pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation Analysis: \n",
    "* The bar graph showed the Drug Regimen Capomulin has the maximum mice number (230), and Zoniferol has the smaller mice number (182).By removing duplicates the total number of mice is 248. The total count of mice by gender also showed that 124 female mice and 125 male mice.\n",
    "* The correlation between mouse weight, and average tumor volume is 0.84. It is a strong positive correlation, when the mouse weight increases the average tumor volume also increases.\n",
    "* The regression analysis helped to understand how much the average tumor volume (dependent variable) will change when weight of mice change(independent variables). The R-squared value is 0.70, which means 70% the model fit the data, wich is fairely good to predict the data from the model. Higher R-squared values represent smaller differences between the observed data, and the fitted value. 70% the model explains all of the variation in the response variable around its mean.\n",
    "* From the selected treatments Capomulin and Ramicane reduces the size of tumors better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x401</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m601</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>g791</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen     Sex  Age_months  Weight (g)\n",
       "0     k403     Ramicane    Male          21          16\n",
       "1     s185    Capomulin  Female           3          17\n",
       "2     x401    Capomulin  Female          16          15\n",
       "3     m601    Capomulin    Male          22          17\n",
       "4     g791     Ramicane    Male          11          16"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data \n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "mouse_metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites\n",
       "0     b128          0                45.0                 0\n",
       "1     f932          0                45.0                 0\n",
       "2     g107          0                45.0                 0\n",
       "3     a457          0                45.0                 0\n",
       "4     c819          0                45.0                 0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the study results\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "study_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine the above data into a single dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the above data into a single dataset\n",
    "combined_df = pd.merge(mouse_metadata, study_results, how='outer', on=\"Mouse ID\")\n",
    "# Display the data table for preview\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "num_mice = combined_df[\"Mouse ID\"].nunique()\n",
    "num_mice\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Duplicate:\n",
    "*Before beginning the analysis, check the data for any mouse ID with duplicate time points and remove any data associated with that mouse ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint.\n",
    "dup_mice_ID = combined_df.loc[combined_df.duplicated(subset=['Mouse ID', 'Timepoint']),'Mouse ID'].unique()\n",
    "dup_mice_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
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       "      <td>45.000000</td>\n",
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       "      <th>910</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
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       "      <th>912</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "908     g989     Propriva  Female          21          26          0   \n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "910     g989     Propriva  Female          21          26          5   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "912     g989     Propriva  Female          21          26         10   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "914     g989     Propriva  Female          21          26         15   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "916     g989     Propriva  Female          21          26         20   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "918     g989     Propriva  Female          21          26         25   \n",
       "919     g989     Propriva  Female          21          26         30   \n",
       "920     g989     Propriva  Female          21          26         35   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "908           45.000000                 0  \n",
       "909           45.000000                 0  \n",
       "910           48.786801                 0  \n",
       "911           47.570392                 0  \n",
       "912           51.745156                 0  \n",
       "913           49.880528                 0  \n",
       "914           51.325852                 1  \n",
       "915           53.442020                 0  \n",
       "916           55.326122                 1  \n",
       "917           54.657650                 1  \n",
       "918           56.045564                 1  \n",
       "919           59.082294                 1  \n",
       "920           62.570880                 2  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "dup_mice_df = combined_df.loc[combined_df[\"Mouse ID\"] == \"g989\", :]\n",
    "dup_mice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "clean_df = combined_df[combined_df['Mouse ID'].isin(dup_mice_ID)==False]\n",
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "clean_mice = clean_df[\"Mouse ID\"].nunique()\n",
    "\n",
    "clean_mice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary Statistics\n",
    "* Use the cleaned data for the remaining steps.\n",
    "* Generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Tumor Volume</th>\n",
       "      <th>Median Tumor Volume</th>\n",
       "      <th>Tumor Volume Variance</th>\n",
       "      <th>Tumor Volume Std. Dev.</th>\n",
       "      <th>Tumor Volume Std. Err.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</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.  Tumor Volume Std. Err.  \n",
       "Drug Regimen                                                  \n",
       "Capomulin                   4.994774                0.329346  \n",
       "Ceftamin                    6.268188                0.469821  \n",
       "Infubinol                   6.567243                0.492236  \n",
       "Ketapril                    8.279709                0.603860  \n",
       "Naftisol                    8.134708                0.596466  \n",
       "Placebo                     7.821003                0.581331  \n",
