Pymaceuticals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8302e130",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d11ee3",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "* Of the top four drugs that were analyzed in the dataset, Capomulin and Ramicane both appeared to be most effective at reducing tumor volumes. As well, in taking a look at the statistical data, there is less variance from the mean of the tumor volumes of Capomulin and Ramicane in comparison to the other drugs. This is further depicted when analyzing the data of the interquartile ranges and boxplots produced. \n",
    "\n",
    "* For Capomulin, there is a strong positive correlation between the moust weight and average tumor volume, indicating that as weight goes up, the average tumor volume goes up. Specifically for Mouse l509 treated with Capomulin, we see an initial slight increase in tumor volume, however, there is a decline in tumor volume as time goes on. \n",
    "\n",
    "* Evidence can presume that the drugs would be effective on both males and females, as the distribution was nearly 50/50. \n",
    "\n",
    "* Further considerations for ongoing studies and/or trials: Evaluation and analysis over longer treatment timepoints of Capomulin and Ramicane and analyzing if the data obtained is demonstrating statistical significance of tumor volumes, effects of diet and/or exercise on mice in conjunction with the drug, upon ceasing drug administration do tumor volumes change and hormonal effects on drug regimens (i.e., are there potential discrepancies between males and females based off of hormonal regulation)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd60589",
   "metadata": {},
   "source": [
    "## Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d07662f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import and dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "from scipy.stats import linregress\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ea9032d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "464f589d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV files\n",
    "mousefile = \"Resources/Mouse_metadata.csv\"\n",
    "studyfile = \"Resources/Study_results.csv\"\n",
    "\n",
    "mouse_df = pd.read_csv(mousefile)\n",
    "study_df = pd.read_csv(studyfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37bfc278",
   "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",
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       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>1893 rows × 8 columns</p>\n",
       "</div>"
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       "     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",
       "[1893 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge CSV file into single DataFrame\n",
    "py_df = pd.merge(mouse_df,study_df, on=[\"Mouse ID\",\"Mouse ID\"], how=\"left\")\n",
    "py_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f2ab152f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for unique mice IDs and show number of unique\n",
    "mice_id = py_df[\"Mouse ID\"].nunique()\n",
    "mice_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "63760ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID              1893\n",
       "Drug Regimen          1893\n",
       "Sex                   1893\n",
       "Age_months            1893\n",
       "Weight (g)            1893\n",
       "Timepoint             1893\n",
       "Tumor Volume (mm3)    1893\n",
       "Metastatic Sites      1893\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e495545b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>10</td>\n",
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       "      <td>g989</td>\n",
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       "      <td>26</td>\n",
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       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
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       "      <td>15</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "    </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",
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      "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",
       "\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  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check mouse ID for duplicate time points\n",
    "duplicate_row_df = py_df[py_df.duplicated(['Mouse ID', 'Timepoint'],keep=False)]\n",
    "duplicate_row_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6c9631fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the duplicated Mouse ID\n",
    "duplicated_mouse_ids = duplicate_row_df['Mouse ID'].unique()\n",
    "duplicated_mouse_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e36ebe8f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>g989</td>\n",
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       "      <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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the duplicates\n",
    "duplicated_mouse_row_s = py_df[\"Mouse ID\"].isin(duplicated_mouse_ids)\n",
    "py_df[duplicated_mouse_row_s]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9b0fc00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reveals total amount of duplicates\n",
    "py_df.duplicated(['Mouse ID', 'Timepoint']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "547a1aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaned DataFrame - remove the duplicates by taking out the duplicated rows \n",
    "clean_pydf = py_df[~duplicated_mouse_row_s]\n",
    "clean_pydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0a4fce76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mouse ID              1880\n",
       "Drug Regimen          1880\n",
       "Sex                   1880\n",
       "Age_months            1880\n",
       "Weight (g)            1880\n",
       "Timepoint             1880\n",
       "Tumor Volume (mm3)    1880\n",
       "Metastatic Sites      1880\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_pydf.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "087fba5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display updated number of unique mice IDs\n",
    "clean_miceid = clean_pydf[\"Mouse ID\"].nunique()\n",
    "clean_miceid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886fa3d5",
   "metadata": {},
   "source": [
