pymaceuticals_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Observation and analysis\n",
    "\n",
    "In this study, 249 animals diagnosed with squamous cell carcinoma (SCC) tumors were treated with various drug regimens. The researchers aimed to compare the performance of Pymaceuticals' drug of interest, Capomulin, against other treatments. Tumor development was observed and measured over a 45-day period.\n",
    "The analysis revealed a balanced ratio of male and female mice, indicating no gender bias in the study. The following conclusions were drawn from the analysis:\n",
    "\n",
    "1) Capomulin and Ramicane treatments exhibited greater effectiveness compared to the other drugs, as evidenced by the boxplot and statistics table. <br></br>\n",
    "\n",
    "\n",
    "2) The administration of Capomulin to a specific mouse (Mouse ID = \"l509\") resulted in positive outcomes, with the tumor size decreasing over the duration of study. <br></br>\n",
    "\n",
    "\n",
    "3) The correlation coefficient of 0.84 between mouse weight and average tumor volume suggests a strong tendency for the average tumor size to increase as the weight of the mouse increases. <br></br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "    </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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "\n",
    "# Display the metadata table for preview\n",
    "mouse_metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 249 entries, 0 to 248\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Mouse ID      249 non-null    object\n",
      " 1   Drug Regimen  249 non-null    object\n",
      " 2   Sex           249 non-null    object\n",
      " 3   Age_months    249 non-null    int64 \n",
      " 4   Weight (g)    249 non-null    int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 9.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display the info from the data table\n",
    "mouse_metadata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the study results data table for preview\n",
    "study_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single DataFrame\n",
    "combined_mouse_df = pd.merge(study_results, mouse_metadata, on = \"Mouse ID\", how = \"left\")\n",
    "\n",
    "# Display the merged data table for preview\n",
    "combined_mouse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1893 entries, 0 to 1892\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Mouse ID            1893 non-null   object \n",
      " 1   Timepoint           1893 non-null   int64  \n",
      " 2   Tumor Volume (mm3)  1893 non-null   float64\n",
      " 3   Metastatic Sites    1893 non-null   int64  \n",
      " 4   Drug Regimen        1893 non-null   object \n",
      " 5   Sex                 1893 non-null   object \n",
      " 6   Age_months          1893 non-null   int64  \n",
      " 7   Weight (g)          1893 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(3)\n",
      "memory usage: 133.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display the merged data table for information\n",
    "combined_mouse_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "len(mouse_metadata[\"Mouse ID\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice (another way)\n",
    "len(mouse_metadata[\"Mouse ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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>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>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>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>1111</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "137      g989          0           45.000000                 0     Propriva   \n",
       "360      g989          5           47.570392                 0     Propriva   \n",
       "681      g989         10           49.880528                 0     Propriva   \n",
       "869      g989         15           53.442020                 0     Propriva   \n",
       "1111     g989         20           54.657650                 1     Propriva   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "137   Female          21          26  \n",
       "360   Female          21          26  \n",
       "681   Female          21          26  \n",
       "869   Female          21          26  \n",
       "1111  Female          21          26  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our data should be uniquely identified by Mouse ID and Timepoint\n",
    "# Get the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "mouse_id = pd.DataFrame(combined_mouse_df.loc[combined_mouse_df.duplicated(['Mouse ID','Timepoint'])])\n",
    "mouse_id[\"Mouse ID\"].unique()\n",
    "mouse_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the unique mouse id -  another method\n",
    "combined_mouse_df.loc[combined_mouse_df.duplicated(subset = ['Mouse ID','Timepoint']), \"Mouse ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID\n",
       "a203    56.312708\n",
       "a251    55.237095\n",
       "a262    58.611631\n",
       "a275    52.770965\n",
       "a366    54.639906\n",
       "          ...    \n",
       "z435    46.942214\n",
       "z578    38.411510\n",
       "z581    53.979584\n",