       "Propriva                    6.622085                0.544332  \n",
       "Ramicane                    4.846308                0.320955  \n",
       "Stelasyn                    7.710419                0.573111  \n",
       "Zoniferol                   6.966589                0.516398  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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",
    "mean = clean_df['Tumor Volume (mm3)'].groupby(clean_df['Drug Regimen']).mean()\n",
    "median = clean_df['Tumor Volume (mm3)'].groupby(clean_df['Drug Regimen']).median()\n",
    "var = clean_df['Tumor Volume (mm3)'].groupby(clean_df['Drug Regimen']).var()\n",
    "std = clean_df['Tumor Volume (mm3)'].groupby(clean_df['Drug Regimen']).std()\n",
    "sem = clean_df['Tumor Volume (mm3)'].groupby(clean_df['Drug Regimen']).sem()\n",
    "\n",
    "summary_stat = pd.DataFrame({\"Mean Tumor Volume\":mean, \n",
    "                            \"Median Tumor Volume\":median, \n",
    "                           \"Tumor Volume Variance\":var, \n",
    "                           \"Tumor Volume Std. Dev.\":std, \n",
    "                           \"Tumor Volume Std. Err.\":sem})\n",
    "# Display the Summary statistics table grouped by 'Drug Regimen' column\n",
    "summary_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</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": [
       "             Tumor Volume (mm3)                                          \n",
       "                           mean     median        var       std       sem\n",
       "Drug Regimen                                                             \n",
       "Capomulin             40.675741  41.557809  24.947764  4.994774  0.329346\n",
       "Ceftamin              52.591172  51.776157  39.290177  6.268188  0.469821\n",
       "Infubinol             52.884795  51.820584  43.128684  6.567243  0.492236\n",
       "Ketapril              55.235638  53.698743  68.553577  8.279709  0.603860\n",
       "Naftisol              54.331565  52.509285  66.173479  8.134708  0.596466\n",
       "Placebo               54.033581  52.288934  61.168083  7.821003  0.581331\n",
       "Propriva              52.320930  50.446266  43.852013  6.622085  0.544332\n",
       "Ramicane              40.216745  40.673236  23.486704  4.846308  0.320955\n",
       "Stelasyn              54.233149  52.431737  59.450562  7.710419  0.573111\n",
       "Zoniferol             53.236507  51.818479  48.533355  6.966589  0.516398"
      ]
     },
     "execution_count": 43,
     "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",
    "#stat = ['mean', 'median', 'var', 'std', 'sem']\n",
    "# Using the aggregation method, produce the same summary statistics in a single line\n",
    "summary_agg =  clean_df.groupby(['Drug Regimen'])[['Tumor Volume (mm3)']].agg(['mean', 'median', 'var', 'std', 'sem'])\n",
    "summary_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar and Pie Charts\n",
    "*Generate a bar plot using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the number of total mice for each treatment regimen throughout the course of the study.\n",
    "\n",
    "NOTE: These plots should look identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capomulin    230\n",
       "Ramicane     228\n",
       "Ketapril     188\n",
       "Naftisol     186\n",
       "Zoniferol    182\n",
       "Placebo      181\n",
       "Stelasyn     181\n",
       "Infubinol    178\n",
       "Ceftamin     178\n",
       "Propriva     148\n",
       "Name: Drug Regimen, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first create a group series using the 'Drug Regimen' column and, counting the number of data points for each Drug Regimen \n",
    "mice_count = clean_df[\"Drug Regimen\"].value_counts()\n",
    "mice_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Mice per Treatment')"
      ]
     },
     "execution_count": 46,
     "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": [
    "# Create a Pandas bar plot based off of the group series from before and label the title\n",
    "plot_pandas = mice_count.plot.bar(color='b')  \n",
    "# Set the xlabel, ylabel, and title using class methods\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice\")\n",
    "plt.title(\"Number of Mice per Treatment\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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 bar plot showing the total number of unique mice tested on each drug regimen using pyplot.\n",
    "x_axis = mice_count.index.values\n",
    "y_axis = mice_count.values\n",
    "\n",
    "# Create a Pyplot bar plot based off of the group series from before and label the title\n",
    "plt.bar(x_axis, y_axis, color='b', alpha=0.8, align='center')\n",
    "\n",
    "# Set the xlabel and ylabel, title using class methods\n",
    "plt.title(\"Number of Mice Tested per Treatment\")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
    "gender_data = clean_df[\"Sex\"].value_counts()\n",
    "plt.title(\"Female vs. Male Mice\")\n",
    "gender_data.plot.pie(autopct= \"%1.1f%%\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
    "labels = ['Female', 'Male']\n",
    "sizes = [49.7999197, 50.200803]\n",
    "plot = gender_data.plot.pie(y='Total Count', autopct=\"%1.1f%%\")\n",
    "plt.title('Male vs Female Mouse Population')\n",
    "plt.ylabel('Sex')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quartiles, Outliers and Boxplots\n",
    "* Put treatments into a list (and later for plot labels)\n",
    "* Create empty list to fill with tumor vol data (for plotting)\n",
    "* Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "* Locate the rows which contain mice on each drug and get the tumor volumes add subset \n",
    "* Determine outliers using upper and lower bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "Capomulin_df = clean_df.loc[clean_df[\"Drug Regimen\"] == \"Capomulin\",:]\n",
    "Ramicane_df = clean_df.loc[clean_df[\"Drug Regimen\"] == \"Ramicane\", :]\n",
    "Infubinol_df = clean_df.loc[clean_df[\"Drug Regimen\"] == \"Infubinol\", :]\n",
    "Ceftamin_df = clean_df.loc[clean_df[\"Drug Regimen\"] == \"Ceftamin\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>45</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>38.982878</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b742</td>\n",
       "      <td>45</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>38.939633</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f966</td>\n",
       "      <td>20</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>30.485985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g288</td>\n",