    "# Generate Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3ef17413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Tumor Volume</th>\n",
       "      <th>Median Tumor Volume</th>\n",
       "      <th>Tumor Volume Variance</th>\n",
       "      <th>Tumor Volume Std. Dev.</th>\n",
       "      <th>Tumor Volume Std. Err.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean Tumor Volume  Median Tumor Volume  Tumor Volume Variance  \\\n",
       "Drug Regimen                                                                  \n",
       "Capomulin             40.675741            41.557809              24.947764   \n",
       "Ceftamin              52.591172            51.776157              39.290177   \n",
       "Infubinol             52.884795            51.820584              43.128684   \n",
       "Ketapril              55.235638            53.698743              68.553577   \n",
       "Naftisol              54.331565            52.509285              66.173479   \n",
       "Placebo               54.033581            52.288934              61.168083   \n",
       "Propriva              52.320930            50.446266              43.852013   \n",
       "Ramicane              40.216745            40.673236              23.486704   \n",
       "Stelasyn              54.233149            52.431737              59.450562   \n",
       "Zoniferol             53.236507            51.818479              48.533355   \n",
       "\n",
       "              Tumor Volume Std. Dev.  Tumor Volume Std. Err.  \n",
       "Drug Regimen                                                  \n",
       "Capomulin                   4.994774                0.329346  \n",
       "Ceftamin                    6.268188                0.469821  \n",
       "Infubinol                   6.567243                0.492236  \n",
       "Ketapril                    8.279709                0.603860  \n",
       "Naftisol                    8.134708                0.596466  \n",
       "Placebo                     7.821003                0.581331  \n",
       "Propriva                    6.622085                0.544332  \n",
       "Ramicane                    4.846308                0.320955  \n",
       "Stelasyn                    7.710419                0.573111  \n",
       "Zoniferol                   6.966589                0.516398  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate summary statistics table - mean, median, variance, standard deviation and SEM of tumor volume for each drug\n",
    "\n",
    "# Can use describe function to pull data - know describe returns mean (mean), mean (50%), standard deviation (std)\n",
    "stats_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].describe()\n",
    "\n",
    "# Calculate variance\n",
    "var_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].var()\n",
    "\n",
    "# Calculate SEM \n",
    "sem_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].sem()\n",
    "\n",
    "# Create Statistical DataFrame\n",
    "stats = {'Mean Tumor Volume':stats_drug['mean'], 'Median Tumor Volume':stats_drug['50%'], 'Tumor Volume Variance':var_drug, 'Tumor Volume Std. Dev.': stats_drug['std'], 'Tumor Volume Std. Err.':sem_drug}\n",
    "stats_df = pd.DataFrame(stats)\n",
    "stats_df\n",
    "\n",
    "\n",
    "# ** OF NOTE - could have used below, however, longer to compute - provides same information as using describe function\n",
    "    # and pulling appropriate column from describe\n",
    "# # Calculate mean\n",
    "# mean_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].mean()\n",
    "\n",
    "# # Calculate median\n",
    "# med_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].median()\n",
    "\n",
    "# # Calculate standard dev\n",
    "# std_drug = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1d7a27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_788ab\">\n",
       "  <caption>Tumor Volume (mm3)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_788ab_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
       "      <th id=\"T_788ab_level0_col1\" class=\"col_heading level0 col1\" >median</th>\n",
       "      <th id=\"T_788ab_level0_col2\" class=\"col_heading level0 col2\" >var</th>\n",
       "      <th id=\"T_788ab_level0_col3\" class=\"col_heading level0 col3\" >std</th>\n",
       "      <th id=\"T_788ab_level0_col4\" class=\"col_heading level0 col4\" >sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Drug Regimen</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row0\" class=\"row_heading level0 row0\" >Capomulin</th>\n",
       "      <td id=\"T_788ab_row0_col0\" class=\"data row0 col0\" >40.675741</td>\n",
       "      <td id=\"T_788ab_row0_col1\" class=\"data row0 col1\" >41.557809</td>\n",
       "      <td id=\"T_788ab_row0_col2\" class=\"data row0 col2\" >24.947764</td>\n",
       "      <td id=\"T_788ab_row0_col3\" class=\"data row0 col3\" >4.994774</td>\n",
       "      <td id=\"T_788ab_row0_col4\" class=\"data row0 col4\" >0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row1\" class=\"row_heading level0 row1\" >Ceftamin</th>\n",
       "      <td id=\"T_788ab_row1_col0\" class=\"data row1 col0\" >52.591172</td>\n",
       "      <td id=\"T_788ab_row1_col1\" class=\"data row1 col1\" >51.776157</td>\n",
       "      <td id=\"T_788ab_row1_col2\" class=\"data row1 col2\" >39.290177</td>\n",
       "      <td id=\"T_788ab_row1_col3\" class=\"data row1 col3\" >6.268188</td>\n",
       "      <td id=\"T_788ab_row1_col4\" class=\"data row1 col4\" >0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row2\" class=\"row_heading level0 row2\" >Infubinol</th>\n",
       "      <td id=\"T_788ab_row2_col0\" class=\"data row2 col0\" >52.884795</td>\n",
       "      <td id=\"T_788ab_row2_col1\" class=\"data row2 col1\" >51.820584</td>\n",
       "      <td id=\"T_788ab_row2_col2\" class=\"data row2 col2\" >43.128684</td>\n",
       "      <td id=\"T_788ab_row2_col3\" class=\"data row2 col3\" >6.567243</td>\n",
       "      <td id=\"T_788ab_row2_col4\" class=\"data row2 col4\" >0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row3\" class=\"row_heading level0 row3\" >Ketapril</th>\n",
       "      <td id=\"T_788ab_row3_col0\" class=\"data row3 col0\" >55.235638</td>\n",
       "      <td id=\"T_788ab_row3_col1\" class=\"data row3 col1\" >53.698743</td>\n",
       "      <td id=\"T_788ab_row3_col2\" class=\"data row3 col2\" >68.553577</td>\n",
       "      <td id=\"T_788ab_row3_col3\" class=\"data row3 col3\" >8.279709</td>\n",
