pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations and Insights "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 observations or inferences that can be made from the data:\n",
    "\n",
    "1. According to the graph for mouse weight and average tumor volume for the Capomulin regimen there is a strong positive relationship between the two. The correlation coefficient is .84\n",
    "\n",
    "2. The sex of the mouse studied does not change the effects of the regimen used. During the tests the sex of the mouse is almost at a 50/50 split meaning there is no observation of the regimen having different effects based on the sex of the mouse.\n",
    "\n",
    "3. Based off the mean for the tumor volume captured during the analysis, Ramicane has the lowest average tumor volume at 40.216745 while Ketapril has the highest average tumor volume at 55.235638."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 67,
     "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",
    "from scipy import stats\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "merged_df = pd.merge(mouse_metadata, study_results, on=\"Mouse ID\", how=\"left\")\n",
    "\n",
    "# Display the data table for preview\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "merged_df[\"Mouse ID\"].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "909           45.000000                 0  \n",
       "911           47.570392                 0  \n",
       "913           49.880528                 0  \n",
       "915           53.442020                 0  \n",
       "917           54.657650                 1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "merged_df[merged_df.duplicated(['Mouse ID','Timepoint'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "908     g989     Propriva  Female          21          26          0   \n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "910     g989     Propriva  Female          21          26          5   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "912     g989     Propriva  Female          21          26         10   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "914     g989     Propriva  Female          21          26         15   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "916     g989     Propriva  Female          21          26         20   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "918     g989     Propriva  Female          21          26         25   \n",
       "919     g989     Propriva  Female          21          26         30   \n",
       "920     g989     Propriva  Female          21          26         35   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "908           45.000000                 0  \n",
       "909           45.000000                 0  \n",
       "910           48.786801                 0  \n",
       "911           47.570392                 0  \n",
       "912           51.745156                 0  \n",
       "913           49.880528                 0  \n",
       "914           51.325852                 1  \n",
       "915           53.442020                 0  \n",
       "916           55.326122                 1  \n",
       "917           54.657650                 1  \n",
       "918           56.045564                 1  \n",
       "919           59.082294                 1  \n",
       "920           62.570880                 2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "merged_df[merged_df['Mouse ID'] == 'g989']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>63.145652</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>65.841013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>69.176246</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70.314904</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0        k403     Ramicane  Male          21          16          0   \n",
       "1        k403     Ramicane  Male          21          16          5   \n",
       "2        k403     Ramicane  Male          21          16         10   \n",
       "3        k403     Ramicane  Male          21          16         15   \n",
       "4        k403     Ramicane  Male          21          16         20   \n",
       "...       ...          ...   ...         ...         ...        ...   \n",
       "1888     z969     Naftisol  Male           9          30         25   \n",
       "1889     z969     Naftisol  Male           9          30         30   \n",
       "1890     z969     Naftisol  Male           9          30         35   \n",
       "1891     z969     Naftisol  Male           9          30         40   \n",
       "1892     z969     Naftisol  Male           9          30         45   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "0              45.000000                 0  \n",
       "1              38.825898                 0  \n",
       "2              35.014271                 1  \n",
       "3              34.223992                 1  \n",
       "4              32.997729                 1  \n",
       "...                  ...               ...  \n",
       "1888           63.145652                 2  \n",
       "1889           65.841013                 3  \n",
       "1890           69.176246                 4  \n",
       "1891           70.314904                 4  \n",
       "1892           73.867845                 4  \n",
       "\n",
       "[1880 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "clean_df = merged_df[merged_df['Mouse ID'] != 'g989']\n",
    "clean_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "clean_df[\"Mouse ID\"].nunique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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": 9,
     "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",
    "mean = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].mean()\n",
    "median = clean_df.groupby(\"Drug Regimen\")[\"Tumor Volume (mm3)\"].median()\n",
    "variance = clean_df.groupby(\"Drug Regimen\")[\"Tumor Volume (mm3)\"].var()\n",
    "stdv = clean_df.groupby(\"Drug Regimen\")[\"Tumor Volume (mm3)\"].std()\n",
    "sem = clean_df.groupby(\"Drug Regimen\")[\"Tumor Volume (mm3)\"].sem()\n",
    "\n",
    "summary_stats_df = pd.DataFrame({\"Mean\": mean, \"Median\": median, \"Variance\": variance, \"Standard Deviation\": stdv, \"SEM\" : sem})\n",
    "summary_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean     median        var       std       sem\n",
       "Drug Regimen                                                     \n",
       "Capomulin     40.675741  41.557809  24.947764  4.994774  0.329346\n",
       "Ceftamin      52.591172  51.776157  39.290177  6.268188  0.469821\n",
       "Infubinol     52.884795  51.820584  43.128684  6.567243  0.492236\n",
       "Ketapril      55.235638  53.698743  68.553577  8.279709  0.603860\n",
       "Naftisol      54.331565  52.509285  66.173479  8.134708  0.596466\n",
       "Placebo       54.033581  52.288934  61.168083  7.821003  0.581331\n",
       "Propriva      52.320930  50.446266  43.852013  6.622085  0.544332\n",
       "Ramicane      40.216745  40.673236  23.486704  4.846308  0.320955\n",
       "Stelasyn      54.233149  52.431737  59.450562  7.710419  0.573111\n",
       "Zoniferol     53.236507  51.818479  48.533355  6.966589  0.516398"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "grp = clean_df.groupby('Drug Regimen')['Tumor Volume (mm3)']\n",
    "# Using the aggregation method, produce the same summary statistics in a single line\n",
    "grp.agg(['mean','median','var','std', 'sem'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></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>230</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mouse ID  Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "Drug Regimen                                                     \n",
       "Capomulin          230  230         230         230        230   \n",
       "Ceftamin           178  178         178         178        178   \n",
       "Infubinol          178  178         178         178        178   \n",
       "Ketapril           188  188         188         188        188   \n",
       "Naftisol           186  186         186         186        186   \n",
       "Placebo            181  181         181         181        181   \n",
       "Propriva           148  148         148         148        148   \n",
       "Ramicane           228  228         228         228        228   \n",
       "Stelasyn           181  181         181         181        181   \n",
       "Zoniferol          182  182         182         182        182   \n",
       "\n",
       "              Tumor Volume (mm3)  Metastatic Sites  \n",
       "Drug Regimen                                        \n",
       "Capomulin                    230               230  \n",
       "Ceftamin                     178               178  \n",
       "Infubinol                    178               178  \n",
       "Ketapril                     188               188  \n",
       "Naftisol                     186               186  \n",
       "Placebo                      181               181  \n",
       "Propriva                     148               148  \n",
       "Ramicane                     228               228  \n",
       "Stelasyn                     181               181  \n",
       "Zoniferol                    182               182  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_info_df = clean_df.groupby(\"Drug Regimen\").count()\n",
    "bar_info_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Drug Regimen')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yxhgzg9CK5fAgcIOkq8jJcABExDdrk8oYY0xXaUU5PFpes5SXMcaY6ZxJKoeI+FInBDHGGNM7DDYJ7tsRcYikK8jspAmIiC1rlcwYY0zXGMxyOL+8f70TghhjjOkdBpsEd0d5v1HSLMAK5aP7I+LNTghnjDGmO7QyCW494FzgYUDAEpJ2j4ib6hXNGGNMt2glW+kbwMYRcT+ApBWAnwDvrVMwY4wx3aOVSXBDG4oBICL+gZ8EZ4wx0zWtWA63SzqLvgD1zsAd9YlkjDGm27SiHPYHDgQ+ScYcbgK+V6dQxhhjuksrk+D+K+k04DpyVdb7I+KN2iUzxhjTNSYZc5C0GfAv4BTgNOABSZN8TKikH0l6WtK9lbJjJf1b0l3ltWnls89KekDS/ZI+MmU/xxhjTDtoNVtp/Yh4AEDSssBV5FPiBuMcUpmc16/8WxExwcQ6Se8EdgRWBhYFfitphYh4uwX5jDHGtJlWspWebiiGwoPA05P6UpkH0erjRMcAP42I/0bEQ8ADwBotftcYY0ybaUU53Cfpakl7SNoduAL4s6RtJG0zBXUeJOnu4naar5QtRj43osHYUtaEpH0l3S7p9nHjxk1B9cYYYyZFK8phGPmI0HWB9YBxwHBgC2DzyazvdGBZYFXgCdJlBZkF1Z+mxf4AIuLMiBgVEaMWXHDByazeGGNMK7SSrbRnuyqLiKca25J+AFxZdscCS1QOXRx4vF31GmMGZuRRV031OR4+cbM2SGJ6jVaylVaQdF0j60jSKpKOnpLKJI2o7G4NNDKZLgd2lDSrpKWB5YHbpqQOY4wxU08rbqUfAJ8F3gSIiLvJzKJBkfQT4BZgRUljJe0NnCTpHkl3A+sDh5Zz3gdcDPwfcA1woDOVjDGme7SSyjp7RNwmTRAWeGtSX4qInQYoPmuQ478CfKUFeYwxxtRMK5bDM2VuQwBI2pYMJhtjjJlOacVyOBA4E1hJ0r+Bh4BdapXKGGNMV2klW+lBYCNJcwBDIuKl+sUyxhjTTVp5Ety8wG7ASGDmRuwhIj5Zq2TGGGO6RitupauBPwH3kKuyGmOMmc5pRTkMi4jDapfEGGNMz9BKttL5kvaRNELS8MardsmMMcZ0jVYshzeAk4HP07feUQDL1CWUMcaY7tKKcjgMWC4inqlbGGOMMb1BS0t2A6/WLYgxxpjeoRXL4W3gLknXA/9tFDqV1Rhjpl9aUQ6XlZcxxpgZhFZmSJ/bCUGMMcb0DhNVDpIujojtJd3DAE9li4hVapXMGGNM1xjMcvhUeZ/cR4EaY4yZxhlMOZwLbBwRj3RKGGOMMb3BYKmsC3ZMCmOMMT3FYJbDPJK2mdiHEfHzGuQxxhjTAwyqHMh4gwb4LAArB2OMmU4ZTDk8EhF7dUwSY4wxPcNgMYeBLAZjjDEzAIMph107JoUxxpieYqLKISLu7aQgxhhjeodWVmU1xhgzgzFR5SDpuvL+tc6JY4wxphcYLFtphKR1gS0l/ZR+AeqI+EutkhljjOkagymHLwJHAYsD3+z3WQAb1CWUMcaY7jJR5RARlwKXSvpCRBzXQZmMMcZ0mVae53CcpC2BdUrRDRFxZb1iGWOM6SaTzFaSdAK5fPf/ldenSpkxxpjplFYeE7oZsGpEjAeQdC5wJ/DZOgUzxhjTPVqd5zBvZXueOgQxxhjTO7RiOZwA3CnpejKddR1sNRhjzHRNKwHpn0i6AXgfqRyOjIgn6xbMGGNM92jFciAingAur1kWY4wxPYLXVjLGGNNEbcpB0o8kPS3p3krZcEm/kfTP8j5fKZek70h6QNLdklavSy5jjDGTZlDlIGlItXOfTM4BNulXdhRwXUQsD1xX9gFGA8uX177A6VNYpzHGmDYwqHIocxv+KmnJyT1xRNwEPNeveAxwbtk+F9iqUn5eJH8C5pU0YnLrNMYY0x5aCUiPAO6TdBvwSqMwIracgvoWLsFtIuIJSQuV8sWAxyrHjS1lT/Q/gaR9SeuCJZecbJ1ljDGmBVpRDl+qXYqBn1cdAx0YEWcCZwKMGjVqwGOMMcZMHa3Mc7hR0lLA8hHxW0mzAzNNYX1PSRpRrIYRwNOlfCywROW4xYHHp7AOY4wxU0krC+/tA1wKnFGKFgMum8L6Lgd2L9u7A7+slO9WspbWAl5suJ+MMcZ0nlbcSgcCawC3AkTEPyuxgoki6SfAesACksYCxwAnAhdL2ht4FNiuHH41sCnwAPAqsOfk/QxjjDHtpBXl8N+IeEPKsICkmZlIPKBKROw0kY82HODYIJWQMcaYHqCVSXA3SvocMJukDwOXAFfUK5Yxxphu0opyOAoYB9wD7Ee6gI6uUyhjjDHdpZVspfHlAT+3ku6k+4sbyBhjzHTKJJWDpM2A7wP/IucjLC1pv4j4Vd3CGWOM6Q6tBKS/AawfEQ8ASFoWuAqwcjDGmOmUVmIOTzcUQ+FB+iavGWOMmQ6ZqOUgaZuyeZ+kq4GLyZjDdsCfOyCbMcaYLjGYW2mLyvZTwLplexwwX20SGWOM6ToTVQ4R4VnKxhgzg9JKttLSwMHAyOrxU7hktzHGmGmAVrKVLgPOImdFj69XHGOMMb1AK8rh9Yj4Tu2SGGOM6RlaUQ6nSDoGuBb4b6MwIv5Sm1TGGGO6SivK4d3ArsAG9LmVouwbY4yZDmlFOWwNLBMRb9QtjDHGmN6glRnSfwXmrVsQY4wxvUMrlsPCwN8l/ZkJYw5OZTXGmOmUVpTDMbVLYYwxpqdo5XkON3ZCEGOMMb1DKzOkX6LvmdGzAEOBVyJi7joFM8YY0z1asRzmqu5L2gpYozaJjDHGdJ1WspUmICIuw3McjDFmuqYVt9I2ld0hwCj63