].plot(x='Timepoint', y='Tumor Volume (mm3)')\n",
    "plt.xlabel('Timepoint')\n",
    "plt.ylabel('Tumor Volume (mm3)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c999ca198>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen\n",
    "mice_clean.loc[mice_clean['Drug Regimen']== 'Capomulin'].plot.scatter(x='Weight (g)', y='Tumor Volume (mm3)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.53\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "\n",
    "Capomulin_mice = mice_clean.loc[mice_clean['Drug Regimen']== 'Capomulin']\n",
    "x_values = Capomulin_mice['Weight (g)']\n",
    "y_values = Capomulin_mice['Tumor Volume (mm3)']\n",
    "correlation = st.pearsonr(x_values,y_values)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Mouse Weight in Grams')\n",
    "plt.ylabel('Tumor Volume (mm3)')\n",
    "plt.show()\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Analysis:\n",
    "# I couldn't get the correct boxplot information to show up instead of a total for all four where I needed different\n",
    "# values in a list for each of the Drugs being studied.\n",
    "\n",
    "# My scatter plot and correlation are not matching the example as well.\n",
    "\n",
    "# It is clear that Capomulin is the best treatment option.\n",
    "\n",
    "# The heavier the mouse the larger the tumor is for the most part."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}