Pymaceuticals_Solution_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis : you will be writing your analysis here\n",
    "Capomulin and Ramicane had the smallest tummor size and volume and likely were the most effective drugs.\n",
    "Capomulin had a gradule increase in tumor size before reducing the overall volume between 25-30 days.\n",
    "There is a strong corelation between mouse weight and average tumor volume as the correlation factor is >0.7 (correlation=0.84).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>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": 5,
     "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",
    "\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",
    "study_data_complete = pd.merge(study_results,mouse_metadata, on=\"Mouse ID\", how='left')\n",
    "\n",
    "# Display the data table for preview\n",
    "study_data_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "number_mice=study_data_complete['Mouse ID'].nunique()\n",
    "number_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "mouse_data=study_data_complete.loc[study_data_complete.duplicated(subset=['Mouse ID','Timepoint']),'Mouse ID'].unique()\n",
    "mouse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "study_data_clean=study_data_complete[study_data_complete['Mouse ID'].isin(mouse_data)==False]\n",
    "study_data_clean['Mouse ID'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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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       "      <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",
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       "      <td>45.000000</td>\n",
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       "      <td>Ketapril</td>\n",
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       "    </tr>\n",
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       "      <td>29</td>\n",
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       "      <th>3</th>\n",
       "      <td>a457</td>\n",
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       "      <td>45.000000</td>\n",
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       "      <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",
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       "      <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": [
    "study_data_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",
    "study_data_clean['Mouse ID'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "      <th>Age_months</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
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       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
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       "      <td>29</td>\n",
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       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0     b128          0                45.0                 0    Capomulin   \n",
