pymaceuticals.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis\n",
    "\n",
    "- Add your analysis here.\n",
    " \n",
    "The results show that in mice, Capomulin was able to reduce the size of tumors over time.  Ms. Mouse B128 was able to significantly reduce the size her tumor. \n",
    "\n",
    "While Capomulin and Ramicane both reduced tumor volume,  Capomulin showed the greatest overall decrease.\n",
    "\n",
    "Katapil and Infubinol on the other hand were shown to cause an increase in tumor volume.\n",
    "\n",
    "The correlation between the weight per mouse and the volume per tumor is .84.  This could indicate that the larger the mouse, the larger the tumor.  However, datmore data would be needed to make that claim accurately.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "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 = \"data/Mouse_metadata.csv\"\n",
    "study_results = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Data\n",
    "mouse_metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
       "</div>"
      ],
      "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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Data\n",
    "study_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "number_mice = len(combined_df[\"Mouse ID\"].value_counts())\n",
    "number_mice\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gett the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "duplicate_mice = combined_df.loc[combined_df.duplicated(subset=[\"Mouse ID\", \"Timepoint\"]),\"Mouse ID\"].unique()\n",
    "\n",
    "duplicate_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>g989</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>g989</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>g989</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "107      g989          0           45.000000                 0     Propriva   \n",
       "137      g989          0           45.000000                 0     Propriva   \n",
       "329      g989          5           48.786801                 0     Propriva   \n",
       "360      g989          5           47.570392                 0     Propriva   \n",
       "620      g989         10           51.745156                 0     Propriva   \n",
       "681      g989         10           49.880528                 0     Propriva   \n",
       "815      g989         15           51.325852                 1     Propriva   \n",
       "869      g989         15           53.442020                 0     Propriva   \n",
       "950      g989         20           55.326122                 1     Propriva   \n",
       "1111     g989         20           54.657650                 1     Propriva   \n",
       "1195     g989         25           56.045564                 1     Propriva   \n",
       "1380     g989         30           59.082294                 1     Propriva   \n",
       "1592     g989         35           62.570880                 2     Propriva   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "107   Female          21          26  \n",
       "137   Female          21          26  \n",
       "329   Female          21          26  \n",
       "360   Female          21          26  \n",
       "620   Female          21          26  \n",
       "681   Female          21          26  \n",
       "815   Female          21          26  \n",
       "869   Female          21          26  \n",
       "950   Female          21          26  \n",
       "1111  Female          21          26  \n",
       "1195  Female          21          26  \n",
       "1380  Female          21          26  \n",
       "1592  Female          21          26  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "optional_duplicate_mice = combined_df.loc[combined_df[\"Mouse ID\"] == \"g989\",:]\n",