       "      <td>45</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>37.074024</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>g316</td>\n",
       "      <td>45</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>40.159220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint Drug Regimen     Sex  Age_months  Weight (g)  \\\n",
       "0     b128         45    Capomulin  Female           9          22   \n",
       "1     b742         45    Capomulin    Male           7          21   \n",
       "2     f966         20    Capomulin    Male          16          17   \n",
       "3     g288         45    Capomulin    Male           3          19   \n",
       "4     g316         45    Capomulin  Female          22          22   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           38.982878                 2  \n",
       "1           38.939633                 0  \n",
       "2           30.485985                 0  \n",
       "3           37.074024                 1  \n",
       "4           40.159220                 2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "# Capomulin\n",
    "Capomulin_last = Capomulin_df.groupby('Mouse ID').max()['Timepoint']\n",
    "Capomulin_vol = pd.DataFrame(Capomulin_last)\n",
    "Capomulin_merge = pd.merge(Capomulin_vol, clean_df, on=(\"Mouse ID\",\"Timepoint\"),how=\"left\")\n",
    "Capomulin_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of Capomulin tumors: 32.37735684\n",
      "The upper quartile of Capomulin tumors: 40.1592203\n",
      "The interquartile range of Capomulin tumors: 7.781863460000004\n",
      "The median of Capomulin tumors: 38.125164399999996 \n",
      "Values below 20.70456164999999 could be outliers.\n",
      "Values above 51.83201549 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "Capomulin_tumors = Capomulin_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "quartiles =Capomulin_tumors.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "\n",
    "print(f\"The lower quartile of Capomulin tumors: {lowerq}\")\n",
    "print(f\"The upper quartile of Capomulin tumors: {upperq}\")\n",
    "print(f\"The interquartile range of Capomulin tumors: {iqr}\")\n",
    "print(f\"The median of Capomulin tumors: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of Ramicane tumors is: 31.56046955\n",
      "The upper quartile of Ramicane tumors is: 40.65900627\n",
      "The interquartile range of Ramicane tumors is: 9.098536719999998\n",
      "The median of Ramicane tumors is: 36.56165229 \n",
      "Values below 17.912664470000003 could be outliers.\n",
      "Values above 54.30681135 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "\n",
    "Ramicane_last = Ramicane_df.groupby('Mouse ID').max()['Timepoint']\n",
    "Ramicane_vol = pd.DataFrame(Ramicane_last)\n",
    "Ramicane_merge = pd.merge(Ramicane_vol, clean_df, on=(\"Mouse ID\",\"Timepoint\"),how=\"left\")\n",
    "Ramicane_merge.head()\n",
    "Ramicane_merge.to_csv(\"output.csv\")\n",
    "Ramicane_tumors = Ramicane_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "quartiles =Ramicane_tumors.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "\n",
    "print(f\"The lower quartile of Ramicane tumors is: {lowerq}\")\n",
    "print(f\"The upper quartile of Ramicane tumors is: {upperq}\")\n",
    "print(f\"The interquartile range of Ramicane tumors is: {iqr}\")\n",
    "print(f\"The median of Ramicane tumors is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a577</td>\n",
       "      <td>30</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>57.031862</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a685</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>66.083066</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c139</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>72.226731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint Drug Regimen     Sex  Age_months  Weight (g)  \\\n",
       "0     a203         45    Infubinol  Female          20          23   \n",
       "1     a251         45    Infubinol  Female          21          25   \n",
       "2     a577         30    Infubinol  Female           6          25   \n",
       "3     a685         45    Infubinol    Male           8          30   \n",
       "4     c139         45    Infubinol    Male          11          28   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           67.973419                 2  \n",
       "1           65.525743                 1  \n",
       "2           57.031862                 2  \n",
       "3           66.083066                 3  \n",
       "4           72.226731                 2  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infubinol \n",
    "# Determine outliers using upper and lower bounds\n",
    "\n",
    "Infubinol_last = Infubinol_df.groupby('Mouse ID').max()['Timepoint']\n",
    "Infubinol_vol = pd.DataFrame(Infubinol_last)\n",
    "Infubinol_merge = pd.merge(Infubinol_vol, clean_df, on=(\"Mouse ID\",\"Timepoint\"),how=\"left\")\n",
    "Infubinol_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of Infubinol tumors is: 54.04860769\n",
      "The upper quartile of Infubinol tumors is: 65.52574285\n",
      "The interquartile range of Infubinol tumors is: 11.477135160000003\n",
      "The median of Infubinol tumors is: 60.16518046 \n",
      "Values below 36.83290494999999 could be outliers.\n",
      "Values above 82.74144559000001 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "Infubinol_tumors = Infubinol_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "quartiles =Infubinol_tumors.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "\n",
    "print(f\"The lower quartile of Infubinol tumors is: {lowerq}\")\n",
    "print(f\"The upper quartile of Infubinol tumors is: {upperq}\")\n",
    "print(f\"The interquartile range of Infubinol tumors is: {iqr}\")\n",
    "print(f\"The median of Infubinol tumors is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")\n",
    "Infubinol_merge.to_csv(\"output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b447</td>\n",
       "      <td>0</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b487</td>\n",
       "      <td>25</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>56.057749</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b759</td>\n",
       "      <td>30</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>55.742829</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f436</td>\n",
       "      <td>15</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>48.722078</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint Drug Regimen     Sex  Age_months  Weight (g)  \\\n",
