pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations and Insights "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as sts\n",
    "import numpy as np\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "merged_df=pd.merge(mouse_metadata, study_results)\n",
    "\n",
    "# Display the data table for preview\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking the number of total mice.\n",
    "tot_mice=len(merged_df.loc[:, 'Mouse ID'].unique())\n",
    "\n",
    "# Find duplicated Mouse ID\n",
    "duplicated_id=merged_df.loc[merged_df.duplicated(subset=['Mouse ID', 'Timepoint']), 'Mouse ID']\n",
    "\n",
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "filter_1=merged_df.loc[:, 'Mouse ID']== 'g989'\n",
    "\n",
    "dropped_data_df=merged_df.loc[filter_1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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",
       "    </tr>\n",
       "    <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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "filter_1=merged_df.loc[:, 'Mouse ID']== 'g989'\n",
    "\n",
    "dropped_data_df=merged_df.loc[filter_1, :]\n",
    "dropped_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean dataframe by only including from the original mice whose id does not equal g989\n",
    "filter_2=merged_df.loc[:, 'Mouse ID']!='g989'\n",
    "merged_df_clean=merged_df.loc[filter_2, :]\n",
    "\n",
    "merged_df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of mice is 248\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame. \n",
    "tot_mice_clean=merged_df_clean.loc[:, 'Mouse ID'].nunique()\n",
    "print(f'The total number of mice is {tot_mice_clean}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get initial information on clean dataframe\n",
    "# merged_df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Mean Tumor Volume (mm3)</th>\n",
       "      <th>Median Tumor Volume (mm3)</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Standard Error of Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean Tumor Volume (mm3)  Median Tumor Volume (mm3)   Variance  \\\n",
       "Drug Regimen                                                                  \n",
       "Capomulin                   40.675741                  41.557809  24.947764   \n",
       "Ceftamin                    52.591172                  51.776157  39.290177   \n",
       "Infubinol                   52.884795                  51.820584  43.128684   \n",
       "Ketapril                    55.235638                  53.698743  68.553577   \n",
       "Naftisol                    54.331565                  52.509285  66.173479   \n",
       "Placebo                     54.033581                  52.288934  61.168083   \n",
       "Propriva                    52.320930                  50.446266  43.852013   \n",
       "Ramicane                    40.216745                  40.673236  23.486704   \n",
       "Stelasyn                    54.233149                  52.431737  59.450562   \n",
       "Zoniferol                   53.236507                  51.818479  48.533355   \n",
       "\n",
       "              Standard Deviation  Standard Error of Mean  \n",
       "Drug Regimen                                              \n",
       "Capomulin               4.994774                0.329346  \n",
       "Ceftamin                6.268188                0.469821  \n",
       "Infubinol               6.567243                0.492236  \n",
       "Ketapril                8.279709                0.603860  \n",
       "Naftisol                8.134708                0.596466  \n",
       "Placebo                 7.821003                0.581331  \n",
       "Propriva                6.622085                0.544332  \n",
       "Ramicane                4.846308                0.320955  \n",
       "Stelasyn                7.710419                0.573111  \n",
       "Zoniferol               6.966589                0.516398  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "drug_df=merged_df_clean.groupby('Drug Regimen')\n",
    "\n",
    "tv_mean=drug_df['Tumor Volume (mm3)'].mean()\n",
    "tv_median=drug_df['Tumor Volume (mm3)'].median()\n",
    "tv_var=drug_df['Tumor Volume (mm3)'].var()\n",
    "tv_std=drug_df['Tumor Volume (mm3)'].std()\n",