       "z795    54.880317\n",
       "z969    60.194652\n",
       "Name: Tumor Volume (mm3), Length: 249, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_mouse_df.groupby([\"Mouse ID\"]).mean(numeric_only = True)[\"Tumor Volume (mm3)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID               object\n",
       "Timepoint               int64\n",
       "Tumor Volume (mm3)    float64\n",
       "Metastatic Sites        int64\n",
       "Drug Regimen           object\n",
       "Sex                    object\n",
       "Age_months              int64\n",
       "Weight (g)              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_mouse_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "duplicate_mouse_data = combined_mouse_df.loc[combined_mouse_df[\"Mouse ID\"] == \"g989\"] \n",
    "duplicate_mouse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([107, 137, 329, 360, 620, 681, 815, 869, 950, 1111, 1195, 1380,\n",
       "            1592],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "# Find the indexes of duplicate mouse id and drop the rows\n",
    "duplicate_mouse_data = combined_mouse_df[combined_mouse_df[\"Mouse ID\"] == \"g989\"].index\n",
    "duplicate_mouse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f932</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>r944</td>\n",
       "      <td>45</td>\n",
       "      <td>41.581521</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>u364</td>\n",
       "      <td>45</td>\n",
       "      <td>31.023923</td>\n",
       "      <td>3</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>p438</td>\n",
       "      <td>45</td>\n",
       "      <td>61.433892</td>\n",
       "      <td>1</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>x773</td>\n",
       "      <td>45</td>\n",
       "      <td>58.634971</td>\n",
       "      <td>4</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>b879</td>\n",
       "      <td>45</td>\n",
       "      <td>72.555239</td>\n",
       "      <td>2</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0        b128          0           45.000000                 0    Capomulin   \n",
       "1        f932          0           45.000000                 0     Ketapril   \n",
       "2        g107          0           45.000000                 0     Ketapril   \n",
       "3        a457          0           45.000000                 0     Ketapril   \n",
       "4        c819          0           45.000000                 0     Ketapril   \n",
       "...       ...        ...                 ...               ...          ...   \n",
       "1888     r944         45           41.581521                 2    Capomulin   \n",
       "1889     u364         45           31.023923                 3    Capomulin   \n",
       "1890     p438         45           61.433892                 1     Ceftamin   \n",
       "1891     x773         45           58.634971                 4      Placebo   \n",
       "1892     b879         45           72.555239                 2     Stelasyn   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "0     Female           9          22  \n",
       "1       Male          15          29  \n",
       "2     Female           2          29  \n",
       "3     Female          11          30  \n",
       "4       Male          21          25  \n",
       "...      ...         ...         ...  \n",
       "1888    Male          12          25  \n",
       "1889    Male          18          17  \n",
       "1890  Female          11          26  \n",
       "1891  Female          21          30  \n",
       "1892  Female           4          26  \n",
       "\n",
       "[1880 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the rows\n",
    "combined_mouse_df.drop(duplicate_mouse_data, inplace = True)\n",
    "combined_mouse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "len(combined_mouse_df[\"Mouse ID\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>SEM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mean     Median   Variance  Standard Deviation       SEM\n",
       "Drug Regimen                                                               \n",
       "Capomulin     40.675741  41.557809  24.947764            4.994774  0.329346\n",
       "Ceftamin      52.591172  51.776157  39.290177            6.268188  0.469821\n",
       "Infubinol     52.884795  51.820584  43.128684            6.567243  0.492236\n",
       "Ketapril      55.235638  53.698743  68.553577            8.279709  0.603860\n",
       "Naftisol      54.331565  52.509285  66.173479            8.134708  0.596466\n",
       "Placebo       54.033581  52.288934  61.168083            7.821003  0.581331\n",
       "Propriva      52.320930  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "\n",
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "# Assemble the resulting series into a single summary DataFrame.\n",
    "\n",
    "drug_gb = combined_mouse_df.groupby(\"Drug Regimen\")\n",
    "\n",
    "# summary_df.head(5)\n",
    "\n",
    "tumor_mean = drug_gb[\"Tumor Volume (mm3)\"].mean() \n",
    "tumor_median = drug_gb[\"Tumor Volume (mm3)\"].median() \n",
    "tumor_variance = drug_gb[\"Tumor Volume (mm3)\"].var() \n",