    "optional_duplicate_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f932</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0     b128          0                45.0                 0    Capomulin   \n",
       "1     f932          0                45.0                 0     Ketapril   \n",
       "2     g107          0                45.0                 0     Ketapril   \n",
       "3     a457          0                45.0                 0     Ketapril   \n",
       "4     c819          0                45.0                 0     Ketapril   \n",
       "\n",
       "      Sex  Age_months  Weight (g)  \n",
       "0  Female           9          22  \n",
       "1    Male          15          29  \n",
       "2  Female           2          29  \n",
       "3  Female          11          30  \n",
       "4    Male          21          25  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "clean= combined_df[combined_df[\"Mouse ID\"].isin(duplicate_mice)==False]\n",
    "clean.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "total_mice = len(clean[\"Mouse ID\"].value_counts())\n",
    "total_mice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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": 150,
     "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",
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "# Assemble the resulting series into a single summary DataFrame.\n",
    "\n",
    "mean = clean[\"Tumor Volume (mm3)\"].groupby(clean[\"Drug Regimen\"]).mean()\n",
    "median = clean[\"Tumor Volume (mm3)\"].groupby(clean[\"Drug Regimen\"]).median()\n",
    "variance = clean[\"Tumor Volume (mm3)\"].groupby(clean[\"Drug Regimen\"]).var()\n",
    "standard_deviation = clean[\"Tumor Volume (mm3)\"].groupby(clean[\"Drug Regimen\"]).std()\n",
    "SEM = clean[\"Tumor Volume (mm3)\"].groupby(clean[\"Drug Regimen\"]).sem()\n",
    "\n",
    "summary_table = pd.DataFrame({\"Mean Tumor Volume\" : mean,\n",
    "               \"Median Tumor Volume\" :median,\n",
    "               \"Tumor Volume Variance\" : variance,\n",
    "               \"Tumor Volume Std. Dev\" : standard_deviation,\n",
    "               \"Tumor Volume Std. Err.\" : sem})\n",
    "\n",
    "summary_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, \n",
    "# and SEM of the tumor volume for each regimen\n",
    "\n",
    "# Using the aggregation method, produce the same summary statistics in a single line.\n",
    "aggregate = clean.groupby(\"Drug Regimen\")[[\"Tumor Volume (mm3)\"]].agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2zsijiIj6WksKtnca5vCRozz/UODQtuKJiIh562eZCyS9VNK7m9uTmyaeiIhYwPSzSupBwMcoi+IBLAL8qM2gIiJiMPqpKbwV2AZ4CMD27cAybQYVERGD0U9SeNS2mb0g3lLthhQREYPST1I4SdJ3geUl7QGcRdljISIiFjD9bMf5JUmvAe4Hngt8yvaZrUcWERHVzTMpNCON/tBJBJKWkDTF9k1tBxcREXX103x0MvBEz/3Hm2MREbGA6ScpLGy7uw5Sc3vR9kKKiIhB6ScpzJS0TeeOpG2Bu9sLKSIiBqWfZS7eBxwr6ZuUJa5vBXZpNaqIiBiIfkYf/RXYQtLSgOZjO86IiHiaGTEpSHqn7R9J2n/IcQBsf7nl2CIiorLRagqdmctZ0iIiYoIYMSnY/m7z/ZB64URExCCN1nz09dFeaHufsQ8nIiIGabTmo/dRtss8ibJfsqpEFBERAzNaUlgN2AHYkbJF5onAj23fWyOwiIiob8TJa7bvsf0d268EdgOWB6ZLelet4CIioq5+FsTbFNgJeA3wK+DitoOKiIjBGK2j+RDgTcDVwAnAgbZn1QosIiLqG62m8D/AjcBGzdfnmolrAmx7w/bDi4iImkZLCutWiyIiIsaF0Sav3VwzkIiIGLx+ls6OiIgJIkkhIiK6RkwKks5uvn+hXjgRETFIo85olvQKYBtJJzBkmQvbl7QaWUREVDdaUvgUcACwJjB07wQDr2orqIiIGIzRRh+dApwi6X9sf6ZiTBERMSD9bMf5GUnbAC9vDv3O9i/aDSsiIgZhnqOPJH0e2Bf4S/O1b3MsIiIWMPOsKQBbAxvbfgJA0jHApcCBbQYWERH19TtPYfme28u1EUhERAxePzWFzwOXSjqHMiz15aSWEBGxQOqno/l4Sb8DNqMkhY/ZvrPtwCIior6+mo9s32H7NNun9psQJB0laYakq3qOrSjpTEnXN99X6HnsQEk3SLpW0uvm/0eJiIinqs21j74PvH7IsQOAs22vB5zd3EfS+sA7gA2a13xL0kItxhYREcNoLSnYPhf4x5DD2wLHNLePAd7Sc/wE24/Y/htwA7B5W7FFRMTwRk0Kkib1Nv+MgVVt