EzGGGOmQ1rJVqo+1+Et4GFgTC3SGGOM6QlaiTn4uQ7GGDODMdhjQr84yPciIo6rQR5jjDE9wGCWwysDlM0B7A3MD1g5GGPMdMpgjwn9RmNb0lzAp4A9gZ8C35jY94wxxkz7DBpzkDQcOAzYGTgXWD0inu+EYMYYY7rHYDGHk4FtgDOBd0fEyx2TyhhjTFcZbBLcp4FFgaOBxyX9p7xekvSfzohnjDGmGwwWc5js2dPGGGOmD6wAjDHGNGHlYIwxpgkrB2OMMU1YORhjjGmilYX32o6kh4GXgLeBtyJiVJlTcREwklzcb3vPqTDGmO7QTcth/YhYNSJGlf2jgOsiYnngurJvjDGmC/SSW2kMOQub8r5VF2UxxpgZmm4phwCulXSHpH1L2cIR8QRAeV9ooC9K2lfS7ZJuHzduXIfENcaYGYuuxByAD0bE45IWAn4j6e+tfjEiziSX9GDUqFF+Ip0xxtRAVyyHiHi8vD8N/AJYA3hK0giA8v50N2QzxhjTBeUgaY6yBDiS5gA2Bu4FLgd2L4ftDvyy07IZY4xJuuFWWhj4haRG/RdGxDWS/gxcLGlv4FFguy7IZowxhi4oh4h4EHjPAOXPAht2Wh5jjDHN9FIqqzHGmB7BysEYY0wTVg7GGGOasHIwxhjThJWDMcaYJqwcjDHGNGHlYIwxpgkrB2OMMU1YORhjjGnCysEYY0wTVg7GGGOasHIwxhjThJWDMcaYJqwcjDHGNGHlYIwxpgkrB2OMMU1YORhjjGnCysEYY0wTVg7GGGOasHIwxhjThJWDMcaYJqwcjDHGNGHlYIwxpgkrB2OMMU1YORhjjGnCysEYY0wTVg7GGGOasHIwxhjThJWDMcaYJqwcjDHGNGHlYIwxpgkrB2OMMU1YORhjjGnCysEYY0wTVg7GGGOasHIwxhjTRM8pB0mbSLpf0gOSjuq2PMYYMyPSU8pB0kzAd4HRwDuBnSS9s7tSGWPMjEdPKQdgDeCBiHgwIt4AfgqM6bJMxhgzw6GI6LYM/0PStsAmEfHxsr8rsGZEHFQ5Zl9g37K7InD/VFa7APDMVJ5jaukFGaA35OgFGaA35OgFGaA35OgFGaA35GiHDEtFxIKDHTDzVFbQbjRA2QTaKyLOBM5sW4XS7RExql3nm1Zl6BU5ekGGXpGjF2ToFTl6QYZekaNTMvSaW2kssERlf3Hg8S7JYowxMyy9phz+DCwvaWlJswA7Apd3WSZjjJnh6Cm3UkS8Jekg4NfATMCPIuK+mqttm4tqKugFGaA35OgFGaA35OgFGaA35OgFGaA35OiIDD0VkDbGGNMb9JpbyRhjTA9g5WCMMaYJKwczRUgaKO14hkLS0G7LYExdWDlMJZ3sJAeqqxudtKR3AD+QNFsX6l5E0vmSutp2Ja0GfEnSQt2UwwyMBy8gaUNJ+0zp960cJpNGo5O0aCkaUi2vs94o2QOSFpc0M0BERBf+CFFe3+yCgngGmAO4pMsK4ilgTeBASQt0Q4BeGSz0Gv3+K/N3+v700H15BThD0l5T8mUrh8mg0egkjQauk3QOsLukeerspCUNqTT2TwHXkh3zJ6ArCuJ+4OvAeOCUTigISctI+nJEvAXsArwG/KLTCkLJkIh4HNgVWB04rBsdUKVNfEDS6CJDNyzJxoDp/ZI+JumdkmbvtBwNKtflMOBH5EDiyE7WL2k9SbtI2rxS1kkvw0wR8Sfg/WRfMdkWhJXDZFBu8PuA7YD9gJuAlYG961QQETEeQNL6wHLAbsAtwGrlD1B746ueO5L7gZOAN4DvdEBBPAr8StKSEfEqsDvwEh1UEI0OOSLGS5q9KIg9gZWAT3dSQVQ6wEOA04G9gIuBbSXN1Sk5GrKUTvAM4N3kwGFPSfN1Uo4qknYANgG2Bh4G1u1Anf9TksB5ZN9wrKSjoTMKonH+iHi7DGJuBT4CnKRcl65lrBwmgaTlJH2obM8LfB9YMCJuAs4G/ggsCuwvad7Gn7ZNdY+StLKkIZJWAq4DXomI28mJgpcDK0r6PPR1GHVQ6Yz2kfQlSd8CXiCXWH+VHJ3UMlqUNDQi3oqIW4BLJf08It4G9iAVREdcTJVrsCdwlqSvAKuSFsQKwCGdjEFIWhrYAlg3IrYjR8mbAkt1SoaKHPuTnfGfyAUxVwF2lTRPJ2Wp8F/gS8Dh5P9zDIBqfARAZfC4NfCJiPgsaeVu34n/aD9rcmvgM5K2LApiHeBESXu3ej4rh0mzBBCS5oqIF4AvAO+TtE8ZRf4MuB0YAbR7pPQe4Flg7oj4O3Aw8ElJK0fEc6Tlcg2wsKThba67CUm7AQcAN5AdwMnkn/BbZBzgqzXUqYh4U9IWkr4KfABYVtJ5xcW0Rzm0I8uslGtwCPBj4GmyU9ycHLl/CNinLkU1wHmfIi23tQAi4sekwj6IDiFp+Yh4iLwmiwPHABsCfyOt6/1U4mM1yjDQaHwu8h69NyI+UtrQJ4Aj6rJylUv+rAxsAywtaZbyv92e9C4cW0e9DSqKYX/gy+QqE/uW/83jwEZkIslurZ7QrwFeZEP/YNmeE3gE2LrsbwTcBexdOX6RNtatyvZKwM3AWmX/k8BzwKplfy5gjg5dkzOAXfvtX1q2l2vnNehX7+rkH33dsj8UuIdcXgVyGZj31FS3+u1/Btim0i42Il0IIkeoS9QkxyyV7eWAFcv20aQyWK3s704qadUhRz+ZhpIDlF3L/vbAd8v2esBPgJVqlqH6X/k4cBSwadk/HbixtJ9DgHuBd9YhA7AscCcwjBxAXQmMAmYux7wDWLsD10DAhcC7yv67gROBg8r+Go22M8nz1t2AptUX+cyIPzVuKLkI4D+Azcr++sADwH513ehK2edLY1uj7O9PBoPfXePvHzJA2ZGlM5q7UnYVMFeNcsxOWihPAfNXyocCDwEX1Fh39U+3HGlpfwa4FZizlA8v92aZGuVYCTiwcu9vL23xSNJiOQW4FDgX+L9Otgsy/nVo2V6BdDGeTiYtbFiXHAPI9eHSOZ8AnAUcVcq/DHybHFy8o87rAZwKbFC2jwEuIwPCMw/Upmpoo3uW9viDch2GlvLNSTf0nJN17k7dvGnxRY4AfgusV/a3AR4ERpf9jahvNLArcARlpAMcSrqQ3lf296HFEcBUyrE56UtemjSZryYD8suW63Fru5VDo8HTN+panByFnl9t4EVB1HL9+8nzafKphAuV/ZOBnwGLlGvwe2DhGuvfgVxs7fPAL0lLaSngG6VdjARWA3YiH+JShwzzUgYFZJxlnrK9CgaYpqQAABolSURBVDkiX7/svxM4DFin7vtSke3jwK8oCpr0r59OWhGNNjRLTXUvWtn+JHBeZf+48n+ZpwPXYDPSSlqUdOt9DdijfDYGuASYfbLO2akbOK29gOHlfd9+CmJrYByweZvrq44AtiGXLz+LDDJ+rJR/isxSem+HrsGeZJbQt0sHsBI5EjqrdFI3AKvUVPdmZPD/e+SIdNXS4H/QXxlRowuFDCj+vtIxzl+uw7HkaOx3NV6DaptoBJxvo1hQRSncS3Fz1XgN5iIDu4uRltxXSLfeVsBC5DPfzwbm7VC77O/q24S0pBsWzCzA2sA5wBfraiPluvyDtBJGk5blFcCOlWOW7cD1WLncj09V2uheRSFcC9zBFLhde2rJ7l5B0pLAFyVdExFnloDX0SUZ4BeSZiInmLSrvmqWwcJkEHz3iPg/5QSWD5RDTpH0Bqmc2o6k+SPi2bL9ETIgvm5EPFSyHK4k4y57N9I2I6Ltj0yU9F7yD/dl0o99MGk1/ZC0pk6VtHdkxhKNa9emutcAtoyIo0vRwqQSXFXSemTQ+XnSnTJTqf61dtVfkUPV3xURl0h6nLRidpR0aUQ8LOlKSiJE/++0i4h4SdLZZOe3F3lfbietlc+U7fnJpIQX2l1/lQH+K69FxDXKuUc/l/TviLhY0q3A28C/ym9oy3Vp1C9pTbIP2JSMr3yMTI4YR3bWDR5sR70DyVApeoL8f+wn6caIuEvS+aS1uyzwZERMfp/RCU0/Lb7IRn86MKbs70umra5fOWaqRyNMODo8lLQYHgFOKmVDyBH8ucB2Nf7eZcmg3eylzp+SQfcPATOVY/YiO8YP1ijH8qT76MRK2WH0uVOWpYagYj8ZFiOfXQ7wPtKldROwM+myOJWa3DcDyPJJcj7Jj8gA+BjSmvolaUneR43uRYpPnezwPkO6tz4OzFbK30vGO56nAy6+ilyHAz8nLbePlLKNyOy+3WqueyvgL/SLqZCDmNPJuMuoOu9H2d6MVErLky7OQ0u7aIsl25EbOa28yKyGqkl4MOnGaGQ/7N/oNGqoe93yJxtOpmu+ABzcaBCke6OWbKBSx4KkX/ndpf6h5bd/B1isctzOwHI134Mflj/9GpXyG6nRnUYJ3lX2rwcuqCjGYeV9G9KEH9GB9rgPObdlQTIg/6VSPrqUf4N6A+GN2M97SatxSeCjRTkeACxQPp+LEo/pxItMkf1t2f4d6VrbqexvSrpC56IeV9KS5CBx8bK/Av3cemT22AE1X4ODixyfL7933YqCuJ42DKDsViqUHPIVSdPs7Yi4JCJOlXQa8NWSs3x6TXUvT1omCwBvRcQfJW0EXCVptog4icy2qKPuxqzfcSX/ewfSRfAG2ch/SOaGfyMiHo2IC+qoX7mY37OkD/docmS4laQFyaywRckRWduRtAiwpaRLSN/+HBGxvqRfA+dL2hl4Wznr9lhg24h4ogY5GtdiSOSs+GVIa+1jpN/4qwAR8StJ44E7I+LpdsvRoMiyBhnoviwiHgUeLfMW1iQnuZ0f6Vp8qS45BnCjjCfnk3y61Ptj4GTlrPWzJK0UOYu+DsaT6aJ7KGeALwWsX+pszPNZhFQi32tXpWVS29qkW3EpUhmsTXoV/gncVO7XhaQ77T9TXWmntH0vvugbGQ2nLzVxBypBJdKtciVtzNdmgBENsDGZn7w/fVkxa5HpmvMN9J02X4tGwHUOMkXyVDJPe1YyJe8kyii6hro3A/5KZnf8tDT+xYoM95BunQ0ndu2msu7R5Ej0SLKjuYuKhUYG9M4mLal3AkvW2RbL9pLl/VukL/lCYNZSdgSwT51toZ9c25MT2k6mMp+GtGRPq+t6TOS6bEyxYskO+GpKBg6ZNHIB9WXOjaAvQ2vj0kc0gtAfBr5Iuj2HkgOIldsow8JkIsoHihxzlPoupDxSuRz3cTJBoC3/kY40sF5+kdlHvyd9iBuXsu2Bu0nNfzdl8lU7G1vZ3pMcnTdM4tHkchT7U1IjG51CDb/7fZTsEjLWcD25Ns8+peyzpXNai8z+WLQmOd5TOuSlSsd3H+kyWYG0pE4uf7bla6h7KVL5Llr+gBeS+fkL9zvuD8CZHWqPnwAuLttrk+7Fj5b9j5HKcoUa6290hsuTFqSAD5JuvS0p7rVyTO2utUpdB5b/4siyP3Npr18gFdXFtDkOVLkWY0qbvJS05KrX4MOkW2t0paytg6jyPzifzBq8nEzt/mZpl0uXY3YqcrRNWXfkxvbaq3LTZwEuIrMuGnMYGpPcVisd5Lo1yXAomQWzU7nJJ5U/4sZk8Pnj5KikFouBHA3fAmxQrsHqpTMaSyqLIcDxpCtjWBvrnZU+pTScnLH5jiLHX8r2GeQExJXIQOip5V60TY6KPGeRluFNZf+zVGbSUixGio+55na5E6kol62UbVGuxfmkAn9XjfU3/hcbkq68n5LzB95BBuKvA7at4z4MIEt1wuNq5RosWvaHlNfo0lZuo70j9Zkq2+uW8y9ABpv/Trp2liYHFTeT2W3/u35tlOO99M2E/waZHbVv2V+eVIjnkpb1Xe1uG7Xe4F5+kRr/cDILpJGR8VHS5719DfVVswxWIHOwZwY+R5qGPyZHAyp/zrqWoqjK8R1y5Hx8pWxko4MiTff521j3TKWT2Yl05ZxFcZmRZnJjWYojSRdBI2NoFdoc8Kzc868CL1OWfShlXyTdXMcAj9V1LwaQ6XPALmV7WEXGuclMpbbdi0Fk+AA5n2QNUnnvWq7FEuQA6qZ234sBZBhJxp2GVfbPHuC6NJRF2yaZlTZ/En0WykfJ+MoW5ITPncv7l0nLszHnpI7g9/6ke3Ue0nrZp/QV25bPFyZdv5tRw7IttTf4XnrRNzJ6F5mbfRppLh9D8aeSMYfHyoVvj+8u00NXL9uNkfIIcindm0unuT2Zwnpyh67FguX9BNKVM7zy2XnUlBlUOvprgH8DOzfuC+nC+jHp5ruNmpaA6H9PyUl9m5OKqpo+uyuppGpZG2igtkVmoPyciluLdCW9vwPtYSZyNH4X8CQTzkT/Mn0TrBbrgCwjSIW4KjnBbZ7SRrerHLM7uWbQzG2ue1bSdXMqZVBAehh+RN/6VT8gXUxL1d02SMv5V43/I7mMzw1kOm3TEjdtlaPuG91rL9J1cgllhnPpoL9FjlQaQem2LYVAjjrGkFbKheXPN0v5bCvg2LK9Gzli7YT7YnXSHG0Eeb9LurZ2It0G/6SMnNpYZ7XBn0gGuQ+iz2yemZwRfSY1zfjtJ8NWpS00liNZkzTPj6scU1cAvirHuqQrcTb6Rq2fJzPndiDdKZ1IV22swzM7Gdc4p3LMEcCpZbu2DqnfdZmNXP7ifNK9uDqptL5WrtFdtHkAQSVtmbRcv09fyuqZRSGsQwa/12hn3QNdg0rZV0iF1VBO25LrSG1a172ImDGVw1rkjMJvlv2h5c95Brn++5B2dQpkIO9e0j94BpmqeVjl8w+Qbp0zyFzl2uYP9JNr1qKITqNvTZyvkT7Nw9vdGVU6oPeQ8ygWJl0Fp5A+/uH0xR9mqX6nJjkay5AcRubJNwK+jXz+z9d03dXv/WDSRXFK6ZCXJhXWV8n4wjXUtDRHPzk2IF2Mny33ZxYyMH8jmdp7A7BFzW2yqhgaGUhzk/Gvs0lX7DKkO/II2hyUr1yLRkbSUDKN+0zSklmq/E9vruta9LsGWwN70+e2+lxpm43VmLei5omYtZ24V16Vm74sfaOAFcmAWyO4MzMZ3GrbzFvKWuqkwlm9dEiHk26cvehzY32IjDHUMjosjWivsr0ZfRbTLOTI7AfAB0rZcTXKsQmZEnk08CKZB74eGWg7l3w29AdqqnvJftf71+Qg4ITS8V0P7FA+X42aXCeUzJKyPZq01mYtHeCTpKJoBMLnYTIXSptCmdYh3Zl7koOFM0hLdxipsO6iZCXRZhfOROQ5otyfs0llPZRM3vgBNSWHVOr+CDnD+GgytjAz6eo8heLTp8YYQ0WO3cp1v7r8NxoDuKPIzMraBgwTyNGJSrr9IoNJt5C5yeeVDmLF0lkdVGO9I8hnL4yjz2zfrzS2nciA02F1/enI0flDpMtkUdKPfi2wUfl8VtJUvpk2L31Apts1AopLlg542fIHvJ++hQ2XJlMk163pGixcrvfhpKtiATK4ugs5Mh5GurkeooZEhCLDTGRu+rP0zXKercixG/DrUnYh+VCW2lJVSz3VEer+9K31P5wcsf6w7M9BBqPP6ZAsi5Kr3a5FpvTeRVqTjcSNU4pMdViV65DKcE3SlXVZKZ+VdEOfRb9Z9DVdj02A39BnQR9FptSvV/YPo6ZnhjTJ0olKuvkqncOdpEtjydIRXUmaqWuRo6Yl2tXg6LNUZiLN4qtKx7N7KZ+ZNBdPKZ1knamJjXzor5A+/oXJjIfL6ZvTsQc5OmpbBkr5jX+gb8QzOzn625cMNi9fyrei5gwc+pYe+SYZ42isCXQ4fe6kQ8m03aVqkqHxR1+2tLcvVj47HjiibO9Jjpo7EXfahLQQ9i73pJH5MxeVlWbJLKnbyIFOnc8i2KoogC9UyvYm05s/WNrU8HbW30+WHcgMxveT65s1JiLOT1rZtT9MilRE+5OTMRtts2Hhn0cHl0GPmI6VQ6WTXgK4oVI+D+nTPaDsz1fTjV6JvgD3UmSQtzFCG1I6zNoae0WOi8ip9I3MoLlIt9Y/SH/qX2jzssKlQV9PyT0n12z6HRnraVhQo0qnU4tyJOM8jWC3SOvxNNK9N4ycVHU7mYlzH/U9we3DZKD7C6TLatFyHY4un3+sfH4m6eKqbVRY+U+8jxwQrE0OYo4jlecS5EONbiUHT0Oq36tRrm3LvfhFaTcfou85DAeSlm0759rMQV8iwgZknGUTco7PXfRZtR8hB1a1WAz9+ot56Bu4fIL0cjQGcLOSFkNHUqobr+lubaXKOizzA89ExGOSHpZ0RkTsFxEvSnqa7DygjWvCROOOS58hO4UXJf2TzD7ZiVynZ1hEfJ1cJ6jt678MsA7NFaRiWlPSs+SCZT+S9A/SZD85Iv7VprqXAZ6PiOcl/YdybSPiBUl7ktbEVyS9Tlpwx0TEve2ou58c85NW2TOSvkSuNXMm+QdcGtgzIr4r6TkyQ2j7iHisBjk2IZXP+aTVdhgZ9F0b+KOklyPi2+VarU8OWOqQYxFyMPJqaQPfAV6MiN+Xzy8n5zBcRrbJkyLiH+2WYyKyfZRcxG+j0k6+Qo7iJemWcp9+HBGvt6m+EeQ9P6Zc93fR98yWX5CDp5C0NhkPOyoi3mxH3f3kmCUi3ijbnybdWktKOoHMUHsVOFDS0Ii4ilTenaWTmqhTL3IU8EcyqPV5cqT0dUrgkew4Nmhjfe8lO9r5yD/+taX8ItKP3BgFrUkuoNY2a6WfHNWRyGbkEsYN6+UzZPbFRtQU6Cznfp60FC6gpN5VPl+AzNDZj77nQdc1A3wDcpG0g0nFcElpD2eQfu19qTHASvrvx1MyW8hR+UWUXH1yZP44xYKoUY6VSAvtN6TffCtyZP4ksH+/Y0fQN/+lrvvSf57J1qTy3q/sz0q62s6hzQkK5Vr8jXQdbV/aauM55ENIy+6Ecr2upb6ZzxvS95S20aW+uUvf9H3SnTYPuVz7RaRir/2Z4E1ydrrC2n9QKoILyw3YijQLzyOXPT6aHL19pI31NRaN2710ABuSvtPPkJNXGgumjSrvdSwBISZUDHuSKbS/J+cwNOo+lFwSoW2KcQBZNgEeJoN7XyT9pTuXP+Nm1LTO/URk+XDpDGYp92b3ck+eJV1JtT6+sfze++hb1PDHpGJs5NO/g3TvLVDHn59cKPBO0ipZnHTRnFmuxwbls04u4ldto3PR50bZvrTX7cv+MNIN1zY3Cn1Pzdu77C9NDuj+SnnedL/j5+ovc5vk2ICckf+Psr8LcH6/Nns/GZ+agw49XW9AWbtVcS0/Jl1J9wI/L/tDydzoc6jhOQzkBKYHqucmRyfXkcsMNPzrB5NB8LoyLaoPMN+MdCWp/P7jSZO0McPyQGpeMI1MUR1PpuEdT47YryZ9x+t1uE1sVjrghh95PnLlypEdqn806dY7jZz93MjgaliTtWXAkBbC+H5t8xr6FnVcu1yb/Tp8Tw4nBym/p8/3P4aMf+1aU517AqeUbZGzrzcmB3JPkGuZfYT0LsxHDZP9yvn/QmaoXVDKVu3fP5GzsWtJ654sebstQA03YGdyJcttK2VnU8y4Ntd1GH3LCjT+7HOSo56vk8svHECuJllX4HUBcqTe6Py+SAagG+sSLVg66DMpE2g6dB82IEdAtcwynkxZRpdOsPa1iSZS/0ZFWTaWYq+u6ll3sHcT4MGyvTXpLpm78vl6wIdqrL+/G+lAcvA0EzmAeoQJV0P+AzU8qIccyP2hdNA/IpMA/kZ6Fm4mYw5XU9/s/IYV+/6yfw+ZQTk3ufLw10grew9yAdCOpKsOKnO3BWhHwyN9/htQUhHJxbIeICfUrFZuxLo11HsqZdE6cjTSyO6YlxyR/LA0vrofa7kFuVpkY3bnV8ngWiMdceGiNNq2LEiLco0mJ7fNV71uXWorY0g3Sq3r0UziWtxHB5+YVql7U9KVcTt9s487ci/ot+Q8mS22BLmy6cXk4Ok5+lZDnrMmOWYnJxveRc7tWZu0EFYvnfNw+lYLbrdimpn0Hnyw7M9UFGTjCZPzl//wGeTKr21bYXaq5O62AFN4sWepdMSNUeHR5GSzdUr5DmS2zBX0pTS2tWMoCum39LlshtBnQRxKmvEd6YxKB/AAGchSUU4/o2/Bv251ipvSYVfSILLU0vFMRv0N10ltS7EPUvcGwNjKfidmO29cBinH0Je3LzK1+3f0JUv8kQyYz9YBmYb321+PnCBb68Cp0i80+q0vAYdXPt+e9Hp0tY1WX0OYxpC0Ium//bCkD5Kj5NHkxJW3gLMkjY6Ii8gLvjA5Gxog2izOraRJuoOk90bE+Ih4S9KOZKDplcjHPdZORFxNZjfcTpqqJ5Bm7KclzUr7f3vLckXEDZLUjfr7yfJyl+v/JTl4GR+lR+hg3b8DPi7paUnzRcRbddZX0niPIwdPQ4BNJC1XfvezZKbWRyXtRrpdPx4Rr9UpE0BEPFfkGyppU3Iy6vER8VTN9Taud+O+v0qurYakXUhlcXu32+gEdFs7Tab2fSfpIvokff7bZckZlLeX/SPI5x83ZuduT3bgdT1wfDHSZXMjGWc4nnTx1DbzeRLybEoqhYaJ3BU/u1+9+aIDlhzNabyLk8kJjTiYyADx90lXW61u1wHkG1r6jN9S84KCg8iwCvlkt4+SA9uOXoNWXg3fec8jaW7SRL0gchLX/yZ7SdqDXEL3AEnrko+V/GxE/Kl8PmfUqJElzUbGPTYiMx+ujw5NIpqIPFuRQfFRMa3cYNNRBpgs2e7zb0Yurf3+iPiPpKtJv/9fycHLFWQ8avaIeL4uOQaRbyg5cHqy7msxkfqXJBNJ/glsFRF/62T9rTAtKYeh5CSegyNnOc8MvB0RIWk9csr5k+SI4KCIuLUbN71XqFshGjMpJI0mZ2NfQ9/jX4eTaaN3A4dERNtWKJiWKP3ZN4HTIuL+bsszENOScpiXzIs+KnI6OZKGRMR4SYuRE94WBP4UEdd0UVRjTEHSRmT67Igofn1JQ8jA8DNdFa7LlKUx2r40R7uYZgLSEfECmTr6UUmrluJGkHM10q1zUkRc0wvBT2MMRMRvyYmIv5O0cCkbP6MrBoBeVgwwDSmHwi9In/4nJG0AjC8ZSycBl0bEq9C3AJ4xpvtExK/I1OpfFavBTANMM26lBmX0sT05eeYvZLbSiRFx2YwcYzCm13EcbNpimlMODYqSGE/OwBxrxWCMMe1jmlUOxhhj6sP+P2OMMU1YORhjjGnCysEYY0wTVg7GGGOasHIwxhjThJWDma6Q9LakuyTdJ+mvkg6ra+KVpPUkvSjpTkl/l/T1qTzf1WWZGGO6zszdFsCYNvNaRKwKIGkh4ELyAUjHVA+SNHO055kGv4+IzcvKvHdK+kVE/GFKThQRm7ZBHmPagi0HM90SEU8D+wIHKdlD0iWSrgCuLSP/KxvHSzqtLP+OpE2LNXCzpO9Uj5tIXa+Rj6BcrHx/Dkk/kvTnYlmMKeWzS7pY0t2SLpJ0q6RR5bOHJS0gaWSp+4eS7pV0gaSNJP1B0j8lrTGJOvaQ9HNJ15TjT2r3tTXTP7YczHRNRDxY3EoLlaL3k8/Wfq4s9d6EpGHk8tLrRMRDkn4yqXokzQcsD9xUij4P/C4i9iquotsk/RbYH3g+IlaR9C5SoQzEcsB2pHL7M/Ax4EPAluQ6RVsNUgfAquSClP8F7pd0akQ8NqnfYUwDWw5mRqC6Su9vojwqchBWAh6MiIfK/mDKYW1Jd5PPErkyIp4s5RsDR0m6C7gBGAYsSXbwPwWIiHvJ5xoMxEMRcU/kY2bvA64ry8PcA4ycRB2U41+MiNeB/yOf22xMy9hyMNM1kpYB3gaeLkWvVD5+iwkHSMMaX5uMKhoxhxWAm0vM4a5yjo/2f5DLZCwn/9/K9vjK/nj6/rcTq2PNft9/G//XzWRiy8FMt0hakHxO8WkTWZTxEeCdkmaVNA+wYSn/O7CMpJFlf4dJ1VUeC3sCcGQp+jVwcEMZSFqtlN9MriqMpHcC757Mn1VlYnUYM9V4NGGmN2YrbpahpGVwPvk4xiYi4jFJF5OunX8Cd5by1yQdAFwj6Rngthbr/j5wuKSlgePIB8jfXTrvh4HNge8B5xZX1J2l7hen5IcOUocxU41XZTVmABrPHiid7neBf0bEt9pw3pmAoRHxuqRlgeuAFSLijak9tzHtxJaDMQOzj6TdgVnIEf4ZbTrv7MD1ygfMC9jfisH0IrYcjDHGNOGAtDHGmCasHIwxxjRh5WCMMaYJKwdjjDFNWDkYY4xpwsrBGGNME/8PqPqYdDUY0YcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of timepoints for all mice tested for each drug regimen using Pandas.\n",
    "df = pd.DataFrame({'drug regimen':['Capomulin', 'Ceftamin', 'Infubinol', 'Ketapril', 'Naftisol', 'Placebo', 'Propriva', 'Ramicane', 'Stelasyn', 'Zoniferol'], \n",
    "                   'val':[230, 178, 178, 188, 186, 181, 148, 228, 181, 182]})\n",
