Pymaceuticals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis\n",
    "* Overall, it is clear that Capomulin is a viable drug regimen to reduce tumor growth.\n",
    "* Capomulin had the most number of mice complete the study, with the exception of Remicane, all other regimens observed a number of mice deaths across the duration of the study. \n",
    "* There is a strong correlation between mouse weight and tumor volume, indicating that mouse weight may be contributing to the effectiveness of any drug regimen.\n",
    "* There was one potential outlier within the Infubinol regimen. While most mice showed tumor volume increase, there was one mouse that had a reduction in tumor growth in the study. "
   ]
  },
  {
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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>45.000000</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>1893 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",
       "[1893 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors as mcolors\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"Resources/Mouse_metadata.csv\"\n",
    "study_results_path = \"Resources/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",
    "mouse_data_combined = pd.merge(study_results, mouse_metadata, how='left', on='Mouse ID')\n",
    "\n",
    "# Display the data table for preview\n",
    "mouse_data_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "249"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "mouse_count = len(mouse_data_combined[\"Mouse ID\"].unique())\n",
    "mouse_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "mouse_duplicates = mouse_data_combined.loc[mouse_data_combined.duplicated(subset=['Mouse ID','Timepoint']),'Mouse ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Weight (g)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>g989</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>g989</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>g989</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "107      g989          0           45.000000                 0     Propriva   \n",
       "137      g989          0           45.000000                 0     Propriva   \n",
       "329      g989          5           48.786801                 0     Propriva   \n",
       "360      g989          5           47.570392                 0     Propriva   \n",
       "620      g989         10           51.745156                 0     Propriva   \n",
       "681      g989         10           49.880528                 0     Propriva   \n",
       "815      g989         15           51.325852                 1     Propriva   \n",
       "869      g989         15           53.442020                 0     Propriva   \n",
       "950      g989         20           55.326122                 1     Propriva   \n",
       "1111     g989         20           54.657650                 1     Propriva   \n",
       "1195     g989         25           56.045564                 1     Propriva   \n",
       "1380     g989         30           59.082294                 1     Propriva   \n",
       "1592     g989         35           62.570880                 2     Propriva   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "107   Female          21          26  \n",
       "137   Female          21          26  \n",
       "329   Female          21          26  \n",
       "360   Female          21          26  \n",
       "620   Female          21          26  \n",
       "681   Female          21          26  \n",
       "815   Female          21          26  \n",
       "869   Female          21          26  \n",
       "950   Female          21          26  \n",
       "1111  Female          21          26  \n",
       "1195  Female          21          26  \n",
       "1380  Female          21          26  \n",
       "1592  Female          21          26  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "mouse_data_combined[mouse_data_combined['Mouse ID']=='g989']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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>45.000000</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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "mouse_clean = mouse_data_combined[mouse_data_combined[\"Mouse ID\"].isin(mouse_duplicates)==False]\n",