3wGlSQpYpTm+BnBrz/Nua44NF9OekqZJmjZz5swxDC0iIkZNCs3chMslrd1yHBrmmId7ou0jbE+1PXXy5MkthxURMbH0MyR1NWC6pAuBhzoHbW/zJMq7S9Jqtu+QtBowozl+G7BWz/PWBG5/Eu8fERFPQT9J4ZAxLO80YFfgsOb7qT3Hj5P0ZWB1YD3gwjEsNyIi+tDPPIXfS1oHWM/2WZKWBOY5MkjS8cCWwMqSbgMOoiSDkyTtDtwC7NCUMV3SSZS1lWYBe9t+/En+TBER8STNMylI2gPYE1gReBalA/g7wFajvc72TiM8NOzrbB8KHDqveCIioj39DEndG3gJcD+A7euZPWooIiIWIP0khUdsP9q5I2lhRhgZFBERT2/9JIXfS/o4sISk1wAnAz9vN6yIiBiEfpLCAcBM4EpgL+B04JNtBhUREYPRz+ijJ5qNdS6gNBtdazvNRxERC6B+Rh9tTRlt9FfKzON1Je1l+1dtBxcREXX1M3ntcOCVtm8AkPQs4JdAkkJExAKmnz6FGZ2E0LiR2ctTRETEAmTEmoKk7Zqb0yWdDpxE6VPYAbioQmwREVHZaM1Hb+65fRfwiub2TGCFuZ8eERFPdyMmBdvvrhlIREQMXj+jj9YFPgRM6X3+k1w6OyIixrF+Rh/9DDiSMov5iXbDiYiIQeonKfzb9tdbjyQiIgaun6TwNUkHAb8BHukctH1Ja1FFRMRA9JMUXgC8C3gVs5uP3NyPiIgFSD9J4a3AM3uXz46IiAVTPzOaLweWbzuQiIgYvH5qCqsC10i6iDn7FDIkNSJiAdNPUjio9SgiImJc6Gc/hd/XCCQiIgavnxnNDzB7T+ZFgUWAh2wv22ZgERFRXz81hWV670t6C7B5axFFRMTA9DP6aA62f0bmKERELJD6aT7arufuJGAqs5uTIiJiAdLP6KPefRVmATcB27YSTUREDFQ/fQrZVyEiYoIYbTvOT43yOtv+TAvxRETEAI1WU3homGNLAbsDKwFJChERC5jRtuM8vHNb0jLAvsC7gROAw0d6XUREPH2N2qcgaUVgf2Bn4BhgU9v31ggsIiLqG61P4YvAdsARwAtsP1gtqoiIGIjRJq99BFgd+CRwu6T7m68HJN1fJ7yIiKhptD6F+Z7tHBERT2858UdERFeSQkREdPWzzMWYk3QT8ADwODDL9tRmpNOJwBTKUhpvz0iniIi6BllTeKXtjW1Pbe4fAJxtez3g7OZ+RERUNJ6aj7alzIWg+f6WAcYSETEhDSopGPiNpIsl7dkcW9X2HQDN91WGe6GkPSVNkzRt5syZlcKNiJgYBtKnALzE9u2SVgHOlHRNvy+0fQRlQh1Tp07Nvg4REWNoIDUF27c332cAP6Vs73mXpNUAmu8zBhFbRMREVj0pSFqqWWAPSUsBrwWuAk4Ddm2etitwau3YIiImukE0H60K/FRSp/zjbJ8h6SLgJEm7A7cAOwwgtoiICa16UrB9I7DRMMfvAbaqHU9ERMw2noakRkTEgCUpREREV5JCRER0JSlERERXkkJERHQlKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHQlKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHQlKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHQlKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHQlKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHQlKURERNe4SwqSXi/pWkk3SDpg0PFEREwk4yopSFoI+F/gDcD6wE6S1h9sVBERE8e4SgrA5sANtm+0/ShwArDtgGOKiJgwZHvQMXRJ2h54ve33NvffBbzI9gd7nrMnsGdz97nAtWMcxsrA3WP8nk+n8hPD+Cg/MYyP8hfUGNaxPXm4BxYew0LGgoY5NkfWsn0EcERrAUjTbE9t6/3He/mJYXyUnxjGR/kTMYbx1nx0G7BWz/01gdsHFEtExIQz3pLCRcB6ktaVtCjwDuC0AccUETFhjKvmI9uzJH0Q+DWwEHCU7emVw2itaeppUj4khvFQPiSG8VA+TLAYxlVHc0REDNZ4az6KiIgBSlKIiIiuJIWIiOhKUoiIiK5xNfpoECQtBrwNmELP78P2pyvGsCrwOWB1229o1nt6se0jK5S93WiP2/5JhRhWnEcM/2i5/J8zZJLkkPK3abP8IbG8BDgYWIfy96gSgp9ZK4YmjlWBzZq7F9qeUaHM8fQ5DPy80BPLKsDiPTHc0mZ5Ez4pAKcC/wQuBh4ZUAzfB44GPtHcvw44EWg9KQBvHuUxA60nBcrv3ow8o73tE+KXWn7/+XEksB/ld/L4IAKQ9Hbgi8DvKJ/JNyT9l+1TWi56PH0OAz8vSNoGOBxYHZhBuVC4Gtig1XIn+pBUSVfZ/j8DjuEi25tJutT2Js2xy2xvPMi4JqJm0uRzmrvX2n6scvkX2H5RzTKHieFy4DWd2oGkycBZtjeqGMOgP4fxcF64HHgV5Xe/iaRXAjvZ3nMeL31KUlOA8yW9wPaVA4zhIUkr0VSdJW1BuUppnaR32v6RpP2He9z2l2vE0RPPNsDLm7u/s/2LimVvCRwD3ES5Ql5L0q62z60VA3COpC9SamjdK1Tbl1SMYdKQ5qJ7qNj/OE4+h/FwXnjM9j2SJkmaZPscSV9ou9AkBXgpsJukv1H+CTttuBtWjGF/ynIez5L0R2AysH2lspdqvi9TqbwRSTqM0o59bHNoX0kvsX1gpRAOB15r+9omnucAxwMvrFQ+QKeW0Lv4mSlXjLWcIenXlJ8dYEfg9Irlj4fPYTycF+6TtDRwLnCspBnArLYLTfORtM5wx23fXDmOhSlLgYvK1eVmc6N9bH+lVpkjxHEFsLHtJ3riurTWP6KkK4aWNdyxlmNYyPZA+hKGxLEd5cQo4FzbP61Y9nj4HAZ+XpC0FPAwpZa2M7AccKzte9osd8LWFCQta/t+4IFBx9LYnNkjHTaVhO0f1CjY9uNNs81Ak0JjeaAz2mi5ymVfLOlI4IfN/Z0pHY013SDpFMq6X1dXLrvX+ZSO7icoC1XWNG1Qn8M4Oy/sCZxs+zZKc1oVE7amIOkXtt/UVA+HjnypOgRQ0g+BZwGXMXvEiW3vUzGGQykn4ROBhzrHa7ZlS9oJOAw4h/J5vBw40PYJlcpfDNibnitk4Fu2q40+kbQMZXXgd1OuEI8CTmhOVLVieC/wKeC3lN/DK4BP2z6qUvkD+xzG2XnhIODtlIukE4BTbN/VerkTNSmMJ5KuBtb3AD8MSecMc9i2a7ZlI2k1Sr+CgAts31mp3EnAFYMecdJL0sspbenLA6cAn7F9Q4VyrwX+o9NM0QyCON/2c9suO+YmaUNKv87bgNtsv7rN8iZy89Gmoz1eebTHVcAzgDsqljkH268cVNkdzcSty2yfJumdwH9L+lqNdlzbT0i6XNLabU8OGk3Tj7I1paYwhdLpeizwMkpn73NGfPHYuY05m08eAG6tUC4w7AQ+AAYwgW9D5p68VmPezlAzgDspo8BWabuwCZsUKP9sI6k92mNl4C+SLmTOYYg1Z3CuBBxEqbIbOI/SZNBqp9YQ3wY2krQR8F+UppMfUJovalgNmN58Dr1NaNU+B+B6SvPZF22f33P8lKbm0JqeYcl/By6QdCrlb2Fb4MI2yx5iPEzgOwrYEJhO6VeBepM5OzG8n1JDmEypKe5h+y9tlzthk8J4uDLucfCgA6C0WZ5LqaJC6dw7EWi1qjrELNuWtC3wddtHStq1YvmHVCxrJBvafnC4Byr0MXWGJf+1+eo4teVyh/qn7V9VLnOoLWyvP+AY1gE+bPuymoVO+D4FSbsMd7zWyJ/xQtLFtl845FjVDcsl/R44g9J08nJgJqU56QUVY1gHWM/2WZKWBBayXW0kiqT/C3yWMhTxDGAjyonhR7Vi6IllKdsPzfuZY17uYZSdFwc2ga8Z/XR4jSvzYcpe1vb9I60J1vZaYBO2ptBjs57biwNbAZdQmi2qaGYwfwN4PrAo5R/iIdvL1oqBMpP2HcBJzf3tgV9WLB9KVfk/gd1t3ylpbcoaPFVI2oMyDHBFymiwNYDvUP4manmt7f+W9FZK2/4OlOakaklB0ospTThLA2s3zXl72f5ApRDGwwS+Y4A/SbqT+pPXjgPexPBrgrW+FtiErykMJWk54IeV2/OnUYYhnkz5R9iFcrX68YoxPECZ3dxpP53E7HZ1V05QAyHpMsp8kQt61qC6snJNZbrtDSR9D/ix7TMkXV553aELKBcFp/X8Hga+FlBNkm6grDRwJbP/J6pNXpMkYK1BDHpITWFu/wLWq12o7Rt6ZrMeLen8eb5obMsf2DIXks6z/dImMfVepXSuzmolpEdsP1r+H7uzzGtfNf1c0jWU5qMPqCxG9+/KMWD71s7vodF6h+84W4frFtunVSxvDk3f2k+pu7QHkKQwdA33ScD6zG5CqeVfKqtCXta0Kd/B7DWJqpG0AiUh9q7d3voiZLZf2nwf9PpLv5f0cWAJSa8BPgD8vGYAtg9oFj27v5lp/hBl9E9Nt0r6D8DN3+U+lCWb2zZu1uECrpF0HOXz7+3XqDkk9c+SNrNddUb5hG8+ktQ73HEWcHMzrbxmDOsAd1H6E/ajzCz+Vo2JSj0xvBfYF1iTMrN6C+BPNSevSfqh7XfN61iL5U8CdgdeS6ml/Br4f7UnFUr6P5SLk97kXLOPa2Xga5SRZwJ+A+xbeXjyQEk6epjDtv2eijH8hTIv5WZKU26Vfo0JnxQ6JC3LnJNUWu3hH1L2UsDDnnMhuMVs/6tiDFdSOt3/bHtjSc8DDrG9Y8UYLrG9ac/9hSmzjFsdGijpbNtbSfqC7Y+1WVYfsRwEbElJCqcDbwDOs11r1dyBk/RMSlLaglKL/xOwn+0bBxpYZYNalG/C79EsaU9JdwFXANMoPf7TKodxNrBkz/0lgLMqx/Bv2/+GsvaM7Wsoq7a2Tg8R/MEAAA4OSURBVNKBTX/ChpLub74eoNSeaoyRX62pMW4jaRNJm/Z+VSi/1/aU0U532n43ZUjqYjUDkHSMpOV77q/QTOaq5ThKE+5qlF3HTmb2Mt5VSFpc0t6SviXpqM5XzRiak/9KlObDbYCVanR0T/g+BcrM2Q1s3z3AGBbvnbBk+8FmjHxNtzUngp8BZ0q6F7i9Utk32F5G0km2316pzF6fAg6gNJ0N7cysPRTy4WbJjVlN7XUG7W9HOtSGtu/r3LF9r6RNKpYv2z/suf8jSR+sWD6UFVqvAV4HfJoymbPqqrWSPkUZktzpxzha0sm2P9tmuUkKZeZmtWaaETwkadPO5BxJL6SMPqnG9lubmwerLI63HFBrVumBlKvBZ1cqbw4uew+fIul/bH9mEDH0mNYk5+9Raq0PUneJCYBJklawfS9AM4mq9XNFz2StcyQdQJllb8r8ldpzZp5tewdJ29o+pul0/nXlGHYCNumpwR9GmUOVpNCyAylb713AnKMMqi1bDXwYOFlS58p8Nco/QjW9Hbq2f985BtTo5L2nSUTrSpprGGDbc0YkPa9pLvvlcM1FNWfS9kwQ+46kM4BlbV9Rq/zG4ZT/iVOa+zsAh1Yod+hkrb16HjNQM2F3Nrm6r+n4v5OyOF5NN1EGG3SGJC/GnMuPtGLCdzSrLH52HnNPUqm2qUUTxyLM3nntGtffqHxoJ+9CwJU11n9phj1uSqmyv3fo450k1WL5R9jeUwNcPnxefRc1ExOApA2AV1L+Hs8exHIPg9SMxvsx8ALg+5TZ3f9j+7sVY/gZZfDHmZSk+BrKuWoGtHfhmqQgnW/7PwZU9qts/1Zl68O51BgTLelA4OOUzu1/Mfsq7VHgCNfbHxlJk23PrFXeeDJCQuqokpiGkrQKcw6LrTK7VuNgPTJJ69r+27yOtRzDqItBtnXhmqRQdhy7mbknqbQ+JFXSIbYPGidjoj9fMwGMEMNk4GPMPUa/5lyJ/2DuNfQn2uKI21CakFanXJWuA1xte4NK5X+j5253PbKaw3KH1pybY3MtGlkhjkWZvYdGlb3bkxTKtntD2ZU39Bi0ZuLWfwLr2v6MpLWA1WxX6+SU9BvKct0fBd4H7ArMrDV3QONjW9QlKWvurN00aa0HPNf2LyrGcDllxNVZtjeR9EpgJ9t71ophSDzV1iNr5udsAPxfysjEjmWB/6qVGJtYtqQszHcTpQa/FrBr26sMTPiOZtvrDjqGZrTJLsx9hVqzs/t/KX0qr6J06D3YHNtstBeNsZVc9lDYt+lH+L3Kctq1TGXA26ICR1M6XDtNmrdRRmZVSwrAY7bvkTRJ0iTb56gsvTEoNdcjey5lhdLlgTf3HH8A2KNSDB2HU1bNvRZA0nMo8zVara1M+KTQdPC+n7J+P8DvgO9W7ug9HfgzQzq7K3uR7U0lXQrdsemLVo6h8zu/Q9LWlHkSa1Ysf+DbogLPsr2jpJ0AbD8szbkyXQX3SVqasunSsZJmUJaAqUKDXY9se9vvkvRx25+rVOZIFukkBADb1zXnq1ZN+KRA2QJyEeBbzf13NcfmGgXTosVtD7syZEWPNSOODN32/doJ6rNNU8FHKPtLLEtZC6qWgW+LCjwqaQlmfw7P6o2lkm0pwyD3o0zaWo4ygatVKjPpHwG+1HO49npkL1RZXmJHSd9mzr0Mqi5/A1ysstlPZyLfzpRaZKvSpzDMWvXDHWs5hv0ozTW/oHJnd08MO1PmRmxKacfcHvik7ZNrxTBomnNxxK62h8QOieG1wCcoV8e/AV4CvNv2aKOTFgidzl1VXARxmBj2obQcPJO5Z/RX7WuUtBiwN2XfdFFqbt9qEmd75SYp6BJgB9t/be4/Ezhl6MiDlmPYmzI56D5mV5ur/AFKWrNzFdZ0sm1FMzadMquz9aWjm+n8I3HNWcaSVmV2P8qFtmfUKrsnhpUoi8GJskBhlSVYNHs/i87Vcedvscq+FpKuouy09ynm7OQtwVRctlrSt22/v1Z5w5Q/ibIYZPWNjZIUpK0onXs3Uv7416HylZmkv1La9KuvvyTpWuB1tm8acvw9wCdsP6tCDB8Z5vBSlGWsV7K9dNsxNHG8nXJS+h3lb+FllBEnp4z2ujGO4WzbW83r2IJI0kspTSRvB4bObK86RLsnnvVsH62ynPgylecpHAscWGt+SMeE71OwfXZn2B+zZxPXbsOdzuDWX9qPsgDeG21fD6Cy7szOwLDNKWPN9uGd25KWoezr8G7K2jeHj/S6FnwC2KxTO2j6Vc4CWk8KkhanrJS7sspmR52r9WUp8wVa18TwPsoaVFcAR9mu1sFs+zzgPEnTbB9Zq9zhqCxhPpVyXjiastfJjyjNebWsBkxv+rg6W+O23sc14ZNC84/wAUq7nYE/SPqOm0WoKnmcsuvaOVRef8n26ZIeAX4l6S2UDvbNgJe7WRCtBpXF0PanJKNjgE1rlt+YNKS56B7qLS+/F2UNrNWZszPxAcrQ4BqOoYwA+wPwRsp4/X0rld3VDEse9CTCtwKbUBagw/btzQVLTYdULg9IUgD4AeUfrzOLcidKb/8OFWP4WfM1EE1taTdKs8n5wFY1k6KkLwLbAUcAL3DPMuKVnSHp18xeu39HynDhGs6nDLvc3vY3miUO3kaZuHRcpRjWt/0CgGbUS+3VWWnKHnYSIeV/tZZHbVtSZxRYte1xh9TYrgSOrFljS5/COBh9NEhDOhcXo1wpPk6lzsUmhicoNaRZzO7cpFYMkl5n+9fN7e2Yc7THwjVGYDUDHl5t+x+SXk5pOvsQsDHw/BpLPAxd2mG4pR5qkHQ1A55EKOmjlAlzrwE+D7wHOM72N0Z94diUfSKza2xvoAzJrVZjS00BLpW0he0/A0h6EfDHmgE0fRqfZ+41f1offWR74Juk2x70DoCnSzoXeGczwqU7yqU5WdcYlrtQzxDkHSmLEf4Y+LGkyyqUD7CRpPub2wKWaO5Xu0BoDGwSoaRnA6va/pKk1wD3U/oVfkW9WuNAa2xJCvAiYBdJnR7+tYGrVfYstlveJLtxNHAQ8BXKcsXvZsikmWjVFZQmmj9L2n9IzaDW57CQpIWbZoKtgN51hqr8n9peqEY5fRjkJMKvUlYNxvaZlGWrkTS1eezNI790zHRXU7A9q/aE9iQFeP2gAwCWaNr15bIH68GS/kBJFNE+2/5es87SsZLeCOxt+1/M2ZzVpuMpaz3dTdl17w/QvXL9Z6UYxouDB1j2FA+zqZHtaZKmVIphoDW2CZ8UmpPwwNaOb/y7maxyvcpetH8HVqlYftBdW+bFlO0OL9UI6/q3VPahks6mDEP8TU97+iRK38KEUXMG+TAWH+WxJWoEMOgaWzqaB7x2fBPDZpRNwZenrFC6HPAF2xfUimEik3Sp7U2GHNsSOAqYPB76XSaCnkEPcz1EvUEPxwO/tf29Icd3p6xYWnWb3EFIUhhna8c3MS0M7Gj72EHFMJFIeovtuYYEN5PI9rJ92ADCigFoljn5KWXnwc58kamUyWtvtX3noGKrJUmhzJ6c2iSHTWw/IelC25tXKHtZyoJXa1Cm9Z/Z3P8ocLntbduOISLm1lwcdtYdmm77t4OMp6YkBeks4C2UIaErU5qQNnOFfZslnQrcC/yJMuJkBcoVyb62aw1DjIjomrBJoTMemTJr8mFKh97OlD6FX9puf91y6cqe8cgLAXdTtmF8oO2yIyKGM+hJQ4P0VeAB2w/ZfsL2LNvHUCaoHFwpht7xyI8Df0tCiIhBmsg1has8wlrlvVfwLcfwOLNXPxRlyNu/qD+DNCICmNjzFCb8eOSIiKEmcvPRRZL2GHqwGY/cen9CRMR4NJGbjyb8eOSIiKEmbFLomMjjkSMihprwSSEiImabyH0KERExRJJCRER0JSnEAkXS45IukzRd0uWS9m+WJW+jrC0l/VPSpZKukfSlp/Be20g6YCzji3gy0qcQCxRJD9peurm9CmVHtT/aPmjI8zq7nD2VsrYEPmr7TZKWAC4FdrdddTvXiLGUmkIssGzPoGxr+UEVu0k6WdLPgd80V/q/6Dxf0jcl7dbcfmNz9X+epK/3Pm+Esh6mrKO1RvP610r6k6RLmjKXHu19m9i+2dz+vqRvSzpH0o2SXiHpKElXS/p+T7wjlXGTpEOa41dKet6Y/VJjgZekEAs02zdS/s47O9m9GNjV9qtGeo2kxYHvAm+w/VJg8rzKafZeWA84V9LKwCeBV9veFJgG7D+f77sCZZ+P/YCfU/bv3gB4gaSNRyqj5/V3N8e/TVmKPaIvSQoxEfTufH6m7X/M4/nPA260/bfm/vGjPPdlkq4A7gR+0Ux63AJYH/ijpMuAXSmr787P+/682ZLzSuAu21fafgKYDkwZpYyOnzTfL26eH9GXibz2UUwAkp4JPE7ZJwNmL0AIMIs5L4w662H1JpF5+UPTp/Ac4DxJP21ef6btnYbEssmw7zC8R5rvT/Tc7txfmPIzzVXGMK9/nPyfx3xITSEWWJImA98BvunhR1TcDKwvaTFJy1E2OgK4BnimpCnN/Xnuy2v7OspGTR8D/gy8pNmzA0lLNkljvt93FCOVEfGU5AoiFjRLNM0pi1BqAj8EvjzcE23fKukk4ArgesroIWw/LOkDwBmS7gYu7LPs71Da75cGdgOOl7RY89gnbV/3JN93uNhnNp3ic5QBXPdk3zMCMiQ1YliSlrb9oCQB/wtcb/sr4/V9I8ZKmo8ihrdHU+OYDixHGTU0nt83YkykphAREV2pKURERFeSQkREdCUpREREV5JCRER0JSlERERXkkJERHT9f5/MLFnHrH66AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph a bar plot showing the total number of timepoints for all mice tested for each drug regimen using Pandas.\n",
    "\n",
    "all_mice = clean.groupby(\"Drug Regimen\")[\"Timepoint\"].count().sort_values(ascending=False)\n",
    "\n",
    "all_mice.plot(kind=\"bar\", width=.75),\n",
    "plt.title(\"Total # of Mice Tested per Drug \")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice Tested\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the same thing with pyplot \n",
    "# Indices\n",
    "x = all_mice.index\n",
    "y = all_mice.values\n",
    "\n",
    "# Make bar graph\n",
    "plt.bar(x, y, alpha=.8, align='center')\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice Tested\")\n",
    "plt.title(\"Total # of Mice Tested per Drug \")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "\n",
    "#show plot \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>Total Count</th>\n",
       "      <th>Percentage of Sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>124</td>\n",
       "      <td>49.799197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>125</td>\n",
       "      <td>50.200803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total Count  Percentage of Sex\n",
       "Sex                                   \n",
       "Female          124          49.799197\n",