    "ax = df.plot.bar(x='drug regimen', y='val', rot=45)\n",
    "plt.title(\"Timepoints for Each Drug Regimen\")\n",
    "plt.ylabel('Number of Timepoints')\n",
    "plt.xlabel(\"Drug Regimen\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Timepoints')"
      ]
     },
     "execution_count": 23,
     "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 bar plot showing the total number of timepoints for all mice tested for each drug regimen using pyplot.\n",
    "\n",
    "x_axis = ['Capomulin', 'Ceftamin', 'Infubinol', 'Ketapril', 'Naftisol', 'Placebo', 'Propriva', 'Ramicane', 'Stelasyn', 'Zoniferol']\n",
    "drug_count = [230, 178, 178, 188, 186, 181, 148, 228, 181, 182]\n",
    "\n",
    "plt.bar(x_axis, drug_count, color = 'g', align = \"center\")\n",
    "\n",
    "tick_locations = [value for value in x_axis]\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.title(\"Timepoints for Each Drug Regimen\")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Timepoints\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fe6b8a700b8>,\n",
       "  <matplotlib.patches.Wedge at 0x7fe6e9d944a8>],\n",
       " [Text(0.013934097579311265, 1.0999117423341975, ''),\n",
       "  Text(-0.0139340975793114, -1.0999117423341975, '')],\n",
       " [Text(0.007600416861442507, 0.5999518594550168, '49.6%'),\n",
       "  Text(-0.007600416861442581, -0.5999518594550168, '50.4%')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using Pandas\n",
    "\n",
    "df = pd.DataFrame({'sex count': [123, 125]}, \n",
    "                   index=['Female', 'Male'])\n",
    "\n",
    "plot = df.plot.pie(y='sex count', figsize=(5, 5))\n",
    "\n",
    "plt.title(\"Sex Distribution\")\n",
    "sex_count = [123, 125]\n",
    "plt.pie(sex_count, autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1192168963081732,\n",
       " 1.1079352522711863,\n",
       " -1.1167571903773796,\n",
       " 1.1085810220189514)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 = [\"Female\", \"Male\"]\n",
    "\n",
    "sizes = [123, 125]\n",
    "\n",
    "colors = [\"pink\", \"blue\"]\n",
    "\n",
    "plt.pie(sizes, labels = labels, colors = colors,\n",
    "       autopct = \"%1.1f%%\", shadow=True, startangle=140)\n",
    "\n",
    "plt.title(\"Sex Distribution\")\n",
    "\n",
    "plt.axis(\"equal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>z581</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>62.754451</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>e476</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>62.435404</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>c758</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>33.397653</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>i557</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>47.685963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>m957</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>33.329098</td>\n",
       "      <td>1</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>75</th>\n",
       "      <td>z578</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>l490</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>l558</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>l661</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>814 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "1872     z581    Infubinol  Female          24          25         45   \n",
       "800      e476    Infubinol    Male          23          26         45   \n",
       "154      c758     Ramicane    Male           9          17         45   \n",
       "440      i557    Capomulin  Female           1          24         45   \n",
       "144      m957    Capomulin  Female           3          19         45   \n",
       "...       ...          ...     ...         ...         ...        ...   \n",
       "75       z578     Ramicane    Male          11          16          0   \n",
       "1113     l490     Ceftamin    Male          24          26          0   \n",
       "1120     l558     Ceftamin  Female          13          30          0   \n",
       "1123     l661     Ceftamin    Male          18          26          0   \n",
       "0        k403     Ramicane    Male          21          16          0   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "1872           62.754451                 3  \n",
       "800            62.435404                 1  \n",
       "154            33.397653                 1  \n",
       "440            47.685963                 1  \n",
       "144            33.329098                 1  \n",
       "...                  ...               ...  \n",
       "75             45.000000                 0  \n",
       "1113           45.000000                 0  \n",
       "1120           45.000000                 0  \n",
       "1123           45.000000                 0  \n",
       "0              45.000000                 0  \n",
       "\n",
       "[814 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "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",
    "final_data_df = clean_df[clean_df[\"Drug Regimen\"].isin([\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"])]\n",
    "final_data_df = final_data_df.sort_values([\"Timepoint\"], ascending=False)\n",
    "final_data_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "\n",
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "\n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes    \n",
    "    \n",
    "    # add subset \n",
    "    \n",
    "    # Determine outliers using upper and lower bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>i557</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>47.685963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>m957</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>33.329098</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>r554</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>32.377357</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>y793</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>31.896238</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>u364</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>31.023923</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>t565</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>34.455298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>g288</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>37.074024</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>l897</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>38.846876</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>38.939633</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>w914</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>36.041047</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>b128</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>38.982878</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>j119</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>38.125164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>i738</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>37.311846</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>v923</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>40.658124</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>g316</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>40.159220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>s710</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>40.728578</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>l509</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>41.483008</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>r944</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>41.581521</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>x401</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>28.484033</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>m601</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>28.430964</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>23.343598</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>j246</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>38.753265</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>f966</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>30.485985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>r157</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>46.539206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>w150</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>39.952347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "440      i557    Capomulin  Female           1          24         45   \n",
       "144      m957    Capomulin  Female           3          19         45   \n",
       "134      r554    Capomulin  Female           8          17         45   \n",
       "124      y793    Capomulin    Male          17          17         45   \n",
       "104      u364    Capomulin    Male          18          17         45   \n",
       "164      t565    Capomulin  Female          20          17         45   \n",
       "244      g288    Capomulin    Male           3          19         45   \n",
       "282      l897    Capomulin    Male          17          19         45   \n",
       "299      b742    Capomulin    Male           7          21         45   \n",
       "224      w914    Capomulin    Male          24          21         45   \n",
       "309      b128    Capomulin  Female           9          22         45   \n",
       "337      j119    Capomulin  Female           7          23         45   \n",
       "204      i738    Capomulin  Female          23          20         45   \n",
       "350      v923    Capomulin  Female          19          21         45   \n",
       "360      g316    Capomulin  Female          22          22         45   \n",
       "370      s710    Capomulin  Female           1          23         45   \n",
       "380      l509    Capomulin    Male          17          21         45   \n",
       "390      r944    Capomulin    Male          12          25         45   \n",
       "29       x401    Capomulin  Female          16          15         45   \n",
       "39       m601    Capomulin    Male          22          17         45   \n",
       "19       s185    Capomulin  Female           3          17         45   \n",
       "317      j246    Capomulin  Female          21          21         35   \n",
       "64       f966    Capomulin    Male          16          17         20   \n",
       "1455     r157    Capomulin    Male          22          25         15   \n",
       "340      w150    Capomulin    Male          23          23         10   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "440            47.685963                 1  \n",
       "144            33.329098                 1  \n",
       "134            32.377357                 3  \n",
       "124            31.896238                 2  \n",
       "104            31.023923                 3  \n",
       "164            34.455298                 0  \n",
       "244            37.074024                 1  \n",
       "282            38.846876                 1  \n",
       "299            38.939633                 0  \n",
       "224            36.041047                 2  \n",
       "309            38.982878                 2  \n",
       "337            38.125164                 1  \n",
       "204            37.311846                 2  \n",
       "350            40.658124                 2  \n",
       "360            40.159220                 2  \n",
       "370            40.728578                 1  \n",
       "380            41.483008                 3  \n",
       "390            41.581521                 2  \n",
       "29             28.484033                 0  \n",
       "39             28.430964                 1  \n",
       "19             23.343598                 1  \n",
       "317            38.753265                 1  \n",
       "64             30.485985                 0  \n",
       "1455           46.539206                 0  \n",
       "340            39.952347                 0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Capomulin\n",
    "\n",
    "Capomulin_sort = final_data_df.loc[final_data_df[\"Drug Regimen\"] == \"Capomulin\"]   \n",
    "\n",
    "Capomulin_df = Capomulin_sort.sort_values(\"Timepoint\", ascending = False)\n",