       "      <td id=\"T_788ab_row3_col4\" class=\"data row3 col4\" >0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row4\" class=\"row_heading level0 row4\" >Naftisol</th>\n",
       "      <td id=\"T_788ab_row4_col0\" class=\"data row4 col0\" >54.331565</td>\n",
       "      <td id=\"T_788ab_row4_col1\" class=\"data row4 col1\" >52.509285</td>\n",
       "      <td id=\"T_788ab_row4_col2\" class=\"data row4 col2\" >66.173479</td>\n",
       "      <td id=\"T_788ab_row4_col3\" class=\"data row4 col3\" >8.134708</td>\n",
       "      <td id=\"T_788ab_row4_col4\" class=\"data row4 col4\" >0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row5\" class=\"row_heading level0 row5\" >Placebo</th>\n",
       "      <td id=\"T_788ab_row5_col0\" class=\"data row5 col0\" >54.033581</td>\n",
       "      <td id=\"T_788ab_row5_col1\" class=\"data row5 col1\" >52.288934</td>\n",
       "      <td id=\"T_788ab_row5_col2\" class=\"data row5 col2\" >61.168083</td>\n",
       "      <td id=\"T_788ab_row5_col3\" class=\"data row5 col3\" >7.821003</td>\n",
       "      <td id=\"T_788ab_row5_col4\" class=\"data row5 col4\" >0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row6\" class=\"row_heading level0 row6\" >Propriva</th>\n",
       "      <td id=\"T_788ab_row6_col0\" class=\"data row6 col0\" >52.320930</td>\n",
       "      <td id=\"T_788ab_row6_col1\" class=\"data row6 col1\" >50.446266</td>\n",
       "      <td id=\"T_788ab_row6_col2\" class=\"data row6 col2\" >43.852013</td>\n",
       "      <td id=\"T_788ab_row6_col3\" class=\"data row6 col3\" >6.622085</td>\n",
       "      <td id=\"T_788ab_row6_col4\" class=\"data row6 col4\" >0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row7\" class=\"row_heading level0 row7\" >Ramicane</th>\n",
       "      <td id=\"T_788ab_row7_col0\" class=\"data row7 col0\" >40.216745</td>\n",
       "      <td id=\"T_788ab_row7_col1\" class=\"data row7 col1\" >40.673236</td>\n",
       "      <td id=\"T_788ab_row7_col2\" class=\"data row7 col2\" >23.486704</td>\n",
       "      <td id=\"T_788ab_row7_col3\" class=\"data row7 col3\" >4.846308</td>\n",
       "      <td id=\"T_788ab_row7_col4\" class=\"data row7 col4\" >0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row8\" class=\"row_heading level0 row8\" >Stelasyn</th>\n",
       "      <td id=\"T_788ab_row8_col0\" class=\"data row8 col0\" >54.233149</td>\n",
       "      <td id=\"T_788ab_row8_col1\" class=\"data row8 col1\" >52.431737</td>\n",
       "      <td id=\"T_788ab_row8_col2\" class=\"data row8 col2\" >59.450562</td>\n",
       "      <td id=\"T_788ab_row8_col3\" class=\"data row8 col3\" >7.710419</td>\n",
       "      <td id=\"T_788ab_row8_col4\" class=\"data row8 col4\" >0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_788ab_level0_row9\" class=\"row_heading level0 row9\" >Zoniferol</th>\n",
       "      <td id=\"T_788ab_row9_col0\" class=\"data row9 col0\" >53.236507</td>\n",
       "      <td id=\"T_788ab_row9_col1\" class=\"data row9 col1\" >51.818479</td>\n",
       "      <td id=\"T_788ab_row9_col2\" class=\"data row9 col2\" >48.533355</td>\n",
       "      <td id=\"T_788ab_row9_col3\" class=\"data row9 col3\" >6.966589</td>\n",
       "      <td id=\"T_788ab_row9_col4\" class=\"data row9 col4\" >0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7c600958b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate summary statistics table - mean, median, variance, standard deviation and SEM of tumor volume for each drug\n",
    "\n",
    "# Use aggregation method to produce same summary statistics in a single line\n",
    "stats_df_agg = clean_pydf.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].agg(['mean', 'median', 'var', 'std', 'sem'])\n",
    "\n",
    "stats_df_agg.style.set_caption('Tumor Volume (mm3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83759087",
   "metadata": {},
   "source": [
    "# Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "89d992af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart using Pandas DataFrame.plot() to show total number of time points for all mice tested for each drug regimen\n",
    "\n",
    "# Create DataFrame in order to plot - value_count only used on one column in dataframe.\n",
    "\n",
    "tp_count = clean_pydf.groupby(['Drug Regimen'])['Timepoint'].count().sort_values(ascending=False)\n",
    "\n",
    "tp_count.plot(kind = 'bar', figsize=(9,5)) \n",
    "\n",
    "# Labels\n",
    "plt.title(\"Total Number of Time Points per Drug Regimen\")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Time Points\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84eb058c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart using Matplotlib's pyplot to show total number of time points for all mice tested for each drug regimen\n",
    "drug_count_s = clean_pydf.groupby(['Drug Regimen'])['Timepoint'].count().sort_values(ascending=False)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(9,4))\n",
    "plt.bar(drug_count_s.index, drug_count_s.values, color='b', alpha=0.5, align='center')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Total Number of Time Points per Drug Regimen\")\n",
    "plt.xlabel(\"Drug Regimen\")\n",
    "plt.ylabel(\"Number of Time Points\")\n",
    "\n",
    "plt.xlim(-0.75, len(drug_count_s.index)-0.25)\n",
    "plt.ylim(0, max(drug_count_s.values)+10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffd7ed4",
   "metadata": {},
   "source": [
    "# Pie Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a2eb00b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of males versus females in the mice study using Pandas\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "sex = clean_pydf['Sex'].value_counts()\n",
    "\n",
    "colors = ['blue','pink']\n",
    "\n",
    "explode = (0.1, 0)\n",
    "\n",
    "# Pie plot\n",
    "plot = sex.plot.pie(explode=explode, figsize=(5, 5),autopct=\"%1.1f%%\", title='Distribution by Sex', \n",
    "                      colors=colors, shadow=True, startangle=90)\n",
    "\n",
    "# Hide y label\n",
    "plot.set_ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a91b0ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of males versus females in the mice study using Matplotlib\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "sex = clean_pydf['Sex'].value_counts()\n",
    "\n",
    "# Parameters\n",
    "colors = [\"blue\", \"pink\"]\n",
    "explode = (0.1, 0)\n",
    "plt.axis(\"equal\")\n",
    "plt.title('Distribution by Sex')\n",
    "\n",
    "# Pie plot\n",