       "0     a275         45     Ceftamin  Female          20          28   \n",
       "1     b447          0     Ceftamin    Male           2          30   \n",
       "2     b487         25     Ceftamin  Female           6          28   \n",
       "3     b759         30     Ceftamin  Female          12          25   \n",
       "4     f436         15     Ceftamin  Female           3          25   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           62.999356                 3  \n",
       "1           45.000000                 0  \n",
       "2           56.057749                 1  \n",
       "3           55.742829                 1  \n",
       "4           48.722078                 2  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ceftamin\n",
    "Ceftamin_last = Ceftamin_df.groupby('Mouse ID').max()['Timepoint']\n",
    "Ceftamin_vol = pd.DataFrame(Ceftamin_last)\n",
    "Ceftamin_merge = pd.merge(Ceftamin_vol, clean_df, on=(\"Mouse ID\",\"Timepoint\"),how=\"left\")\n",
    "Ceftamin_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of treatment is: 48.72207785\n",
      "The upper quartile of temperatures is: 64.29983003\n",
      "The interquartile range of temperatures is: 15.577752179999997\n",
      "The the median of temperatures is: 59.85195552 \n",
      "Values below 25.355449580000002 could be outliers.\n",
      "Values above 87.66645829999999 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Ceftamin quartiles and IQR\n",
    "# Determine outliers using upper and lower bounds\n",
    "\n",
    "Ceftamin_tumors = Ceftamin_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "quartiles = Ceftamin_tumors.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "print(f\"The lower quartile of treatment is: {lowerq}\")\n",
    "print(f\"The upper quartile of temperatures is: {upperq}\")\n",
    "print(f\"The interquartile range of temperatures is: {iqr}\")\n",
    "print(f\"The the median of temperatures is: {quartiles[0.5]} \")\n",
    "\n",
    "# Determine outliers using upper and lower bounds\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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 box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "data_to_plot = [Capomulin_tumors, Ramicane_tumors, Infubinol_tumors, Ceftamin_tumors]\n",
    "Regimen = ['Capomulin', 'Ramicane', 'Infubinol','Ceftamin']\n",
    "#data_to_plot=data_to_plot.values\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Tumor Volume at Selected Mouse')\n",
    "ax1.set_ylabel('Final Tumor Volume (mm3)')\n",
    "ax1.set_xlabel('Drug Regimen')\n",
    "ax1.boxplot(data_to_plot, labels=Regimen, widths = 0.4, vert=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recalculated: Quartiles, Outliers and Boxplots using for loop \n",
    "\n",
    "   * Create empty list to fill with tumor vol data (for plotting)\n",
    "   * Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "   * Locate the rows which contain mice on each drug and get the tumor volumes add subset \n",
    "   * Determine outliers using upper and lower bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 59,
     "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",
    "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "drug_list = [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "drugs = combined_df[combined_df[\"Drug Regimen\"].isin(drug_list)]\n",
    "drugs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin\n",
      "Ramicane\n",
      "Infubinol\n",
      "Ceftamin\n"
     ]
    }
   ],
   "source": [
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "last_timepoint = drugs.groupby([\"Drug Regimen\", \"Mouse ID\"]).agg(tumor_size=(\"Tumor Volume (mm3)\", lambda x: x.iloc[-1]))\n",
    "#Reshape dataframe\n",
    "last_timepoint = last_timepoint.stack(level=0).unstack(level=0)\n",
    "for drug in drug_list:\n",
    "    print(drug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "The lower quartile of Capomulin treatments is: 32.38\n",
      "The upper quartile of Capomulin treatments is: 40.16\n",
      "The interquartile range of Capomulin treatments is: 7.78\n",
      "Values below 20.71 could be Capomulin outliers.\n",
      "Values above 51.83 could be Capomulin outliers.\n",
      "------------------------------------------------------------\n",
      "The lower quartile of Ramicane treatments is: 31.56\n",
      "The upper quartile of Ramicane treatments is: 40.66\n",
      "The interquartile range of Ramicane treatments is: 9.1\n",
      "Values below 17.91 could be Ramicane outliers.\n",
      "Values above 54.31 could be Ramicane outliers.\n",
      "------------------------------------------------------------\n",
      "The lower quartile of Infubinol treatments is: 54.05\n",
      "The upper quartile of Infubinol treatments is: 65.53\n",
      "The interquartile range of Infubinol treatments is: 11.48\n",
      "Values below 36.83 could be Infubinol outliers.\n",
      "Values above 82.75 could be Infubinol outliers.\n",
      "------------------------------------------------------------\n",
      "The lower quartile of Ceftamin treatments is: 48.72\n",
      "The upper quartile of Ceftamin treatments is: 64.3\n",
      "The interquartile range of Ceftamin treatments is: 15.58\n",
      "Values below 25.35 could be Ceftamin outliers.\n",
      "Values above 87.67 could be Ceftamin outliers.\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)   \n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "# Locate the rows which contain mice on each drug and get the tumor volumes add subset \n",
    "# Determine outliers using upper and lower bounds\n",
    "treatment = 0\n",
    "for drug in drug_list:\n",
    "    quartiles = last_timepoint[drug].quantile([.25,.5,.75]).round(2)\n",
    "    lowerq = quartiles[0.25].round(2)\n",
    "    upperq = quartiles[0.75].round(2)\n",
    "    iqr = round(upperq-lowerq,2)\n",
    "    lower_bound = round(lowerq - (1.5*iqr),2)\n",
    "    upper_bound = round(upperq + (1.5*iqr),2)\n",
    "\n",
    "\n",
    "    if treatment == 0:\n",
    "        print(f\"------------------------------------------------------------\")\n",
    "    print(f\"The lower quartile of {drug} treatments is: {lowerq}\")\n",
    "    print(f\"The upper quartile of {drug} treatments is: {upperq}\")\n",
    "    print(f\"The interquartile range of {drug} treatments is: {iqr}\")\n",
    "    print(f\"Values below {lower_bound} could be {drug} outliers.\")\n",
    "    print(f\"Values above {upper_bound} could be {drug} outliers.\")\n",
    "    print(f\"------------------------------------------------------------\")\n",
    "    treatment+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_list = []\n",