    "Capomulin_df = Capomulin_sort.drop_duplicates(subset=\"Mouse ID\", keep='first')\n",
    "Capomulin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "440     47.685963\n",
       "144     33.329098\n",
       "134     32.377357\n",
       "124     31.896238\n",
       "104     31.023923\n",
       "164     34.455298\n",
       "244     37.074024\n",
       "282     38.846876\n",
       "299     38.939633\n",
       "224     36.041047\n",
       "309     38.982878\n",
       "337     38.125164\n",
       "204     37.311846\n",
       "350     40.658124\n",
       "360     40.159220\n",
       "370     40.728578\n",
       "380     41.483008\n",
       "390     41.581521\n",
       "29      28.484033\n",
       "39      28.430964\n",
       "19      23.343598\n",
       "317     38.753265\n",
       "64      30.485985\n",
       "1455    46.539206\n",
       "340     39.952347\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumor_vol_Capomulin = Capomulin_df['Tumor Volume (mm3)']\n",
    "tumor_vol_Capomulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile is: 32.37735684\n",
      "The upper quartile is: 40.1592203\n",
      "The interquartile range is: 7.781863460000004\n",
      "The the median is: 38.125164399999996 \n",
      "Values below 20.70456164999999 could be outliers.\n",
      "Values above 51.83201549 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "#Capomulin - quartile calculations & outlier calculations\n",
    "\n",
    "quartiles = tumor_vol_Capomulin.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "print(f\"The lower quartile is: {lowerq}\")\n",
    "print(f\"The upper quartile is: {upperq}\")\n",
    "print(f\"The interquartile range is: {iqr}\")\n",
    "print(f\"The the median is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>c758</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>33.397653</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>n364</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>31.095335</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>j913</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>31.560470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>z578</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>30.638696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>m546</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>30.564625</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>a444</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>43.047543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>q597</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>45.220869</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>u196</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>40.667713</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>i334</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>36.374510</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>d251</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>37.311236</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>r811</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>37.225650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>a520</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>38.810366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>a411</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>38.407618</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>j989</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>36.134852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>i177</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>33.562402</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>a644</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>32.978522</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>e662</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>40.659006</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>s508</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>30.276232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>22.050126</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>g791</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>29.128472</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>q610</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>36.561652</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>c458</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>38.342008</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>r921</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>43.419381</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>y449</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>44.183451</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>w678</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>43.166373</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "154     c758     Ramicane    Male           9          17         45   \n",
       "114     n364     Ramicane    Male           4          17         45   \n",
       "94      j913     Ramicane  Female           4          17         45   \n",
       "84      z578     Ramicane    Male          11          16         45   \n",
       "74      m546     Ramicane    Male          18          16         45   \n",
       "430     a444     Ramicane  Female          10          25         45   \n",
       "420     q597     Ramicane    Male          20          25         45   \n",
       "410     u196     Ramicane    Male          18          25         45   \n",
       "254     i334     Ramicane  Female           8          20         45   \n",
       "272     d251     Ramicane  Female           8          19         45   \n",
       "234     r811     Ramicane    Male           9          19         45   \n",
       "214     a520     Ramicane    Male          13          21         45   \n",
       "327     a411     Ramicane    Male           3          22         45   \n",
       "194     j989     Ramicane    Male           8          19         45   \n",
       "184     i177     Ramicane    Male          10          18         45   \n",
       "174     a644     Ramicane  Female           7          17         45   \n",
       "400     e662     Ramicane    Male           8          24         45   \n",
       "59      s508     Ramicane    Male           1          17         45   \n",
       "9       k403     Ramicane    Male          21          16         45   \n",
       "49      g791     Ramicane    Male          11          16         45   \n",
       "262     q610     Ramicane  Female          18          21         35   \n",
       "289     c458     Ramicane  Female          23          20         30   \n",
       "447     r921     Ramicane  Female           5          25         30   \n",
       "453     y449     Ramicane    Male          19          24         15   \n",
       "449     w678     Ramicane  Female           5          24          5   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "154           33.397653                 1  \n",
       "114           31.095335                 1  \n",
       "94            31.560470                 1  \n",
       "84            30.638696                 0  \n",
       "74            30.564625                 1  \n",
       "430           43.047543                 0  \n",
       "420           45.220869                 2  \n",
       "410           40.667713                 3  \n",
       "254           36.374510                 2  \n",
       "272           37.311236                 2  \n",
       "234           37.225650                 1  \n",
       "214           38.810366                 1  \n",
       "327           38.407618                 1  \n",
       "194           36.134852                 1  \n",
       "184           33.562402                 3  \n",
       "174           32.978522                 1  \n",
       "400           40.659006                 2  \n",
       "59            30.276232                 0  \n",
       "9             22.050126                 1  \n",
       "49            29.128472                 1  \n",
       "262           36.561652                 2  \n",
       "289           38.342008                 2  \n",
       "447           43.419381                 1  \n",
       "453           44.183451                 0  \n",
       "449           43.166373                 0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ramicane\n",
    "\n",
    "Ramicane_sort = final_data_df.loc[final_data_df[\"Drug Regimen\"] == \"Ramicane\"]   \n",
    "\n",
    "Ramicane_df = Ramicane_sort.sort_values(\"Timepoint\", ascending = False)\n",
    "Ramicane_df = Ramicane_sort.drop_duplicates(subset=\"Mouse ID\", keep='first')\n",
    "Ramicane_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154    33.397653\n",
       "114    31.095335\n",
       "94     31.560470\n",
       "84     30.638696\n",
       "74     30.564625\n",
       "430    43.047543\n",
       "420    45.220869\n",
       "410    40.667713\n",
       "254    36.374510\n",
       "272    37.311236\n",
       "234    37.225650\n",
       "214    38.810366\n",
       "327    38.407618\n",
       "194    36.134852\n",
       "184    33.562402\n",
       "174    32.978522\n",
       "400    40.659006\n",
       "59     30.276232\n",
       "9      22.050126\n",
       "49     29.128472\n",
       "262    36.561652\n",
       "289    38.342008\n",
       "447    43.419381\n",
       "453    44.183451\n",
       "449    43.166373\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumor_vol_Ramicane = Ramicane_df['Tumor Volume (mm3)']\n",
    "tumor_vol_Ramicane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile is: 31.56046955\n",
      "The upper quartile is: 40.65900627\n",
      "The interquartile range is: 9.098536719999998\n",
      "The the median is: 36.56165229 \n",
      "Values below 17.912664470000003 could be outliers.\n",
      "Values above 54.30681135 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "#Ramicane - quartile calculations & outlier calculations\n",
    "\n",
    "quartiles = tumor_vol_Ramicane.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "print(f\"The lower quartile is: {lowerq}\")\n",
    "print(f\"The upper quartile is: {upperq}\")\n",
    "print(f\"The interquartile range is: {iqr}\")\n",
    "print(f\"The the median is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>z581</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>62.754451</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>e476</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>62.435404</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>a203</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>a251</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>c139</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>72.226731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>f345</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>60.918767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>k483</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>66.196912</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>a685</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>66.083066</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>y163</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "      <td>67.685569</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>i386</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>67.289621</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>k804</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>62.117279</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>o809</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>55.629428</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>q132</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>54.656549</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>n671</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>60.165180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>a577</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>57.031862</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>w584</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>58.268442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>c895</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>60.969711</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>s121</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>55.650681</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>w193</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>50.005138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>v719</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>54.048608</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>v766</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>51.542431</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>c326</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>36.321346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>o813</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>45.699331</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>v339</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>46.250112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>m756</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>47.010364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "1872     z581    Infubinol  Female          24          25         45   \n",