    "plt.pie(sex, explode=explode, labels=sex.index.values, colors=colors,\n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ad3e74",
   "metadata": {},
   "source": [
    "# Quartiles, Outliers and Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aaaa286e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>1</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>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>63.440686</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>243</th>\n",
       "      <td>z435</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>48.710661</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</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>245</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>246</th>\n",
       "      <td>z795</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>65.741070</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</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>248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0       a203    Infubinol  Female          20          23         45   \n",
       "1       a251    Infubinol  Female          21          25         45   \n",
       "2       a262      Placebo  Female          17          29         45   \n",
       "3       a275     Ceftamin  Female          20          28         45   \n",
       "4       a366     Stelasyn  Female          16          29         30   \n",
       "..       ...          ...     ...         ...         ...        ...   \n",
       "243     z435     Propriva  Female          12          26         10   \n",
       "244     z578     Ramicane    Male          11          16         45   \n",
       "245     z581    Infubinol  Female          24          25         45   \n",
       "246     z795     Naftisol  Female          13          29         45   \n",
       "247     z969     Naftisol    Male           9          30         45   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "0             67.973419                 2  \n",
       "1             65.525743                 1  \n",
       "2             70.717621                 4  \n",
       "3             62.999356                 3  \n",
       "4             63.440686                 1  \n",
       "..                  ...               ...  \n",
       "243           48.710661                 0  \n",
       "244           30.638696                 0  \n",
       "245           62.754451                 3  \n",
       "246           65.741070                 3  \n",
       "247           73.867845                 4  \n",
       "\n",
       "[248 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate final tumor volume of each mouse across - Capomulin, Ramicane, Infubinol, Ceftamin\n",
    "\n",
    "# Find last timepoint (greatest time = max time) for each mouse ID & create dataframe\n",
    "maxtime = clean_pydf.groupby(['Mouse ID'])['Timepoint'].max()\n",
    "maxtime_df = pd.DataFrame(maxtime)\n",
    "\n",
    "# Merge max dataframe on clean dataframe - place timepoint ON mouse ID to have ONLY max timepoints \n",
    "last_time_df = pd.merge(clean_pydf, maxtime_df, on=('Mouse ID', 'Timepoint'), how='right')\n",
    "last_time_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ef8c61d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR for Capomulin: 7.781863460000004.\n",
      "Lower Bound for Capomulin: 20.70456164999999.\n",
      "Upper Bound for Capomulin: 51.83201549.\n",
      "Number of outliers for Capomulin: 0.\n",
      "IQR for Ramicane: 9.098536719999998.\n",
      "Lower Bound for Ramicane: 17.912664470000003.\n",
      "Upper Bound for Ramicane: 54.30681135.\n",
      "Number of outliers for Ramicane: 0.\n",
      "IQR for Infubinol: 11.477135160000003.\n",
      "Lower Bound for Infubinol: 36.83290494999999.\n",
      "Upper Bound for Infubinol: 82.74144559000001.\n",
      "Number of outliers for Infubinol: 1.\n",
      "IQR for Ceftamin: 15.577752179999997.\n",
      "Lower Bound for Ceftamin: 25.355449580000002.\n",
      "Upper Bound for Ceftamin: 87.66645829999999.\n",
      "Number of outliers for Ceftamin: 0.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "\n",
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "drug = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "drug_values = []\n",
    "\n",
    "# Locate each drug from our list 'drugs' in the orginal dataframe\n",
    "for drug in drug:\n",
    "    \n",
    "    temp_df = last_time_df.loc[last_time_df['Drug Regimen'] == drug]\n",
    "\n",
    "    # Create subset dataframes that narrow down to rows only containing the final timepoint for each regimen\n",
    "    final_volume_df = temp_df.loc[temp_df['Timepoint'] == temp_df['Timepoint']]\n",
    "    \n",
    "    # Create a series that consists of all final volume values for each regimen\n",
    "    values = final_volume_df['Tumor Volume (mm3)']\n",
    "    drug_values.append(values)\n",
    "    \n",
    "    # Calculate and print the interquartile range (IQR) for each regimen\n",
    "    quartiles = values.quantile([.25,.5,.75])\n",
    "    lowerq = quartiles[0.25]\n",
    "    upperq = quartiles[0.75]\n",
    "    iqr = upperq-lowerq\n",
    "    print(f'IQR for {drug}: {iqr}.')\n",
    "    \n",
    "    # Find upper and lower bounds to help identify outliers for each regimen\n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "    print(f'Lower Bound for {drug}: {lower_bound}.')\n",
    "    print(f'Upper Bound for {drug}: {upper_bound}.')\n",
    "    \n",
    "    # Quantitatively check for outliers\n",
    "    outliers_count = (values.loc[(final_volume_df['Tumor Volume (mm3)'] >= upper_bound) | \n",
    "                                        (final_volume_df['Tumor Volume (mm3)'] <= lower_bound)]).count()\n",
    "    print(f'Number of outliers for {drug}: {outliers_count}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7cbbc4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot that shows the distrubution of the tumor volume for each treatment group.\n",
    "\n",
    "# X-axis is list of top drug regimen, y-axis is the drug_values obtained from above\n",
    "drug = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "vols = drug_values\n",
    "\n",
    "\n",
    "# Edit characteristic of outlier\n",
    "flierprops = dict(marker='o', markerfacecolor='r', markersize=8, markeredgecolor='black')\n",
    "\n",
    "\n",
    "# Create boxplot and label\n",
    "plt.boxplot(vols, flierprops=flierprops)\n",
    "\n",
    "plt.title('Final Tumor Volumes for Top Drug Regimens')\n",