       "Male            125          50.200803"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby to find the # of Mickeys and Minnies \n",
    "groupby_gender = merge_data.groupby([\"Mouse ID\",\"Sex\"])\n",
    "groupby_gender\n",
    "\n",
    "mouse_gender_df = pd.DataFrame(groupby_gender.size())\n",
    "\n",
    "# DF for Mickeys and Minnies\n",
    "mouse_gender = pd.DataFrame(mouse_gender_df.groupby([\"Sex\"]).count())\n",
    "mouse_gender.columns = [\"Total Count\"]\n",
    "\n",
    "# % of M/F\n",
    "mouse_gender[\"Percentage of Sex\"] = (100*(mouse_gender[\"Total Count\"]/mouse_gender[\"Total Count\"].sum()))\n",
    "\n",
    "# % Format\n",
    "mouse_gender[\"Percentage of Sex\"] = mouse_gender[\"Percentage of Sex\"]\n",
    "\n",
    "#gender_df\n",
    "mouse_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 295.2x295.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "#plot = name of the data frame .plot.pie (sumplots true) legend(frameon = False)\n",
    "\n",
    "colors = [ 'pink','lightskyblue']\n",
    "explode = (0.1, 0)\n",
    "plot = mouse_gender.plot.pie(title=(\"Female vs Male Population of Mice\"),y='Total Count', figsize=(4.1,4.1),colors = colors, startangle=140, explode = explode, legend = False, shadow = True, autopct=\"%1.1f%%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "\n",
    "# #labels for chart\n",
    "labels = [ \"Female\", \"Male\"]\n",
    "\n",
    "#List the values of each section of the pie chart\n",
    "sizes = [49.799197,50.200803]\n",
    "\n",
    "# Colors\n",
    "colors = [ 'pink','lightskyblue']\n",
    "\n",
    "# Set which section to detach\n",
    "explode = (0.1, 0)\n",
    "\n",
    "#Create the pie chart based upon the values \n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct=\"%1.1f%%\", shadow=True, startangle=140)\n",
    "plt.ylabel(\"Total Count\")\n",
    "plt.title(\"Female vs Male Population of Mice\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>45</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>4</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>30</td>\n",
       "      <td>63.440686</td>\n",
       "      <td>1</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>z435</td>\n",
       "      <td>10</td>\n",
       "      <td>48.710661</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>z578</td>\n",
       "      <td>45</td>\n",
       "      <td>30.638696</td>\n",
       "      <td>0</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>z581</td>\n",
       "      <td>45</td>\n",
       "      <td>62.754451</td>\n",
       "      <td>3</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>z795</td>\n",
       "      <td>45</td>\n",
       "      <td>65.741070</td>\n",
       "      <td>3</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>z969</td>\n",
       "      <td>45</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0       a203         45           67.973419                 2    Infubinol   \n",
       "1       a251         45           65.525743                 1    Infubinol   \n",
       "2       a262         45           70.717621                 4      Placebo   \n",
       "3       a275         45           62.999356                 3     Ceftamin   \n",
       "4       a366         30           63.440686                 1     Stelasyn   \n",
       "..       ...        ...                 ...               ...          ...   \n",
       "243     z435         10           48.710661                 0     Propriva   \n",
       "244     z578         45           30.638696                 0     Ramicane   \n",
       "245     z581         45           62.754451                 3    Infubinol   \n",
       "246     z795         45           65.741070                 3     Naftisol   \n",