       "800      e476    Infubinol    Male          23          26         45   \n",
       "463      a203    Infubinol  Female          20          23         45   \n",
       "473      a251    Infubinol  Female          21          25         45   \n",
       "637      c139    Infubinol    Male          11          28         45   \n",
       "834      f345    Infubinol    Male          23          26         45   \n",
       "1053     k483    Infubinol  Female          20          30         45   \n",
       "540      a685    Infubinol    Male           8          30         45   \n",
       "1813     y163    Infubinol  Female          17          27         45   \n",
       "949      i386    Infubinol  Female          23          29         40   \n",
       "1076     k804    Infubinol  Female          23          29         35   \n",
       "1312     o809    Infubinol    Male           3          25         35   \n",
       "1397     q132    Infubinol  Female           1          30         30   \n",
       "1225     n671    Infubinol    Male          18          25         30   \n",
       "530      a577    Infubinol  Female           6          25         30   \n",
       "1726     w584    Infubinol    Male           3          29         30   \n",
       "742      c895    Infubinol  Female           7          29         30   \n",
       "1485     s121    Infubinol    Male          23          26         25   \n",
       "1683     w193    Infubinol    Male          22          30         20   \n",
       "1632     v719    Infubinol  Female          17          30         20   \n",
       "1640     v766    Infubinol    Male          16          27         15   \n",
       "669      c326    Infubinol  Female          18          25          5   \n",
       "1314     o813    Infubinol    Male          24          28          5   \n",
       "1616     v339    Infubinol    Male          20          26          5   \n",
       "1205     m756    Infubinol    Male          19          30          5   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "1872           62.754451                 3  \n",
       "800            62.435404                 1  \n",
       "463            67.973419                 2  \n",
       "473            65.525743                 1  \n",
       "637            72.226731                 2  \n",
       "834            60.918767                 1  \n",
       "1053           66.196912                 3  \n",
       "540            66.083066                 3  \n",
       "1813           67.685569                 3  \n",
       "949            67.289621                 4  \n",
       "1076           62.117279                 2  \n",
       "1312           55.629428                 1  \n",
       "1397           54.656549                 4  \n",
       "1225           60.165180                 0  \n",
       "530            57.031862                 2  \n",
       "1726           58.268442                 1  \n",
       "742            60.969711                 2  \n",
       "1485           55.650681                 2  \n",
       "1683           50.005138                 0  \n",
       "1632           54.048608                 1  \n",
       "1640           51.542431                 1  \n",
       "669            36.321346                 0  \n",
       "1314           45.699331                 0  \n",
       "1616           46.250112                 0  \n",
       "1205           47.010364                 1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Infubinol\n",
    "\n",
    "Infubinol_sort = final_data_df.loc[final_data_df[\"Drug Regimen\"] == \"Infubinol\"]   \n",
    "\n",
    "Infubinol_df = Infubinol_sort.sort_values(\"Timepoint\", ascending = False)\n",
    "Infubinol_df = Infubinol_sort.drop_duplicates(subset=\"Mouse ID\", keep='first')\n",
    "Infubinol_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1872    62.754451\n",
       "800     62.435404\n",
       "463     67.973419\n",
       "473     65.525743\n",
       "637     72.226731\n",
       "834     60.918767\n",
       "1053    66.196912\n",
       "540     66.083066\n",
       "1813    67.685569\n",
       "949     67.289621\n",
       "1076    62.117279\n",
       "1312    55.629428\n",
       "1397    54.656549\n",
       "1225    60.165180\n",
       "530     57.031862\n",
       "1726    58.268442\n",
       "742     60.969711\n",
       "1485    55.650681\n",
       "1683    50.005138\n",
       "1632    54.048608\n",
       "1640    51.542431\n",
       "669     36.321346\n",
       "1314    45.699331\n",
       "1616    46.250112\n",
       "1205    47.010364\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumor_vol_Infubinol = Infubinol_df['Tumor Volume (mm3)']\n",
    "tumor_vol_Infubinol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile is: 54.04860769\n",
      "The upper quartile is: 65.52574285\n",
      "The interquartile range is: 11.477135160000003\n",
      "The the median is: 60.16518046 \n",
      "Values below 36.83290494999999 could be outliers.\n",
      "Values above 82.74144559000001 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "#Infubinol - quartile calculations & outlier calculations\n",
    "\n",
    "quartiles = tumor_vol_Infubinol.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "print(f\"The lower quartile is: {lowerq}\")\n",
    "print(f\"The upper quartile is: {upperq}\")\n",
    "print(f\"The interquartile range is: {iqr}\")\n",
    "print(f\"The the median is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>a275</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>o287</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>59.741901</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>j296</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>61.849023</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>k210</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>68.923185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>l471</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>67.748662</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>l661</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>59.851956</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>l733</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>64.299830</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>y769</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "      <td>68.594745</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>x822</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>61.386660</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>p438</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>61.433892</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>y865</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>64.729837</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>w151</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>67.527482</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1773</th>\n",
       "      <td>x581</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>64.634949</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>q483</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>64.192341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>l490</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>57.918381</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>b759</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>55.742829</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>b487</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>56.057749</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>u149</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>52.925348</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>f436</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>48.722078</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>l558</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>46.784535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>h531</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>47.784682</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>x226</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>b447</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>t573</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>u153</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "493      a275     Ceftamin  Female          20          28         45   \n",
       "1260     o287     Ceftamin    Male           2          28         45   \n",
       "993      j296     Ceftamin  Female          24          30         45   \n",
       "1033     k210     Ceftamin    Male          15          28         45   \n",
       "1112     l471     Ceftamin  Female           7          28         45   \n",
       "1132     l661     Ceftamin    Male          18          26         45   \n",
       "1162     l733     Ceftamin  Female           4          30         45   \n",
       "1845     y769     Ceftamin  Female           6          27         45   \n",
       "1797     x822     Ceftamin    Male           3          29         45   \n",
       "1379     p438     Ceftamin  Female          11          26         45   \n",
       "1855     y865     Ceftamin    Male          23          26         45   \n",
       "1670     w151     Ceftamin    Male          24          25         45   \n",
       "1773     x581     Ceftamin  Female          19          28         45   \n",
       "1406     q483     Ceftamin    Male           6          26         40   \n",
       "1119     l490     Ceftamin    Male          24          26         30   \n",
       "617      b759     Ceftamin  Female          12          25         30   \n",
       "600      b487     Ceftamin  Female           6          28         25   \n",
       "1582     u149     Ceftamin    Male          24          29         25   \n",
       "840      f436     Ceftamin  Female           3          25         15   \n",
       "1122     l558     Ceftamin  Female          13          30         10   \n",
       "940      h531     Ceftamin    Male           5          27          5   \n",
       "1743     x226     Ceftamin    Male          23          28          0   \n",
       "594      b447     Ceftamin    Male           2          30          0   \n",
       "1551     t573     Ceftamin  Female          15          27          0   \n",
       "1583     u153     Ceftamin  Female          11          25          0   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "493            62.999356                 3  \n",
       "1260           59.741901                 4  \n",
       "993            61.849023                 3  \n",
       "1033           68.923185                 3  \n",
       "1112           67.748662                 1  \n",
       "1132           59.851956                 3  \n",
       "1162           64.299830                 1  \n",
       "1845           68.594745                 4  \n",
       "1797           61.386660                 3  \n",
       "1379           61.433892                 1  \n",
       "1855           64.729837                 3  \n",
       "1670           67.527482                 3  \n",
       "1773           64.634949                 3  \n",
       "1406           64.192341                 1  \n",
       "1119           57.918381                 3  \n",
       "617            55.742829                 1  \n",
       "600            56.057749                 1  \n",
       "1582           52.925348                 0  \n",
       "840            48.722078                 2  \n",