    "tumor_std = drug_gb[\"Tumor Volume (mm3)\"].std() \n",
    "tumor_sem = drug_gb[\"Tumor Volume (mm3)\"].sem() \n",
    "\n",
    "summary_df = pd.DataFrame(\n",
    "    {\n",
    "    \"Mean\" : tumor_mean, \n",
    "    \"Median\" : tumor_median, \n",
    "    \"Variance\" : tumor_variance,\n",
    "    \"Standard Deviation\" : tumor_std, \n",
    "    \"SEM\" : tumor_sem\n",
    "    }\n",
    ")\n",
    "\n",
    "summary_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>SEM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mean     Median   Variance  Standard Deviation       SEM\n",
       "Drug Regimen                                                               \n",
       "Capomulin     40.675741  41.557809  24.947764            4.994774  0.329346\n",
       "Ceftamin      52.591172  51.776157  39.290177            6.268188  0.469821\n",
       "Infubinol     52.884795  51.820584  43.128684            6.567243  0.492236\n",
       "Ketapril      55.235638  53.698743  68.553577            8.279709  0.603860\n",
       "Naftisol      54.331565  52.509285  66.173479            8.134708  0.596466\n",
       "Placebo       54.033581  52.288934  61.168083            7.821003  0.581331\n",
       "Propriva      52.320930  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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A more advanced method to generate a summary statistics table of mean, median, variance, standard deviation,\n",
    "# and SEM of the tumor volume for each regimen (only one method is required in the solution)\n",
    "# Using the aggregation method, produce the same summary statistics in a single line\n",
    "# use drop function to drop the columns to resolve the error.\n",
    "# summary_stat = combined_mouse_df.groupby(\"Drug Regimen\").agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])[\"Tumor Volume (mm3)\"]\n",
    "summary_stat = combined_mouse_df.drop([\"Mouse ID\", \"Sex\"], axis=1).groupby(\"Drug Regimen\").\\\n",
    "                agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "summary_stat = summary_stat.rename(columns = \n",
    "                                   {\n",
    "                                       \"mean\" : \"Mean\",\n",
    "                                       \"median\" : \"Median\",\n",
    "                                       \"var\" : \"Variance\",\n",
    "                                       \"std\" : \"Standard Deviation\",\n",
    "                                       \"sem\" : \"SEM\"\n",
    "                                   })\n",
    "summary_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using Pandas.\n",
    "mouse_per_drug = combined_mouse_df[\"Drug Regimen\"].value_counts()\n",
    "plot_pandas = mouse_per_drug.plot.bar(color='tab:blue')  \n",
    "\n",
    "# Set the xlabel, ylabel, and title using class methods\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice Tested\")\n",
    "plt.title(\"Number of Mice Tested for each Drug Regimen\")\n",
    "plt.grid(linewidth = 1.5, alpha = 0.25)\n",
    "plt.savefig(\"images/bar_plot_drug_vs_number_of_mice.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using pyplot.\n",
    "mouse_per_drug = combined_mouse_df[\"Drug Regimen\"].value_counts()\n",
    "x_axis = np.arange(len(mouse_per_drug))\n",
    "plt.bar(x_axis, mouse_per_drug, width = 0.7, zorder=3)\n",
    "\n",
    "# Label the tick marks according to the index values in the 'counts' series\n",
    "tick_locations = [value for value in x_axis]\n",
    "plt.xticks(tick_locations, mouse_per_drug.index.values)\n",
    "\n",
    "# Rotate tick marks 90 degrees for visibility\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Set labels for axes\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Mice Tested\")\n",
    "plt.title('Number of Mice Tested for each drug  Regimen')\n",
    "\n",
    "# Create a grid - using 'zorder' places grid behind the bars since we set their z-order higher\n",
    "plt.grid(linestyle = \"dashed\")\n",
    "plt.savefig(\"images/pyplot_bar_drug_vs_number_of_mice.png\", bbox_inches = \"tight\")\n",
    "\n",
    "# Display resulting plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using Pandas\n",
    "data = combined_mouse_df[\"Sex\"].value_counts()\n",
    "labels = [\"Male\", \"Female\"]\n",
    "explode = [0.1, 0]\n",
    "colors = [\"blue\", \"pink\"]\n",
    "data.plot(kind=\"pie\", explode=explode, labels=data.index.values, colors=colors, \n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=140, title=\"Mouse distribution by Sex\")\n",
    "plt.savefig(\"images/pandas_pie_male_vs_female.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "# Tried a new look to visualize\n",
    "colors = [\"brown\", \"coral\"]\n",
    "plt.pie(data, labels=data.index.values, colors=colors, startangle=100, explode=explode, shadow=True, autopct=\"%1.1f%%\")\n",
    "plt.legend(title=\"Mouse distribution by Sex\", loc =\"upper right\")\n",
    "\n",
    "# Set equal axis\n",
    "plt.axis(\"equal\") \n",
    "plt.savefig(\"images/pyplot_pie_male_vs_female.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "last_timepoint = combined_mouse_df.groupby([\"Mouse ID\"])[\"Timepoint\"].max()\n",
    "last_timepoint = last_timepoint.reset_index()\n",