       "247     z969         45           73.867845                 4     Naftisol   \n",
       "\n",
       "        Sex  Age_months  Weight (g)  \n",
       "0    Female          20          23  \n",
       "1    Female          21          25  \n",
       "2    Female          17          29  \n",
       "3    Female          20          28  \n",
       "4    Female          16          29  \n",
       "..      ...         ...         ...  \n",
       "243  Female          12          26  \n",
       "244    Male          11          16  \n",
       "245  Female          24          25  \n",
       "246  Female          13          29  \n",
       "247    Male           9          30  \n",
       "\n",
       "[248 rows x 8 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "\n",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "timepoint = clean.groupby(\"Mouse ID\")[\"Timepoint\"].max()\n",
    "timepoint= pd.DataFrame(timepoint)\n",
    "# Merge to get tumor valume at turnpoit\n",
    "merged_tumor = pd.merge(timepoint,clean, on=(\"Mouse ID\", \"Timepoint\"), how=\"left\")\n",
    "\n",
    "merged_tumor\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin Regimen\n",
      "The Potential Outliers are: 20.7,51.83\n",
      "----------------------------------------------------------\n",
      "Ramicane Regimen\n",
      "The Potential Outliers are: 17.91,54.31\n",
      "----------------------------------------------------------\n",
      "Infubinol Regimen\n",
      "The Potential Outliers are: 36.83,82.74\n",
      "----------------------------------------------------------\n",
      "Ceftamin Regimen\n",
      "The Potential Outliers are: 25.36,87.67\n",
      "----------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatments = [ \"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumor_vol_data = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "for treatment in treatments:\n",
    "\n",
    "    \n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes\n",
    "    tumor_vol = merged_tumor.loc[merged_tumor[\"Drug Regimen\"] == treatment, \"Tumor Volume (mm3)\"]\n",
    "    \n",
    "    # add subset \n",
    "    tumor_vol_data.append(tumor_vol)\n",
    "    \n",
    "    # Determine outliers using upper and lower bounds\n",
    "    quartiles = tumor_vol.quantile([.25,.5,.75])\n",
    "    lowerq = quartiles[0.25]\n",
    "    upperq = quartiles[0.75]\n",
    "    iqr = upperq-lowerq\n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "\n",
    "    print(f\"{treatment} Regimen\")\n",
    "    print(f\"The Potential Outliers are: {round(lower_bound,2)},{round(upper_bound,2)}\")\n",
    "    print(f\"----------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x25609b36fc8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b2b608>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b44808>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b44d88>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bedec8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bf4488>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c016c8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c01c08>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x25609b2bb48>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b2bcc8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b44f08>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b14848>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bf4a08>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bf4f88>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c01d88>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c08708>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x2560942e148>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b25dc8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b44208>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bed908>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x25609b25688>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b14dc8>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bfb588>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c08c88>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x25609b25c48>,\n",