       "1122           46.784535                 0  \n",
       "940            47.784682                 0  \n",
       "1743           45.000000                 0  \n",
       "594            45.000000                 0  \n",
       "1551           45.000000                 0  \n",
       "1583           45.000000                 0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ceftamin\n",
    "\n",
    "Ceftamin_sort = final_data_df.loc[final_data_df[\"Drug Regimen\"] == \"Ceftamin\"]   \n",
    "\n",
    "Ceftamin_df = Ceftamin_sort.sort_values(\"Timepoint\", ascending = False)\n",
    "Ceftamin_df = Ceftamin_sort.drop_duplicates(subset=\"Mouse ID\", keep='first')\n",
    "Ceftamin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "493     62.999356\n",
       "1260    59.741901\n",
       "993     61.849023\n",
       "1033    68.923185\n",
       "1112    67.748662\n",
       "1132    59.851956\n",
       "1162    64.299830\n",
       "1845    68.594745\n",
       "1797    61.386660\n",
       "1379    61.433892\n",
       "1855    64.729837\n",
       "1670    67.527482\n",
       "1773    64.634949\n",
       "1406    64.192341\n",
       "1119    57.918381\n",
       "617     55.742829\n",
       "600     56.057749\n",
       "1582    52.925348\n",
       "840     48.722078\n",
       "1122    46.784535\n",
       "940     47.784682\n",
       "1743    45.000000\n",
       "594     45.000000\n",
       "1551    45.000000\n",
       "1583    45.000000\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumor_vol_Ceftamin = Ceftamin_df['Tumor Volume (mm3)']\n",
    "tumor_vol_Ceftamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile is: 48.72207785\n",
      "The upper quartile is: 64.29983003\n",
      "The interquartile range is: 15.577752179999997\n",
      "The the median is: 59.85195552 \n",
      "Values below 25.355449580000002 could be outliers.\n",
      "Values above 87.66645829999999 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "#Ceftamin - quartile calculations & outlier calculations\n",
    "\n",
    "quartiles = tumor_vol_Ceftamin.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "\n",
    "print(f\"The lower quartile is: {lowerq}\")\n",
    "print(f\"The upper quartile is: {upperq}\")\n",
    "print(f\"The interquartile range is: {iqr}\")\n",
    "print(f\"The the median is: {quartiles[0.5]} \")\n",
    "\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cZmZWKk5cZmZWKk5cZmZWKk5cZmZWKk5cZmZWKqV/Hd7M6pOa/cm7RdzKjnUKJy6zIayRZCPJSclKxVWFZmZWKk5cZmZWKm1JXJI2lnRr4e9ZSV+TNFrSVZLuzf9HtSM+MzPrXG1JXBFxT0RsGRFbAtsAL5J+zv5wYGpEbARMzd1mZmYLdUJV4S7A/RExC9gLmJz7Twb2bltUZmbWkTohcX0CmJI/rxkRjwPk/2vUmkDSREnTJU2fO3fuIIVpZmadoK2JS9KywJ7AhX2ZLiJOj4juiOju6irlz8mYmVk/tbvE9Z/AzRHxZO5+UtJYgPx/TtsiMzOzjtTuxLUfi6oJAS4HJuTPE4DLBj0iM7MaJLX0z/qvbS1nSFoB2A34QqH3ccAFkg4CHgb2bUdsZmbVGm1dxC2RDLy2Ja6IeBFYvarf06S3DM3MzGpqd1WhmZlZnzhxmZlZqThxmZlZqThxmZlZqThxmZlZqThxmZlZqThxmZlZqbTte1xm1n+jR49m/vz5LZtfq1pyGDVqFPPmzWvJvMzqceIyK6H58+d3ZOsMbsrIBoOrCs3MrFRc4jKzpZ6rXsvFicvMlnquei0XVxWamVmpOHGZmVmpOHGZmVmpOHGZmVmpOHGZmVmpOHGZmVmpOHGZmVmpOHGZmVmptC1xSVpN0kWS7pZ0l6R3Sxot6SpJ9+b/o9oVn5mZdaZ2lrh+DFwREW8D3gHcBRwOTI2IjYCpudvMzGyhtjT5JGkV4L3AgQAR8SrwqqS9gJ3yaJOBa4DDBj9Cs84WR60Ck1ZtdxhLiKNWaXcIthRQO9rnkrQlcDpwJ6m0NQP4KvBoRKxWGG9+RCxRXShpIjARYNy4cdvMmjVrUOI26xSSOrZtvU6Mq1cdeBOw0KRnBmS2kmZERPeAzHyAtStxdQP/ALaPiBsk/Rh4FvhyI4mrqLu7O6ZPnz6wAZt1mE5NEJ0aV286Ne6BjKvMiatdz7hmA7Mj4obcfRGwNfCkpLEA+f+cNsVnZmYdqi2JKyKeAB6RtHHutQup2vByYELuNwG4rA3hmZlZB2vn73F9GThX0rLAA8BnSIn0AkkHAQ8D+7YxPjMz60BtS1wRcStQq351l8GOxczMysMtZ5iZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWak4cZmZWam0s5FdM2uCpHaHsIRRo3r8+TyzlnDiMiuhVv64YKf+iKJZPa4qNDOzUnHiMjOzUnHiMjOzUnHiMjOzUnHiMjOzUnHiMjOzUnHiMjOzUmnb97gkPQQ8B7wBvB4R3ZJGA78BxgMPAR+LiPntitHMzDpPu0tcO0fElhHRnbsPB6ZGxEbA1NxtZma2ULsTV7W9gMn582Rg7zbGYmZmHaidiSuAKyXNkDQx91szIh4HyP/XqDWhpImSpkuaPnfu3EEK18zMOkHTiUvSJyQdmT+vI2mbBifdPiK2Bv4TOFTSextdZkScHhHdEdHd1dXVj6jNzKysmkpckk4BdgY+lXu9AJzWyLQR8Vj+Pwe4FNgWeFLS2DzvscCcZuIzM7Ohp9kS13si4gvAywARMQ9YtreJJK0oaeXKZ2B34J/A5cCEPNoE4LIm4zMzsyGm2dfhX5M0jPS8CkmrA282MN2awKX594RGAOdFxBWSbgIukHQQ8DCwb5PxmZk1xL9vVh7NJq5TgYuBLklHAx8Dju5tooh4AHhHjf5PA7s0GZOZWZ/4983KpanEFRG/ljQD2BUQsG9E/LMlkZmZmdXQipYzHgGuyvMaJmmLiLi9BfMthVZXL/hOzcysZ00lLklHAROBB8nPufL/hl9tL7tGE42rD8zMWqPZEtcngfUj4pVWBGNmZtabZl+Hnwms3IpAzMzMGtFsiesY4BZJtwMLS10R8eEm52tmZlZTs4lrMnAScAeNfX/LzAZRoy8PNTKen9Fap2g2cc2LiBNbEomZtZyTjQ1FzSaumyR9n9RUU7GqcKl5Hd5ay18vsE7Vl2PTJdiB1Wzi2jb/36nQb6l6Hd5aq5GT2V8tsHbwMdc5mm05Y8dWBWJmZtaIZr+AfESt/hFxbDPzNTMzq6fZqsI3Cp9HAh8gfbfLzMxsQDRbVXh8sVvS8cBvm4rIzMysB822nFFtOWCDFs/TzMxsoWafcd3CosZ1hwNjAT/fMjOzAdPsM66PFj6/DjzhBnfNzGwg9StxSVolf5xbNWg5SctFxLPNhWVmZlZbf0tcM0lVhLW+Hh7AuH5HZGZm1oN+Ja6IWKfVgZiZmTWi2WdcSNqDRU08XRMRV/Rh2uHAdODRiPigpPWA84HRwM3ApyPi1WZjNDOzoaOp1+ElHQN8B3gg/31H0g/6MIuvAncVuo8HToqIjYD5wEHNxGedY/To0UhqyR/QsnmNHj26zVvGzPqq2e9xfQjYJSJOj4jTgd2BPRuZUNLapJY2fpm7BbwPuCiPMhnYu8n4mtKJF9uyXmjnz59PRHTc3/z589u9acysj5quKgRWIZWOAFbuw3Qnk0prlWlWBxZExOu5ezawVq0JJU0EJgKMGzdw74FULradpNU/+2FmVjbNlrhOAG6W9EtJZ5CeVx3fyzRI+iAwJyJmFHvXGLVm1sglvO6I6O7q6upP3GZmVlLNtlV4jqSrge1Iiee7EfFoA5NuD+yZX+wYSSq1nQysJmlELnWtDTzWTHxmZjb09KvEJek2SYdJWjciHo2ISyLi4gaTFhHxXxGxdkSMBz4B/CUi9geuZlFrHBOAy/oTn5mZDV39rSr8LDAG+Kukv0n6kqQ1WhDPYcA3JN1HeuZ1RgvmaWZmQ4iafflA0vakUtM+wJ3AlIj4VQtia0h3d3dMnz59YGY+adWBmW+zJj3T7gj6TFLHvegCnRuX2UCTNCMiutsdR380nbgWzkjaEfgJsFlELNuSmTZgIBNXJ17UOjGmRnRq3J0al9lAK3PiavYLyFtJOkHSg8BxwJmAm4MyK4Ep557L5uPHM3zYMDYfP54p557b7pDMGtLf1uG/B3wceInURNNOETGrlYGZ2cCZcu65HDlxIme8+CI7ANNmzeKgiRMB2G///dsbnFkv+lVVKOn7pGdZd7Y+pL5xVWE5dGrcnRrXQNt8/Hh+OmsWOxf6XQ18ed11+edDD7UpKhtMZa4qbNkzrnZx4iqJTn3RBUr5skuzhg8bxssRLFPo9xowUuKNN99sV1g2iMqcuFrR5JNZr3T0sx2ZcCURk9odxeDbZNw4plWVuKbl/madrt8vZygZ28pgzGxwHHnMMRy0wgpcTSppXQ0ctMIKHHnMMW2OzKx3/S5xRURI+j2wTQvjMbNBUHkB48tHHsldDz/MJuPGccwxx/jFDCuFZqsKb5S0dUTc3JJozGzQ7Lf//k5UVkrNJq4dgM9Luh94gdTQbkTE1k1HZmZmVkOziautP/RoZmZLn6ZazoiI+4Hlgd3y38jcz8zMbEA02+TTl4ALgHH57wJJh7QiMDMzs1qarSqcCGwbEc8DSDoW+Dvws2YDMzMzq6WpEhfpZYzXCt2v5X5mZmYDotkS19nAPyRdnLv3ASY3OU8zM7O6mkpcEXGCpKuBHUklrYMj4qaWRNYhpM4qQI4aNardIZiZtVUr2iq8h/QdrhEAkraIiNtbMN+2a2XbeqVtHNfMrMM0lbgkHUV6QeNBoHJVDuC9TcZlQ1CnlV7BJVizMmq2xPVJYP2IeKUVwdjQ5dKrmbVKs28VzgRW7utEkkZKulHSbZJmSjo6919P0g2S7pX0G0nLNhmfmZkNMc2WuI4BbpF0O7Cw1BURH+5luleA90XE85KWAaZJ+iPwDeCkiDhf0mnAQcDPm4zRzMyGkGYT12TgJOAOoOGfTY1Uz/N87lwm/wXwPlL1Y2Xek3DiMjOzgmYT17yIOLE/E0oaDswANgROBe4HFkTE63mU2cBadaadSHophHH+xVYzs6VKs8+4bpL0fUnvlLRF5a+RCSPijYjYElgb2BbYpNZodaY9PSK6I6K7q6ur/9GbmVnpNFvi2jb/36nQr0+vw0fEAknXAO8CVpM0Ipe61gYeazI+MzMbYpptOWPH/kwnqQt4LSet5YFdgeOBq4GPAucDE4DLmonPzMyGnma/gHxErf4RcWwvk44FJufnXMOACyLi95LuBM6X9APgFuCMZuIzM7Ohp9mqwjcKn0cCHyB9t6tHuUmorWr0f4BF1Y9mZmZLaLaq8Phit6Tjgd82FZGZmVkPmn2rsNpywAYtnqeZmdlC/SpxVd78k3QLi15ZH056dtXb8y0zM7N+629V4Y3A1qQ3ACteB55wg7tmZjaQ+pu4BBAR97cwFjMzs171N3F1SfpGvYH9bQbKzMysN/1NXMOBlcglLzMzs8HS38T1eER8r6WRmJmZNaC/r8O7pGVmZm3R38S1S0ujMDMza1C/EldEzGt1IGZmZo1odcsZZmZmA8qJy8zMSqXZ1uGXelLj76k0Mm5EzR99NjOzzImrSU40ZmaDy1WFZmZWKk5cZmZWKq4qtI7S6DPDRsdzVa7Z0OPEZR3FicbMeuOqQjMzK5W2JC5J60i6WtJdkmZK+mruP1rSVZLuzf9HtSM+MzPrXO0qcb0OfDMiNgHeBRwqaVPgcGBqRGwETM3dZmZmC7UlcUXE4xFxc/78HHAXsBawFzA5jzYZ2Lsd8ZmZWedq+zMuSeOBrYAbgDUj4nFIyQ1Yo840EyVNlzR97ty5gxWqmZl1gLYmLkkrARcDX4uIZxudLiJOj4juiOju6uoauADNzKzjtC1xSVqGlLTOjYhLcu8nJY3Nw8cCc9oVn5mZdaZ2vVUo4Azgrog4sTDocmBC/jwBuGywYzMzs87Wri8gbw98GrhD0q253xHAccAFkg4CHgb2bVN8ZmbWodqSuCJiGlCvzZ5dBjMWMzMrl7a/VWhmZtYXTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqTlxmZlYqbUlcks6UNEfSPwv9Rku6StK9+f+odsRmZmadrV0lrrOA91f1OxyYGhEbAVNzt5mZ2WLakrgi4lpgXlXvvYDJ+fNkYO9BDcrMzEqhk55xrRkRjwPk/2vUG1HSREnTJU2fO3fuoAVoZmbt10mJq2ERcXpEdEdEd1dXV7vDMTOzQdRJietJSWMB8v85bY7HzMw6UCclrsuBCfnzBOCyNsZiZmYdql2vw08Brgc2ljRb0kHAccBuku4FdsvdZmZmixnRjoVGxH51Bu0yqIGYmVnpdFJVoZmZWa+cuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFScuMzMrFQ6LnFJer+keyTdJ+nwdsdjZmadpaMSl6ThwKnAfwKbAvtJ2rS9UZmZWSfpqMQFbAvcFxEPRMSrwPnAXm2OyczMOsiIdgdQZS3gkUL3bGC76pEkTQQm5s7nJd0zCLE1awzwVLuDGCK8LVvL27O1yrI91213AP3VaYlLNfrFEj0iTgdOH/hwWkfS9IjobnccQ4G3ZWt5e7aWt+fA67SqwtnAOoXutYHH2hSLmZl1oE5LXDcBG0laT9KywCeAy9sck5mZdZCOqiqMiNclfQn4EzAcODMiZrY5rFYpVdVmh/O2bC1vz9by9hxgiljiEZKZmVnH6rSqQjMzsx45cZmZWaks9YlL0lsknS/pfkl3SvqDpLe2O64KSddI6s6f/yBptXbH1BtJb0i6VdI/Jf2uVTFL2nNpbwZM0vMNjLOjpJl5Hyzfw3gPSRpTo//Bkg7oZ3w7Sfp9f6YdbH099yV9RdJdks7N6/meFsbyS7cS1LilOnFJEnApcE1EbBARmwJHAGu2N7LaImKPiFjQ7jga8FJEbBkRmwPzgENbMdOIuDwijmvFvIa4/YEf5X3wUl8njojTIuLXAxBXx+jnuScPwRYAAAgjSURBVH8IsEdE7A/sBLQscUXE5yLizlbNb6hbqhMXsDPwWkScVukREbcCt0iaKulmSXdI2gtA0nhJd0uaLOl2SRdJWiEP20XSLXn8MyUtl/s/JOlYSddLmi5pa0l/ynd5B+dxFrtLlXSKpAOrg63cIec47pL0i3xnfWVPd9Ztdj2pRRQkrdTLdv1lLqWdK2lXSX+TdK+kbfN4B0o6JX9eU9Klkm7Lf+/J/X8raUbeLpXWVZD0vKRj8rj/kLRm7t8l6WJJN+W/7Qd5+/RLPmauycfg3XmbSdLngI8B3y2UDHo6tr4t6cb8t2EeZ5Kkb+XP10g6Pg//l6Qdc/+Rkn6V9+MtknYevLVviZrnfkRcJ+nb+Vi4XdLRAJJOA9YHLpf0deBg4OtKpdodJX1I0g15W/y5cHxNyteLK/P5+2FJJ+TtdoWkZfJ4xZqVmseqLbK0J67NgRk1+r8M7BMRW5MO8P+VVGnVY2Pg9IjYAngWOETSSOAs4OMR8XbS1wy+WJjfIxHxbuC6PN5HgXcB32si9o2AUyNiM2AB8JEm5jUglBpN3oVF38XrabtuCPwY2AJ4G/BJYAfgW6Q74Wo/Af4aEe8AtgYqX5v4bERsA3QDX5G0eu6/IvCPPP61wOdz/x8DJ0XEO0nb8JdNr/jg2Qr4GqlB6vWB7SPil6Tt/e1cMujNsxGxLXAKcHKdcUbkcb4GHJX7HQqQj/f9gMn5PCiLmue+pN1J59a2wJbANpLeGxEHkxpD2DkiTgJOIx03W0bEdcA04F0RsRWpjdXvFGa7AfABUrur5wBX5+32Uu5frd6xatnSnrjqEXCspNuBP5NKDJW7nkci4m/58zmki+vGwIMR8a/cfzLw3sL8KhfuO4AbIuK5iJgLvKz+P/95MJcOIZ2A4/s5n4GwvKRbgaeB0cBVuX9P2/XBiLgjIt4kJaGpkb6rcQe11+19wM8BIuKNiHgm9/+KpNuAf5BaYdko938VqJQ8ittrV+CUHO/lwCqSVm5i3QfTjRExO2+zW+nfMTCl8P/ddca5JP8vbrcdgLMBIuJuYBbQMc+Gm7B7/rsFuJl0E7VRj1MkawN/knQH8G1gs8KwP0bEa6RjeThwRe5f79iud6xatrQnrpnANjX67w90AdtExJbAk0DlbrL6i29B7TYWi17J/98sfK50jwBeZ/F90cida3E+b9BZXyZ/KW+3dYFlWfSMq6ftWr1ditusoXWTtBMpEb07363eUpj/a7HoS4vF7TUsj79l/lsrIp5reE3bq5FjoLdjK+p8rrWc4jJ6O+Y7Xb1zX8APC8fDhhFxRgPz+ylwSi5JfYHFt/MrAPkGo3gc1ju26x2rli3tiesvwHKSFhbFJb2TdMGdExGv5br7YivK4yRV7kz3I1UR3A2MrzwjAD4N/LUPccwCNpW0nKRVSdVrpZdLQV8BvpXr8lel/nbtq6nk6lhJwyWtkuc/PyJelPQ2UnVsb64EvlTpkLRlEzF1ot6OrY8X/l/fh/leS7oRQelNvHFAGX6loaLeuf8s8FlJK+V+a0lao8b0zwHFkvmqwKP584SBCdkqlurEle9q9gF2U3pZYiYwCfgD0C1pOunkvLsw2V3AhFzdNRr4eUS8DHwGuDBXFbxJqgNvNI5HgAuA24FzSSWFISEibgFuI7U7eS71t2tffRXYOW/vGaSqmSuAEXnffJ9UXdibr+SYbpd0J+mh+5DRwLG1nKQbSNvz632Y9c+A4Xn7/wY4MCJe6WWajtHDuX9e/rs+r9tFLJ6gKn4H7FN5OSNPe6Gk6yjHT5qUmpt86gNJ44Hf59e8zcysDZbqEpeZmZWPS1xmZlYqLnGZmVmpOHGZmVmpOHGZmVmpOHHZkKJFLdPPzG29fUPSgBznuR3AZ3L7dHdL+lGT8ytF6/9m7eZvZNtQU2m1g/zF0fNIXw49qjiSpBER8XoLlnddRHxQqZHjWyRdWmgSrE8iYo8WxGM25LnEZUNWRMwBJgJfUnKgpAsl/Q64Uj20nC5pj1yKmibpJ+rlN6byz4fcyqKW8FdU+pWAm3KJrNIS/gqSLshfeP5NblG80ip4sfX/RlrLr7eMAyVdotT6+L2STmj1tjVrJ5e4bEiLiAdyVWGl2Z53A1tExLzctuEScivn/we8NyIelDSl1nhV04wiNcZ6be51JPCXiPhsrv67UdKfSc1UzY+ILSRtTkp2tWwI7EtKvDexqLX8PUmt5e/dwzIgtWy+FamdvHsk/TS3omFWei5x2dKg2CDsVRExr5fx3wY8EBEP5u6eEteOuYmpJ0itqjyR++8OHJ5bnb+G1OjqOFLyOR8gIv5JaoqplkZay6+3DPL4z+TmyO6kuXYhzTqKS1w2pElan9TC9pzc64XC4Hotp/el5fPKM663AtPyM65b8zw+EhGLNTwrqdF5N9Jafr1lbEdn/3qAWVNc4rIhS1IXqbHjU6J2EzH1Wk6/G1g/t00Ji1pQryv/FtsPgcNyrz8BX64kKklb5f7TSL9QjKRNgbf3cbWK6i3DbEjzXZgNNZUfsVyGVKI6Gzix1ogR8YikSsvp95JbTo+IlyQdAlwh6SngxgaXfRrpJ1zWI7VOfzJwe04sDwEfJLWqPjlXL96Sl/1M7dn1qt4yzIY0t1VoVoOklSLi+ZwQTgXuzT/Z3ux8hwPLRMTLkjYg/a7YWyPi1Wbnbba0cInLrLbPS5pA+gXnW0hvGbbCCsDVSj+sKeCLTlpmfeMSl5mZlYpfzjAzs1Jx4jIzs1Jx4jIzs1Jx4jIzs1Jx4jIzs1L5/8tuuKTEMogxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "\n",
    "fig7, ax7 = plt.subplots()\n",
    "ax7.set_title('Final Tumor Volume of Each Mouse Across Four Regimens of Interest')\n",
    "ax7.set_ylabel('Tumor Volume')\n",
    "ax7.set_xlabel('Drug Regimen')\n",
    "data = [tumor_vol_Capomulin, tumor_vol_Ramicane, tumor_vol_Infubinol, tumor_vol_Ceftamin]\n",
    "plt.ylim(0, 75)\n",
    "red_dot = dict(markerfacecolor='r', marker='o')\n",
    "ax7.boxplot(data, flierprops=red_dot, labels=['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin',])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>43.878496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>37.614948</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>38.177232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>36.866876</td>\n",
       "      <td>0</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>440</th>\n",
       "      <td>i557</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>47.685963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>r157</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>r157</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>45.597064</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>r157</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>46.059608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>r157</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>46.539206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "10       s185    Capomulin  Female           3          17          0   \n",