    "for drug in drug_list:\n",
    "    boxplot_list.append(list(last_timepoint[drug].dropna()))\n",
    "    \n",
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "fig = plt.figure()\n",
    "plt.xlabel(\"Regimen\")\n",
    "plt.xticks([1,2,3,4], drug_list, rotation=45)\n",
    "plt.ylabel(\"Tumor Volume\")\n",
    "plt.title(\"Tumor Volume by Drug Regimen\")\n",
    "plt.boxplot(boxplot_list)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line and Scatter Plots\n",
    "* Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "* Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "* Start by getting the last (greatest) timepoint for each mouse\n",
    "* Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n",
    "* Put treatments into a list for for loop (and later for plot labels)\n",
    "* Create empty list to fill with tumor vol data (for plotting)\n",
    "* Calculate the IQR and quantitatively determine if there are any potential outliers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tumor Volume (mm3)')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9+zfuXHWlRC5GrVvDkUfCVVd5c08E/lBuWiI752nAK8BGAGb21cwGSZcAn7RAGSpavjNfPfQQnHGGZ8sE76Z54YU+jWEx1dALYeTItYH/ggvK//2GylLQwC+pJ3AocAHwk1rbBByNT7geCiDfma9mzvSA/8gj/rxnT/jVr+C447w2XAn22Qeqqvxm9QsvwBe/mHaJQmg+hb65exkwGlhTx7Z9gHlm9npdB0oaJWm6pOkLFiwoZBnL0pIlPgnK6ad718SlSz0Hjpn/XLp07cxXBx7o+8+Z45Od7LabB/0uXeB3v/N++ccfXzlBH3z08JFH+uPo3RPKTcECv6RhwHwzm7GeXb4GjF/f8WY21syqzay6qqqqIGUsVzU1Pt3htGkN58NftsybgHbcEXbYAcaP91w5p58Os2f7N4JCzHpVCiJ3TyhXhazx7wWMkPQW8DdgoKSbASS1AY4E/l7A169Y+cx89Z//eDrk447zGv7vfw9duxa2nMVuv/2gWze/HrNmpV2aEJpPwQK/mZ1lZj3NrDdwDPComR2bbD4IeNXM5hbq9StVU2a+2nJL+MtfYOutm79cpahNGzjiCH8cuXtCOUlrANcx1NPME/LXlJmvPv7Yjw9rRXNPKEctEvjNbLKZDct6fryZXd0Sr11pSm3mq2J3wAGw6abw6quefyiEchApG8pMqc18VeyiuSeUowj8ZaYUZ74qdtmpmkMoBxH4y0z//vkH/rRmvip2Awd6D6eXX/YlhFIXgb/MbL65JxnLR1ozXxW7tm3h8MP9cdT6QzmIwF8mzODaa6FvX/j008Yfn/bMV8Uu09wT7fyhHETgLwNz5/pI3VGjPPXCEUfAl79cWjNfFbsDD4SNN4YXX/QePiGUsgj8JczMB1ztvDM8+KC3Q48f7xOeP/JIac18VezatYvmnlA+IvCXqA8+gMMO8+Rpn3wCw4d7bfSYY7y5phRnvip22YO5QihlMQNXiTGDv/0NTj3VZ8Lq0gUuvxy++c31t8+XysxXxW7lSk9l/ckn3tyz444NHxNCmlKbgSs0nwULfCLzO+7w54ccAtdd5/ny61MqM18Vu3bt/FvWjTf6Td6f/zztEoWQn2jqKRF33AFf+IL/7NQJxo6F++9vOOiH5hXNPaEcROAvch995JOjHHWU1/gPOMBTBH/3u9FMk4aDD4aNNoLnn4fX65xCKITiF4G/iE2Y4LX88eO9980VV3hvnd690y5Z5WrfHkaM8MfRpz+Uqgj8Rejjj723zvDh3ntn7729hnnKKdAqfmOpi+aeUOoijBSZBx+EXXbx/vkbbACXXAKTJ8N226VdspAxaJD3jHr2WXjzzbRLE0Lj5dSrR1I1Pjn6FsBy4EXgETP7qIBlK3mZbpRTp67bjXKPPT7bPr94sc9ze+21/vzLX4YbboCddmrxYocGbLCBN/f89a9e6z/zzLRLFELj1Fvjl3S8pJnAWcCGwGvAfGBv4GFJf5HUq4FztJb0rKQJWet+IOk1SS9Juqjpb6O41NTA1VfDttt67fCMM+Dcc/3noEG+/uqr1+bNf+wxr+Vfe613Gfztb+HJJyPoF7PI3RNKWUM1/o7AXma2vK6NkvoB2wPv1HOO04BXgI2SYw4ADgP6mtkKSd0bXeoitmSJ582ZOfOz896uXOnLnDleu7/pJk+3MHasb99117UpGEJxO+QQ71Y7YwbMnu2joEMoFfXW+M3syvUF/WT7c2Y2aX3bJfUEDgWuy1p9EnChma1IzpHnDLHFp6bGg/60aQ1Pdr5sGfzrXx70W7eG88+Hp5+OoF8qNtzQb75D1PpD6WmoqUeSjpY0Mnl8oKQ/SDpZUi43hi8DRgNrstbtAOwj6RlJj0uqc+oPSaMkTZc0fcGCBTm/oTRdf73X9FesyP0YyduIzzknkqSVmmjuCaWqoeB9JXA0cBxwE/B9YDqwL3BpfQdKGgbMN7MZtTa1ATYBBgA/A26VPjsUyczGmlm1mVVXVVXl8l5SZQYXXdRwTb+u48aP95+htAwZ4snupk2Dt95KuzQh5K6hwL+PmR0FfAUYAnzDzG4EjgUGNnDsXsAISW8BfwMGSroZmAvcaW4q/m2gWxPeQ1GYMgXm59loNW+eHx9Ky4YbwrBh/jhq/aGUNBT4VwGYWQ0wzcxWJs9XAfVO8GdmZ5lZTzPrDRwDPGpmxwJ3k3xoSNoBaAcsbMqbKAZTp67tpdNYq1Z5rTGUnhjMFUpRQ4H/A0mdAMxscGalpM2AlXm+5jigj6QX8W8C37JSyA3dgMWL8w/8K1f68aH0DBni6TSmToW33067NCHkpqFePUPMbEkdmxYBw3J9ETObbGbDkscrzexYM9vZzHY1s0cbV+Ti1Llz/jdn27Xz40Pp6dABDj3UH2fSZYdQ7HJO2SCpr6QRko4EBuODuEKif39ok+fsBm3awO519m0KpSCae0KpyTVlwzigL/ASa7tmGnBngcpVUlav9m6cy9c74qF+PXp4GodQmoYO9Ru9Tz8N774LW22VdolCqF+uddQBZvb5gpakRM2aBaNG+T89+GCs1fXe9l5Xhw4wenTk1i9lHTt68L/jDl9+9KO0SxRC/XJt6pkiKQJ/luXLfeq9XXf1oL/55vD3v3vNvX373M7Rvj3sthuccEJhyxoKL5p7QinJNfD/BQ/+r0l6QdIsSS8UsmDF7NFHoW9f+M1vvCvmSSfBK6/A0Uf7dIj9+3tNvj4dOvh+EyfGiN1ycOihnrXzX/+CuXPTLk0I9cs18I/DR+8OBobjPXqGF6pQxerDD+Hb34YDD4Q33oDPf96zaP7pT9Cli+/TqRNMmgRjxnjiro4dvWYv+c+OHX39mDG+X6dO6b6n0Dw6dfKunRC9e0LxUy5d6CU9amYNjdQtmOrqaps+fXpaL48Z3HKLt90uXOjdL3/xC2+bb9eu/uOmTPHBWdn5+AcMiDb9cjR+vM+PvPfe8MQTaZcmBJA0w8yqP7M+x8D/J2Bj4F7gfynIzKxFevWkGfhnz/amnIce8uf77QfXXAM77phKcUIRW7wYqqp8QN7cubDFFmmXKFS69QX+XJt6NsQD/iC8iSfT3FO2Vq2Ciy/2NMkPPQSbbOLZNx97LIJ+qFvnzt7cYxbNPaG45dSd08y+XeiCFJNp07yL5nPP+fOvfQ0uvdT724dQn6OOgrvv9t49P/hB2qUJoW65DuDaBvgB0Dv7GDMbUZhipWPJEm+7/8MfYM0a6N0brroKBg9u8NAQAJ+cpX17v+n//vvezTeEYpPrAK67gevxNv41DeybusZMcp5x331w8snwzjvQqpVPjXj++d4LJ4RcbbSRT8t4zz1w551wyilplyiEz8o18H9qZn8oaEmaQU2Nt8NfdJHnxq+p8aVtW1+6d/eeOCeeuLbv/AcfwGmnwa23+vNdd/VJz3fdNb33EUrbyJEe+G+7LQJ/KE659ur5Oj6p+kOs26tnZuGKtlYuvXrqm+Q8W4cOPlp2wgQfaTt6NHz8sa//1a/ghz/MP9laCACffOKVjJoab+6Je0MhLevr1ZNriNsFH8A1kHWTtKXWtz9b9iTnDc13u2wZPPOMJ9JatMjXDRnig7B69y54UUMF6NIFBg3yysWdd3p34BCKSa7dOY8A+pjZfmZ2QLIURdCHxk9yvnKlB/1OnXzQzX33RdAPzSty94Rilmvgfx4fwNVoklpLelbShOT5eZLek/RcsgzN57wZ+U5yDrDppvDVr8Yo2tD8Rozw+0iPP57/XMwhFEqugb8H8KqkByXdk1lyPPY04JVa6y41s37JMjHn0tahKZOcL1wYk5yHwth4Yzj4YO8WfNddaZcmhHXlGvjPxZt7fgNckrXUS1JP4FDgunwL2JCY5DwUq2juCcWq3pu7kmTu8Yb2Wc/my4DRQO0ZZU+V9E1gOnC6mf23jvOOAkYB9OrVa71ljEnOQ7E67DDvIfbYY7BggefxKTb5jHkJpa+hGv9jkn4gaZ3IK6mdpIGS/gJ8q64DJQ0D5pvZjFqbrgK2BfoB77Oebw5mNtbMqs2suqqe/5iY5DwUq002gYMOKs7mnpoauPpq2HZb74F0xhlw7rn+c9AgX3/11flXqkJxayjwDwZWA+Ml/UfSy5JmA68DX8Pb6m9Yz7F7ASMkvQX8DRgo6WYzm2dmq81sDXAt0L8pb6B///wDf0xyHgot09xz++3pliPbkiUwcKCPTp8zB5Yu9W+/Zv5z6VJff/rpPvfEkiVplzg0t5wGcAFIagt0A5ab2ceNehFpf+CnZjZM0uZm9n6y/sfAl83smPqOr28Al5nXTubMaUyJXJ8+PqFKfKUNhfLRRz6Ay8xHiXfrlm55amo86Ocy5gU871D//j5pUMwUV3qampYZM6sxs/cbG/TrcFHW1I0HAD9uyskkH33b0FSHtcUk56EldO3qtebVqz1rZ9oaO+ZlxQqYMQPGjStsuULLyrnGn6aGUjZELSYUs+uug+9+19vOH3wwvXLEt+PK0+QafzFr2zYmOQ/F6/DDoXVrr2h8+GF65WjKmJd582LMSznJOfBL2lrSQcnjDSUVVX+YmOQ8FKtu3fwb6erV8I9/pFeOGPMSMnKdiOW7eJ/6rnhXzJ7A1cCBhSta47VtC9/7ns+eFZOch2IyciQ8/LAP5jrhhHTKEGNeQkau2TlPwbtdPgNgZq9L6l6wUjWRBHvu6UsIxeDwwz1L5yOPeE+frl1bvgyZMS8rVzb+2BjzUl5ybepZYWb/+3OR1AZPyxxCyEFVFey/vzeZ3JNrlqtmtHIlzJ2bf40/xryUl1wD/+OS/g/YUNLBwG34NIwhhBylkbtn9Wq48UbYaSe45BLv2ZOPHj08jUMoD7kG/jOBBcAs4HvARODsQhUqhHJ0xBE+n/PDD/usb4VkBnfcAX37wre+5V04d9rJu5XGmJeQU+A3szVmdq2ZjTSzo5LH0dQTQiN07w777efNLYVq7jHzsQK77w5HHQUvv+yTDN1wA8yaBVde6fNJt2+f2/nat/epStO6IR0KI6fAL2lYMpnKR5IWSVosaVGhCxdCuSlkc88TT/gHy+DBPtp2s8080L/2mtf627SJMS/B5drUcxmehXNTM9vIzDqb2UYFLFcIZenII73J5KGHfFL25jBzJgwdCvvu68G/a1f43e/gzTfh5JO9R062+sa8tEoiwqabxpiXcpZr4H8XeDGad0Jomh49PECvXNn05p5XXvFvELvt5rX4Tp3gnHNg9uyG81dlxry88YZ/CP3ud/DLX8LXvubb+/Xz7VHTL0+59uMfDUyU9Djwv2w4ZjamIKUKoYyNHOlz8Y4d6ykcGjsBypw5cP75cNNNnut/gw3glFPgzDMbn/2z9piXhQth/Hj45z9h0SLYKL7Xl6VcA/8FwBJgA6BdA/uGENajpsbz3QM8+eTaNApt2/rSvbvX1k888bO17fffh1//Gq691o9p08ZHqZ99Nmy5ZfOUr1s3//B56in/JnDUUc1z3lBccg38Xc1sUEFLEkKZW7IEhgzxNvmMzCjalSt9yUyAcsstflO1Uyf/VvC738EVV8Dy5V5LP+44OO88b6NvbsOHe+CfMCECf7nKtY3/EUkR+