    "tv_sem=drug_df['Tumor Volume (mm3)'].sem()\n",
    "\n",
    "# Assemble the resulting series into a single summary dataframe.\n",
    "new_columns={\n",
    "    \"Mean Tumor Volume (mm3)\": tv_mean,\n",
    "    \"Median Tumor Volume (mm3)\": tv_median,\n",
    "    \"Variance\": tv_var,\n",
    "    \"Standard Deviation\": tv_std,\n",
    "    \"Standard Error of Mean\": tv_sem\n",
    "}\n",
    "\n",
    "drug_summary_df=pd.DataFrame(new_columns)\n",
    "\n",
    "drug_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Tumor Volume (mm3)</th>\n",
       "      <th>Median Tumor Volume (mm3)</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Standard Error of Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean Tumor Volume (mm3)  Median Tumor Volume (mm3)   Variance  \\\n",
       "Drug Regimen                                                                  \n",
       "Capomulin                   40.675741                  41.557809  24.947764   \n",
       "Ceftamin                    52.591172                  51.776157  39.290177   \n",
       "Infubinol                   52.884795                  51.820584  43.128684   \n",
       "Ketapril                    55.235638                  53.698743  68.553577   \n",
       "Naftisol                    54.331565                  52.509285  66.173479   \n",
       "Placebo                     54.033581                  52.288934  61.168083   \n",
       "Propriva                    52.320930                  50.446266  43.852013   \n",
       "Ramicane                    40.216745                  40.673236  23.486704   \n",
       "Stelasyn                    54.233149                  52.431737  59.450562   \n",
       "Zoniferol                   53.236507                  51.818479  48.533355   \n",
       "\n",
       "              Standard Deviation  Standard Error of Mean  \n",
       "Drug Regimen                                              \n",
       "Capomulin               4.994774                0.329346  \n",
       "Ceftamin                6.268188                0.469821  \n",
       "Infubinol               6.567243                0.492236  \n",
       "Ketapril                8.279709                0.603860  \n",
       "Naftisol                8.134708                0.596466  \n",
       "Placebo                 7.821003                0.581331  \n",
       "Propriva                6.622085                0.544332  \n",
       "Ramicane                4.846308                0.320955  \n",
       "Stelasyn                7.710419                0.573111  \n",
       "Zoniferol               6.966589                0.516398  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the aggregation method, produce the same summary statistics in a single line\n",
    "agg_summary_df=merged_df_clean.groupby('Drug Regimen')['Tumor Volume (mm3)'].agg(['mean', 'median', 'var', 'std', 'sem'])\n",
    "\n",
    "\n",
    "# Rename columns\n",
    "new_columns={\n",
    "    \"mean\":\"Mean Tumor Volume (mm3)\",\n",
    "    \"median\": \"Median Tumor Volume (mm3)\",\n",
    "    \"var\": \"Variance\",\n",
    "    \"std\": \"Standard Deviation\",\n",
    "    \"sem\": \"Standard Error of Mean\"\n",
    "}\n",
    "\n",
    "agg_df_renamed=agg_summary_df.rename(columns=new_columns)\n",
    "agg_df_renamed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of measurements taken on each drug regimen using pandas.\n",
    "\n",
    "# Pull a series representing number of measurements by drug regimen\n",
    "drug_count=drug_df.count()['Mouse ID']\n",
    "\n",
    "# Plot using pandas\n",
    "drug_count.plot(kind='bar', figsize=(10, 6), ylabel='Number of Measurements', title='Measurements by Drug Regimen', color=\"green\",fontsize=9)\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of measurements taken on each drug regimen using pyplot.\n",
    "\n",
    "x_axis=np.arange(len(merged_df_clean.loc[:, \"Drug Regimen\"].unique()))\n",
    "y_axis=drug_count\n",
    "tick_locations = [value for value in x_axis]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x_axis, y_axis, width=0.45, color='g',align='center')\n",
    "plt.xticks(tick_locations, merged_df_clean['Drug Regimen'].unique(), rotation='vertical')\n",
    "plt.xlim(-0.5, len(x_axis)-0.5)\n",
    "plt.ylim(0, max(drug_count)+50)\n",