       "11       s185    Capomulin  Female           3          17          5   \n",
       "12       s185    Capomulin  Female           3          17         10   \n",
       "13       s185    Capomulin  Female           3          17         15   \n",
       "14       s185    Capomulin  Female           3          17         20   \n",
       "...       ...          ...     ...         ...         ...        ...   \n",
       "440      i557    Capomulin  Female           1          24         45   \n",
       "1452     r157    Capomulin    Male          22          25          0   \n",
       "1453     r157    Capomulin    Male          22          25          5   \n",
       "1454     r157    Capomulin    Male          22          25         10   \n",
       "1455     r157    Capomulin    Male          22          25         15   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "10             45.000000                 0  \n",
       "11             43.878496                 0  \n",
       "12             37.614948                 0  \n",
       "13             38.177232                 0  \n",
       "14             36.866876                 0  \n",
       "...                  ...               ...  \n",
       "440            47.685963                 1  \n",
       "1452           45.000000                 0  \n",
       "1453           45.597064                 0  \n",
       "1454           46.059608                 0  \n",
       "1455           46.539206                 0  \n",
       "\n",
       "[230 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = clean_df[clean_df[\"Drug Regimen\"].isin([\"Capomulin\"])]\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>43.878496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>37.614948</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>38.177232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>36.866876</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>33.949940</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>32.959671</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>28.328531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>25.472143</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>23.343598</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "10     s185    Capomulin  Female           3          17          0   \n",
       "11     s185    Capomulin  Female           3          17          5   \n",
       "12     s185    Capomulin  Female           3          17         10   \n",
       "13     s185    Capomulin  Female           3          17         15   \n",
       "14     s185    Capomulin  Female           3          17         20   \n",
       "15     s185    Capomulin  Female           3          17         25   \n",
       "16     s185    Capomulin  Female           3          17         30   \n",
       "17     s185    Capomulin  Female           3          17         35   \n",
       "18     s185    Capomulin  Female           3          17         40   \n",
       "19     s185    Capomulin  Female           3          17         45   \n",
       "\n",
       "    Tumor Volume (mm3)  Metastatic Sites  \n",
       "10           45.000000                 0  \n",
       "11           43.878496                 0  \n",
       "12           37.614948                 0  \n",
       "13           38.177232                 0  \n",
       "14           36.866876                 0  \n",
       "15           33.949940                 0  \n",
       "16           32.959671                 1  \n",
       "17           28.328531                 1  \n",
       "18           25.472143                 1  \n",
       "19           23.343598                 1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mouse_df = test_df.loc[test_df[\"Mouse ID\"] == \"s185\"]\n",
    "test_mouse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mouse s185 Tumor Volume vs. Time Point with Capomulin')"
      ]
     },
     "execution_count": 58,
     "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",
    "lineplt_df = pd.DataFrame({\n",
    "   'tumor volume': [45.000000, 43.878496, 37.614948, 38.177232, 36.866876, 33.949940, 32.959671, 28.328531, 25.472143, 23.343598]\n",
    "      }, index = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45])\n",
    "lines = lineplt_df.plot.line()\n",
    "\n",
    "plt.xlabel('Time Point')\n",
    "plt.ylabel('Tumor Volume')\n",
    "plt.title(\"Mouse s185 Tumor Volume vs. Time Point with Capomulin\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s185</td>\n",
       "      <td>17</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s185</td>\n",
       "      <td>17</td>\n",
       "      <td>43.878496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>s185</td>\n",
       "      <td>17</td>\n",
       "      <td>37.614948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s185</td>\n",
       "      <td>17</td>\n",
       "      <td>38.177232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>s185</td>\n",
       "      <td>17</td>\n",
       "      <td>36.866876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>i557</td>\n",
       "      <td>24</td>\n",
       "      <td>47.685963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>r157</td>\n",
       "      <td>25</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>r157</td>\n",
       "      <td>25</td>\n",
       "      <td>45.597064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>r157</td>\n",
       "      <td>25</td>\n",
       "      <td>46.059608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>r157</td>\n",
       "      <td>25</td>\n",
       "      <td>46.539206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Weight (g)  Tumor Volume (mm3)\n",
       "10       s185          17           45.000000\n",
       "11       s185          17           43.878496\n",
       "12       s185          17           37.614948\n",
       "13       s185          17           38.177232\n",
       "14       s185          17           36.866876\n",
       "...       ...         ...                 ...\n",
       "440      i557          24           47.685963\n",
       "1452     r157          25           45.000000\n",
       "1453     r157          25           45.597064\n",
       "1454     r157          25           46.059608\n",
       "1455     r157          25           46.539206\n",
       "\n",
       "[230 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df = test_df.loc[:, [\"Mouse ID\", \"Weight (g)\", \"Tumor Volume (mm3)\"]]\n",
    "scatter_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID\n",
       "b128    41.963636\n",
       "b742    40.083699\n",
       "f966    36.505973\n",
       "g288    41.990097\n",
       "g316    44.613344\n",
       "i557    44.805810\n",
       "i738    39.141053\n",
       "j119    44.465236\n",
       "j246    43.216925\n",
       "l509    44.434474\n",
       "l897    42.507261\n",
       "m601    34.847470\n",
       "m957    39.049816\n",
       "r157    45.798970\n",
       "r554    36.509212\n",
       "r944    43.367364\n",
       "s185    34.559143\n",
       "s710    42.803733\n",
       "t565    39.328725\n",
       "u364    39.887495\n",
       "v923    41.581595\n",
       "w150    41.384825\n",
       "w914    41.352452\n",
       "x401    36.182040\n",
       "y793    38.506829\n",
       "Name: Tumor Volume (mm3), dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_tumor_vol = scatter_df.groupby(scatter_df[\"Mouse ID\"])[\"Tumor Volume (mm3)\"].mean()\n",
    "avg_tumor_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID\n",
       "b128    22\n",
       "b742    21\n",
       "f966    17\n",
       "g288    19\n",
       "g316    22\n",
       "i557    24\n",
       "i738    20\n",
       "j119    23\n",
       "j246    21\n",
       "l509    21\n",
       "l897    19\n",
       "m601    17\n",
       "m957    19\n",
       "r157    25\n",
       "r554    17\n",
       "r944    25\n",
       "s185    17\n",
       "s710    23\n",
       "t565    17\n",
       "u364    17\n",
       "v923    21\n",
       "w150    23\n",
       "w914    21\n",
       "x401    15\n",
       "y793    17\n",
       "Name: Weight (g), dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "each_mouse_weight = scatter_df.groupby(scatter_df[\"Mouse ID\"])[\"Weight (g)\"].mean()\n",
    "each_mouse_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": [
    "# Scatter Plot\n",
    "\n",
    "y_values = [41.963636, 40.083699, 36.505973, 41.990097, 44.613344, 44.805810, 39.141053, 44.465236, 43.216925, 44.434474, 42.507261, 34.847470, 39.049816, 45.798970, 36.509212, 43.367364, 34.559143, 42.803733, 39.328725, 39.887495, 41.581595, 41.384825, 41.352452, 36.182040, 38.506829]\n",
    "x_values = [22, 21, 17, 19, 22, 24, 20, 23, 21, 21, 19, 17, 19, 25, 17, 25, 17, 23, 17, 17, 21, 23, 21, 15, 17]\n",
    "plt.scatter(x_values, y_values)\n",
    "model = st.linregress(each_mouse_weight,avg_tumor_vol)\n",
    "y_values = each_mouse_weight*model[0]+model[1]\n",
    "plt.plot(each_mouse_weight, y_values, color ='r')\n",
    "plt.xlabel('mouse weight (g)')\n",
    "plt.ylabel('average tumor volume (mm3)')\n",
    "plt.title('Average Tumor Volume vs. Mouse Weight for Capomulin ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient for mouse weight and average tumor volume for the Capomulin regimen is 0.84.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "\n",
    "corr = round(st.pearsonr(each_mouse_weight, avg_tumor_vol)[0],2)\n",
    "print(f\"The correlation coefficient for mouse weight and average tumor volume for the Capomulin regimen is {corr}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[44.50660022 43.50660022 39.50660022 41.50660022 44.50660022 46.50660022\n",
      " 42.50660022 45.50660022 43.50660022 43.50660022 41.50660022 39.50660022\n",
      " 41.50660022 47.50660022 39.50660022 47.50660022 39.50660022 45.50660022\n",
      " 39.50660022 39.50660022 43.50660022 45.50660022 43.50660022 37.50660022\n",
      " 39.50660022]\n"
     ]
    }
   ],
   "source": [
    "#linear regression model on average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = stats.linregress(x_values, y_values)\n",
    "\n",
    "regress_values = x_values + slope + intercept\n",
    "print(regress_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.95x +21.55\n"
     ]
    }
   ],
   "source": [
    "line_eq = \"y = \" + str(round(slope, 2)) + \"x +\" + str(round(intercept, 2))\n",
    "print(line_eq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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"
    },
    {
     "data": {