    "\n",
    "# Merge this group df with the original DataFrame to get the tumor volume at the last timepoint\n",
    "last_timepoint_df = last_timepoint.merge(combined_mouse_df, on=[\"Mouse ID\", \"Timepoint\"], how=\"left\")\n",
    "last_timepoint_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Capomulin, the outliers are: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "For Ramicane, the outliers are: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "For Infubinol, the outliers are: 31    36.321346\n",
      "Name: Tumor Volume (mm3), dtype: float64\n",
      "For Ceftamin, the outliers are: Series([], Name: Tumor Volume (mm3), dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "drug_list = [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "total_tumor_volume = []\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "for drug in drug_list:\n",
    "\n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes\n",
    "    tumor_volume = last_timepoint_df.loc[last_timepoint_df[\"Drug Regimen\"] == drug, \"Tumor Volume (mm3)\"]\n",
    "    \n",
    "    # add subset \n",
    "    total_tumor_volume.append(tumor_volume)\n",
    "    \n",
    "    # Determine outliers using upper and lower bounds\n",
    "    quartiles = tumor_volume.quantile([.25, .5, .75])\n",
    "    lowerq = quartiles[.25]\n",
    "    upperq = quartiles[.75]\n",
    "    iqr = upperq - lowerq \n",
    "    \n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "    outliers = tumor_volume.loc[(tumor_volume < lower_bound) | (tumor_volume > upper_bound)]\n",
    "    print(f\"For {drug}, the outliers are: {outliers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot that shows the distrubution of the tumor volume for each treatment group.\n",
    "flierprops = dict(marker='o', markerfacecolor='r', markersize=8, markeredgecolor='black')\n",
    "plt.boxplot(total_tumor_volume, labels=drug_list, flierprops=flierprops)\n",
    "plt.title('Tumor Volume for each Drug Regimen',fontsize =12)\n",
    "plt.ylabel('Final Tumor Volume (mm3)',fontsize = 10)\n",
    "plt.xlabel('Drug Regimen', fontsize = 10)\n",
    "plt.savefig(\"images/boxplot_final_tumor_volume.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>231</th>\n",
       "      <td>l509</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>l509</td>\n",
       "      <td>5</td>\n",
       "      <td>45.851193</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>l509</td>\n",
       "      <td>10</td>\n",
       "      <td>46.759074</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>l509</td>\n",
       "      <td>15</td>\n",
       "      <td>47.488599</td>\n",
       "      <td>1</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>l509</td>\n",
       "      <td>20</td>\n",
       "      <td>48.070452</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>l509</td>\n",
       "      <td>25</td>\n",
       "      <td>44.006257</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>l509</td>\n",
       "      <td>30</td>\n",
       "      <td>44.708055</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>l509</td>\n",
       "      <td>35</td>\n",
       "      <td>40.207288</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>l509</td>\n",
       "      <td>40</td>\n",
       "      <td>40.770813</td>\n",
       "      <td>2</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>l509</td>\n",
       "      <td>45</td>\n",
       "      <td>41.483008</td>\n",
       "      <td>3</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "231      l509          0           45.000000                 0    Capomulin   \n",
       "253      l509          5           45.851193                 0    Capomulin   \n",
       "539      l509         10           46.759074                 0    Capomulin   \n",
       "881      l509         15           47.488599                 1    Capomulin   \n",
       "982      l509         20           48.070452                 2    Capomulin   \n",
       "1146     l509         25           44.006257                 2    Capomulin   \n",
       "1414     l509         30           44.708055                 2    Capomulin   \n",
       "1499     l509         35           40.207288                 2    Capomulin   \n",
       "1723     l509         40           40.770813                 2    Capomulin   \n",
       "1771     l509         45           41.483008                 3    Capomulin   \n",
       "\n",
       "       Sex  Age_months  Weight (g)  \n",
       "231   Male          17          21  \n",
       "253   Male          17          21  \n",
       "539   Male          17          21  \n",
       "881   Male          17          21  \n",
       "982   Male          17          21  \n",
       "1146  Male          17          21  \n",
       "1414  Male          17          21  \n",
       "1499  Male          17          21  \n",
       "1723  Male          17          21  \n",
       "1771  Male          17          21  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a line plot of tumor volume vs. time point for a single mouse treated with Capomulin\n",
    "# Select the dataframe with Capomulin\n",
    "Capomulin_df = combined_mouse_df.loc[combined_mouse_df[\"Drug Regimen\"] == \"Capomulin\",:]\n",