    "plt.title('Measurement Count by Drug Regimen')\n",
    "plt.xlabel('Drug Regimen')\n",
    "plt.ylabel('Number of Measurements')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "\n",
    "sex_count_df=merged_df_clean[['Mouse ID', 'Sex']].groupby('Sex').count()\n",
    "\n",
    "new_columns={\n",
    "    \"Mouse ID\":\"Number of Mice\"\n",
    "}\n",
    "\n",
    "sex_count_df_renamed=sex_count_df.rename(columns=new_columns)\n",
    "\n",
    "# Create pie plot\n",
    "sex_count_df_renamed.plot(y=\"Number of Mice\", kind='pie', title=\"Sex of Mice\", autopct='%1.1f%%', legend=False)\n",
    "plt.ylabel(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "labels = sex_count_df_renamed.index\n",
    "sizes = sex_count_df_renamed['Number of Mice']\n",
    "colors = ['tab:blue', 'tab:orange']\n",
    "plt.pie(sizes,labels=labels, colors=colors, autopct=\"%1.1f%%\")\n",
    "plt.title('Sex of Mice')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint_y</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Drug Regimen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>Infubinol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>Infubinol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>45</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>Placebo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>Ceftamin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>30</td>\n",
       "      <td>63.440686</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint_y  Tumor Volume (mm3) Drug Regimen\n",
       "0     a203           45           67.973419    Infubinol\n",
       "1     a251           45           65.525743    Infubinol\n",
       "2     a262           45           70.717621      Placebo\n",
       "3     a275           45           62.999356     Ceftamin\n",
       "4     a366           30           63.440686     Stelasyn"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pull a reference df that you will later use for merging\n",
    "mouse_drug_df=merged_df_clean.loc[:, ['Mouse ID', 'Drug Regimen']].drop_duplicates()\n",
    "\n",
    "\n",
    "# Find max timepoint to find end tumor values, merge with original df\n",
    "mouse_last_measure_df=merged_df_clean.loc[:, ['Mouse ID', 'Timepoint']].groupby('Mouse ID').max()\n",
    "mouse_last_measure_df_vol=pd.merge(mouse_last_measure_df, merged_df_clean[['Mouse ID', 'Timepoint', 'Tumor Volume (mm3)']], left_index=True, right_on='Mouse ID')\n",
    "\n",
    "\n",
    "# Create a filter to only show the rows where timepoint y = final timepoint, and fill in tumor volume\n",
    "filter_df=mouse_last_measure_df_vol['Timepoint_x']==mouse_last_measure_df_vol['Timepoint_y']\n",
    "mouse_last_measure_df_with_volume=mouse_last_measure_df_vol[filter_df][['Mouse ID', 'Timepoint_y', 'Tumor Volume (mm3)']]\n",
    "\n",
    "\n",
    "mouse_final_tumor_df=pd.merge(mouse_last_measure_df_with_volume, mouse_drug_df)\n",
    "mouse_final_tumor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "\n",
      "Ramicane's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "\n",
      "Infubinol's potential outliers: 31    36.321346\n",
      "Name: Tumor Volume (mm3), dtype: float64\n",
      "\n",
      "Ceftamin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Loop through df to create lists of tumor volumes for each drug,\n",
    "# calculate quartiles and outliers for each treatment type using the lists\n",
    "\n",
    "treatment_list= ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "tumor_vol_list= []\n",
    "\n",
    "for each_drug in treatment_list:\n",
    "    final_tumor_volume=mouse_final_tumor_df.loc[mouse_final_tumor_df['Drug Regimen']==each_drug, 'Tumor Volume (mm3)']\n",
    "    \n",
    "    tumor_vol_list.append(final_tumor_volume)\n",
    "    \n",
    "    \n",
    "    quartiles = final_tumor_volume.quantile([.25, .5, .75])\n",
    "    lower_q = quartiles[0.25]\n",
    "    upper_q = quartiles[0.75]\n",
    "    iqr = upper_q - lower_q\n",
    "    \n",
    "    lower_bound = lower_q - (1.5*iqr)\n",
    "    upper_bound = upper_q + (1.5*iqr)\n",
    "    \n",
    "    print(f\"{each_drug}'s potential outliers: {final_tumor_volume.loc[(final_tumor_volume < lower_bound) | (final_tumor_volume > upper_bound)]}\")\n",