       "1     f932          0                45.0                 0     Ketapril   \n",
       "2     g107          0                45.0                 0     Ketapril   \n",
       "3     a457          0                45.0                 0     Ketapril   \n",
       "4     c819          0                45.0                 0     Ketapril   \n",
       "\n",
       "      Sex  Age_months  Weight (g)  \n",
       "0  Female           9          22  \n",
       "1    Male          15          29  \n",
       "2  Female           2          29  \n",
       "3  Female          11          30  \n",
       "4    Male          21          25  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_data_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Tumor Volume</th>\n",
       "      <th>Median Tumor Volume</th>\n",
       "      <th>Tumor Volume Variance</th>\n",
       "      <th>Tumor Volume Std Dev</th>\n",
       "      <th>Total Volume Std. Err.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</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 Volume Std Dev  Total Volume Std. Err.  \n",
       "Drug Regimen                                                \n",
       "Capomulin                 4.994774                0.329346  \n",
       "Ceftamin                  6.268188                0.469821  \n",
       "Infubinol                 6.567243                0.492236  \n",
       "Ketapril                  8.279709                0.603860  \n",
       "Naftisol                  8.134708                0.596466  \n",
       "Placebo                   7.821003                0.581331  \n",
       "Propriva                  6.622085                0.544332  \n",
       "Ramicane                  4.846308                0.320955  \n",
       "Stelasyn                  7.710419                0.573111  \n",
       "Zoniferol                 6.966589                0.516398  "
      ]
     },
     "execution_count": 13,
     "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",
    "mean=study_data_clean.groupby('Drug Regimen').mean()\n",
    "median=study_data_clean.groupby('Drug Regimen').median()\n",
    "std_dev=study_data_clean.groupby('Drug Regimen').std()\n",
    "variance=study_data_clean.groupby('Drug Regimen').var()\n",
    "std_error= study_data_clean.groupby('Drug Regimen').sem()\n",
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "# Assemble the resulting series into a single summary dataframe.\n",
    "sum_stats=pd.DataFrame({'Mean Tumor Volume':mean['Tumor Volume (mm3)'],'Median Tumor Volume':median['Tumor Volume (mm3)'],'Tumor Volume Variance':variance['Tumor Volume (mm3)'],'Tumor Volume Std Dev':std_dev['Tumor Volume (mm3)'], \n",
    "                       'Total Volume Std. Err.':std_error['Tumor Volume (mm3)']})\n",
    "sum_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "#above\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug Regimen\n",
      "Capomulin    230\n",
      "Ceftamin     178\n",
      "Infubinol    178\n",