       "  <matplotlib.lines.Line2D at 0x25609b14f48>,\n",
       "  <matplotlib.lines.Line2D at 0x25609bfbb08>,\n",
       "  <matplotlib.lines.Line2D at 0x25609c08e08>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot that shows the distrubution of the tumor volume for each treatment group.\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Ending Tumor Volume by Treatment\")\n",
    "ax.set_ylabel(\"Tumor Volume (mm3)\")\n",
    "ax.set_xlabel( \"Drug Regimens\")\n",
    "\n",
    "ax.boxplot(tumor_vol_data, labels = treatments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Volume of Tumor vs Timepoint for Mouse(b128)')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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",
    "Capomulin = clean.loc[clean[\"Mouse ID\"] == \"b128\"]\n",
    "\n",
    "x_axis = Capomulin[\"Timepoint\"]\n",
    "\n",
    "y_axix = Capomulin[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "plt.plot(x_axis, y_axix, linewidth=2, marker=\"o\" )\n",
    "\n",
    "plt.xlabel(\"Timepoint(days)\")\n",
    "\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "\n",
    "plt.title(\"Volume of Tumor vs Timepoint for Mouse(b128)\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "\n",
    "\n",
    "# DF copy\n",
    "Capom = clean.loc[clean[\"Drug Regimen\"] == \"Capomulin\"]\n",
    "\n",
    "# Find mean by mouse\n",
    "cap_avg = Capom.groupby(\"Mouse ID\").mean()\n",
    "\n",
    "mice_weight = cap_avg[\"Weight (g)\"]\n",
    "mice_tumor =  cap_avg[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Make the graph\n",
    "plt.scatter(mice_weight, mice_tumor, marker=\"o\" )\n",
    "\n",
    "plt.xlabel(\"Mouse Weight for Capomulin Regimen\")\n",
    "\n",
    "plt.ylabel(\"Average Tumor Volume\")\n",
    "\n",
    "plt.title(\"Average Volume of Tumor vs Weight of Mice for Capomulin\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient between mouse and average tumor volume is 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient & linear regression model for mouse weight & avg tumor vol for Capomulin \n",
    "\n",
    "# DF copy\n",
    "capo = clean.loc[clean[\"Drug Regimen\"] == \"Capomulin\"]\n",
    "\n",
    "# Find Mean\n",
    "capo_mean = capo.groupby(\"Mouse ID\").mean()\n",
    "\n",
    "#Generate a scatter plot of average tumor volume vs. mouse weight for the Capom\n",
    "mice_weight = capo_mean[\"Weight (g)\"]\n",
    "mice_tumor =  capo_mean[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "\n",
    "# Print \n",
    "print(f\"The correlation coefficient between mouse and average tumor volume is {round(st.pearsonr(mice_weight,mice_tumor)[0],2)}\")\n",
    "\n",
    "# Line regress\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(mice_weight, mice_tumor)\n",
    "regress_values = mice_weight * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "# Graph\n",
    "plt.scatter(mice_weight, mice_tumor, marker=\"o\" )\n",
    "plt.plot(mice_weight,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(20,36),fontsize=18,color=\"red\")\n",
    "plt.xlabel(\"Mouse (g)\")\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\")\n",
    "plt.title(\" Average Volume of Tumor vs Weight of Mice for Capomulin\")\n",
    "\n",
    "\n",
    "#Show\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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