    "single_mouse_data = Capomulin_df.loc[Capomulin_df[\"Mouse ID\"] == \"l509\",:]\n",
    "single_mouse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "231     45.000000\n",
       "253     45.851193\n",
       "539     46.759074\n",
       "881     47.488599\n",
       "982     48.070452\n",
       "1146    44.006257\n",
       "1414    44.708055\n",
       "1499    40.207288\n",
       "1723    40.770813\n",
       "1771    41.483008\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify the x and y axis data\n",
    "x_axis = single_mouse_data[\"Timepoint\"]\n",
    "y_axis = single_mouse_data[\"Tumor Volume (mm3)\"]\n",
    "y_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of tumor volume vs. time point for a single mouse treated with Capomulin\n",
    "fig1, ax1 = plt.subplots()\n",
    "plt.plot(x_axis, y_axis)\n",
    "plt.title(\"Capomulin treatmeant of Mouse ID l509\",fontsize =12)\n",
    "plt.xlabel(\"Timepoint (Days)\",fontsize =10)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\",fontsize =10)\n",
    "plt.savefig(\"images/line_plot_tumor_volume_vs_time_point.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mouse ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b128</th>\n",
       "      <td>22.5</td>\n",
       "      <td>41.963636</td>\n",
       "      <td>0.70</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b742</th>\n",
       "      <td>22.5</td>\n",
       "      <td>40.083699</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f966</th>\n",
       "      <td>10.0</td>\n",
       "      <td>36.505973</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g288</th>\n",
       "      <td>22.5</td>\n",
       "      <td>41.990097</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g316</th>\n",
       "      <td>22.5</td>\n",
       "      <td>44.613344</td>\n",
       "      <td>1.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i557</th>\n",
       "      <td>22.5</td>\n",
       "      <td>44.805810</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i738</th>\n",
       "      <td>22.5</td>\n",
       "      <td>39.141053</td>\n",
       "      <td>0.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j119</th>\n",
       "      <td>22.5</td>\n",
       "      <td>44.465236</td>\n",
       "      <td>0.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j246</th>\n",
       "      <td>17.5</td>\n",
       "      <td>43.216925</td>\n",
       "      <td>0.25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l509</th>\n",
       "      <td>22.5</td>\n",
       "      <td>44.434474</td>\n",
       "      <td>1.40</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l897</th>\n",
       "      <td>22.5</td>\n",
       "      <td>42.507261</td>\n",
       "      <td>0.40</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m601</th>\n",
       "      <td>22.5</td>\n",
       "      <td>34.847470</td>\n",
       "      <td>0.90</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m957</th>\n",
       "      <td>22.5</td>\n",
       "      <td>39.049816</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r157</th>\n",
       "      <td>7.5</td>\n",
       "      <td>45.798970</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r554</th>\n",
       "      <td>22.5</td>\n",
       "      <td>36.509212</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r944</th>\n",
       "      <td>22.5</td>\n",
       "      <td>43.367364</td>\n",
       "      <td>0.90</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s185</th>\n",
       "      <td>22.5</td>\n",
       "      <td>34.559143</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s710</th>\n",
       "      <td>22.5</td>\n",
       "      <td>42.803733</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t565</th>\n",
       "      <td>22.5</td>\n",
       "      <td>39.328725</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u364</th>\n",
       "      <td>22.5</td>\n",
       "      <td>39.887495</td>\n",
       "      <td>1.80</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v923</th>\n",
       "      <td>22.5</td>\n",
       "      <td>41.581595</td>\n",
       "      <td>0.90</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w150</th>\n",
       "      <td>5.0</td>\n",
       "      <td>41.384825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w914</th>\n",
       "      <td>22.5</td>\n",
       "      <td>41.352452</td>\n",
       "      <td>0.50</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x401</th>\n",
       "      <td>22.5</td>\n",
       "      <td>36.182040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y793</th>\n",
       "      <td>22.5</td>\n",
       "      <td>38.506829</td>\n",
       "      <td>1.40</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Timepoint  Tumor Volume (mm3)  Metastatic Sites  Age_months  \\\n",
       "Mouse ID                                                                \n",
       "b128           22.5           41.963636              0.70         9.0   \n",
       "b742           22.5           40.083699              0.00         7.0   \n",
       "f966           10.0           36.505973              0.00        16.0   \n",