      "Ketapril     188\n",
      "Naftisol     186\n",
      "Placebo      181\n",
      "Propriva     148\n",
      "Ramicane     228\n",
      "Stelasyn     181\n",
      "Zoniferol    182\n",
      "Name: Mouse ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of unique mice tested on each drug regimen using pandas.\n",
    "print (study_data_clean.groupby('Drug Regimen')['Mouse ID'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b0d25f340>"
      ]
     },
     "execution_count": 35,
     "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": [
    "df=study_data_clean.groupby('Drug Regimen')['Mouse ID'].count()\n",
    "df.plot(x='Drug Regimen', y=df[1], kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Unique Mice Tested')"
      ]
     },
     "execution_count": 36,
     "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 unqiue mice tested on each drug regimen using using pyplot.\n",
    "y_axis=(study_data_clean.groupby('Drug Regimen')['Mouse ID'].count()).sort_values(ascending=False)\n",
    "plt.bar(study_data_clean['Drug Regimen'].unique(), y_axis, alpha=1,align='center')\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Number of Unique Mice Tested\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E2FRO353SAVSoecCt0iHWsq2cdwMrpUOo0HoGx31XOsRadpXTcduBa6VjqNCyZtYE28rpuwlwpUOo0FkOPCIdoif7yulfqXKTdAwVOr/AcbukQ/RkXzl91yJwJ2EVWmngd9IherOznI67EviDdAwVGpfZNmuCreX0XQ1Y9wemAmcxlp6dZm85HXcZFm5qqMC5FMe1cqkce8vpuwR931Plz+vAQ9Ih+mN3Of3FfBPSMVRgXYzjetIh+mN3OX23Ay9Kh1CB8zcc9wnpEBtifzn9/9nOQpfQVMPHBX4kHWIg9pcTwHHnYck1dioQLszdwNlqxVFO38XowSG16Z4Bfi8dYjCKp5yOuxo4TzpGoWWyHrvf2sLh9/gnTL25IsPUP7ay680tfOveNpo6+j6eMfMf3ex4YwvbXd9M8vl1d0z/2aw1fPnmFk55pP3zr931ZifXvVTwu6pLaAPOtPkgUE/FU04Ax72HkF3zed3LnUxsWPfXdMbf2kl+vYL5P6rm6J1KufqFL5Yqk/U46/F2njhxJAvPqubeBV0sXJnBXeMxd1mGt35UTcbzmP9xhvYujzve7OLHU8oL+WNJuRTHfU86xGAVVzl9Z+Gf1RF4y5qypJZ0c8ZX1xXnnU+z7Ld1BICDty3lz4u6v/C6Vz7MsF1dCduOLqE8YjhulzL+uribEgOdGQ/P82jvgrIIXD23k3P2LKcsYgr2cwl5hSK7HLH4yum4rcAMwIqlJPLpvJlr+PVBlZT06M2ksREefccv5IMLu/hX0xdv5fFhs8eEUev+arccZfiwOUtNheE7E8vY/dZWtqktIVphePWjDEfuVJb3n0XYKuAEW88E6k/xlRPAcd8CfiodI58ee7eLsVWGyeMj6339tiMruenVTib/roXmDijvY8bz+tijWvusC/ep4I0fVvObQyu55JkOLt+/gj+83smxD7Zx5XOB3O/M4hezaDZn1yrOcgI47i1YskpaPrzwQYZH3+kmdm0zxz3Uzuyl3Zz0cDs7NUR46uQqXvt+NcfvWsqXRn+xnFuOMuvNqMuaPMbXrP9XPW+5P4nsUF/Cn97s4oFjRrLgkwxLGotqchmMS3DcmdIhhqJ4y+k7E1gqHSIfrjqokmX/XkP6vBrumz6CA7cp5e5vj+CTVr90Wc/jyuc6+eEeXzyQM2WLCEsasyxdlaUz43Hf210csWPpes+55JkOLj+ggq4sZHIzbYmBtmBdB/RnHPeX0iGGqrjL6a+acDTQJB2lUO6d38UON7Sw042tjK8xfHc3f3/xo+Ysh/2v/3ZLaYnhxsMqOfTuNibe1MKxO5exy9h1m8d/WdzFlPERxteUUFtpmLplhF1vbsEY+MrmkT7HLUILgdOkQ2wK4/W1g1JsnOgBwEwgFO8HqAGtBvbEcZdIB9kUxT1zruW4zwAn4+/8q3DrBk4s9mJCUMoJ4LgPEMIziNR6POC7OO7j0kGGQ3DKCeC4NwBXScdQYn6C4wbmnjvB2OfszYneTpEfDFAb7ec4bqD+Yw7WzLnOmcDD0iFUwVwStGJCUMvpuN3Asegds8PgYhz3SukQ+RDMcgK58yhPQ1ePD7KLcNxfSIfIl2Duc/bmRC/HX8lPBUMHcHruEsLACkc5AZzomcDNQGBOgQmplcBROO5c6SD5Fp5yAjjROHA/UCUdRQ3JQuBwHDeQ51P3Ftx9zr44bgrYB7DmBqlq0GYB08JSTAhbOQEc901gD+A+6Shq0G4BDstd6BAa4dqs7c2J/gB/6YpK6SiqT03AuTjuHdJBJIS7nABO9CvAA8AO0lHUep4DTsFx/ykdREr4Nmt78zdzJwP3SkdRAHQCFwIHhLmYoDPn+pzoccBvgXHSUUJqPnBSbo2o0NOZsyfHvQ/YCbgevTdLIWXwb5Y8RYu5js6c/XGiu+GftLC3dJSAexI4H8ddIB3ENlrODXGiBjgDSAJ1wmmCZgF+KZ+UDmIrLedgONF64DL8olYIpyl2HwOXAn8stkWeC03LuTGc6DjgfOAH6CmAG6sduAZI4rjN0mGKgZZzKJxoA/6K8z8BRgmnsd0K/Mv2bsFxP5UOU0y0nJvCidYCZwPnAvXCaWzzBv5MeR+O2ykdphhpOYeDE60CpgOnAvuz7tYkYZMFHgOuwXHnCGcpelrO4eZEt8ZfQ/dUYDvhNIWyEP++NXfjuP+QDhMUWs58cqLT8Es6A4gKpxluC/AL+SCOu0g6TBBpOQvBiZYDU4GDco8pFN+KDB7wFvAQfiHfEc4TeFpOCU40ir9vurasO4nm6Vsb8CowN/d4EcdtlI0ULlpOGzjR8cBuwM69HjUFStAKpIG3WVfGebklRpUQLafNnOgE1hV1C2B07lHX49ejgep+vkMH0NLj8SH+/UzTuY/+w3FX5u1nUEOm5QwCJ1qGX9AM/l22/I96elxR03IqZSm9nlMpS2k5lbKUllMpS2k5A84Y4xlj7urxeakxZqUx5rEBXrf/QM9R+aXlDL5WYJIxZkTu84Px31JRltNyhsMTQDz36+PpsQyoMWZPY8xcY8y83Mcde7/YGFNljLnNGPNq7nlHFih3qGk5w+E+4DhjTCXwZeDlHr+3GNjP87zd8ZcP+WUfr/9PYLbneVOAA4CrjTG6EkSelUoHUPnned5bxpgY/qz5eK/fjgJ3GmO2xz+5vayPb3EIcIQx5vzc55XAVoBejZJHWs7weBT4b/wT7nuu2nAF8IzneUfnCjynj9ca4Due5+mVKAWkm7XhcRtwued583t9Pcq6A0Sn9fPaJ4GzjTEGwBize14SqvVoOUPC87xlnudd18dv/Rq4yhjzAv1fY3oF/ubuW8aYBbnPVZ7pubVKWUpnTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUspeVUylJaTqUs9f9sLpAia3cp8AAAAABJRU5ErkJggg==\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",
    "labels=['Female', 'Male']\n",
    "count = study_data_clean['Sex'].value_counts()\n",
    "explode = (0.1, 0)\n",
    "plt.pie(count, labels=labels,autopct=\"%1.1f%%\");\n",
    "# (x, explode=None, labels=None, colors=None, \n",
    "# autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, \n",
    "# startangle=0, radius=1, counterclock=True, wedgeprops=None, \n",
    "# textprops=None, center=0, 0, frame=False, rotatelabels=False, *, \n",
    "# normalize=None, data=None)[source]¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      958\n",
       "Female    922\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>45</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>4</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>30</td>\n",
       "      <td>63.440686</td>\n",
       "      <td>1</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>29</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>243</th>\n",
       "      <td>z435</td>\n",
       "      <td>10</td>\n",
       "      <td>48.710661</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>z578</td>\n",
       "      <td>45</td>\n",
       "      <td>30.638696</td>\n",
       "      <td>0</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>z581</td>\n",
       "      <td>45</td>\n",
       "      <td>62.754451</td>\n",
       "      <td>3</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>z795</td>\n",
       "      <td>45</td>\n",
       "      <td>65.741070</td>\n",
       "      <td>3</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>z969</td>\n",
       "      <td>45</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0       a203         45           67.973419                 2    Infubinol   \n",
       "1       a251         45           65.525743                 1    Infubinol   \n",