    "    print()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "\n",
    "treatment_list= ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "tick_locations = [1, 2, 3, 4]\n",
    "outliers = dict(marker='^', markerfacecolor='blue',markersize=7,linestyle='none')\n",
    "\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Final Tumor Volumes of Mice')\n",
    "ax1.set_ylabel('Final Tumor Volume (mm3)')\n",
    "ax1.set_xlabel('Drug Regimen')\n",
    "ax1.boxplot(tumor_vol_list, flierprops=outliers)\n",
    "plt.xticks(tick_locations, labels=treatment_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * According to the final mean tumor volume, mice treated with Ramicane had the lowest final mean tumor volume (40.216745 mm3), followed by mice treated with Capomulin (40.675741 mm3).\n",
    "\n",
    "\n",
    " * Infubinol and Ceftamin had a greater final average tumor volumes than Capomulin and Ramicane. While Infubinol had one potential outlier with an average tumor     volume of 36.321346 mm3, the final tumor volumes tended to be greater than those of the two leading drugs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pull time and tumor volume for one mouse id that also is capomulin\n",
    "merged_df_clean.head()\n",
    "capomulin_filter=merged_df_clean.loc[:, 'Drug Regimen']=='Capomulin'\n",
    "\n",
    "cap_mice=merged_df_clean.loc[capomulin_filter, 'Mouse ID']\n",
    "\n",
    "cap_mouse_filter=merged_df_clean.loc[:, 'Mouse ID']=='s185'\n",
    "cap_mouse_df=merged_df_clean.loc[cap_mouse_filter,]\n",
    "cap_mouse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate line plot of tumor volume vs time point for mouse s185\n",
    "\n",
    "y_axis=cap_mouse_df['Tumor Volume (mm3)']\n",
    "x_axis=cap_mouse_df['Timepoint']\n",
    "\n",
    "plt.plot(x_axis, y_axis, marker='^', color='purple')\n",
    "plt.ylabel('Tumor Volume (mm3)')\n",
    "plt.xlabel('Timepoint')\n",
    "plt.title('Tumor progression for mouse s185 on Capomulin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * For mouse s=185, treated with Capomulin, as time progressed tumor volume decreased at a nearly linear rate. When combining this observation with final average tumor volume for Capomulin, it can be inferred that this drug regimen is one of the more effective in this study for decreasing tumor volume. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "capomulin_filter_1=merged_df_clean.loc[:, 'Drug Regimen']=='Capomulin'\n",
    "capomulin_df=merged_df_clean.loc[capomulin_filter_1, :]\n",
    "\n",
    "average_tumor_volume=capomulin_df.groupby('Mouse ID')['Tumor Volume (mm3)'].mean()\n",
    "\n",
    "mouse_weight=capomulin_df.groupby('Mouse ID')['Weight (g)'].mean()\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(x=mouse_weight, y=average_tumor_volume, marker='o', facecolors='blue', edgecolors='black', alpha=0.50)\n",
    "plt.xlabel('Mouse Weight (g)')\n",
    "plt.ylabel('Average Tumor Volume (mm3)')\n",
    "plt.title('Average Tumor Volume by Weight of Mouse treated by Capomulin')\n",
    "plt.xlim(14, 27)\n",
    "plt.ylim(33, 47)\n",
    "\n",
    "\n",
    "# Calculate correlation coefficient and linear regression model\n",
    "x_values = mouse_weight\n",
    "y_values = average_tumor_volume\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = sts.linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "correlation = sts.pearsonr(x_values, y_values) \n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "plt.plot(x_values, regress_values, \"r-\")\n",
    "plt.annotate(line_eq,(20,36),fontsize=10,color=\"red\")\n",
    "\n",
    "print(f'The correlation between both factors is {round(correlation[0],2)}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * According to the scatter plot, regression line, and correlation coefficient comparing average tumor volume by weight of mouse treated with Capomulin,\n",
    "  * There is a strong correlation beween mouse weight and average tumor volume. As mouse weight increases, tumor volume increases. This makes sense, as a larger mouse would logically be able to have a larger tumor. It may be more beneficial to analysis to compare weight with final tumor volume to analyze potential benefits of treatment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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