EPIU02NB/1p02DZsvr3XbbMvwkMGuRt9ttsAxdf7EH/yCO9W+aNNxYm6AMMG+Y/77vPB4CF8tOYXD2jJa0AagABFj17QshNPhOgTJmyNhXyIYd4M0/1ZzKrN7/Pf94/bObM8Q+gGLFbfnIdwNXZzFqZ2YaN7c4pqXUyBmBCrfU/lWSSUp6MLoTCMoOLLmq4pl+X9u1h8mR44IGWCfrgTUnDh/vjeyMxS1nKdQDXvnUtOb7GacArtc63FXAw8E7jihtC6WnKBCiZQVctLdPcM2FC/fuF0pRrG//PspZf4AnazmvoIEk9gUOB62ptuhTvIhrjAkLZK8UJUPbbz28sz5oFb7/d8q8fCivXpp7hWcvBwM7AvBwOvQwP8GsyKySNAN4zs+fzKXAIpaYUJ0Bp187vK0DU+stRvnPuzsWD/3pJGgbMN7MZWes6AD8HzmnoBSSNkjRd0vQFCxbkWcwQ0peZACUfaU6AEu385SvXqRf/yNpmmVZAP6ChGvtewAhJQ/GBXxsBNwHbAM/Lhyf2BGMgojAAABbLSURBVGZK6m9mH2QfbGZjgbEA1dXV0SQUSlb//vnPfJXmBChDhviN3sce8zEIkbOnfORa458OzEiWKcAZZnZsfQeY2Vlm1tPMegPHAI+a2VfMrLuZ9U7WzwV2rR30Qygne+zhI3LzkeYEKN27+zzVK1f6HAKhfOTaxv+XrOWvZvZUoQsWQrmQGk6aVpdimAAleveUJ9WXcFPSLOrueZMZwNW3UAXLVl1dbdOnT2+JlwqhIGpqYOBA76GTyyCu9u29iWjSpHQzZM6a5bN49egB//nP2rTNoTRImmFmnxkB0lAb/7AClSeEipKZAGXoUJ8kpb7BXB06eKrlYpgAZeedoVcveOcdmD7dP4xC6av389vM3s4swKfALsmyPFkXQshRfROgtG/vz/v0Ka4JUGIUb3mqt6nnfztJRwMXA5PxZp59gJ+Z2e0FLV0imnpCuTHzEb3Tpq2bj3/AgOKb1PyBB7yHT79+8OyzaZcmNMb6mnpyDfzPAweb2fzkeRXwiJl9sdlLWocI/CGk59NPPU//0qXe5LPVVmmXKORqfYE/11s1rTJBP/FhI44NIZSwDTaAgw/2x/fdl25ZQvPINXg/IOlBScdLOh64D5hYuGKFEIpJtPOXl3p79Ug6CphgZj+TdCSwN97GP9bM7mqJAoYQ0jd0qP+cNMmbfDp2TLc8oWkaqvF/A3hH0o3AcvyG7o8j6IdQWTbbzG8+r1jhwT+Utoa6cx4BbAdMAn4IvCvpqkbk4g8hlInMKN5o7il9Dbbxm9miJFXDELwP/3PAHyW9W/DShRCKRqad/777YM2a+vcNxS3nnjmSNgGOBL4KdAXuKFShQgjF54tfhJ494f33ff7gULrqDfySOks6TtJEfPrE3YFfA73M7EctUcAQQnGQImlbuWioxj8HGAxcBWxlZqPM7FHLZdRXCKHsRDt/eWgoSVsvM6snnVQIoZIMHAgbbuhNPe+9B1tumXaJQj4a6tUTQT+E8D8bbggHHeSPYxRv6Yq0CyGERsn07ol2/tLVYOCX1FrSxS1RmBBC8Tv0UP/5yCOwfHm6ZQn5yaUf/2pgNym/ZLHJB8ezkiYkz38l6QVJz0l6SNIW+Zw3hJCOLbbwiWKWL4dHH027NCEfuTb1PAv8I+naeWRmyfHY0/CuoBkXm1lfM+sHTADOaUR5QwhFIJK2lbZcA39XPBXzQGB4sjQ4LaOknsChwHWZdWa2KGuXjtQ9p28IoYhl9+ePzt2lp6HunACY2bfzPP9lwGigc/ZKSRcA3wQ+AQ6o60BJo4BRAL169crz5UMIhbDrrt7k89578Nxz8KUvpV2i0Bg51fgl9ZR0l6T5kuZJuiOpzdd3zDBgvpnNqL3NzH5uZlsBfwVOret4MxtrZtVmVl1VVZVLMUMILURae5M3eveUnlybev4M3ANsAWwJ3Jusq89ewAhJbwF/AwZKurnWPrcAX8m5tCGEohHt/KUr18BfZWZ/NrNVyXIDUG813MzOMrOeZtYbOAZ41MyOlbR91m4jgFfzKXgIIV0HHujTMk6bBh98kHZpQmPkGvgXSjo26ZrZWtKx+M3efFwo6UVJLwCD8F4/IYQS06GDB3+IUbylJtfAfwJwNPAB8D5wVLIuJ2Y22cyGJY+/YmY7J106h5vZe40tdAihOES2ztKUU+A3s3fMbISZVZlZdzM73MzeLnThQgjFLRP4H3oIPv003bKE3OXaq2cbSWMk3SnpnsxS6MKFEIpbz57Qrx8sWwaTJ6ddmpCrnPrxA3cD1+O9eWLStRDC/wwf7n35770XBg9OuzQhF7m28X9qZn8ws8fM7PHMUtCShRBKQoziLT25Bv7LJZ0raQ9Ju2aWgpYshFASqquhRw945x2YNSvt0oRc5NrUswtwHJ6rJ9PUY8nzEEIFa9XKR/GOG+e1/r590y5RaEiuNf4jgD5mtp+ZHZAsEfRDCECM4i01uQb+54GNC1mQEELpOuggaNcOnnkG5s9PuzShIbkG/h7Aq5IejO6cIYTaOnXyidjNYOLEtEsTGpJrG/+5BS1FCKHkDRsGDzzgzT3HH592aUJ9cs3HH103Qwj1GjYMTj3VR/GuWAHt26ddorA+uY7cXSxpUbJ8Kmm1pEUNHxlCqBRbbw277AJLlsDjUVUsarnm6ulsZhslywZ4Dv0rClu0EEKpyfTuiaRtxa3ewC+pzqYgM7ub6MMfQqglM4r33ntjFG8xa6iNfyqwq6Qjs9a1AqqJSdJDCLX07w9VVfDWW/Dyy/CFL6RdolCXXLtzDgeGJcsgYBE+e1YIIfxP69YwdKg/jsFcxauhwN9d0k+AF4GXkuU14FM8hUODkhm7npU0IXl+saRXJb2QTOAeA8NCKCPRzl/8Ggr8rYFOdSydkyUXpwGvZD1/GNjZzPoC/wbOakyBQwjFbdAgaNsWpkyBhQvTLk2oS0Nt/O+b2S/zPbmknsChwAXATwDM7KGsXZ7Gp3EMIZSJzp1h//3h4Yfh/vvhuJzaBkJLaqjGryae/zJgNOufvOUE4P46X1gaJWm6pOkLFixoYjFCCC0pkrYVt4YC/4H5nljSMGC+mc1Yz/afA6uAv9a13czGmlm1mVVXVVXlW4wQQgoy3ToffBBWrky3LOGz6g38ZvZRE869FzBC0lvA34CBkm4GkPQtvIfQN8yit28I5Wabbbwr56JF8MQTaZcm1JZrd85GM7OzzKynmfUGjgEeNbNjJQ0GzgBGmNmyQr1+CCFd2VMyhuJSsMBfjyvwHkEPS3pO0tUplCGEUGDZ7fzxvb645JqWuUnMbDIwOXm8XUu8ZgghXQMGwKabwptvwmuvwU47pV2i0mHm3WGnToXFi72nVP/+sMceoKZ2uaGFAn8IofJkRvHedJPX+iPwN6ymBq6/Hi66yGcyq6nxpW1bX7p3h9Gj4cQT/Xm+0mjqCSFUiGjnz92SJT6L2emnw5w5sHSp94gy859Ll/r600+HAw/0/fMVgT+EUDCHHAJt2sBTT8FHTekjWOZqamDIEJg2DZY10OVl2TJvAho61I/LRwT+EELBdOkC++4Lq1f7tIyhbtdfDzNn+sxluVixAmbMgHHj8nu9CPwhhIKKUbz1M/M2/YZq+rUtW+bH5dNjKgJ/CKGgMu38DzyQf9NEOZsyxW/k5mPePD++sSLwhxAKarvtvEfPxx97W39Y19Sp+X8grlrl9wUaKwJ/CKHgonfP+i1enH/gX7nSj2+sCPwhhIKLdv7169w5/z757dr58Y0VgT+EUHB77gmbbAL//rcvwc2fD//6V/4ZTNu0gd13b/xxEfhDCAXXpo33U4do7gH45BM45xzo0wduuy3/8/To4WkcGisCfwihRWTa+Su5uefTT+GSS2DbbeFXv/LRuMOGwdlnQ4cOjTtXhw6eviGf3D2RqyeE0CIGD/b8PU884T18Nt447RK1nFWr4IYb4PzzYe5cX7fPPvDb38Jee/nN3cmTvYdOLoO42reH3XaDE07IrzxR4w8htIhNNoG9966sUbxmcPvtsPPO8N3vetD/4hdh4kR4/HEP+uA3d++/3zNwNlTz79DB95s4Mf+bwhH4QwgtJtO7p9zb+c3goYf8xuvIkZ6WetttYfx4T80wZMhnm2g6dYJJk2DMGG/779jRa/aS/+zY0dePGeP7deqUf/lUCjMfVldX2/Tp09MuRgihiTJ5+TfZxHu0tCnDxuZnnoGzzoLHHvPnm28O557rzTK51tAz+finTVs3H/+AAY1r05c0w8yqa68v+GWX1BqYDrxnZsMkjQTOAz4H9DeziOghVIgdd4Ttt4fXX/fAts8+aZeo+bz0kt+kvftuf77JJnDmmXDqqY2/cSt5F9g992z+ckLL3Nw9DXgF2Ch5/iJwJHBNC7x2CKHIDB/uzRX33lt8gT+fma/eegvOO88nnFmzxoP8j34EP/tZ8d7ALmjgl9QTOBS4APgJgJm9kmwr5EuHEIrUsGEe+CdM8OySxSCfma/mz4cLLoCrrvJ927SBk07yWv9mm6X7fhpS6Br/ZcBofHL1RpE0ChgF0KtXr2YuVgghLXvv7Xn6X3nF5+Pddtt0y7Nkid9snTnzs6mRV670JTPz1S23+A3aa67xD6+lS/2bwLHHelfNPn3SeQ+NVbBePZKGAfPNbEY+x5vZWDOrNrPqqqqqZi5dCCEtbdt6n35Iv3dPY2e+mjIFevdeO/hq+HB4/nlv5imVoA+F7c65FzBC0lvA34CBkm4u4OuFEEpEsSRta+zMV6tW+bLddvDkk3DPPbDLLoUtYyEULPCb2Vlm1tPMegPHAI+a2bGFer0QQukYPBhatfJBTIsWpVOGfGe+Ah+EVqgeNy2hxQdwSTpC0lxgD+A+SQ+2dBlCCOnadFMftbpqFTyYUgRoysxX8+fnN/NVsWiRwG9mk81sWPL4ruSbQHsz62Fmh7REGUIIxSXtyVnSmPmqWETKhhBCKjLt/BMnetNJS0tj5qtiEYE/hJCKnXbynjALF3qag5a0apV30cxXvjNfFYsI/CGEVEgt37tn3jwfdNWnD/z5z36DNx/5znxVLCLwhxBS0xLt/Jk0DN/4Bmy1lY+sffdd75LZtWt+58x35qtiEYE/hJCafff1JpMXX/ScN81p2TLvp7/bbt718pZbvIln+HDvSfTaa177b8mZr4pFBP4QQmratYNDkn59zVXrf/NN+OlPoWdP+M534NlnvfvoGWfA7Nk+6GrQIB9HcOKJsOuunu8+F02d+apYlGE27BBCKRk+3Gepuukmr5HnmhUz25o1PoPVlVf67F6Ztvvdd/e0yEcfDRts8NnjMjNfDR0KM2bUP5irQwcP+k2Z+apYROAPIaSmpmbtIKqpU+G55xrOipnto49g3DjPkDl7tq9r3x6OOQZOOSW3G7CZma/GjfORvPPm+QfQypX+jaRNG2/THz26cZOpFLOYgSuEkIr6smJmy65pZ6YbnDkTrrjCM2V++qmv23prOPlkD87duuVXpuaa+apYpDYDVwgh1JadFbOhBGnLlvm3gcGDvc3+mmvg6afXbj/kEK/dDx0KrVs3rVyFnvmqWETgDyG0uMZmxVyxAp56yhfwfP7f/rZPfLLDDoUrZ7mKwB9CaFFNyYrZrh388Y/eJ79jx+YvW6WI7pwhhBbVlKyYbdvCzjtH0G+qCPwhhBZVyVkxi0UE/hBCi6rkrJjFIgJ/CKFFde6cf1/4Us+KWSwKHvgltZb0rKQJyfOukh6W9Hryc5NClyGEUDz6988/8Jd6Vsxi0RI1/tOAV7KenwlMMrPtgUnJ8xBChdhjDx+Rm49Sz4pZLAoa+CX1BA4FrstafRjwl+TxX4DDC1mGEEJxkTz9QSVmxSwWha7xXwaMBtZkrethZu8DJD/r/OyXNErSdEnTFyxYUOBihhBaUqVmxSwWBQv8koYB881sRj7Hm9lYM6s2s+qqqqpmLl0IIU2ZrJj9+zdc8+/Qwfcrh6yYxaKQNf69gBGS3gL+BgyUdDMwT9LmAMnPPIdyhBBKWSYr5pgxPhVix45es5f8Z8eOvn7MGN8vk6AtNF2LZOeUtD/wUzMbJuli4EMzu1DSmUBXMxtd3/GRnTOE8lZuWTGLRTFl57wQuFXSicA7wMgUyhBCKCKVkhWzWJREPn5JC4C38zy8G7CwGYtT6uJ6rBXXYl1xPdZVDtdjazP7zE3Skgj8TSFpel1fdSpVXI+14lqsK67Husr5ekTKhhBCqDAR+EMIocJUQuAfm3YBikxcj7XiWqwrrse6yvZ6lH0bfwghhHVVQo0/hBBClgj8IYRQYco68EsaLOk1SW8ko4QriqRxkuZLejFrXUXOhyBpK0mPSXpF0kuSTkvWV9z1kLSBpKmSnk+uxfnJ+oq7Ftkqae6Qsg38kloDVwJDgM8DX5P0+XRL1eJuAAbXWlep8yGsAk43s88BA4BTkr+HSrweK4CBZvZFoB8wWNIAKvNaZKuYuUPKNvAD/YE3zGy2ma3EE8UdlnKZWpSZ/RP4qNbqipwPwczeN7OZyePF+D/4llTg9TC3JHnaNlmMCrwWGZU2d0g5B/4tgXezns9N1lW6nOZDKGeSegNfAp6hQq9H0qzxHJ4d92Ezq9hrkch77pBSVM6Bv66cftF3tcJJ6gTcAfzIzBalXZ60mNlqM+sH9AT6S9o57TKlpalzh5Sicg78c4Gtsp73BP6TUlmKScXOhyCpLR70/2pmdyarK/Z6AJjZx8Bk/F5QpV6Lips7pJwD/zRge0nbSGoHHAPck3KZisE9wLeSx98C/pFiWVqMJAHXA6+Y2ZisTRV3PSRVSdo4ebwhcBDwKhV4LQDM7Cwz62lmvfE48aiZHUsZX4+yHrkraSjedtcaGGdmF6RcpBYlaTywP55edh5wLnA3cCvQi2Q+BDOrfQO47EjaG3gCmMXadtz/w9v5K+p6SOqL36xsjVf+bjWzX0ralAq7FrXVmjSqbK9HWQf+EEIIn1XOTT0hhBDqEIE/hBAqTAT+EEKoMBH4QwihwkTgDyGEChOBPxQdSZtKei5ZPpD0XvJ4iaQ/pVSmEQ1leJXUW9LX69m+eVbmx/0lfZJkg3xN0j+TEaTNWeZdJN3QnOcM5aFN2gUIoTYz+xDPGomk84AlZvb7lMt0Dw0PAOwNfB24ZT3bfwJcm/X8CTMbBiCpH3C3pOVmNqmJxQXAzGZJ6impl5m90xznDOUhavyhZCS15EyN+TxJf5H0kKS3JB0p6SJJsyQ9kKRnQNJukh6XNEPSg1lD8CdLukzSvyS9KKl/sr6rpLslvSDp6WSwE5KOl3RF8vgGSX9Ijp0t6aikiBcC+yTfTn5cx1v4CvBAXe/NzJ4DfgmcmrzGcEnPJN8IHpHUQ1KrJDd8VbJPK/lcE90kjUzex/OS/pl16nvx0agh/E8E/lDKtsVT6R4G3Aw8Zma7AMuBQ5Pg/0fgKDPbDRgHZI/e7mhmewInJ9sAzgeeNbO++MjeG9fz2psDewPD8IAPnq/9CTPrZ2aXZu8saRvgv2a2op73MxPYKXn8JDDAzL6E548ZbWZrkvf5jWSfg4DnzWwhcA5wSJJjf0TWOacD+9TzmqECRVNPKGX3m1mNpFl4+oFMbXoW3uyyI7Az8LCn6qE18H7W8ePB5y2QtFGSv2ZvvGaOmT2a3G/oUsdr350E4pcl9cihrJsDCxrYJzujbE/g78k3lHbAnGT9ODxnzGXACcCfk/VPATdIuhW4M+s884EtcihfqCBR4w+lbAVAEoBrbG3+kTV4pUbAS0kNvJ+Z7WJmg7KOr52vxMg9nXd2zb2uY2pbDmzQwD5fYu0MUH8Erki+wXwvc6yZvYtnjRwIfBm4P1n/feBsPCPtc0meGZLjludQvlBBIvCHcvYaUCVpD/C0zJK+kLX9q8n6vYFPzOwT4J8kTSlJwq6FjcjbvxjovJ5t/8a/hdQpuZfwC3y6UIAuwHvJ42/V2v06vMnnVjNbnRy/rZk9Y2bnAAtZm5J8B+BFQsgSTT2hbJnZyuTG6x+S5po2eBPJS8ku/5X0L2AjvNkE4Dzgz5JeAJbx2aBbnxeAVZKeB27Ibuc3s6WS3pS0nZm9kazeR9KzQAe8SeaHWT16zgNuk/Qe8DSwTdbr3IM38fw5a93FkrbHv31MAp5P1h8A3NeI9xAqQGTnDBVJ0mQ8/e70FnzNI4DdzOzsJp6nGrjUzOq9aSupPfA4sLeZrWrKa4byEjX+EFqImd2V1fael2QQ2Ums7dlTn17AmRH0Q21R4w8hhAoTN3dDCKHCROAPIYQKE4E/hBAqTAT+EEKoMBH4Qwihwvw/+zlerU9RA5gAAAAASUVORK5CYII=\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",
    "\n",
    "forline_df = Capomulin_df.loc[Capomulin_df[\"Mouse ID\"] == \"l509\",:]\n",
    "forline_df.head()\n",
    "x_axis = forline_df[\"Timepoint\"]\n",
    "tumsiz = forline_df[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "plt.title('Capomulin treatmeant of mouse l509')\n",
    "plt.plot(x_axis, tumsiz,linewidth=2, markersize=15,marker=\"o\",color=\"blue\", label=\"Fahreneit\")\n",
    "plt.xlabel('Timepoint (Days)')\n",
    "plt.ylabel('Tumor Volume (mm3)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Averag Tumor Volume (mm3)')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bMa2aeiTpUElvzK9fJenLko6WNGxvIEn7A8siYmGhbDIwG/jP4daPiLkR0R8R/VOmTCl5OGZmNpxWTT1fA7YA1gUOAtYDzgP2BV5AartvZTfgQEn7AusDGwHfB54L1Gr7U4GrJb0sIu5ei+MwM7OSWiX+3SNiR0mTgLuBrSJipaQfAtcMt+GIOJ50YhhJM4APRcTBxWUkLQL63avHzKxzWjXZPAEQEY8Dv4+IlXn6CeDJDsRmZmYVaFXjv1vSMyPikYjYp1Yo6VnAynZ2EhHzgfkNyvva2Y6Zma29Vr16/rXJrIeA/ZvMMzOzUa7U6JySdgL66pY/q4qAzMysWmW6ZZ4CnAIcDByQH67xm5lVqMrbTZap8b88Il40crs0M7NWqr7dZJlhmX8nyYnfzKxDqr7dZJnEP4+U/G+RdL2kGyRdPzK7N7N2VdkEYKND1bebLNPUcwpwOHAD8NTI7NbM1kTVTQA2OlR9u8kyNf4lEXFuRNwWEYtrj5HZvZm1o+omABsdqr7dZJka/815mIbzgBW1wohwd06zDqu6CcBGh9qvt9mz09922rSU9EfqV12ZxL8BKeG/plAWuB+/WcdV3QRgo8fAQHXNd8Mm/oh4ezW7NrN2zZkztI0fRrYJwHpDmXvuPhc4hrordyPiwOrCMrNGqm4CsN5Q5uTuOcAi4CvA/xQeZtYFAwOwaBE89VR67oWk7y6sI6tMG/8/IuLLlUdiZtaAu7COPEVE6wWkt5Juqv5Lhvbqubra0Fbr7++PBQsWdGp3ZjaK9PU1PqE9fXr6xWPNSVoYEf315WVq/DuSLuCayeoLuCJPm5lVyl1YR16ZxP964Hm1O3CZmXWSu7COvDInd68DNqk6ELM14ZN+41/VV7H2ojI1/i1JV+/+nqFt/O7OaV3lk369wV1YR16Zk7t7NiqPiEsriagBn9y1RnzSz6y1tk/uSlIkTRN8bZmRCtKsHT7pZ7ZmWrXxXyLpGElDTqFIWlfSTEnzgCOqDc+suWYn93zSz6y1Vol/H+BJ4DRJd0r6o6S/ArcCbwG+GBHf7UCMZg35pJ/Zmmna1BMR/wC+Dnxd0iRgc+CxiHiwU8GZteKTfmZrpkyvHiLiceCuimMxa1uVQ9eajVdl+vGbmdk4UnnilzRR0jWSzs/Tn5N0c75x+9mSfHGYrTFfwGXWvlKJX9J0Sa/OrzeQtGEb+zgWuKkwfRGwQ0TsBPwJOL6NbZmtUruAa/FiiFh9AZeTfzX8JTt+DJv4Jb0LOBP4Zi6aShqjf1iSpgL7Ad+ulUXELyPiiTx5Rd6eWdt84/HO8Zfs+FKmxv8+YDfgIYCIuBXYouT2TwKOY/WonvWOAi5oNEPSLEkLJC249957S+7Oeokv4Oocf8mOL2US/4riyJyS1iENy9ySpP2BZRGxsMn82cATQMM6Q0TMjYj+iOifMmVKiTCt1/gCrs7xl+z4UibxXyrpP4ANJO0NnAGcV2K93YADJS0CfgTMlPQDAElHAPsDAx7ywdaUL+DqHH/Jji9lEv9HgXuBG4B3Az8HPjbcShFxfERMjYg+4M3AxRFxmKR9gI8AB0bE8pYbMWthYADmzk2Dsknpee5c9+uvgr9kx5dhE39EPBUR34qIN0bEIfn12tTSvwpsCFwk6VpJ31iLbVmP843HO3OC1V+y40uZYZn3B/4LmE660ldARMRG1YeXeFhms6T+HgSQat5OwtZIs2GZyzT1nEQahfOfImKjiNiwk0nfzFZz7xobCWUS/1LgRp+ENes+966xkVAm8R8H/FzS8ZI+WHtUHZiZPV2v9q7xVcMjq0zinwMsB9YnnZStPcysw/bdt73y8cBXDY+8Mid3FzQ6OdBJPrlrlvTifYZ78ZhHytqc3P2VpNdUEJOZtakX2/h78ZirVnasngslPSbpIUkPS3qo6sDM7Ol6sY2/F4+5amUu4NowIiZExAbuzmnWXb14BW0vHnPVhr31oqQ9GpVHxGUjH46ZtdKL9xnuxWOuWpmTu8UB2dYHXgYsjIiZVQZW5JO7Zmbta3Zyd9gaf0QcULehrYHPjmBsZmbWQWtyz93bgR1GOhAzM+uMMm38X2H1jVcmADsD11UZlJmZVWfYxA8UG9efAE6LiN9UFI+ZmVWsTBv/vE4EYmZmndE08Uu6gcb31q2Nx79TZVGZmVllWtX49+9YFGZm1jFNE39ErBoWSdKWwEvz5FURsazqwMzMrBrDdueUdChwFfBG4FDgSkmHVB2YmZlVo0yvntnAS2u1fElTgF8BZ1YZmJmZVaPMBVwT6pp27i+5npmZjUJlavwXSvoFcFqefhPw8+pCMjOzKrXqznkIcH5EfFjSG4BXkrpyzo2IszsVoJmZjaxWNf4B4OuSLiTV9j8cEU92JiwzM6tK07b6iHg9sC3wv8D/BZZKOrnZ+PxmZjY2tDxJGxEPRcS8iPhXYEfgWuArkpZ2JDoze5rBwXQD8gkT0vPgYLcjsrGmVO8cSZsCbyCd2N0M+EnZHUiaKOkaSefn6c0kXSTp1vy86ZoEbtaLBgdh1ixYvBgi0vOsWU7+1p6miV/ShpIOl/Rz4CbSlbsnAtMi4v1t7OPYvH7NR4H/jYjnk5qRPtp+2Ga9afZsWL58aNny5ancrKxWNf7bgH2Ak4GtI2JWRFwcw92rsUDSVGA/4NuF4oOA2oif84DXtReyNeMmgPFvyZL2ys0aadWrZ1pELG8xv4yTgOOADQtlW0bEXQARcZekLRqtKGkWMAtg2rRpaxnG+FdrAqjVBmtNAOCbUo8n06alv22jcrOyWvXqWaukL2l/YFlELFyT9SNibkT0R0T/lClT1iaUnuAmgN4wZw5Mnjy0bPLkVG5WVpVDL+wGHChpEfAjYKakHwD3SNoKID97pM8R4CaA3jAwAHPnwvTpIKXnuXP9q87a0zLx5x45n1uTDUfE8RExNSL6gDcDF0fEYcC5wBF5sSOAn67J9m2oZj/13QQw/gwMwKJF8NRT6dlJ39o1XD/+J4GXSNII7vMzwN6SbgX2ztO2ltwEYGZllRmk7Rrgp5LOAB6tFUbEWWV3EhHzgfn59f3Aq9qK0oZVq/XNnp2ad6ZNS0nftUEzq1cm8W9GGop5ZqEsgNKJ3zpjYMCJ3syGN2zij4i3dyIQMzPrjDK3Xpwq6WxJyyTdI+kn+cIsMzMbg8p05zyV1BPn2cBzgPNymY0yvnLXzMook/inRMSpEfFEfnwX8BVVo4wH7zKzssok/vskHZb79E+UdBjpZK+NIr5y18zKKpP4jwIOBe4G7gIOyWU2ivjK3c5ys5qNZWV69SwBDuxALLYWPHhX53hAPBvryvTqea6kL0g6S9K5tUcngrPyevXK3W7UvN2sZmNdmQu4zgG+Q+rN81S14dia6sUrd7tV83azmo11Gu6+KpKujIhdOxRPQ/39/bFgwYJuhmCjUF9f4+at6dPT4GXjbb9m7ZK0MCL668vLnNz9kqRPSHqFpF1qjwpiNGtLt2revdqsZuNHmaaeHYHDSWP11Jp6gqFj95h1XLdOaPdis5qNL2US/+uB50XEyqqDMWvHnDlD2/ihczVvD4hnY1mZpp7rgE2qDsSsXb4bldmaKVPj3xK4WdLvgRW1wohw337rOte8zdpXJvF/ovIozMysY4Zt6omISxs9OhGctcfDCJhZGcPW+CU9TOrFA7AuMAl4NCI2qjIwa4+HETCzssrU+DeMiI3yY33gYOCr1Ydm7fAwAmZWVplePUNExDm4D/+o42EEzKyspk09ktaJiCckvaFQPAHoZ3XTj40SHp3TzMpqVeO/Kj8fUHi8FngYOKjiuKxNHkbAzMpqdXJXABHx9g7FYmvBwwiYWVmtEv8USR9sNjMivlBBPLYWfDGTmZXRKvFPBJ5Jrvmbmdn40Crx3xURn1rTDUtaH7gMWC/v58yI+ISknYFvAOsDTwBHR8RVzbdkZmYjadg2/rWwApgZEY9ImgRcLukC4FPAJyPiAkn7Ap8FZqzlvszMrKRWif9Va7PhSLf2eiRPTsqPyI/aVb8bA3euzX7MzKw9TRN/RDywthuXNBFYCGwLfC0irpT0fuAXkj5P6k76f5qsOwuYBTDNndHNzEZM21futiMinoyInYGpwMsk7QC8F/hARGwNfIB0I/dG686NiP6I6J8yZUqVYZqZ9ZRKE39NRDwIzAf2AY4AzsqzzgBe1okYzMwsGTbxS3pY0kN1j6WSzpb0vBbrTZG0SX69AfBq4GZSm/6eebGZwK1rfxhmZlZWmRuxfIGUrH9I6unzZuBZwC3AKTTvkbMVMC+3808ATo+I8yU9CHxJ0jrAP8jt+GZm1hllEv8+EbFrYXqupCsi4lOS/qPZShFxPfDiBuWXAy9pP1QzMxsJZdr4n5J0qKQJ+XFoYZ5H6TQzG2PKJP4B4HBgGXBPfn1Ybrf/twpjMzOzCgzb1BMRfyUNydzI5SMbjpmZVa3MPXfXB94BbE8aXweAiDiqwrjMzKwiZZp6vk/qxfNa4FLSxVgPVxmUmZlVp0zi3zYiPg48GhHzgP2AHasNy8zMqlIm8T+enx/MQy5sDPRVFpGZmVWqTD/+uZI2BT4GnEu6OcvHK43KzMwq0zLxS5oAPBQRfyPdVKXpEA1mZjY2tGzqiYincF99M7NxpUwb/0WSPiRpa0mb1R6VR2ZmZpUo08Zf66//vkJZ4GYfM7MxqcyVu8/tRCBmZtYZZcbjnyzpY5Lm5unnS9q/+tDMzKwKZdr4TwVWsvreuLcDJ1YWkZmZVapM4t8mIj5LvpArIh4j3ZDFzMzGoDKJf2UegjkAJG0DrKg0KjMzq0yZXj0nABcCW0saBHYDjqwwJjMzq9CwNf6I+CXwBlKyPw3oj4j51YZla+Loo2GddUBKz0cf3e2IzGw0KjMe/7mkhH9uRDxafUi2Jo4+Gk4+efX0k0+unv7617sTk5mNTmXa+P8H2B34o6QzJB2Sb85io8jcue2Vm1nvKnMB16XApZImAjOBdwGnABtVHJu14ckn2ys3s95V5uQuuVfPAcCbgF2AeVUGZe2bOLFxkp84sfOxmNnoVubK3R8DN5Fq+18j9es/purA1tbgIPT1wYQJ6XlwsNsRVWvWrPbKzax3lanxnwq8NSKeBJC0m6S3RsT7hlmvawYHU8JbvjxNL168OgEODHQvrirVTuDOnZtq/hMnpmP2iV0zq6eIGH4haWfgLaSmntuAsyLiKxXHtkp/f38sWLCg9PJ9fSnZ15s+HRYtGrGwzMxGNUkLI6K/vrxpjV/SdsCbSQn/fuDHpC+KvUrucH3SXbvWy/s5MyI+kecdQ7rByxPAzyLiuPYOp7UlS9orNzPrJa2aem4Gfg0cEBF/BpD0gTa2vQKYGRGPSJoEXC7pAmAD4CBgp4hYIWmLNYy9qWnTGtf4p00b6T2ZmY09rU7uHgzcDVwi6VuSXkUbg7NF8kienJQfAbwX+ExErMjLLVujyFuYMwcmTx5aNnlyKjcz63VNE39EnB0RbwJeCMwHPgBsKelkSa8ps3FJEyVdCywDLoqIK4HtgN0lXSnpUkkvXeujqDMwkE5yTp+ehi+YPj1Nj9cTu2Zm7Sh1cnfVwuleu28E3hQRM9tYbxPgbOAY4EfAxcCxwEtJ5w6eF3WBSJoFzAKYNm3aSxY3arsxM7Ommp3cLTNkwyoR8UBEfLOdpJ/Xe5D0q2Ef0o1czspNQVcBTwGbN1hnbkT0R0T/lClT2tmdmZm10Fbib4ekKbmmX7vy99WkE8bnkC4Gq/UcWhe4r6o4zMxsqFJDNqyhrYB5eYyfCcDpEXG+pHWBUyTdSLql4xH1zTxmZladyhJ/RFwPvLhB+UrgsKr2a2ZmrVXW1GNmZqOTE7+ZWY9x4jcz6zFO/GZmPcaJ38ysxzjxm5n1GCd+M7Me48RvZtZjnPjNzHqME7+ZWY9x4jcz6zFO/GZmPcaJfxwZHIS+PpgwIT0PDnY7IjMbjaocltk6aHAQZs2C5cvT9OLFaRp8y0kzG8o1/nFi9uzVSb9m+fJUbmZW5MQ/TixZ0l65mfUuJ/5xYtq09srNrHc58Y8Tc+bApElDyyZNSuVmZkVO/OOI1HrazAyc+MeN2bNh5cqhZStX+uSumT2dE/844ZO7Zo7j2OMAAAjBSURBVFaWE/844ZO7ZlaWE/84MWcOTJ48tGzyZJ/cNbOnc+IfJwYGYO5cmD49ndSdPj1N+6pdM6vnIRvGkYEBJ3ozG55r/GZmPcaJ38ysx1SW+CWtL+kqSddJ+oOkT9bN/5CkkLR5VTGYmdnTVdnGvwKYGRGPSJoEXC7pgoi4QtLWwN6Ae5mbmXVYZTX+SB7Jk5PyI/L0F4HjCtNmZtYhlfbqkTQRWAhsC3wtIq6UdCBwR0RcpxaDyUiaBeRbifCIpFuqjLUimwP3dTuIDuq14wUfc68Yq8c8vVGhIqqvdEvaBDgbOBb4FvCaiPi7pEVAf0SMxTd0WJIWRER/t+PolF47XvAx94rxdswd6dUTEQ8C84GDgOcC1+WkPxW4WtKzOhGHmZlV26tnSq7pI2kD4NXANRGxRUT0RUQfcDuwS0TcXVUcZmY2VJVt/FsB83I7/wTg9Ig4v8L9jUZzux1Ah/Xa8YKPuVeMq2PuSBu/mZmNHr5y18ysxzjxm5n1GCf+ESDpFEnLJN1YV36MpFvykBWf7VZ8VWh0zJJ2lnSFpGslLZD0sm7GONIkbS3pEkk35b/psbl8M0kXSbo1P2/a7VhHSotj/pykmyVdL+nsWkeOsa7Z8Rbmj4uhZtzGPwIk7QE8AnwvInbIZXsBs4H9ImKFpC0iYlk34xxJTY75l8AXI+ICSfsCx0XEjC6GOaIkbQVsFRFXS9qQdHHi64AjgQci4jOSPgpsGhEf6WKoI6bFMU8FLo6IJyT9N8B4OOZmxxsRf8xDzXwbeCHwkrF8/ZFr/CMgIi4DHqgrfi/wmYhYkZcZN0kfmh5zABvl1xsDd3Y0qIpFxF0RcXV+/TBwE/Ac0vUp8/Ji80iJcVxodswR8cuIeCIvdgXpi2DMa/E3hnE01IwTf3W2A3aXdKWkSyW9tNsBdcD7gc9JWgp8Hji+y/FURlIf8GLgSmDLiLgLUuIAtuheZNWpO+aio4ALOh1P1YrHWxxqpqtBjRAn/uqsA2wKvBz4MHC6Wg1OND68F/hARGwNfAD4TpfjqYSkZwI/Ad4fEQ91O55OaHbMkmYDTwCD3YqtCsXjJR3fbOA/uxrUCHLir87twFl5lNKrgKdIAz2NZ0cAZ+XXZwDj6uQuQB5i/CfAYETUjvWe3DZcayMeV816TY4ZSUcA+wMDMY5OFjY43m0YZ0PNOPFX5xxgJoCk7YB1GZuj+7XjTmDP/HomcGsXYxlx+Rfbd4CbIuILhVnnkr70yM8/7XRsVWl2zJL2AT4CHBgRy7sV30hrdLwRccN4G2rGvXpGgKTTgBmkGv09wCeA7wOnADsDK4EPRcTF3YpxpDU55luAL5Gauf4BHB0RC7sV40iT9Erg18ANpF9wAP9BavM+HZhGurnQGyOi/sT3mNTimL8MrAfcn8uuiIj3dD7CkdXseCPi54VlFjHGRxV24jcz6zFu6jEz6zFO/GZmPcaJ38ysxzjxm5n1GCd+M7Me48RvluVRFw9pY/kZIz1So6TvSip9haik9SQtkTRubgRu1XPitzFF0nskPSpp3ULZupKWS7qhbtnn58Q8s+TmtwLOG+F4j5T0SMlldyQN+HZS2e3nQQA/B/z3mkVovciJ38aai4HJDB0OYlfg78B2kqYUymcAK4DfltlwRNxdG021S44BfrIG4/8MAq+UtH0FMdk45MRvY0pE/Ik0NMReheK9gF8BC0jJvlj+u4j4h5LjJP1F0mOSbpB0WHHb9U09knaVdLWkf0i6RtK+eZniPgD+JY/CujzfgGaXvP4M4FTgGXm9kHRCo+OSNBE4lDT8Q7F8S0nn5pgXS3q7pBuL28lXCf8GeEvrd88sceK3segSnp745+dHsXxGXhbgROAdwPuAFwGfBr4pab9GO8ijM54P3Ay8hDQO++eaxPNp4KPALqQhDAbzmC+/JY3uuJzUjLQVabjqRnYi3cNgQV35PGA6aeyjg4DD8nS9q1g9TpJZS+t0OwCzNXAJ8FVJ6wEiDX39TmApaawgJL2QlGgvlvQM4IPAayLi13kbtyndGvJ9wM8a7GMAmAi8IyIeA/4gaQ6Nhx/+eERckvf7KeBy0s1Kbpf0dyBKDOg1nXSDj1XLSXoB8FrgFRFxRS47EljUYP07gb5h9mEGOPHb2HQJsD7wClLivy8i/iLpbmCbPFzuXqSa9pWkgfLWBy6UVBycahKNkyik2+vdmJN+Tf0NSGquL7yu3XVsC9IojmVtADweEU8Vyl5IGihs1a+AiFgqqdGdzR7L2zAblhO/jTkR8VdJi0lNOSI18RARj0pamMtnAJdHxOOSak2aB5BGzyx6vMluRPlb7BW3UVun3WbU+4B1JU0uDHPczo17NgPubXOf1qPcxm9jVa2dv9a+XzOf1B4+g9QDCOCPpN490yPiz3WPxU22fxOwo6RiLXpNbiyzktRkNJxr8/OL6mKYQDrHAICkqcCzG6y/A3D1GsRnPciJ38aqS0ht+7syNPFfCryZ1NRyCay6afbngc9LOkrStpJ2ztcEzGqy/UHgSeBbkl4k6dWkceihvZttLwLWl7S3pM0lTW60UETcS0rcryyU3QL8AviGpJdL2pnUS2h5gxh2By5sIy7rYU78NlZdQrqr2bKI+Euh/HJSW/dDQPEmMB8HTgA+BPwBuAg4GLit0cYj4hFS09D2wDWkHj0n5Nn/KBtkRPwW+AZwGqkp5rgWi88lnVQuOpJ0rmA+qavnIOnWjqtikPQKUo+gM8vGZb3NN2IxK0nSQcDZwBZV3H0p91K6GXhbofdR/TKbk04gvyUifpLLzgCuiYj/N9Ix2fjkk7tmTeSbif+V1E10B9JQCudVdcu9iFiR97lZIYaZwIakWwFuAcwhnQi+MM9fD7gO+GIVMdn45MRv1tyWwCdJ1wPcTerv/5EqdxgRl9UVTSJdfPY8VndP3SMiHs3Lr8jzzUpzU4+ZWY/xyV0zsx7jxG9m1mOc+M3MeowTv5lZj3HiNzPrMf8fr9+Jt3IOgrUAAAAASUVORK5CYII=\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",
    "fig1, ax1 = plt.subplots()\n",
    "avg_capm_vol =Capomulin_df.groupby(['Mouse ID']).mean()\n",
    "\n",
    "marker_size=15\n",
    "plt.scatter(avg_capm_vol['Weight (g)'],avg_capm_vol['Tumor Volume (mm3)'], color=\"blue\")\n",
    "plt.title('Mouse Weight Versus Average Tumor Volume')\n",
    "plt.xlabel('Weight (g)',fontsize =14)\n",
    "plt.ylabel('Averag Tumor Volume (mm3)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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 and regression model for mouse weight and average tumor volume for the Capomulin regimen\n",
    "correlation = st.pearsonr(avg_capm_vol['Weight (g)'],avg_capm_vol['Tumor Volume (mm3)'])\n",
    "print(f\"The correlation between mouse weight and the average tumor volume is {round(correlation[0],2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is: 0.709\n"
     ]
    },
    {
     "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": [
    "# Add the linear regression equation and line to plot\n",
    "# Calculate the correlation coefficient and linear regression model for mouse weight and average tumor volume for the Capomulin regimen\n",
    "(slope, intercept,rvalue, pvalue, stderr)= linregress(avg_capm_vol[\"Weight (g)\"],avg_capm_vol[\"Tumor Volume (mm3)\"])\n",
    "regress_values=avg_capm_vol[\"Weight (g)\"]* slope + intercept\n",
    "line_eq= f\"y = {round(slope, 2)} x + {round(intercept, 2)}\"\n",
    "\n",
    "plt.scatter(avg_capm_vol[\"Weight (g)\"],avg_capm_vol[\"Tumor Volume (mm3)\"],color='b')\n",
    "plt.plot(avg_capm_vol[\"Weight (g)\"], regress_values, color='red')\n",
    "plt.annotate(line_eq,(20,36), fontsize=14)\n",
    "plt.xlabel(\"Weight (g)\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "plt.title(\"Weight vs Tumor Volume for Capomulin\")\n",
    "print(f\"The r-squared is: {round(rvalue**2,3)}\")\n",
    "plt.show()"
   ]
  },
  {
   "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
}