    "mouse_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "mouse_clean_count = len(mouse_clean['Mouse ID'].unique())\n",
    "mouse_clean_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>SEM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.322552</td>\n",
       "      <td>50.854632</td>\n",
       "      <td>42.351070</td>\n",
       "      <td>6.507770</td>\n",
       "      <td>0.512884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mean     Median   Variance  Standard Deviation       SEM\n",
       "Drug Regimen                                                               \n",
       "Capomulin     40.675741  41.557809  24.947764            4.994774  0.329346\n",
       "Ceftamin      52.591172  51.776157  39.290177            6.268188  0.469821\n",
       "Infubinol     52.884795  51.820584  43.128684            6.567243  0.492236\n",
       "Ketapril      55.235638  53.698743  68.553577            8.279709  0.603860\n",
       "Naftisol      54.331565  52.509285  66.173479            8.134708  0.596466\n",
       "Placebo       54.033581  52.288934  61.168083            7.821003  0.581331\n",
       "Propriva      52.322552  50.854632  42.351070            6.507770  0.512884\n",
       "Ramicane      40.216745  40.673236  23.486704            4.846308  0.320955\n",
       "Stelasyn      54.233149  52.431737  59.450562            7.710419  0.573111\n",
       "Zoniferol     53.236507  51.818479  48.533355            6.966589  0.516398"
      ]
     },
     "execution_count": 12,
     "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",
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "drug_groups = mouse_data_combined.groupby(['Drug Regimen'])\n",
    "drug_groups\n",
    "\n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "drug_mean = drug_groups[\"Tumor Volume (mm3)\"].mean()\n",
    "drug_median = drug_groups[\"Tumor Volume (mm3)\"].median()\n",
    "drug_variance = drug_groups[\"Tumor Volume (mm3)\"].var()\n",
    "drug_std = drug_groups[\"Tumor Volume (mm3)\"].std()\n",
    "drug_sem = drug_groups[\"Tumor Volume (mm3)\"].sem()\n",
    "\n",
    "# Assemble the resulting series into a single summary dataframe.\n",
    "drug_table = pd.DataFrame({'Mean': drug_mean, 'Median': drug_median, 'Variance': drug_variance, 'Standard Deviation': drug_std, 'SEM': drug_sem})\n",
    "drug_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of measurements taken on each drug regimen using pandas.\n",
    "measurements = mouse_clean[\"Drug Regimen\"].value_counts()\n",
    "x_axis = measurements.index\n",
    "y_axis = measurements.values\n",
    "\n",
    "#bar chart\n",
    "measurements.plot(kind=\"bar\", width = .25, color = \"thistle\")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Total Number of Measurements\")\n",
    "plt.title (\"Measurements Taken On Each Drug Regimen\")\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of measurements taken on each drug regimen using using pyplot.\n",
    "measurements = mouse_clean[\"Drug Regimen\"].value_counts()\n",
    "x_axis = measurements.index\n",
    "y_axis = measurements.values\n",
    "\n",
    "#bar chart\n",
    "plt.bar(x_axis, y_axis, width = .25, color = \"thistle\") \n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Total Number of Measurements\")\n",
    "plt.title (\"Measurements Taken On Each Drug Regimen\")\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "#obtain gender count\n",
    "gender = mouse_clean[\"Sex\"].value_counts()\n",
    "\n",
    "#variables\n",
    "sex = gender.index\n",
    "gender_count = gender.values\n",
    "\n",
    "#pie chart\n",
    "gender.plot(kind=\"pie\", explode=explode, colors=colors, startangle=90, autopct=\"%1.2f%%\")\n",
    "plt.title(\"Male vs. Female Mice %\")\n",
    "plt.ylabel(\" \")\n",
    "\n",
    "#pie chart formatting \n",
    "explode = [0.05, 0]\n",
    "colors = [\"cornflowerblue\", \"lightpink\"]\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "#obtain gender count\n",
    "gender = mouse_clean[\"Sex\"].value_counts()\n",
    "\n",
    "#variables\n",
    "sex = gender.index\n",
    "gender_count = gender.values\n",
    "\n",
    "#pie chart\n",
    "plt.pie(gender, labels=sex, colors=colors, explode=explode, startangle=90, autopct=\"%1.2f%%\")\n",
    "plt.title(\"Male vs. Female Mice %\")\n",
    "\n",
    "#pie chart formatting \n",