       "2       a262         45           70.717621                 4      Placebo   \n",
       "3       a275         45           62.999356                 3     Ceftamin   \n",
       "4       a366         30           63.440686                 1     Stelasyn   \n",
       "..       ...        ...                 ...               ...          ...   \n",
       "243     z435         10           48.710661                 0     Propriva   \n",
       "244     z578         45           30.638696                 0     Ramicane   \n",
       "245     z581         45           62.754451                 3    Infubinol   \n",
       "246     z795         45           65.741070                 3     Naftisol   \n",
       "247     z969         45           73.867845                 4     Naftisol   \n",
       "\n",
       "        Sex  Age_months  Weight (g)  \n",
       "0    Female          20          23  \n",
       "1    Female          21          25  \n",
       "2    Female          17          29  \n",
       "3    Female          20          28  \n",
       "4    Female          16          29  \n",
       "..      ...         ...         ...  \n",
       "243  Female          12          26  \n",
       "244    Male          11          16  \n",
       "245  Female          24          25  \n",
       "246  Female          13          29  \n",
       "247    Male           9          30  \n",
       "\n",
       "[248 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "#create data frame that holds last timepoint for each mouse, and mouse ID, reset index mouse id=column\n",
    "max_timepoint=study_data_clean.groupby(['Mouse ID'])['Timepoint'].max()\n",
    "max_timepoint=max_timepoint.reset_index()\n",
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "study_data_merged=pd.merge(max_timepoint,study_data_clean, on=['Mouse ID', 'Timepoint'], how='left')\n",
    "study_data_merged\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "Ramicane's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "Infubinol's potential outliers: 31    36.321346\n",
      "Name: Tumor Volume (mm3), dtype: float64\n",
      "Ceftamin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "#code given by instructor\n",
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatment_list = [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumor_vol_list = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "for drug in treatment_list:\n",
    "    \n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes\n",
    "    final_tumor_vol = study_data_merged.loc[study_data_merged[\"Drug Regimen\"] == drug, 'Tumor Volume (mm3)']\n",
    "    \n",
    "    # add subset \n",
    "    tumor_vol_list.append(final_tumor_vol)\n",
    "    \n",
    "    # Determine outliers using upper and lower bounds\n",
    "    quartiles = final_tumor_vol.quantile([.25,.5,.75])\n",
    "    lowerq = quartiles[0.25]\n",
    "    upperq = quartiles[0.75]\n",