       "g288           22.5           41.990097              0.80         3.0   \n",
       "g316           22.5           44.613344              1.00        22.0   \n",
       "i557           22.5           44.805810              0.20         1.0   \n",
       "i738           22.5           39.141053              0.60        23.0   \n",
       "j119           22.5           44.465236              0.60         7.0   \n",
       "j246           17.5           43.216925              0.25        21.0   \n",
       "l509           22.5           44.434474              1.40        17.0   \n",
       "l897           22.5           42.507261              0.40        17.0   \n",
       "m601           22.5           34.847470              0.90        22.0   \n",
       "m957           22.5           39.049816              0.90         3.0   \n",
       "r157            7.5           45.798970              0.00        22.0   \n",
       "r554           22.5           36.509212              2.20         8.0   \n",
       "r944           22.5           43.367364              0.90        12.0   \n",
       "s185           22.5           34.559143              0.40         3.0   \n",
       "s710           22.5           42.803733              0.60         1.0   \n",
       "t565           22.5           39.328725              0.00        20.0   \n",
       "u364           22.5           39.887495              1.80        18.0   \n",
       "v923           22.5           41.581595              0.90        19.0   \n",
       "w150            5.0           41.384825              0.00        23.0   \n",
       "w914           22.5           41.352452              0.50        24.0   \n",
       "x401           22.5           36.182040              0.00        16.0   \n",
       "y793           22.5           38.506829              1.40        17.0   \n",
       "\n",
       "          Weight (g)  \n",
       "Mouse ID              \n",
       "b128            22.0  \n",
       "b742            21.0  \n",
       "f966            17.0  \n",
       "g288            19.0  \n",
       "g316            22.0  \n",
       "i557            24.0  \n",
       "i738            20.0  \n",
       "j119            23.0  \n",
       "j246            21.0  \n",
       "l509            21.0  \n",
       "l897            19.0  \n",
       "m601            17.0  \n",
       "m957            19.0  \n",
       "r157            25.0  \n",
       "r554            17.0  \n",
       "r944            25.0  \n",
       "s185            17.0  \n",
       "s710            23.0  \n",
       "t565            17.0  \n",
       "u364            17.0  \n",
       "v923            21.0  \n",
       "w150            23.0  \n",
       "w914            21.0  \n",
       "x401            15.0  \n",
       "y793            17.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight vs. the average observed tumor volume for the entire Capomulin regimen\n",
    "Capomulin_df_mean = Capomulin_df.groupby(['Mouse ID']).mean(numeric_only=True)\n",
    "Capomulin_df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID\n",
       "b128    22.0\n",
       "b742    21.0\n",
       "f966    17.0\n",
       "g288    19.0\n",
       "g316    22.0\n",
       "i557    24.0\n",
       "i738    20.0\n",
       "j119    23.0\n",
       "j246    21.0\n",
       "l509    21.0\n",
       "l897    19.0\n",
       "m601    17.0\n",
       "m957    19.0\n",
       "r157    25.0\n",
       "r554    17.0\n",
       "r944    25.0\n",
       "s185    17.0\n",
       "s710    23.0\n",
       "t565    17.0\n",
       "u364    17.0\n",
       "v923    21.0\n",
       "w150    23.0\n",
       "w914    21.0\n",
       "x401    15.0\n",
       "y793    17.0\n",
       "Name: Weight (g), dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify the x and y axis data\n",
    "x_axis = Capomulin_df_mean[\"Weight (g)\"]\n",
    "y_axis = Capomulin_df_mean[\"Tumor Volume (mm3)\"]\n",
    "x_axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight vs. the average observed tumor volume for the entire Capomulin regimen\n",
    "plt.scatter(x=x_axis, y=y_axis)\n",
    "plt.title(\"Average Tumor Volume by Weight\")\n",
    "plt.xlabel(\"Weight (g)\")\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between mouse weight and the average tumor volume is 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and a linear regression model \n",
    "# for mouse weight and average observed tumor volume for the entire Capomulin regimen\n",
    "correlation = st.pearsonr(x_axis, y_axis)\n",
    "print(f\"The correlation between mouse weight and the average tumor volume is {round(correlation[0],2)}\")\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_axis, y_axis)\n",
    "regress_values = x_axis * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x = x_axis, y = y_axis)\n",
    "plt.plot(x_axis, regress_values,\"r-\")\n",
    "plt.title(\"Positive Correlation between Average Tumor Volume and Weight\")\n",
    "plt.xlabel(\"Weight (g)\")\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\")\n",
    "plt.annotate(line_eq, xy = (20,38), fontsize = 12, color = \"red\")\n",
    "plt.savefig(\"images/scatter_plot_weight_vs_avg_tumor_volume.png\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}