    "explode = [0.05, 0]\n",
    "colors = [\"cornflowerblue\", \"lightpink\"]\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "greatest_time = mouse_clean.groupby(\"Mouse ID\").max()[\"Timepoint\"]\n",
    "\n",
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "greatest_time_merge = pd.merge(greatest_time, mouse_clean, on=[\"Mouse ID\",\"Timepoint\"], how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25    32.377357\n",
       "0.50    38.125164\n",
       "0.75    40.159220\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatments= [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumor_data = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "iqr_data = greatest_time_merge.loc[:, \"Drug Regimen\"]== \"Capomulin\"    \n",
    "quantiles = [.25, .5, .75]\n",
    "first_treatment = greatest_time_merge.loc[iqr_data, \"Tumor Volume (mm3)\"]\n",
    "quartiles = first_treatment.quantile(quantiles)\n",
    "\n",
    "quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin's potential outliers: 0, Name: Tumor Volume (mm3).\n",
      "Ramicane's potential outliers: 0, Name: Tumor Volume (mm3).\n",
      "Infubinol's potential outliers: 1, Name: Tumor Volume (mm3).\n",
      "Ceftamin's potential outliers: 0, Name: Tumor Volume (mm3).\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatments= [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumor_data = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "for each_treatment in treatments:\n",
    "    iqr_data = greatest_time_merge.loc[:, \"Drug Regimen\"]== each_treatment\n",
    "   \n",
    "    first_treatment = greatest_time_merge.loc[iqr_data, \"Tumor Volume (mm3)\"]\n",
    "    tumor_data.append(greatest_time_merge.loc[iqr_data, \"Tumor Volume (mm3)\"])\n",
    "    \n",
    "    #set up for IQR calculation\n",
    "    quantiles = [.25, .5, .75]\n",
    "    quartiles=first_treatment.quantile(quantiles)\n",
    "    lower_q = quartiles.loc[.25]\n",
    "    upper_q = quartiles.loc[.75]\n",
    "    \n",
    "    #determine IQR\n",
    "    iqr = upper_q - lower_q\n",
    "    \n",
    "    #determine outliers via upper/lower bounds\n",
    "    lower_bound = lower_q - (1.5 * iqr)\n",
    "    upper_bound = upper_q + (1.5 * iqr)\n",
    "    \n",
    "    #locate rows which contain mice on each drug\n",
    "    find_drug = (first_treatment > upper_bound) | (first_treatment < lower_bound)\n",
    "    \n",
    "    #outlier data\n",
    "    outlier = len(first_treatment.loc[find_drug])\n",
    "    \n",
    "    print(f\"{each_treatment}'s potential outliers: {outlier}, Name: Tumor Volume (mm3).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAElFTkSuQmCC\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",
    "plt.boxplot(tumor_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of tumor volume vs. time point for a mouse treated with Capomulin\n",
    "capo_drug = mouse_clean.loc[:, \"Drug Regimen\"] == \"Capomulin\"\n",
    "capomulin = mouse_clean.loc[capo_drug,:]\n",
    "capo_drug = capomulin.loc[:, \"Mouse ID\"] == \"b128\"\n",
    "mouse_b128 = capomulin.loc[capo_drug,:]\n",
    "\n",
    "#lineplot\n",
    "mouse_b128.plot(\"Timepoint\", \"Tumor Volume (mm3)\", color = \"darkcyan\")\n",
    "                \n",
    "#plot formatting\n",
    "plt.title(\"Capomulin Treatment of Mouse b128\")     \n",
    "plt.xlabel(\"Timepoint (days)\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "capo_average = capomulin[[\"Mouse ID\", \"Tumor Volume (mm3)\", \"Weight (g)\"]].groupby(\"Mouse ID\").mean()\n",
    "\n",
    "#scatter plot\n",
    "plt.scatter(capo_average[\"Weight (g)\"], capo_average[\"Tumor Volume (mm3)\"], color = \"rosybrown\")\n",
    "\n",
    "#plot formatting\n",
    "plt.xlabel(\"Weight (g)\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "plt.title(\"Average Tumor Volume vs. Mouse Weight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between mouse weight and the average tumor volume is 0.84\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "\n",
    "correlation = round(st.pearsonr(capo_average[\"Weight (g)\"], capo_average[\"Tumor Volume (mm3)\"])[0], 2)\n",
    "\n",
    "print(f\"The correlation between mouse weight and the average tumor volume is {correlation}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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