      "image/png": 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OOdcZjBoxlJoe3ZuU1fTozqgRQzPfdqcYy8g557qK3NlElTjLyBOCc851MCOHDSxLAsjnYxk555wDPCE451zH1KsXdO/e9nIl5AnBOec6kgMPBAk+/RSWLSvrpj0hOOdcR3DNNSERjB/fWDZvXllD8E5l55yrpJkzYfPNm5bddRfstVfZQ/EagnPOVcLHH4caQTIZ/OxnYFaRZABeQ3DOufIyg255x+JDhsCrr1YmngSvITjnXLlstVXzZLB0aYdIBuAJwTnnsnfeeaF5aNq0xrL33itcW6ggbzJyzrmsTJ0KO+3UtOxf/4Jtt61IOG3xhOCccy1o98xlb78N/fs3LbvkEjj55GwCLRFPCM45V0Bu5rLcZDW5mcuAlpPCkiXQo0fTsl12gQceyDLUkuk4jVfOOdeBFD1z2aqrNk8GZp0mGYAnBOdcShNn1DP8wikMOfNuhl84pSxz/FZS6pnLjj8+dBg3NDSWffppSAadjCcE51ybKjnxe6W0OXPZrbeGRDBuXOODL74YEsFKK5UhwtLzhOCca1MlJ36vlJZmLhuzcY+QCA4+uPGBm28OiWBo9rOaZck7lZ3rZNp95styqOTE75XYX2g+c9nglbvxz7P2aLrQMcfAVVdlHku5eEJwrhNp15kvJTCgtob6Aj/+WU/8Xqn9zflq5jKp6QMrrwwLF2a+/XLzJiPnOpFKNd1UauL3ijdVSc2TweLFVZkMwBOCc51KpZpuRg4byAX7b8rA2hoEDKyt4YL9N63epqrDD2+eCOrrQz/BCtXbsFK9e+ZcFapU0w1UZuL3su/vxImw335Ny666KvQVdAFt1hAk1Uk6RdJYSedKOkhS33IE55xrqlJNN5VStv2trw81gmQy2GmnUCPoIskAWqkhSDoKOAl4DZgOzAF6AtsDZ0j6N/C/ZvZGGeJ0ztH8zJdynnVTCZnv77JlhSey74QXlZWCrIUdl/Q/wDVmVrCxTtIWwGpm9mCG8QFQV1dn05LDxjrn3PLK7yOAkCAKlXdSkqabWV3a5VtsMjKzK1pKBvHxZ9MkA0ndJc2QdFde+eWSqrOr3jnXcW21VfMf/XfeCbWCKkoG7dFiQpC0kqTTJY2S1FPSUZLukPQbSb2K2MbJwOy8ddcBte2M2Tnninf55c0nqbnnnpAI1lijcnF1IK11Kl8HrAkMAe4G6oDfAgKuTLNySYOAvYCrE2XdgbHA6e2K2DnnivHvf4dEcNJJjWXHHx8SwR57tPy8Lqi10043NLODJAl4C9jVzEzSI8BzKdd/CeGHv3ei7ATgDjN7S61UzyQdBxwHsPbaa6fcnHPORZ9/DjUFTk/toh3GabR52qmFXud74v/c/TZfUUl7A++a2fRE2QDgQODyFNsdZ2Z1ZlbXr1+/thZ3zrlGUvNkYObJoA2t1RCmSeplZgvN7OhcoaT1gE9SrHs4sI+kPQmnq/YBnge+AObG2sFKkuaa2frt3gPnnMvp2RO++KJp2cKFYewh16bWzjI6xsyanQVkZq8A325rxWY22swGmdlg4BBgipmtamZfN7PBsfwzTwbOueV2yimhVpBMBtOnhxqBJ4PU2hy6InYC7wUMzlv+dxnF5Jxz6UyZEuYsTrroIjjdz1lpjzRjGd0JfA7MApa1ZyNmNhWYWqC8mNNXnXMueP99WH31pmUbbwwvvFCZeKpEmoQwyMw2yzwS55xrixl0K9DS7Z3FJZFm+OtJknbLPBLnnGuN1DwZLF7syaCE0tQQngAmSOoGLCZcmGZm1ifTyJxzDgoPJzFvHqyzTtlDKZdKTRuapoZwMbAdsJKZ9TGz3p4MnHOZ++53myeD3GT2VZ4MRt8+i/qGRRiN04ZOnFGf+bbT1BBeBv6duzDNOecy9cADIRnk6yI/Qa1NG5p1LSFNQngLmCppEuGiMgDMzE87da4CKtWckDkfagKo4LShpEsIr8W/r8U/51yF5JoTckeQueYEoHMnhS4wN0FalZwmtc2EYGbnZB6Fcy6VSjYnZKLQD/4rr8C665Y/lg5i1IihTZI+lG+a1LRzKk+Q9Iykmbm/zCNzzjVTyeaEklpppebJ4NxzQ/NQF04GEGp6F+y/KQNraxAwsLaGC/bftCwJP02T0Y3AKJbjSmXnXGlUsjmhJK64Ak44oXl5F+snaMvIYQMrUuNLkxAWmNkdmUfinGtTJZsTlsvbb0P//s3LPRF0KGkSwtmSrgYepOlZRrdnFpVzrqDcUWOnOsuoUD+BJ4IOKU1C+BGwEdCDxiYjAzwhOFcBlWpOKFqhRPDxx9C7d/PyVlTtabYdUJqEsLmZbZp5JM656lAoEYwfDwccUPSqqvY02w4qzdAVT0j6RuaROOc6t2OPbZ4MNtssNA+1IxlA66fZutJLU0PYHjhS0muEPoTc4HY+JLZzDmbMgC23bF5egn6CqjnNtpNIkxB2zzwK51zns3QprFDgJ6SEHcad/jTbTqbFhCCpl5ktNLPX21omm9Cca5t3OFZIoX6CJUuge/eSbqbTnmbbSbXWh/APSRdL+o6kr2aplrSupB9LmozXHlwFVXKY4C5Lap4Mnnkm1ApKnAygslftdkVqbVRrSXsChwLDgVWBJcAc4G7gL2b2djmCrKurs2nTppVjU64TGX7hlILNCQNra3jszJ0rEFEVGzQI6vMS7bHHwrhxlYnHpSJpupnVpV2+1T4EM7sHuGe5o3IuA97hWAY33giHHda83C8sq0ppOpWd65C8wzFDH30EtbXNyz0RVLU01yE41yGNGjGUmh5N2629w7EEpObJwMyTQRfgCcF1Wt7hWGKFOozr6z0RdCGpmowkbQ9sYGbXSuoH9DKz17INzbm2dZpxfTqyQqeQXnwxnHpq+WNxFZVmgpyzgTOA0bGoB3BDlkE558rgzDNbHonUk0GXlKaGsB8wDHgGwMzmS0o9XKGk7sA0oN7M9pZ0I1AHLAaeAo43s8VFR+4cfmFau7z8Mmy4YfNybxrq8tIkhC/NzCQZQPIitZROBmYDfeL9G4HceWw3AccAVxa5Tud8JMximUG3Ao0CKROBJ9/ql6ZT+VZJfwZqJR0LPABclWblkgYBewFX58rM7B6LCDWEQcWH7ZyPhFkUqXky+OyzopKBXxVe/dpMCGb2W2A8cBswFDjLzC5Puf5LgNMpMBezpB7A4cC9hZ4o6ThJ0yRNW7BgQcrNua7EL0xLodCZQ5MmhURQk/56DU++XUOq007N7H7gPOB8YLqkvm09R9LewLtmNr2FRf4IPGxmj7SwzXFmVmdmdf369UsTputiWroAzS9MA3bcsXki2HbbkAh2L34IMk++XUOas4yOl/QOMJPQOTw9/m/LcGAfSfOA/wN2lnRDXOfZQD/AT2Vw7eYXphXwwAMhETz0UNNyM/jXv9q9Wk++XUOaGsJpwCZmNtjM1jWzIWa2bltPMrPRZjbIzAYDhwBTzOwwSccAI4AfmFmzpiTn0vIL0xK++CIkgu9+t2l5ia4w9uTbNaQ5y+gV4LMSbvNPwOvAvxSqtLeb2bklXL/rQrrihWn5Z/s8NnqX5gstW1b4GoN2yr3GfpZRdWt1+GsAScOAa4EnCVNoAmBmJ2UbWiMf/tq5IHmq7byL9m6+wOzZsNFG5Q/MdUglHf46+jMwBZhFgbOFnHPlM3byHGb/ao9m5bcMP4CDHx1fgYhcNUmTEJaYmXf+Oldpf/gDj40+sVnx4DPuQsDB5Y/IVZk0CeGfko4D7qRpk9EHmUXlnGv09tvQv3+z4sFn3PXV7Wo+28evkC6fNAnhh/H/6ESZAW2eaeScW04FOoaTiSBnp42q81odH56kvNpMCGY2pByBOOcSCp0h9MEHDP/zDChwMdg/X6zOq/lbu0LaE0LptZkQJB1RqNzM/lr6cJzr4golguuugyOPBLreFcNdbX8rLU2T0VaJ2z2BXQhDYXtCcK5Ujj4arr22adlqq8F77zUp6mrzSHe1/a20NIPbnZj4O5YwN8LXsg/NuS5gxoxQK8hPBmbNkgF0vSuGu9r+VlqqKTTzfAZsUOpAnOtSli2D7t2bl7dxoWhXu2K4q+1vpaW5UvlOwllFEGoU3wBuNbMzM47tK36lsqsqhfoJFi+GFdpzfOZcy7K4Uvm3idtLgNfN7M2iI3OuqyuUCB5/HLbbrvyxOFdAmtNOH2prGedcKzbYAObObVq2//5w222Vice5FrSYECR9QmNTUZOHADOzPgUec87ljB8PBx7YvNwns3cdVIsJwcx6lzMQ56rGJ59AnwLHS54IXAeXqhdL0ubAt+Pdh81sZnYhOdeJFeon8ETgOok0U2ieDNwIrBH/bpTUfMhF57qyQpPZv/GGJwPXqaSZQvPHwDZmdpaZnQVsCxybbVjOdRKFEsH554dEsNZalYnJuXZK02QkIDm61NJY5lzXdfnlcFKBSQO9RuA6sTQJ4VrgSUkT4v2RwF+yC8m5DuzNNwsf+XsicFUgzXUIv5M0FdieUDP4kZnNyDow5zoUM+hWoIXVE4GrImmGv74UuMXMLitDPM51PIXOHPrsM6jxETdddUnTqfwM8EtJcyWNlZR6XAznOrVCHcZTpoRagScDV4XSDH99vZntCWwNvARcJOnlzCNzrlKOPbZ5Ithnn5AIdtqpMjE5VwbFDK+4PrARMBh4IZNonKukp5+GrbduXu79BK6LSNOHcBGwP/AKcAtwnpk1ZB2Yc2WzeDF8rcCcT54IXBeTpobwGrCdmTWfvsm5zq5Qh/GyZYXLO4iJM+p9whiXiTR9CH9anmQgqbukGZLuiveHSHpS0suSbpHk03G68ivUYfzSS6FW0MGTwejbZ1HfsAgD6hsWMfr2WUycUV/p0FwVSHOW0fI6GZiduH8R8Hsz2wD4kDA0hnPlsfnmzX/wx4wJiWCDjj8z7NjJc1i0eGmTskWLlzJ28pwKReSqSaZz9kkaBOwF/Bo4VZKAnYEfxkWuB8YAV2YZR1fhTQmtuOUWOOSQ5uWdrJ9gfsOiosqdK0arCUFSN2CmmX2zneu/BDgdyM2tsBrQYGZL4v03gYK/WJKOA44DWHvttdu5+a4j15SQO3rMNSUAXTspfPgh9O3bvLyTJYKcAbU11Bf48R9Q69dFuOXXapORmS0DnpNU9C+ypL2Bd81serK40GZa2PY4M6szs7p+/foVu/kux5sSCpCaJwOzTpsMAEaNGEpNj+5Nymp6dGfUiKEVishVkzRNRv2B5yU9BXyaKzSzfdp43nBgH0l7Aj2BPoQaQ62kFWItYRAwv12Ruya8KSGhUKfwBx/AqquWP5YSy9X2vGnQZSFNQjinPSs2s9HAaABJOwKnmdmhkv4OfB/4P+BI4B/tWb9rypsSKJwIbrkFDjqo/LFkaOSwgZ4AXCbSnHb6EPAioR+gNzA7lrXXGYQO5rmEPgUfSrsEunRTwpgxzZPBZpuFpqEqSwbOZSnNlcoHAWOBqYQ+gMsljTKz8Wk3YmZT4/Mxs1cJ4yK5EuqSTQkvvQRDCyS8TtxH4FwlpWky+gWwlZm9CyCpH/AAkDohuPLoMk0JPjeBc5lIc2Fat1wyiN5P+TznSk9qngy+/NKTgXMlkKaGcK+kycDN8f7BwKTsQnKugEIdxk8/DXU+PYdzpZKmU3kUMA7YDNgcGGdmp2cdmCvexBn1DL9wCkPOvJvhF06pjvFt9t23eTI47rhQI/Bk4FxJpRq6wsxuk3R/bnlJfc3sg0wjc0WpuiuVp0yBXXZpXu5NQ85lJs1ZRscD5wKLgGWEM40MWDfb0FwxWrtSuVMlhEWLYKWVmpd7InAuc2lqCKcBm/h8CB1bVVypXKifoIMmAh9I0FWjNGcLvQJ8lnUgbvm0dEVyp7hSudDcBG++2aGTgc9J4KpRmoQwGnhc0p8lXZb7yzowV5xOeaXy6qs3TwSXXx4SwcB0R9uV6Ej3gQRdtUrTZPRnYAowi9CH4DqgTnWl8rhxcPzxTcv69IGPPipqNZXqSK+K5jnnCkiTEJaY2amZR+KWW4e/Uvmtt2DAgObl7WwaqlRHug8k6KpVmiajf0o6TlJ/SX1zf5lH5qqL1DwZLOfcBJU6Uu+UzXPOpZCmhpCb7nJ0osxPO3XpFDpzaOFCWHnl5V51pY7UO1XznHNFaDMhmBO8Zq4AABQdSURBVNmQcgTiqkyhRDB5Muy2W8k2MWrE0CZ9CFC+I/UO3zznXDukuTDtiELlZvbX0ofjOr0TT4Q//KFp2e67w6TSD3/lR+rOlVaaJqOtErd7ArsAzwCeEFyjGTNgyy2bl2d8LYEfqTtXOmmajE5M3pe0CvC3zCJyncuSJdCjR/PyDnpRmXOuZakGt8vzGbBBqQNxy6/swykU6idYurTw5DXOuQ4vTR/CnYSziiCcpvoN4O9ZBuWKV9aLtAolghdegI03Lu12nHNllaaG8NvE7SXA62b2ZkbxuHYqy0Va22wDTz3VtOznP4df/7o063fOVVSahLCnmZ2RLJB0UX6Zq6xML9KaMAH23795ufcTOFdV0jT2frdA2R6lDsQtn0xGO/3oo9A8lJ8MlvMKY+dcx9RiQpD0E0mzgKGSZib+XgNmli9El0bJh1OQoLa2aZknAueqWmtNRjcBk4ALgDMT5Z/49JkdT8ku0irUYbxgQRiq2jlX1WSd4Iivrq7Opk2bVukwqluhRHDDDXDooeWPxTlXEpKmm1ld2uX9hPGu7oILmieDjTYKTUOeDJzrUtpzYVoqknoCDwMrxu2MN7OzJe0CjCUko4XAUWY2N6s4XAtefRXWW695eSeoMTrnspEqIUhaB9jAzB6QVAOsYGaftPG0L4CdzWyhpB7Ao5ImAVcC+5rZbEk/BX4JHNX+XXBFMSt8JbEnAue6vDabjCQdC4wnTKUJMAiY2NbzLFgY7/aIfxb/+sTyVYD5Rcbs2ktqngy++MKTgXMOSNeH8D/AcOBjADN7GVgjzcoldZf0LPAucL+ZPQkcA9wj6U3gcODCFp57nKRpkqYtWLAgzeZcS6Tm/QSPPx4Swde+VpmYnHMdTpqE8IWZfZm7I2kFGsc2apWZLTWzLQi1iq0lfRM4hXD18yDgWuB3LTx3nJnVmVldv3790mzO5TvkkOaJ4IgjQiLYbrvKxOSc67DS9CE8JOnnQI2k7wI/Be4sZiNm1iBpKuEK581jTQHgFuDeYtblUnj4Ydhhh+bl3jTknGtFmhrCmcACYBZwPHAPoSO4VZL6SaqNt2uAXYHZwCqSNoyLfTeWuVJYtizUCPKTgV9h7JxLIc0EOcuAq+JfMfoD10vqTkg8t5rZXbGT+jZJy4APgaOLXK8r5De/gTPyxhvMJQjnnEshzXwIs2jeZ/ARMA34lZm9X+h5ZjYTGFagfAIwofhQXUF33w177914f/vt4cEHvbPYOVe0NH0Ik4ClhLGNAA6J/z8GrgO+V/qwXJtmz4ZvfKPxfs+e8MYb4B3wzrl2SpMQhpvZ8MT9WZIeM7Phkg7LKjDXgg8+gCFD4OOPG8ueew4226xyMTnnqkKaTuVekrbJ3ZG0NdAr3l2SSVSuucWLYZddYLXVGpPBhAmhs9iTgXOuBNIkhGOAqyW9JmkecDVwrKSVCUNju6ydcUboE5gyJdw/77yQCEaOrGxczrmqkuYso6eBTSWtQhguuyHx8K2ZRebgxhvhsESr3MiRMH48dO/e8nOcc66d0g5utxewCdBT8TRGMzs3w7i6tqeeChPa5wwaBM8/D336tPwc55xbTmlOO/0TsBKwE6G56PvAUxnH1TXNnw8D82Y4mzu38DDVzjlXYmn6EP7LzI4APjSzc4DtgLWyDauLWbQINt+8aTJ48MHQT+DJwDlXJmkSwufx/2eSBgCLgSHZhdSFmMGPfwwrrQQzZ4ayK64I5TvvXNnYnHNdTpo+hDvjmERjgWcIVy0XO4yFy3fFFXDCCY33jzkGxo3zoSaccxXTakKQ1A14MJ5ZdJuku4CeZvZRWaKrRg8+CLvu2nh/iy3gX/8KVxo751wFtZoQzGyZpIsJ/QaY2ReEqTFdsebOhQ02aFo2fz7071+ZeJxzLk+aPoT7JB0geVtGu3z0EQwY0DQZPP106CfwZOCc60DSJIRTgb8DX0r6WNInkj5u60ld3tKlsO++UFsLb70Vym66KSSCurrKxuaccwW0mRDMrLeZdTOzHmbWJ973K6Rac+65sMIKcMcd4f4ZZ4RE8IMfVDYu55xrRZoL0wQcCgwxs/MkrQX0NzO/OC3fhAmw//6N93fdFSZNCsnBOec6uDS/VH8ElgE7A+cBC4ErgK0yjKtzee65cLZQTm0tvPIK9O1buZicc65IaRLCNma2paQZAGb2oSSfjgvg3XfD1cVLEqOAv/ACbLxxRcKZOKOesZPnML9hEQNqaxg1Yigjhw1s+4nOOUe6TuXFcV5kA5DUj1Bj6Lq+/BK22w7WXLMxGdxzT+gnqGAyGDX+OeobFmFAfcMiRo1/jokz6isSj3Ou80mTEC4jzIG8hqRfA48C52caVUdlBiefDCuuCE88EcrGjg3le+xR0dDOufN5Fi9tOvX14qXGOXc+X6GInHOdTZr5EG6UNB3YBRAw0sxmZx5ZR3PttXD00Y33DzkkzFfQLU1Ozd6Hny0uqtw55/KlOcvoUuAWM7uiDPF0PI89Bttv33h//fXh2Wdh5ZUrF5NzzmUgTafyM8AvJW1IaDq6xcymZRtWB/DGG7DOOk3L5s1rXtZB1Nb0oGFR89pAbU2PCkTjnOuM0lyYdr2Z7QlsDbwEXCTp5cwjW04TZ9Qz/MIpDDnzboZfOCV95+qnn4ZhJpI//I88EvoJOmgyABizzyb06NZ0dJEe3cSYfTapUETOuc6mmAbw9YGNgMHAi5lEUyITZ9Qz+vZZTc64GX37rNaTwrJl4UriXr3CQHQAf/lLSATJJqMOauSwgYw9cHMG1tYgYGBtDWMP3NxPO3XOpZamD+EiYH/gFeBW4Lw4HHaHNXbyHBYtXtqkbNHipYydPKfwD+TFF8NppzXeP/FEuPTSTjc3wchhAz0BOOfaLU0fwmvAdmb2XjErltQTeBhYMW5nvJmdHYfC+BVwILAUuNLMLisu7NbNb1iUrnzSJNhzz8b7224LU6eG00qdc66LSXPa6Z8krSppa6BnovzhNp76BbCzmS2U1AN4VNIkYGPCnMwbxfkW1liO+AsaUFtDfYGkMKC2Jtx48cWmF5CtsALU18MaJQ/FOec6jTb7ECQdQzjSnwycE/+Paet5FiyMd3vEPwN+ApxrZsvicu+2K/JWjBoxlJoe3ZuU1fTozs+3WxNWXbVpMnj2WVi82JOBc67LS9OpfDJhILvXzWwnYBiwIM3KJXWX9CzwLnC/mT0JrAccLGmapEmSNmjhucfFZaYtWJBqc18ZOWwgF+y/6VcdrGv3/hoP3X8+e+34TWiI3R+33RY6jDffvKh1O+dctUqTED43s88BJK1oZi8CQ9Os3MyWmtkWwCBga0nfJPQpfG5mdcBVwDUtPHecmdWZWV2/fv3SbK6JkcMG8tiZO/OaHuXhX+7GGk8+Eh4YMyYkguQw1c4551J1Kr8pqRaYCNwv6UNgfjEbMbMGSVOB3YE3gdviQxOAa4tZV1Huvx8uvDDc/t73wnwF3bu3/hznnOui0lyYtp+ZNZjZGOB/gb8AI9t6nqR+MZEgqQbYlXD9wkTC3AoAOxAudsvG1luHU0obGsLsZZ4MnHOuRUVN5WVmDxWxeH/g+jh0djfgVjO7S9KjwI2STiFMtnNMMTEUZZVV4NRTM1u9c85Vk8zmdjSzmYQO6PzyBmCvrLbrnHOufTrG2M3OOecqzhOCc845wBOCc865yBOCc845wBOCc865yBOCc845wBOCc865yBOCc845wBOCc865yBOCc845wBOCc865yBOCc845IMPB7Vz5TZxRz9jJc5jfsIgBtTWMGjGUkcMGVjos51wn4QmhSkycUc/o22exaPFSAOobFjH69lkAnhScc6l4k1GVGDt5zlfJIGfR4qWMnTynQhE55zobTwhVYn7DoqLKnXMunyeEKjGgtqaocuecy+cJoUqMGjGUHt3UpKxHNzFqxNAKReSc62w8IVQTtXHfOeda4QmhSoydPIfFS61J2eKl5p3KzrnUPCFUCe9Uds4tL08IVcI7lZ1zy8sTQpUYNWIoNT26Nymr6dHdO5Wdc6n5lcpVInc1sg9d4ZxrL08IVWTksIGeAJxz7eZNRs4554AME4KknpKekvScpOclnZP3+OWSFma1feecc8XJssnoC2BnM1soqQfwqKRJZvaEpDqgNsNtO+ecK1JmNQQLcjWAHvHPJHUHxgKnZ7Vt55xzxcu0D0FSd0nPAu8C95vZk8AJwB1m9lYbzz1O0jRJ0xYsWJBlmM455wCZWdtLLe9GpFpgAnA2cD6wo5ktkbTQzHqleP4C4PWMw8zC6sB7lQ6ijLra/oLvc1fRWfd5HTPrl3bhspx2amYNkqYCOwHrA3MlAawkaa6Zrd/G81PvUEciaZqZ1VU6jnLpavsLvs9dRVfZ5yzPMuoXawZIqgF2Baab2dfNbLCZDQY+aysZOOecK48sawj9getjJ3I34FYzuyvD7TnnnFsOmSUEM5sJDGtjmTb7Dzq5cZUOoMy62v6C73NX0SX2uSydys455zo+H7rCOecc4AnBOedc5AmhBCRdI+ldSf/OKz9R0pw4ltNvKhVfFgrts6QtJD0h6dl4UeHWlYyx1CStJemfkmbH9/TkWN5X0v2SXo7/V610rKXQyv6OlfSipJmSJuTOJqwGLe1z4vHTJJmk1SsVY5a8D6EEJH0HWAj81cy+Gct2An4B7GVmX0haw8zerWScpdTCPt8H/N7MJknaEzjdzHasYJglJak/0N/MnpHUG5gOjASOAj4wswslnQmsamZnVDDUkmhlfwcBU+LFpRcBVMP+Qsv7bGYvSFoLuBrYCPiWmXXGC9Va5TWEEjCzh4EP8op/AlxoZl/EZaomGUCL+2xAn3h7FWB+WYPKmJm9ZWbPxNufALOBgcC+wPVxsesJP5qdXkv7a2b3mdmSuNgThARRFVp5jwF+TxiDrWqPoj0hZGdD4NuSnpT0kKStKh1QGfw/YKyk/wC/BUZXOJ7MSBpMOK36SWDN3Nhc8f8alYssG3n7m3Q0MKnc8ZRDcp8l7QPUm9lzFQ0qY54QsrMCsCqwLTAKuFVxvI4q9hPgFDNbCzgF+EuF48mEpF7AbcD/M7OPKx1P1lraX0m/AJYAN1Yqtqwk95mwj78AzqpoUGXgCSE7bwK3x2HAnwKWEQbIqmZHArfH238HqqpTGSDO7XEbcKOZ5fb1ndj2nGuDrprmwRb2F0lHAnsDh1qVdUQW2Of1gCHAc5LmEZrInpH09cpFmQ1PCNmZCOwMIGlD4Gt0ztESizEf2CHe3hl4uYKxlFys4f0FmG1mv0s8dAchGRL//6PcsWWhpf2VtDtwBrCPmX1WqfiyUGifzWyWma2RGIPtTWBLM3u7gqFmws8yKgFJNwM7EmoA7xCG+f4bcA2wBfAlcJqZTalUjKXWwj7PAS4lNJd9DvzUzKZXKsZSk7Q98Agwi1DjA/g5oV39VmBt4A3gQDPL73DvdFrZ38uAFYH3Y9kTZvbf5Y+w9FraZzO7J7HMPKCuGs8y8oTgnHMO8CYj55xzkScE55xzgCcE55xzkScE55xzgCcE55xzkScE5zIiqU7SZW0sMzh/lNzEY0dJGtDKcy+Jgwy2tv69JZ2TLmLX1XlCcC4jZjbNzE5ajlUcBRRMCJL6AtvGQQZbczewj6SVliMO10V4QnAdSjxiflHS1ZL+LelGSbtKeizON7B1XK6vpIlxTP4nJG0Wy8dIOi2xvn/Hda4s6W5Jz8Wyg+Pj34qDD06XNDk3BEXi+d0lvaqgVtKy3FG5pEckrR/XfY2kpyXNkLRvfHxHSXfF2/0U5kp4RtKfJb2eGFO/u6Sr4vj790mqkfR9oA64UWF+iZq8l+r7wL2JOPeMr9ujki7LbTcOKzGVMMyEc63yhOA6ovUJVzxvRhh7/ofA9sBphCtlAc4BZpjZZrHsr22sc3dgvpltHudvuDeOWXM58H0z+xbhyvJfJ59kZkuBl4BvxBimE0axXREYZGZzCQOfTTGzrYCdCCO+rpy3/bPjMlsCEwhXNedsAFxhZpsADcABZjYemEYYK2gLM1uUt77hMRYk9QT+DOxhZtsD/fKWnQZ8u43XxzlPCK5Dei2OH7MMeB54MB7pzgIGx2W2JwwPQhwSZDVJq7SyzlnArpIukvRtM/sIGAp8E7hf0rPALyk8tv8jwHfi3wVx21sBT8fHdwPOjOuYCvSk6Q9+Lt7/i/HeC3yYt7/PxtvTE/vYmv7Agnh7I+BVM3st3r85b9l3aaHpybmkFSodgHMFfJG4vSxxfxmNn9lCQ4kbYaji5IFOTwAze0nSt4A9gQsUZnebADxvZtu1Ec8jwH8TflTPIgxnviOQa78X4ah+TvJJktZM3m1l/cn9XQrkNw8Vsoi4b22sm7hcfg3DuWa8huA6q4eBQyG01QPvxbH65wFbxvItCcMWE8/W+czMbiBM3rMlYTC+fpK2i8v0kLRJgW09CfwXsMzMPgeeBY4nJAqAycCJufkuJA0rsI5HgYPi47sR5spoyydA7xYem01oWgN4EVhXYUIXgIPzlt0QKHgmk3NJnhBcZzUGqJM0E7iQxuGnbwP6xuabnxDa/wE2BZ6K5b8AfmVmXxI6Zy+S9Bzhh/6/8jcUp0H9D2G6SAiJoDehGQrgPKAHMDOeQnpegXjPAXaT9AywB/AW4Qe/NdcBf2qhU/luQi2F2L/wU0K/yKOE0Wc/Siy7U1zeuVb5aKfOlUHshF4aJ6bfDrjSzLZYznU+CuxtZg2SepnZwlhLuQJ42cx+H5utbjKzXZZ/L1y18z4E58pjbcI0qt0I82McW4J1/iyutwE4VmEWs68BMwhnHeW2+7MSbMt1AV5DcM45B3gfgnPOucgTgnPOOcATgnPOucgTgnPOOcATgnPOuej/A3+w4cvQ+1oaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot Joined\n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.xlabel('mouse weight')\n",
    "plt.ylabel('average tumor volume (mmm3)')\n",
    "plt.title('Average Tumor Volume vs. Mouse Weight for Capomulin')\n",
    "plt.show()\n",
    "\n",
    "y_values = [41.963636, 40.083699, 36.505973, 41.990097, 44.613344, 44.805810, 39.141053, 44.465236, 43.216925, 44.434474, 42.507261, 34.847470, 39.049816, 45.798970, 36.509212, 43.367364, 34.559143, 42.803733, 39.328725, 39.887495, 41.581595, 41.384825, 41.352452, 36.182040, 38.506829]\n",
    "x_values = [22, 21, 17, 19, 22, 24, 20, 23, 21, 21, 19, 17, 19, 25, 17, 25, 17, 23, 17, 17, 21, 23, 21, 15, 17]\n",
    "plt.scatter(x_values, y_values)\n",
    "model = st.linregress(each_mouse_weight,avg_tumor_vol)\n",
    "y_values = each_mouse_weight*model[0]+model[1]\n",
    "plt.plot(each_mouse_weight, y_values, color ='r')\n",
    "plt.xlabel('mouse weight (g)')\n",
    "plt.ylabel('average tumor volume (mm3)')\n",
    "plt.title('Average Tumor Volume vs. Mouse Weight for Capomulin ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}