    "    iqr = upperq-lowerq\n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "    outliers = final_tumor_vol.loc[(final_tumor_vol < lower_bound) | (final_tumor_vol > upper_bound)]\n",
    "    print(f\"{drug}'s potential outliers: {outliers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9iPgT4A5gZppsJnB7y6o0M7M9NPI98E8Cp0t6Ajg9DZuZ2RCppwtlp4hYAixJt58FTm1+SWZmVg//EtPMLFMOcDOzTA2oC6WTxRUHw9zR7S5jD3HFwe0uwcyGqWET4Lpyc8d+DzzmtrsKMxuO3IViZpYpB7iZWaYc4GYZWLBgAVOnTmXEiBFMnTqVBQsWtLsk6wDDpg/cbLhasGABc+bM4brrruOkk05i6dKlzJo1C4ALLrigzdVZO2koP/jr7u6O3t7elixbUud+iNmBddXUgd/oAWDu8+2uYMhNnTqVq6++munTp+8ct3jxYmbPns2jjz7axspsqEhaFhHde4x3gLdWp9ZVSyfW3Yk1DYURI0awdetWRo4cuXPctm3bGDVqFNu3b29jZTZU+gtw94GbdbgpU6awdOnS3cYtXbqUKVOmtKki6xQOcLMON2fOHGbNmsXixYvZtm0bixcvZtasWcyZM6fdpVmb+UNMsw5X+qBy9uzZrFixgilTpnDVVVf5A0xzH3irdWpdtXRi3Z1Yk9lQcB+4mdkw4wA3M8uUA9zMLFMOcDOzTDnAzcwy5QA3M8uUA9zMLFMOcDOzTDnAzcwy5QA3M8vUsDoXiqR2l7CHsWPHtrsEMxumhk2AN/McGT7nhpnlwF0oZmaZGjYtcGu+TuuScneU2e4c4FaVu6TMOp+7UMzMMlUzwCWNknSfpIckPSbpyjS+S9JCSU+ka7+/NTMbQvW0wF8E3h4RxwDTgD+U9AfAZcCiiJgMLErDZmY2RGoGeBR+nQZHpksAZwE9aXwPMKMlFZqZWVV19YFLGiFpObABWBgR9wITImIdQLoe37oyzcysUl0BHhHbI2Ia8BrgBElT612BpIsl9Urq3bhx42DrNDOzCgP6FkpEbAKWAH8IrJc0ESBdb+hnnnkR0R0R3ePGjWuwXDMzK6nnWyjjJI1Jt/cHTgNWAncAM9NkM4HbW1WkmZntqZ4f8kwEeiSNoAj8myPiLkn3ADdLmgU8DZzTwjrNzKxCzQCPiIeBY6uMfxY4tRVFmZlZbf4lpplZphzgZmaZcoCbmWXKAW5mlikHuJlZphzgZmaZcoCbmWXKAW5mlikHuJlZphzgZmaZcoCbmWXKAW5mlikHuJlZphzgZmaZcoCbmWXKAW5mlikHuJlZphzgZmaZcoCbmWXKAW5mlikHuJlZpmr+K/1wIqmp00ZEI+WYmTXkZRXgDlwzG07chWJmlikHuJlZphzgZmaZeln1gVtz+UNhs/ZygNugOXDN2stdKGZmmXKAm5llygFuZpapmgEu6XckLZa0QtJjki5J47skLZT0RLoe2/pyzcyspJ4W+EvAhyJiCvAHwPslvR64DFgUEZOBRWnYzMyGSM0Aj4h1EfFAuv0CsAI4DDgL6EmT9QAzWlWkmZntaUB94JImAccC9wITImIdFCEPjO9nnosl9Urq3bhxY2PVmpnZTnUHuKQDgW8Cl0bE5nrni4h5EdEdEd3jxo0bTI1mZlZFXT/kkTSSIrznR8S/pNHrJU2MiHWSJgIbai1n2bJlz0haPfhyh8whwDPtLmIY8fZsHm/L5sple7622siaAa7iN9DXASsi4rNld90BzAQ+ma5vr7WsiMiiCS6pNyK6213HcOHt2Tzels2V+/aspwV+IvBu4BFJy9O4v6UI7pslzQKeBs5pTYlmZlZNzQCPiKVAf2ciOrW55ZiZWb38S8zq5rW7gGHG27N5vC2bK+vtKZ9RzswsT26Bm5llygFuZpap7AJc0qslfUPSk5J+Kulbkn633XWVSFoiqTvd/pakMe2uqRZJ2yUtl/SopDubVbOkj0k6rRnLypGkX9cxzVvTSeKWS9p/L9OtknRIlfHvlfSng6zvFEl3DWbedhjosS/pA+kkfPMlzUjncGpWLR1xbGcV4Ok76bcBSyLiyIh4PcVXGie0t7LqIuKMiNjU7jrqsCUipkXEVOA54P3NWGhEXB4R/96MZQ1jFwKfTtt/y0BnjogvRsSNLairowzy2H8fcEZEXEhxrqamBXinHNtZBTgwHdgWEV8sjYiI5cCDkhZJekDSI5LOguLcLZJWSuqR9LCkWyUdkO47VdKDafqvStovjV8l6ROS7knncPl9Sd9Nr/rvTdPs1nKRdI2kiyqLLbWaUh0rJH05tba+t7fWVpvdQ3GyMiSdIOnHaTv9WNJRafxFkv41tdafkvSXkv4qTfcTSV1puhsknZ1uH5+W8ZCk+yQdlLbL3el5e0DSW9K0p6R3Mrem529+OoCRdJykH0halp6XiW3ZSgPQ3+OR9D+Bc4HL07ha+9VH0ra7T9Lr0jRzJX043V4i6VPp/sclvTWNHyXp+rSvPyhp+tA9+qapeuxHxN2SPiLp/nSMXwkg6YvAEcAdkuYAZwL/V8U7nSMl/Xma5yFJ3yzLhRsk/bOKU2j/XNLbUj6skHRDad2dcmznFuBTgWVVxm8F3hURv0/xRH+mdMADRwHzIuKNwGbgfZJGATcA50XEGyi+D/8XZctbExFvBu5O051NcSrdjzVQ+2TgnyLiaGAT8D8aWFZLSBpB8d3+O9KolcDJEXEscDnwibLJpwJ/DJwAXAX8Jk13D7DbW3pJrwBuAi6JiGOA04AtFKdfOD09b+cBXyib7VjgUopW0xHAiSpO6XA1cHZEHAd8Na07B3s8noj4CsW2/khqJdayOSJOAK4BPtfPNPumaS4Frkjj3g+Q9vULgJ50DOSk6rEv6R0Ux9YJwDTgOEknR8R7gbXA9Ii4il3beVpEPAn8S0Qcn/bHFcCsssWOBd4OfBC4E/hH4GjgDZKmVamtbcf2cPlTYwGfkHQysIOiBVl6a7UmIn6Ubn8N+ACwEHgqIh5P43sodvLSQVEKsEeAA9NpdF+QtFWD7/d6Kr1bgGJHnDTI5bTC/ip+ZTuJoraFafxoioN9MhDAyLJ5Fpdtl+cpdnQottkbK5Z/FLAuIu4HKJ0MTdIrgWvSQbEdKO/PvC8ifpGmK9W2ieJAXphen0cA6xp65EOn2uNZOsBlLCi7/sd+pimdq6h8HzuJ4oWPiFip4nxEHfO5UYPekS4PpuEDKQL1hzXmmyrp48CYNM93y+67MyJC0iPA+oh4BEDSYxTbdHnFstp2bOcW4I9RtIYrXQiMA46LiG2SVgGlFkblF92D/n9ZWvJiut5Rdrs0vC/Fn1yUv3uppzVTvpztQCd1oWyJiGmSRgN3UbyYfQH43xRB/S4VpxJeUjZP5XYp32aV+5XY83mAooWzHjiGYntu7Wf529MyBTyW3h3lptrjqVRrv4p+bldbT/k6au3vOejv2BfwDxHxpQEu7wZgRkQ8lLqpTim7r9bxX6ltx3ZuXSjfB/aT9OelEZKOpzhT14YU3tPZ/cxdh0sqHfAXULR6VgKTSv2IFOd6+cEA6lgNvF7Sfin0hsUpBSLieYp3KB9O3RWjgV+muy9qYNErgUPTc0Xq/943LX9dROygeA5G1FjOz4BxpedT0khJRzdQV6eptV+dV3Z9zwCW+0OKRg4qvrVxOMW2zEl/x/5m4M9UnO4aSYdJqvbfBC8AB5UNHwSsS/t5Pd1XHSmrAI/iZ6PvAk5X8aHiY8Bc4FtAt6ReiidjZdlsK4CZkh4GuoB/joitwHuAW9LbpB3AF6lTRKwBbgYeBuaz6+1b9iLiQeAh4Hzg/wD/IOlH1A7XvS3ztxShc7Wkhyi6aEYB11I8Nz+heEv/n3Us52zgU2k5y4G3DLauTlPHfrWfpHuBSyjevdTrWmBE2tdvAi6KiBdrzNNR9nLsfz1d7kmP71Z2D+qSb1B8CPygpCOBv6f4Y5qF7J4XWRnWP6VPb/vvSl+PMzMbVrJqgZuZ2S7DugVuZjacuQVuZpYpB7iZWaYc4GZmmXKAm5llygFuZpap/w+V5y0q+7t/CAAAAABJRU5ErkJggg==\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",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Final Tumor Volume for Ramicane')\n",
    "ax.boxplot(tumor_vol_list)\n",
    "plt.xticks([1, 2, 3, 4],['Capomulin','Ramicane','Infubinol','Ceftamin'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tumor Volume (mm3)')"
      ]
     },
     "execution_count": 42,
     "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 tumor volume vs. time point for a mouse treated with Capomulin\n",
    "capo_mouse=study_data_clean.loc[(study_data_clean['Drug Regimen'] == 'Capomulin')]\n",
    "capo_mouse\n",
    "j246=capo_mouse.loc[(capo_mouse['Mouse ID']=='j246')]\n",
    "j246\n",
    "time=j246['Timepoint']\n",
    "plt.plot(time, j246['Tumor Volume (mm3)'])\n",
    "plt.title('Tumor Volume Over Time')\n",
    "plt.xlabel('Timepoint')\n",
    "plt.ylabel('Tumor Volume (mm3)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26b0d4beb20>"
      ]
     },
     "execution_count": 43,
     "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 average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "capo_mouse1=capo_mouse.groupby('Mouse ID')\n",
    "avg=capo_mouse1.mean()\n",
    "avg_tumor=avg['Tumor Volume (mm3)']\n",
    "x=avg['Weight (g)']\n",
    "plt.scatter(x,avg_tumor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026B0D49BA00>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capo_mouse1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
    "weight=avg['Weight (g)']\n",
    "tumor_vol=avg['Tumor Volume (mm3)']\n",
    "correlation=st.pearsonr(weight,tumor_vol)\n",
    "print(f\"The correlation between mouse weight and the average tumor volume is {round(correlation[0],2)}\")\n",
    "# age = who_data.iloc[:,3]\n",
    "# cell_phones = who_data.iloc[:,10]\n",
    "# correlation = st.pearsonr(age,cell_phones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}