    "plt.xticks([1,2,3,4], drug)\n",
    "plt.xlabel('Drug Regimen')\n",
    "plt.ylabel('Final Tumor Volume (mm3)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c4d73c",
   "metadata": {},
   "source": [
    "# Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "94de6824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of tumor volume vs. time point for a mouse treated with Capomulin - l509\n",
    "\n",
    "# Find data points of mouse 'l509' in Capomulin\n",
    "mouse = clean_pydf.loc[clean_pydf[\"Mouse ID\"] == \"l509\"]\n",
    "\n",
    "# defined the x axis list by calling the timepoints from the l509 dataframe\n",
    "TP_xaxis = mouse[\"Timepoint\"] \n",
    "\n",
    "# defined the y axis or tumor size list by calling the tumor size from the dataframe\n",
    "TV_yaxis = mouse[\"Tumor Volume (mm3)\"] \n",
    "\n",
    "\n",
    "# Plot to line chart and labels\n",
    "plt.plot(TP_xaxis, TV_yaxis,linewidth=2, markersize=10) \n",
    "plt.title('Capomulin Treatmeant of Mouse l509')\n",
    "plt.xlabel('Time Point (Days)')\n",
    "plt.ylabel('Tumor Volume (mm3)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4198d67",
   "metadata": {},
   "source": [
    "# Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c88a987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "\n",
    "# Find cases of only Capomulin\n",
    "cap = clean_pydf.loc[clean_pydf[\"Drug Regimen\"] == \"Capomulin\"]\n",
    "\n",
    "# use groupby() and mean() to create a dataframe\n",
    "cap_df = cap.groupby(['Mouse ID']).mean()\n",
    "\n",
    "# Define x_axis by weight of each mouse in Capomulin\n",
    "x_cap = cap_df['Weight (g)']\n",
    "\n",
    "# Define y_axis by tumor volume of each mouse in Capomulin\n",
    "y_cap = cap_df['Tumor Volume (mm3)']\n",
    "\n",
    "\n",
    "# Create scatter plot and labels\n",
    "plt.scatter(x_cap, y_cap)\n",
    "plt.xlabel('Weight (g)')\n",
    "plt.ylabel('Average Tumor Volume (mm3)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe15421",
   "metadata": {},
   "source": [
    "# Correlation Coefficient and Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2438c100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between mouse weight and the average tumor volume is 0.84.\n"
     ]
    },
    {
     "data": {
      "image/png": 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c38K89dZbWLx4MVatWoVatWohOTkZc+bM0Tp/Tk4O7ty5o5XQCCFw69YtNG7cuMBjm/M6Fff3nVfbsHz5crzzzjtYvnw5/Pz8NEl23jkkScK+ffs0N5ln5S8r6HfRsmVLtGzZErm5uTh+/Di+/vprjBo1CpUqVULv3r0VXbeAgABNZ/4LFy7gf//7Hz777DM8efKk0HmE8hsyZAiWLFmChIQErF27tsD9DP17NOZ7yDvmhg0bCkyynicvOYmNjcWhQ4cQHh6u2RYSEoKKFSsiJiYGsbGx8PX11SQ6xTm3ks8rMh0mMyVQbm4uVq5ciaCgICxZskRn+/bt2zFnzhz88ssv6Nq1KypUqIAePXpg1apVaNasGW7duqUzwqFr16744osv4OnpicDAwCLFJUkSHBwcYG9vryl7/PgxVq9erbVfq1atAMgjC54dYbJhwwadEStdu3bFzp07ERQUpLdZqzjyRg8kJCSgY8eOmvKtW7ca9TyAPHnX5s2bcfPmTa0b8qpVq1CmTJliDwVt2rQpQkNDsXz5ctSqVQsqlUqrqapt27aYNWsW1qxZg9GjR2vKN27ciIyMDLRt27bAY5vzOhnj9z148GAMGzYM+/fvx7Zt2zBmzBitv8muXbtixowZuHHjhk7TQVHY29ujadOmqF27NtauXYv4+Hj07t27yNetVq1a+OSTT7Bx40bEx8criqVZs2YYMmQIUlJS0LNnzwL3a9u2LaKiohAfH6/1f3DVqlWQJEmTNBjzPXTs2BEODg64dOmSombUZ9WtWxdeXl7Ys2cPjh8/jqioKM02SZLQqlUrREdH4/Dhw3j11Vc124pzbiWfV0o4OzuzpkYBJjMl0C+//IKbN29i5syZemdqDQ0NxTfffIOlS5eia9euAORvbD/++CNGjBiBKlWqoF27dlqvGTVqFDZu3IhWrVph9OjRCAsLg1qtxtWrV7Fr1y6MHTsWTZs2LTSuLl26YO7cuejbty+GDh2Ke/fuYfbs2TrfdOvWrYs+ffpgzpw5sLe3R5s2bXD27FnMmTMHKpUKdnZPW0enTZuG3bt3o3nz5hg5ciSCg4ORmZmJy5cvY+fOnVi4cCGqVKlSpOvo4+ODdu3aISoqChUqVEBAQAB+//13bNq0qUjHK8yUKVM0/UE+/fRTeHh4YO3atdixYwdmzZoFlUpV7HMMGTIEY8aMwfnz5/HOO+/A1dVVs619+/bo2LEjJkyYgNTUVLz88stISEjAlClT0KBBg0In1TPndTLG7zuvJrJPnz7IysrS6Tfy8ssvY+jQoRg8eDCOHz+OVq1awc3NDUlJSdi/fz/q1auHYcOGFXqOhQsXYs+ePejSpQuqVq2KzMxMTa1m3v8tQ69bQkICRowYgddeew01a9aEk5MT9uzZg4SEBHz00UcKr6Bhc02NHj0aq1atQpcuXTBt2jQEBARgx44d+O677zBs2DDUqlXL6O+hWrVqmDZtGiZNmoR//vkHnTp1QoUKFXD79m0cPXoUbm5umDp1aqFxS5KE1q1bY8OGDRBCaNXMAHJT06hRoyCE0BqSXZxzK/m8UqJevXpYv349fvzxR1SvXh0uLi6oV69ekY5VKlis6zGZTI8ePYSTk5NITk4ucJ/evXsLBwcHzTDT3Nxc4e/vrzXCJb/09HTxySefiODgYOHk5CRUKpWoV6+eGD16tNZwVeQbFfSsZcuWieDgYOHs7CyqV68uoqKixNKlSwUAkZiYqNkvMzNTjBkzRnh7ewsXFxfx0ksviUOHDgmVSiVGjx6tdcw7d+6IkSNHisDAQOHo6Cg8PDxEo0aNxKRJk0R6erqhl01v3ElJSaJXr17Cw8NDqFQq0b9/f3H8+HG9o3Tc3Nx0jhkeHi7q1q2rUx4QECC6dOmiVXb69GkRGRkpVCqVcHJyEi+88ILWOYR4Onrkp59+Mvh95blz545wcnISAMTRo0d1tj9+/FhMmDBBBAQECEdHR+Hr6yuGDRumGUr87HvKP8rCnNfJGL/vvn37CgDi5ZdfLnCfZcuWiaZNmwo3Nzfh6uoqgoKCxMCBA7WGihcU96FDh0TPnj1FQECAcHZ2Fp6eniI8PFxs3bpVaz9Drtvt27fFoEGDRO3atYWbm5soW7asCAsLE//9739FTk5Ooe/z2dFMhck/mkkIIa5cuSL69u0rPD09haOjowgODhZffvmlyM3NNel72LJli4iIiBDlypUTzs7OIiAgQPTq1Uv89ttvhb6HPHnTSnh5eelsO3XqlAAgAIiLFy/qbDfk3PqGZhv6eZU3min/qFB9o8IuX74sOnToINzd3QUAERAQYND7L60kIRQsSkFkQQcPHsTLL7+MtWvXom/fvpYOh4ioQPy8Mi8mM2SVdu/ejUOHDqFRo0ZwdXXFH3/8gRkzZkClUiEhIUFnNAwRkaXw88ry2GeGrFK5cuWwa9cuzJs3D2lpaahYsSJeeeUVREVF8YOBiKwKP68sjzUzREREZNM4aR4RERHZNCYzREREZNOYzBAREZFNK/EdgNVqNW7evAl3d3fFU8ATERGRZQghkJaWBj8/v+dOPljik5mbN2/qrERMREREtuHatWvPndm7xCcz7u7uAOSLUa5cOQtHQ0RERIZITU2Fv7+/5j5emBKfzOQ1LZUrV47JDBERkY0xpIsIOwATERGRTWMyQ0RERDaNyQwRERHZNCYzREREZNOsJpmJioqCJEkYNWqUVvmff/6Jbt26QaVSwd3dHS+99BKuXr1qmSCJiIjI6lhFMnPs2DEsWrQIYWFhWuWXLl1CixYtULt2bcTGxuKPP/7A5MmTuQopERERaVh8aHZ6ejr69euHxYsX4/PPP9faNmnSJHTu3BmzZs3SlFWvXt3cIRIREZEVs3jNzPDhw9GlSxe0a9dOq1ytVmPHjh2oVasWOnbsCG9vbzRt2hRbtmwp9HhZWVlITU3VehAREVHJZdFkZv369YiPj0dUVJTOtuTkZKSnp2PGjBno1KkTdu3ahZ49e+LVV19FXFxcgceMioqCSqXSPLiUARERUclmsWama9eu4YMPPsCuXbv09oFRq9UAgO7du2P06NEAgPr16+PgwYNYuHAhwsPD9R534sSJGDNmjOZ53nTIREREZDy5aoGjifeRnJYJb3cXNAn0gL2dZRZ0tlgyc+LECSQnJ6NRo0aastzcXOzduxfffPMNMjIy4ODggJCQEK3X1alTB/v37y/wuM7OznB2djZZ3ERERKVd9JkkTN12DkkpmZoyX5ULpkSGoFOor9njsVgy07ZtW5w+fVqrbPDgwahduzYmTJgAZ2dnNG7cGOfPn9fa58KFCwgICDBnqERERPT/os8kYdiaeIh85bdSMjFsTTwW9G9o9oTGYsmMu7s7QkNDtcrc3Nzg6empKR83bhzeeOMNtGrVChEREYiOjsa2bdsQGxtrgYiJiIhKt1y1wNRt53QSGQAQACQAU7edQ/sQH7M2OVl8NFNhevbsiYULF2LWrFmoV68elixZgo0bN6JFixaWDo2IiKjUOZp4X6tpKT8BICklE0cT75svKFjBPDPP0lfjMmTIEAwZMsT8wRAREZGW5LSCE5mi7GcsVl0zQ0RERNbD292wGfgN3c9YmMwQERGRQZoEesBX5YKCesNIkEc1NQn0MGdYTGaIiIjIMPZ2EqZEylOm5E9o8p5PiQwx+3wzTGaIiIjIYJ1CfbGgf0P4qLSbknxULhYZlg1YWQdgIiIisn6dQn3RPsSHMwATERGR7bK3k9AsyNPSYQBgMxMRERHZOCYzREREZNOYzBAREZFNYzJDRERERdO1KyBJQEyMRcNgB2AiIiJS5u+/gZo1nz5v0wYQ+pafNA8mM0RERGQ4Sc/w67NnzR/HM9jMRERERM/388/6ExkhgJAQ88fzDNbMEBERUcHUasDeXrf86FGgcWPzx6MHa2aIiIhIv3HjdBOZwEC5NsZKEhmANTNERESU3/37gKee2X3v3AEqVjR/PM/BmhkiIiJ6qk4d3URm5Ei5NsYKExmANTNEREQEAPHxQKNGuuU5Ofr7zFgR1swQERGVdpKkm8hs3CjXxlh5IgMwmSEiIiq9liwpeLj1q6+aP54iYjMTERFRafPkCeDsrFt+/jxQq5b54ykm1swQERGVJn376iYyecsR2GAiA7BmhoiIqHS4dg2oWlW3PD0dcHMzfzxGxJoZIiKiks7ZWTeRmTlTro2x8UQGYM0MERGRxeWqBY4m3kdyWia83V3QJNAD9nZ6OuYq9dtvQPv2uuVqtf6OvzaKyQwREZEFRZ9JwtRt55CUkqkp81W5YEpkCDqF+hbtoEIAdnoaX2JjgfDwoh3TirGZiYiIyEKizyRh2Jp4rUQGAG6lZGLYmnhEn0lSftDPP9dNZFQqOcEpgYkMwJoZIiIii8hVC0zddg5CzzYBQAIwdds5tA/xMazJKS0NKFdOt/zGDcDPr5jRWjfWzBAREVnA0cT7OjUyzxIAklIycTTx/vMP1qKFbiIzaJBcG1PCExmANTNERFQIk3VMJSSnFZzIGLzfuXNA3bq65U+eAI6ORYzM9jCZISIivUzSMZU0vN1direfvtFIK1cCAwcWIyrbxGYmIiLSYZKOqaSlSaAHfFUuKKieS4KcPDYJ9NDe8OOPBa+nVAoTGYDJDBER5fO8jqmA3DE1V61vDzKUvZ2EKZEhAKCT0OQ9nxIZ8rRZLydHTmJ699be+dQpOZEpxZjMEBGRFqN2TKVCdQr1xYL+DeGj0m5K8lG5YEH/hk+b8957T7cPTP36chLzwgvmCdaKsc8MERFpMUrHVAuytU7LnUJ90T7ER3/MyclApUq6L3rwAChf3uyxWismM0REZmIrN9lid0y1IFvttGxvJ6FZkKd2YeXKwM2b2mWTJsmT4pEWJjNERGZgSzfZvI6pt1Iy9fabkSA3g+h0TLWwvE7L+WPO67Ss1WxjzQ4dApo31y3PzdW/RAGxzwwRkanZ2sggxR1TrUCJ6bQsSbqJzI4dBa+1RACYzBARmZSt3mQN7phqJWy+03L//gUPt+7c2fzx2Bg2MxERmZCSm6xOnwkLK7RjqpWx2U7LBa2n9M8/QGCg+eOxUUxmiIhMyGZvsv9Pb8dUK2STnZb11cQApX7OmKJgMxMRkQnZ5E3WBhV5Nl1LiI/Xn8ikpTGRKSImM0REJmRTN1kbZjOdliUJaNRIu2zAADmJKVvWMjGVAExmiIhMyGZusiWAVXda/u9/C+7gu2qV+eMpYSQhSnadVmpqKlQqFVJSUlBOXycrIiIzsKV5ZmydVU1OmJsLOOjpnvrTT0CvXuaPx4YouX8zmSEiMhOrusmS6TVtChw9qltesm+7RqPk/s3RTEREZmIrI4OomJKSAD8/3fLERKBaNbOHUxowmSEiIjIWff1iqlcHLl0yfyyliNV0AI6KioIkSRg1apTe7e+88w4kScK8efPMGhcREdFz/fKL/kTmyRMmMmZgFcnMsWPHsGjRIoSFhendvmXLFhw5cgR++qrtiIiILEmSdJcc+PRTuW+Mo6NlYiplLJ7MpKeno1+/fli8eDEqVKigs/3GjRsYMWIE1q5dC0f+URARkbX44IOCh1tPnWr+eEoxiyczw4cPR5cuXdCuXTudbWq1GgMGDMC4ceNQt25dC0RHRESUT2amnMR89ZV2+d69HKlkIRbtALx+/XrEx8fj2LFjerfPnDkTDg4OGDlypMHHzMrKQlZWluZ5ampqseMkIiICIM/Sm5GhW84kxqIsVjNz7do1fPDBB1izZg1cXHTXJDlx4gTmz5+PFStWQCpoMS49oqKioFKpNA9/f39jhk1ERKXRn3/KtTH5E5l795jIWAGLTZq3ZcsW9OzZE/b29pqy3NxcSJIEOzs7zJw5E+PGjYOdnZ3Wdjs7O/j7++Py5ct6j6uvZsbf35+T5hERUdHo+0L9yivAzp3mj6UUsYlJ89q2bYvTp09rlQ0ePBi1a9fGhAkT4Ovri44dO2pt79ixIwYMGIDBgwcXeFxnZ2c4OzubJGYiIipFli0D3npLt1yt1p/gkMVYLJlxd3dHaGioVpmbmxs8PT015Z6e2jNlOjo6wsfHB8HBwWaLk4iIShkhADs9vTCWLNGf3JDFcQZgIiKiPF27Ajt26JazX4xVs6pkJjY2ttDtBfWTISIiKpb79wFPPetmnTsH1Klj/nhIEatKZoiIiMxOX/8XV1fg0SPzx0JFYvFJ84iIiCxi/379iczjx0xkbAyTGSIiKn0kCWjZUrtsxAi5b4yeuc/IujGZISKi0mPq1ILXU/r6a/PHQ0ZRpD4z165dw+XLl/Ho0SN4eXmhbt26nNuFiIisV06O/hWsd+6UJ8Ajm2ZwMnPlyhUsXLgQ69atw7Vr1/DsxMFOTk5o2bIlhg4din/9619as/YSERFZVM2awN9/65ZzuHWJYVDW8cEHH6BevXq4ePEipk2bhrNnzyIlJQVPnjzBrVu3sHPnTrRo0QKTJ09GWFhYgQtHEhERmc2VK3KTUv5E5uZNJjIljEE1M05OTrh06RK8vLx0tnl7e6NNmzZo06YNpkyZgp07d+LKlSto3Lix0YMlIiIyiL5+MS++CPDLdolksYUmzUXJQlVERGTjBgwA1qzRLc/N1b9EARVZrlrgaOJ9JKdlwtvdBU0CPWBvZ7w1q2xioUkiIiKj0lcb8/HHwPTp5o+lhIs+k4Sp284hKSVTU+arcsGUyBB0CvU1ezyK0tQlS5bgzTffxPLlywEAP/74I+rUqYPq1atjypQpJgmQiIioUJJU8HBrJjJGF30mCcPWxGslMgBwKyUTw9bEI/pMktljMjiZmTdvHkaNGoX09HRMmjQJ06dPx/Dhw9G/f38MHjwY8+fPx6JFi0wZKxER0VO3b+tPYhYsYAdfE8lVC0zddg76rm5e2dRt55CrNu/1N7iZ6fvvv8eiRYvQt29fnDx5Ek2aNMHChQvx1v8vh16lShV8++23GDp0qMmCJSKyZabuY1Cq6EtiACYxJnY08b5OjcyzBICklEwcTbyPZkF6Fu40EUXzzLRo0QIA0KBBA9jb2+Oll17SbG/ZsiXGjBlj/AiJiEoAa+tjYLNWrAAGD9Ytv3ED8PMzezilTXJawYlMUfYzFoOTmTJlyiAjI0Pz3MvLC2XLltXaJycnx3iRERGVEHl9DPLXGeT1MVjQvyETGkOwNsbivN0NW7fK0P2MxeA+M7Vr10ZCQoLm+bVr1xAQEKB5/tdff6FatWpGDY6IyNZZax8Dm1KrVsEdfJnImFWTQA/4qlxQUOOoBLnGsUmghznDMjyZmTlzJoKDgwvcfvXqVbzzzjtGCYqIqKRQ0seA8snNlZOYixe1y3v2ZBJjIfZ2EqZEhgCATkKT93xKZIjZ+4IZ3Mz08ssvF7r9vffeK3YwREQljbX2MbB6bFKyWp1CfbGgf0OdPmA+FuwDVqxJ89LT06FWq7XKOMsuEdFT1trHwGqdPg2EhemW//Yb0Lat+eMhvTqF+qJ9iI/VjM5TnMwkJiZixIgRiI2NRWbm04xMCAFJkpCbm2vUAImIbFleH4NbKZl6+81IkL/RmruPgVVibYxNsbeTzDr8ujCKk5l+/foBAJYtW4ZKlSpBKuiPj4iINH0Mhq2JhwRoJTSW7GNgVSZMAGbN0i3PyADKlDF/PGRzFCczCQkJOHHiRKGdgYmI6Clr7GNgNVgbQ0agOJlp3Lgxrl27xmSGiEgBa+tjYHFMYsiIFCczS5YswbvvvosbN24gNDQUjo6OWtvD9HXcIiIiq+pjYDGpqYBKpVv+2WcAFyymIlKczNy5cweXLl3C4Gemk5YkiR2AiYiocGaqjeEaWKWP4mRmyJAhaNCgAdatW8cOwERE9Hw7dgBdu+qW//UXYOQuC1wDq3SShFCWEru5ueGPP/5AjRo1TBWTUaWmpkKlUiElJYVz4BARmZsZ+8YUtAZWXgRcA8u2KLl/G7ycQZ42bdrgjz/+KHJwRERUCnTooD+Ryc01SSLDNbBKN8XNTJGRkRg9ejROnz6NevXq6XQA7tatm9GCIyIiGyMEYKfne3LDhsCJEyY7rZI1sEp9J+wSSHEy8+677wIApk2bprONHYCJiEoxCw635hpYpZviZia1Wl3gg4kMEVEpdPmy/kRm/XqzzRvDNbBKt2ItNElERKWclUx+xzWwSrciJTNHjx5FbGwskpOTdVbNnjt3rlECIyIiKzZvHjB6tG753buAp/n7pHANrNJNcTLzxRdf4JNPPkFwcLDOPDOcc4bINnGSMVLESmpj8uMaWKWX4nlmKlWqhJkzZ2LQoEEmCsm4OM8MUeE4yRgZrGxZeSXr/KxsPSUm5yWDSeeZsbOzw8svv1zk4IjIeuRNMpZ/SOutlEwMWxOP6DNJFoqMrEpWllwbkz+RGTrU6hIZ4OkaWN3rV0azIE8mMqWA4mRm9OjR+Pbbb00RCxGZEScZI4NIEuCiZwSQEMD335s/HiI9FPeZ+fDDD9GlSxcEBQUhJCREZ9K8TZs2GS04IjIdTjJGhdq7FwgP1y0/cgRo0sT88RAVQnEy8/777yMmJgYRERHw9PRkp18iG8VJxqhAVtrBl6ggipOZVatWYePGjejSpYsp4iEiM+EkY6TjtdeADRt0y7OyACcn88dDZCDFyYyHhweCgoJMEQsRmREnGSMtrI0hG6a4A/Bnn32GKVOm4NGjR6aIh4jMJG+SMeDppGJ5OMlYKSJJ+hMZIZjIkM1QPM9MgwYNcOnSJQghUK1aNZ0OwPHx8UYNsLg4zwxR4TjPTCmVnAxUqqRbPnMmMH68+eMhykfJ/VtxM1OPHj2KGhcRWaFOob5oH+LDScZKEzYpUQmjuGbG1rBmhojo/y1bBrz1lm55YiJQrZrZwyEqjElrZgwhhOCQbSIia8LaGCrBDOoAXKdOHfzwww948uRJoftdvHgRw4YNw8yZM40SHBERFVOVKvoTGbWaiQyVGAbVzHz77beYMGEChg8fjg4dOuDFF1+En58fXFxc8ODBA5w7dw779+/HuXPnMGLECLz33numjpuIiAqTmws46PmIb94cOHDA/PEQmZCiPjMHDx7Ejz/+iL179+Ly5ct4/PgxKlasiAYNGqBjx47o378/ypcvb8JwlWOfGSIqddikRCWAyVbNbt68OebPn4+TJ0/iwYMHyMzMxPXr17Ft2zaMGDGiWIlMVFQUJEnCqFGjAADZ2dmYMGEC6tWrBzc3N/j5+WHgwIG4efNmkc9BRPrlqgUOXbqHn0/dwKFL97i4pK06fVp/IvPzz0xkqEQzSQdgpY4dO4ZFixYhLCxMU/bo0SPEx8dj8uTJeOGFF/DgwQOMGjUK3bp1w/Hjxy0YLVHJwnlmSggT1cbkqgWH7ZPVs3gyk56ejn79+mHx4sX4/PPPNeUqlQq7d+/W2vfrr79GkyZNcPXqVVStWtXcoRKVONFnkjBsTbzOcga3UjIxbE08FvRvyITG2o0dC8ydq1uemgq4uxfr0Ex0yVYoXs7A2IYPH44uXbqgXbt2z903JSUFkiQV2pyVlZWF1NRUrQcR6cpVC0zddk7vukx5ZVO3nWOTkzWTJP2JjBBGSWSGrYnXSmSAp4lu9JmkYh2fyJgsmsysX78e8fHxiIqKeu6+mZmZ+Oijj9C3b99COwJFRUVBpVJpHv7+/sYMmajEOJp4X+dG9SwBICklE0cT75svKDKMiddTYqJLtsZiycy1a9fwwQcfYM2aNXBxcSl03+zsbPTu3RtqtRrfffddoftOnDgRKSkpmse1a9eMGTZRiZGcVnAiU5T9yAzS0vQnMSNHGrWDLxNdsjVF6jNz6dIlLF++HJcuXcL8+fPh7e2N6Oho+Pv7o27dugYd48SJE0hOTkajRo00Zbm5udi7dy+++eYbZGVlwd7eHtnZ2Xj99deRmJiIPXv2PHd4lrOzM5ydnYvytohKFW/3wr9EKN2PTMyMw62Z6JKtUVwzExcXh3r16uHIkSPYtGkT0tPTAQAJCQmYMmWKwcdp27YtTp8+jVOnTmkeL774Ivr164dTp05pJTIXL17Eb7/9Bk9PT6XhElEBmgR6wFflgoLGpUiQO3s2CfQwZ1iU3/bt+hOZU6dMNtyaiS7ZGsU1Mx999BE+//xzjBkzBu7PdDCLiIjA/PnzDT6Ou7s7QkNDtcrc3Nzg6emJ0NBQ5OTkoFevXoiPj8f27duRm5uLW7duAQA8PDzg5OSkNHQieoa9nYQpkSEYtiYeEqDVPyLv1jklMoTDcC3JQpPf5SW6t1Iy9fabkQD4MNElK6K4Zub06dPo2bOnTrmXlxfu3btnlKAA4Pr169i6dSuuX7+O+vXrw9fXV/M4ePCg0c5DVJp1CvXFgv4N4aPS/obto3LhsGxLatlSfyKTk2OWye/yEl0AOjV3THTJGimumSlfvjySkpIQGBioVX7y5ElUrly5WMHExsZq/l2tWjUoWGmBiIqoU6gv2of4cGI0ayAEYKfnO6aPD5Bk3qHQeYlu/nlmfDjPDFkhxclM3759MWHCBPz000+QJAlqtRoHDhzAhx9+iIEDB5oiRiIyMXs7Cc2C2CfNoqxwPSUmumQrFC00CcjDpAcNGoT169dDCAEHBwfk5uaib9++WLFiBezt7U0Va5FwoUkishZ6lwa4dhWoVk1358WLgbffNnuMRNZCyf1bcTKT59KlSzh58iTUajUaNGiAmjVrFilYU2MyQ0TWQN/SAJdndtW/M5vYiRTdv4u8NlNQUBCCgoKK+nIiolIj/xpY/z6yCZNil+nueOsWUKmSWWMjKgkUJzNCCGzYsAExMTFITk6GWq3W2r5p0yajBUdEZOvyLw1QUG1Mbq6afVGIikhxMvPBBx9g0aJFiIiIQKVKlSAV1GmNiIg0SwMUlMRUm7AdALAu8T47YRMVkeJkZs2aNdi0aRM6d+5siniIiEqUO/dT9SYyv9RqjmE9P9Y859IAREWnOJlRqVSoXr26KWIhIipZJAnd9BTn1cY8i0sDGI/eUWNswivRFCczn332GaZOnYply5bB1dXVFDEREdm2gweBl1/WKX6jTxSOVK2nVcalAYxL36gxX070V+IpTmZee+01rFu3Dt7e3qhWrRocHR21tsfHxxstOCIim1NAP0J9tTGAvCYWlwYwjvyjxvLcSsnEsDXxXKKjBFOczAwaNAgnTpxA//792QGYiCjPgAHAmjW65Y8fI/rvB8AaftEzpfyjxp4lINeATd12Du1DfJg4lkCKk5kdO3bg119/RYsWLUwRDxGR7SlkKYK8m2yBLwVvssaQN2qsIAJAUkomjnLUWImkeNVsf39/zqRLRATISYy+REYIzSy+Sm6yVHSGjgbjqLGSSXEyM2fOHIwfPx6XL182QThERDbg7l39Scznn+ssRcCbrHkYOhqMo8ZKJsXNTP3798ejR48QFBSEMmXK6HQAvn+f3y6IqARTuLo1b7Lm0STQA74qF9xKydTbb4ajxko2xcnMvHnzTBAGEZGVW70aGDhQt/zvv4FC1qnjTdY87O0kTIkMwbA18ZAArWudl35y1FjJVeRVs20FV80momJTWBuTX96QYUD/TZZDho2H88yUHEru34qTmatXrxa6vWrVqkoOZ3JMZoioyGrWlGte8lOrC05wCsCbrPlwBuCSwaTJjJ2dXaFzy+Tm5io5nMkxmSEixdRqwN5et7xBA6AYE4PyJktkOCX3b8V9Zk6ePKn1PDs7GydPnsTcuXMxffp0pYcjIrIuxWxSKoy9ncQ5TohMQHEy88ILL+iUvfjii/Dz88OXX36JV1991SiBERGZ1blzQN26uuUbNgD/+pf54yEigylOZgpSq1YtHDt2zFiHIyIyHxPWxhCR6SmeNC81NVXrkZKSgr/++guTJ09GzZo1TREjEZFpfPSR/kTm4UMmMkQ2RHHNTPny5XU6AAsh4O/vj/Xr1xstMCIik2JtDFGJoTiZiYmJ0XpuZ2cHLy8v1KhRAw4ORmu1IiIyDSYxRCWO4uwjPDzcFHEQEZlWRgZQtqxu+bvvAgsWmD8eIjIag5KZrVu3GnzAbt26FTkYIiKTYG0MUYlmUDLTo0cPgw4mSZLVTZpHRKVYdDTwyiu65SdOAA0bmj8eIjIJg5IZtVpt6jiIiIyLtTFEpYbiodlERFatfXv9iUx2NhMZohKqSMlMXFwcIiMjUaNGDdSsWRPdunXDvn37jB0bEZEykgT89pt2mUolJzEcbUlUYilOZtasWYN27dqhTJkyGDlyJEaMGAFXV1e0bdsWP/zwgyliJCIqnCTpr40RQp4Aj4hKNMWrZtepUwdDhw7F6NGjtcrnzp2LxYsX488//zRqgMXFVbOJSrDr1wF/f93y774Dhg0zfzxEZDRK7t+KkxlnZ2ecPXsWNWrU0Cr/+++/ERoaiszMTOURmxCTGaISih18iUo0Jfdvxc1M/v7++P3333XKf//9d/jr+4ZERGRMq1frT2Ru3GAiQ1RKKe4RN3bsWIwcORKnTp1C8+bNIUkS9u/fjxUrVmD+/PmmiJGISMbaGCLSw+Bk5s6dO/Dy8sKwYcPg4+ODOXPm4H//+x8AuR/Njz/+iO7du5ssUCIqxV54AUhI0C1nEkNEUJDMVK5cGd26dcNbb72FHj16oGfPnqaMi4gIyMkBHB11y4cOBb7/3vzxEJFVMrjPzMqVK5GamorIyEj4+/tj8uTJ+Oeff0wZGxGVZpKkP5ERgokMEWkxOJnp06cPdu3ahcTERPz73//G2rVrUbNmTURERGDt2rVWN4qJiGxUQoL+vjEHDrBZiYj0Ujw0+1m///47li9fjs2bN8PJyQl9+vTBd999Z8z4io1Ds4lsCDv4EtH/M+nQ7Ge1bdsWa9aswapVq2BnZ4fvWfVLREXx8cf6E5nHj5nIENFzFXmxksuXL2P58uVYuXIlrl+/joiICLz11lvGjI2ISgN9SYy3N3D7tvljISKbpCiZyczMxE8//YTly5dj7969qFy5MgYNGoTBgwejWrVqJgqRiEokNikRkZEYnMwMHToU//vf/5CZmYnu3btjx44d6NChA6SCPpCIiPRJSQHKl9ct/+or4P33zR4OEdk+g5OZw4cPY+rUqRgwYAA8PDxMGRMRlVSsjSEiEzA4mUnQN/smEZEhduwAunbVLU9MBEpRE3WuWuBo4n0kp2XC290FTQI9YG/H2m2i4ipyB2AiIoOwNgYAEH0mCVO3nUNSytM5uXxVLpgSGYJOob4WjIzI9hVraDYRUYG6dtWfyKjVpTKRGbYmXiuRAYBbKZkYtiYe0WeSLBQZUclgNclMVFQUJEnCqFGjNGVCCHz22Wfw8/ODq6srWrdujbNnz1ouSCJ6PiHkJGbHDu3yrl2fbitFctUCU7edg770La9s6rZzyFWXrgSPyJgUJTM5OTmYOnUqrl27ZtQgjh07hkWLFiEsLEyrfNasWZg7dy6++eYbHDt2DD4+Pmjfvj3S0tKMen4iMhJJAuz0fKwIAWzbZv54rMDRxPs6NTLPEgCSUjJxNPG++YIiKmEUJTMODg748ssvkZuba7QA0tPT0a9fPyxevBgVKlTQlAshMG/ePEyaNAmvvvoqQkNDsXLlSjx69Ag//PCD0c5PREaQmKi/xmXHjlLXpJRfcpph69YZuh8R6VLczNSuXTvExsYaLYDhw4ejS5cuaNeunVZ5YmIibt26hQ4dOmjKnJ2dER4ejoMHDxZ4vKysLKSmpmo9yHblqgUOXbqHn0/dwKFL91gVb40kCaheXbdcCKBzZ/PHY2W83V2Muh8R6VI8mumVV17BxIkTcebMGTRq1Ahubm5a27t162bwsdavX4/4+HgcO3ZMZ9utW7cAAJUqVdIqr1SpEq5cuVLgMaOiojB16lSDYyDrxdEfVu6rr4APPtAtf/gQUKnMHo61ahLoAV+VC26lZOrtNyMB8FHJw7SJqGgUJzPDhg0DAMydO1dnmyRJBjdBXbt2DR988AF27doFF5eCv5Hkn2FYCFHorMMTJ07EmDFjNM9TU1Ph7+9vUExkPfJGf+T/8M8b/bGgf0MmNJbE4dYGs7eTMCUyBMPWxEMCtP6m867ilMgQzjdDVAyKm5nUanWBDyV9aU6cOIHk5GQ0atQIDg4OcHBwQFxcHL766is4ODhoamTyamjyJCcn69TWPMvZ2RnlypXTepBt4egPK+btrT+REYKJTCE6hfpiQf+G8FFpf3HzUbkwMScyAotNmte2bVucPn1aq2zw4MGoXbs2JkyYgOrVq8PHxwe7d+9GgwYNAABPnjxBXFwcZs6caYmQyUyUjP5oFuRpvsBKs8xMwNVVt3zSJODzz80fjw3qFOqL9iE+nAGYyASKlMzExcVh9uzZ+PPPPyFJEurUqYNx48ahZcuWBh/D3d0doaGhWmVubm7w9PTUlI8aNQpffPEFatasiZo1a+KLL75AmTJl0Ldv36KETTaCoz+sDJuUjMbeTmICTmQCipuZ1qxZg3bt2qFMmTIYOXIkRowYAVdXV7Rt29boQ6bHjx+PUaNG4b333sOLL76IGzduYNeuXXB3dzfqeci6cPSHlTh0SH8ik5DARIaIrIokhLJPpTp16mDo0KEYPXq0VvncuXOxePFi/Pnnn0YNsLhSU1OhUqmQkpLC/jM2Ilct0GLmnueO/tg/oQ2r6E2FtTFEZGFK7t+Ka2b++ecfREZG6pR369YNiYmJSg9HpCNv9AfwdLRHHo7+MLGhQ/UnMtnZTGSIyGopTmb8/f3x+++/65T//vvvHAJNRsPRHxYgScDixdplYWFyEuNgsbECRETPpfgTauzYsRg5ciROnTqF5s2bQ5Ik7N+/HytWrMD8+fNNESOVUhz9YSZsUiIiG1ekSfN8fHwwZ84c/O9//wMg96P58ccf0b17d6MHSKUbR3+Y0J078rwx+a1aBQwYYP54iIiKSHEHYFvDDsBEerA2hoisnEk7AD8rPT2dizoS2ZIfftCfyCQlMZEhIpuluJkpMTERI0aMQGxsLDIzn05alrdmkpIlDYjIjFgbQ0QllOJkpl+/fgCAZcuWoVKlSoUu+khEVqBxY+D4cd1yJjFEVEIoTmYSEhJw4sQJBAcHmyIeIi25asHRTEWVm6t/SPXgwcCyZeaPh4jIRBQnM40bN8a1a9eYzJDJRZ9JwtRt57QWnfRVuWBKZAjnmXkeNikRUSmiOJlZsmQJ3n33Xdy4cQOhoaFwdHTU2h4WFma04Kj0ij6ThGFr4nWWM7iVkolha+I5cV5Bzp0D6tbVLd+7F1CwECwRkS1RnMzcuXMHly5dwuDBgzVlkiSxAzAZTa5aYOq2c3rXZRKQlzSYuu0c2of4sMnpWayNIaJSSvHQ7CFDhqBBgwY4dOgQ/vnnHyQmJmr9JCquo4n3tZqW8hMAklIycTTxvvmCsmaffaY/kXn0iIkMEZUKimtmrly5gq1bt6JGjRqmiIcIyWkFJzJF2a9E05fElCsHpKSYPxYiIgtRnMy0adMGf/zxB5MZMhlvd5fn76RgvxKJTUoc6UZEGoqTmcjISIwePRqnT59GvXr1dDoAd+vWzWjBUenUJNADvioX3ErJ1NtvRoK8enaTQA9zh2Z5aWlyzUt+s2cDY8eaPx4L4Ug3InqW4rWZ7OwK7mZjjR2AuTaTbcobzQRAK6HJ+95dKkczsTYGQMEj3Ur13wZRCWTStZnUanWBD2tLZMh2dQr1xYL+DeGj0m5K8lG5lL6b1a5d+hOZixeNlsjkqgUOXbqHn0/dwKFL95Crts4E6Xkj3QB5pJu1xk9EpqG4mYnIXDqF+qJ9iE/p7hdhhtoYW2qyUTLSrVmQp/kCIyKLUpzMTJs2rdDtn376aZGDIcrP3k4qnTelf/0L2LRJtzw3FyikqVcpW5uckCPdiEgfxcnM5s2btZ5nZ2cjMTERDg4OCAoKYjJDVBxC6E9W2rUDdu826qlscXJCjnQjIn0UJzMnT57UKUtNTcWgQYPQs2dPowRFVCqZuYOvLTbZcKQbEeljlPrqcuXKYdq0aZg8ebIxDkdUuly9qj+R2bLFpCOVbLHJxt5OwpTIEABPRy/lyXs+JTLEamqSiMg8jNb4/vDhQ6Rw1lEiZSQJCAjQLRcC6N7dpKe21SYbjnQjovwMbma6evUqqlSpgm+++UarXAiBpKQkrF69Gp06dTJ6gEQl0oIFwHvv6Zbfvw9UqGCWEGy5yYYj3YjoWQZPmmdvb4+kpCQ0bdpUq9zOzg5eXl5o06YNJk6cCHd3d5MEWlScNI+sjhVNfsfJCYnIWim5fxtcM5OX8yQmJhYvOqLSyt8fuH5dt9yCM/jmNdnkn2fGx0rnmSEi0oeT5hGZWlYW4KKn38mHHwJffmn+ePJhkw0R2TpFycySJUtQtmzZQvcZOXJksQIiKlGsqEmpMKV2ckIiKhEM7jNjZ2eHKlWqwN7evuCDSRL++ecfowVnDOwzQxZx9CiQr38ZAODkSaB+fbOHQ0Rka0zSZwYAjh8/Dm9v72IFR1Ti2UhtDBFRSWHwPDNSQR/QRCR7/339icyTJ0xkiIhMSPFoJiLSQ18SU6cOcO6c+WMhIiplDE5mpkyZ8tzOv0TGlqsW1j3Khk1KREQWZ3AHYFvFDsC2K/pMks78J77WMv/JvXtAxYq65cuWAYMHmz8eIqISxmQdgInMJW9m2vyZ9q2UTAxbE2/ZmWlZG0NEZFWMttAkkbHkqgWmbjund72gvLKp284hV23m5GHDBv2JzI0bTGSIiCyINTNkdY4m3tdqWspPAEhKycTRxPvmm+iNtTFERFarSDUzOTk5+O233/D9998jLS0NAHDz5k2kp6cbNTgqnZLTCk5kirJfsbRooT+RUauZyBARWQnFNTNXrlxBp06dcPXqVWRlZaF9+/Zwd3fHrFmzkJmZiYULF5oiTipFvN31rGNUjP2KJDcXcNDz36NfP2DNGtOdl4iIFFNcM/PBBx/gxRdfxIMHD+Dq6qop79mzJ37//XejBkelU5NAD/iqXFDQAGwJ8qimJoEepglAkvQnMkIwkSEiskKKk5n9+/fjk08+gZOTk1Z5QEAAbty4YbTAqPSyt5MwJTIEAHQSmrznUyJDjD/fzPnz+puUYmLYpEREZMUUJzNqtRq5ubk65devX4e7u7tRgiLqFOqLBf0bwkel3ZTko3IxzbBsSQJq19YtFwJo3dq45yIiIqNS3Gemffv2mDdvHhYtWgRAXrMpPT0dU6ZMQefOnY0eIJVenUJ90T7Ex7QzAH/xBTBpkm55ejrg5ma88xARkckongH45s2biIiIgL29PS5evIgXX3wRFy9eRMWKFbF3716rW1WbMwBTgfQ1Kbm4AI8fmz8WIiLSYtIZgP38/HDq1CmsW7cO8fHxUKvVeOutt9CvXz+tDsFEVsvREcjJ0S1nvxgiIpvEtZmo9MjIAPQtlhoVBXz0kfnjISKiApm0Zmbr1q16yyVJgouLC2rUqIHAwEClhyUyLc7gS0RUYilOZnr06AFJkpC/QievTJIktGjRAlu2bEGFChWMFihRkezZA7Rtq1t+/jxQq5b54yEiIqNTPDR79+7daNy4MXbv3o2UlBSkpKRg9+7daNKkCbZv3469e/fi3r17+PDDD597rAULFiAsLAzlypVDuXLl0KxZM/zyyy+a7enp6RgxYgSqVKkCV1dX1KlTBwsWLFAaMpVWkqQ/kRGCiQwRUQmiuGbmgw8+wKJFi9C8eXNNWdu2beHi4oKhQ4fi7NmzmDdvHoYMGfLcY1WpUgUzZsxAjRo1AAArV65E9+7dcfLkSdStWxejR49GTEwM1qxZg2rVqmHXrl1477334Ofnh+7duysNnUqLvn2Bdet0y3NzATsuFE9EVNIo/mS/dOmS3o445cqVwz///AMAqFmzJu7evfvcY0VGRqJz586oVasWatWqhenTp6Ns2bI4fPgwAODQoUN488030bp1a1SrVg1Dhw7FCy+8gOPHjysNm0oDIeTamPyJTHi4vI2JDBFRiaT4071Ro0YYN24c7ty5oym7c+cOxo8fj8aNGwMALl68iCpVqig6bm5uLtavX4+MjAw0a9YMANCiRQts3boVN27cgBACMTExuHDhAjp27FjgcbKyspCamqr1oFJAkvQnK0IAsbFmD4eIiMxHcTKzdOlSJCYmokqVKqhRowZq1qyJKlWq4PLly1iyZAkAua/L5MmTDTre6dOnUbZsWTg7O+Pdd9/F5s2bERIir8vz1VdfISQkBFWqVIGTkxM6deqE7777Di1atCjweFFRUVCpVJqHv7+/0rdItuTGDf0jlTZs4EglIqJSokjzzAgh8Ouvv+LChQsQQqB27dpo37497IpQjf/kyRNcvXoVDx8+xMaNG7FkyRLExcUhJCQEs2fPxuLFizF79mwEBARg7969mDhxIjZv3ox27drpPV5WVhaysrI0z1NTU+Hv7895ZkoiDrcmIiqxlMwzY3WT5rVr1w5BQUGYN28eVCoVNm/ejC5dumi2v/3227h+/Tqio6MNOh4nzSuBli4F3n5bt/zuXcDT0/zxEBGR0Zl00jwAyMjIQFxcHK5evYonT55obRs5cmRRDqkhhEBWVhays7ORnZ2tU9tjb28PtVpdrHOQDWNtDBER5aM4mTl58iQ6d+6MR48eISMjAx4eHrh79y7KlCkDb29vRcnMxx9/jFdeeQX+/v5IS0vD+vXrERsbi+joaJQrVw7h4eEYN24cXF1dERAQgLi4OKxatQpz585VGjbZuuBg4MIF3XImMUREpZ7iTi6jR49GZGQk7t+/D1dXVxw+fBhXrlxBo0aNMHv2bEXHun37NgYMGIDg4GC0bdsWR44cQXR0NNq3bw8AWL9+PRo3box+/fohJCQEM2bMwPTp0/Huu+8qDZtsVd5w6/yJzAcfMJEhIiIARegzU758eRw5cgTBwcEoX748Dh06hDp16uDIkSN488038ddff5kq1iJhnxkbtmsXoG8YPpMYIqIST8n9W3HNjKOjI6T/77dQqVIlXL16FQCgUqk0/yYqlsePAQ8P3UTm4EEmMkREpENxMtOgQQPNDLwRERH49NNPsXbtWowaNQr16tUzeoBUynzzDVCmDPDgwdOy6Gg5ifn/yRSJiIiepTiZ+eKLL+Dr6wsA+M9//gNPT08MGzYMycnJWLRokdEDpFLi5k25b8z77z8ta9dOXk+pkBmfiYiIFI1mEkLAy8sLdevWBQB4eXlh586dJgmMSpGhQ4HFi7XL/vwTqF3bMvEQEZFNUVQzI4RAzZo1cf36dVPFQ6VJfLxcG/NsIjNhgtykxESGiIgMpKhmxs7ODjVr1sS9e/dQs2ZNU8VEJV1uLtC4MXDypHY5Z/AlIqIiUNxnZtasWRg3bhzOnDljiniopNu4EXBw0E5kVq2Sa2OYyBARUREonmemQoUKePToEXJycuDk5ARXV1et7ffv3zdqgMXFeWasREoKUL68dlmNGsDZs4CTk0VCIiIi62XStZnmzZtX1LiotPrPf4BPP9UuO3AAaN7cMvEQEVGJojiZefPNN00RB5VE//wDBAVpl/XuDfzwQ8ELRhIRESmkuM8MAFy6dAmffPIJ+vTpg+TkZABAdHQ0zp49a9TgyEYJAbz6qm4ic/kysG4dExkiIjIqxclMXFwc6tWrhyNHjmDTpk1IT08HACQkJGDKlClGD5BszN69gJ0dsHnz07IZM+QEJyDAcnEREVGJpTiZ+eijj/D5559j9+7dcHqm42ZERAQOHTpk1ODIhmRlAVWrAuHhT8scHYHUVHnuGCIiIhNRnMycPn0aPXv21Cn38vLCvXv3jBIU2ZhlywAXF+DatadlP/8MPHkCuLtbLi4iIioVFHcALl++PJKSkhAYGKhVfvLkSVSuXNlogZENSE4GKlXSLmvWDNi3D7C3t0xMRERU6iiumenbty8mTJiAW7duQZIkqNVqHDhwAB9++CEGDhxoihjJGo0apZvInDoFHDzIRIaIiMxKcTIzffp0VK1aFZUrV0Z6ejpCQkLQqlUrNG/eHJ988okpYiRrcvq0PBpp/vynZSNGyB18X3jBcnEREVGppXgG4DyXLl3CyZMnoVar0aBBA6tdq4kzABuJWi137t2/X7v81i3dGhoiIqJiMukMwHFxcQgPD0dQUBCC8s8jQiXTjh1A167aZd9/Dwwdapl4iIiInqE4mWnfvj18fHzQt29f9O/fH6GhoaaIi6xBejpQsaI87DqPr688s6+Li+XiIiIieobiPjM3b97E+PHjsW/fPoSFhSEsLAyzZs3C9evXTREfWcqcOfKw6mcTmT17gJs3mcgQEZFVKXKfGQBITEzEDz/8gHXr1uGvv/5Cq1atsGfPHmPGV2zsM6PQ1au6M/VGRsrzxnAZAiIiMhMl9+9iJTMAkJubi19++QWTJ09GQkICcnNzi3M4o2Myo8CAAcCaNdplFy8CNWpYJh4iIiq1lNy/i7TQJAAcOHAA7733Hnx9fdG3b1/UrVsX27dvL+rhyJIOH5ZrXZ5NZD79VB5uzUSGiIisnOIOwB9//DHWrVuHmzdvol27dpg3bx569OiBMmXKmCI+MqXsbCAsDPjrL+3yBw+A8uUtEhIREZFSimtmYmNj8eGHH+LGjRvYsWMH+vbtq0lkTp06Zez4yFR++AFwctJOZNavl2tjmMgQEZENUVwzc/DgQa3nKSkpWLt2LZYsWYI//vjD6vrMUD4PHgAeHtpl9eoB8fGAg+I/ByIiIosrcp+ZPXv2oH///vD19cXXX3+Nzp074/jx48aMjYxt0iTdROboUSAhgYkMERHZLEV3sOvXr2PFihVYtmwZMjIy8PrrryM7OxsbN25ESEiIqWKk4jp/HqhdW7ts8GBg2TLLxENERGREBtfMdO7cGSEhITh37hy+/vpr3Lx5E19//bUpY6PiEgLo1Ek3kbl+nYkMERGVGAbXzOzatQsjR47EsGHDrHZRSXrGb78B7dtrl82bB3zwgUXCISIiMhWDa2b27duHtLQ0vPjii2jatCm++eYb3Llzx5SxUVE8fiyvp/RsIuPuDmRkMJEhIqISyeBkplmzZli8eDGSkpLwzjvvYP369ahcuTLUajV2796NtLQ0U8ZJhvjuO6BMGeDevadlO3cCqalyORERUQlUrOUMzp8/j6VLl2L16tV4+PAh2rdvj61btxozvmIrFcsZJCUBfn7aZW3aALt3A3ZFHrBmFZ7kqLH60GVcuf8IAR5lMKBZNTg52PZ7IiKi5zPr2kyAvD7Ttm3bsGzZMiYz5vbuu8D332uXnT0LlIDRZVE7z2HxvkSon/kLtZOAf7cMxMTOtv/+iIioYGZPZqxZiU1mTp4EGjbULvvwQ+DLLy0Tj5FF7TyH7/cmFrj9nVZMaIiISjKzLDRJFpKbC7z4om4ic+dOiUlknuSosXhfwYkMACzel4gnOWozRURERNaMyYwt2bRJnqn3xImnZStWyPPJVKxosbCMbfWhy1pNS/qohbwfERER57C3BampgEqlXRYYKC8S6eRkmZhM6Mr9R0bdj4iISjbWzFi76dN1E5n9+4F//imRiQwABHgYNozc0P2IiKhkYzJjrRITAUkCPvnkadnrrwNqNfDyy5aLywwGNKsGO6nwfewkeT8iIiImM9ZGCKBXL6B6de3yf/4BfvxRTnBKOCcHO/y7ZWCh+/y7ZSDnmyEiIgBMZoosVy1w6NI9/HzqBg5duofc5/VYNcS+ffIkdxs3Pi374gs5wQks/OZe0kzsHIJ3WgXq1NDYSRyWTURE2jjPTBFEn0nC1G3nkJSSqSnzVblgSmQIOoX6Kj/gkydArVrAlStPy+zsgIcP5XWVSjHOAExEVDpx0rxnGDuZiT6ThGFr4pH/ouVVICzo31BZQrN8OTBkiHbZ5s1Ajx7FiJKIiMi2Kbl/c2i2ArlqganbzukkMgAgICc0U7edQ/sQH9g/rwfr3buAl5d2WdOmwIEDgL29kSImIiIq+Vhfr8DRxPtaTUv5CQBJKZk4mni/8AONHaubyJw8CRw+zESGiIhIIdbMKJCcVnAiY9B+Z88CoaHaZe+9B3z7bTEjIyIiKr2YzCjg7e5StP3UaqBNGyAuTrs8KQnw8TFSdERERKWTRZuZFixYgLCwMJQrVw7lypVDs2bN8Msvv2jt8+eff6Jbt25QqVRwd3fHSy+9hKtXr1ok3iaBHvBVuaCg3jAS5FFNTQI9nhbu3Ck3HT2byCxYIA+3ZiJDRERUbBZNZqpUqYIZM2bg+PHjOH78ONq0aYPu3bvj7NmzAIBLly6hRYsWqF27NmJjY/HHH39g8uTJcHExrIbE2OztJEyJlOc3yZ/Q5D2fEhkid/7NyADKlgW6dHm6k7c38Pgx8O67ZomXiIioNLC6odkeHh748ssv8dZbb6F3795wdHTE6tWri3w8i8wz89//AmPGaL/o99/lpiYiIiJ6Lpscmp2bm4uffvoJGRkZaNasGdRqNXbs2IHx48ejY8eOOHnyJAIDAzFx4kT0KGQOlqysLGRlZWmep6amGj3WTqG+aB/ig6OJ95Gclglvd7lpyf7mDd3lBrp0AbZtKxXLEBAREVmCxYdmnz59GmXLloWzszPeffddbN68GSEhIUhOTkZ6ejpmzJiBTp06YdeuXejZsydeffVVxOXvSPuMqKgoqFQqzcPf398kcdvbSWgW5Inu9SujWZAn7IcMBvKf68IFYPt2JjJEREQmZPFmpidPnuDq1at4+PAhNm7ciCVLliAuLg7ly5dH5cqV0adPH/zwww+a/bt16wY3NzesW7dO7/H01cz4+/sbtZlJy9Gj8mR3z/rkE+A//zH+uYiIiEoJm2pmcnJyQo0aNQAAL774Io4dO4b58+fj66+/hoODA0JCtBcUrFOnDvbv31/g8ZydneHs7GzSmAHIo5HCwoAzZ7TL798HKlQw/fmJiIgIgBU0M+UnhEBWVhacnJzQuHFjnD9/Xmv7hQsXEBAQYKHonrFpk3Yis26dnOAwkSEiIjIri9bMfPzxx3jllVfg7++PtLQ0rF+/HrGxsYiOjgYAjBs3Dm+88QZatWqFiIgIREdHY9u2bYiNjbVk2LKwMKBZM+DRI+D4ccDB4pVcREREpZJF78C3b9/GgAEDkJSUBJVKhbCwMERHR6N9+/YAgJ49e2LhwoWIiorCyJEjERwcjI0bN6JFixaWDFtWsyZw8KCloyAiIir1LN4B2NRMMc8MERERmZaS+7fV9ZkhIiIiUoLJDBEREdk0JjNERERk05jMEBERkU1jMkNEREQ2jckMERER2TQmM0RERGTTmMwQEVmCWg3MmwfUqwe4ugJeXsBrrwHnzhXteIcPA927AxUrAi4uQK1a8qK3jx7p3/+zzwBJKvjx0UdFfWfW4coV4Ouvgc6dgerVAWdn+dp06gRs3Vrw6+LigKlTgS5d5N+JJAG1axc9jsKusSQBmZm6r2nduvDX/P8s+fQU5+AnIjI3IYA33gA2bADKl5dvnHfvAhs3Ajt2ADExQNOmhh9v7VrgzTeB3FygUSOgalV5mZXp04Ht24F9+wB3d/2vffll4P8X+9XSqFGR3prV6NcPOHBAThSbNpUfly4Bv/4qP0aPBubO1X3dBx8Af/xh3Fjc3IBevfRvs7cv+HX/+hdQtqxueeXKxomrBGEyQ0RkbsuXy4lMzZpyolGpkly+caN80+vXD/jrL8PWfLt+HXj7bTmRWbYMGDxYLs/KAgYMAH76CRg/HliwQP/r334bGDTIKG/LqlStKr///v3lZCLPjh1Ajx7Af/8r19J06KD9ug4dgNdfBxo3lmtyGjYsfiwVKwIrVih/3ezZQLVqxT9/KcBmJiIic5szR/45a9bTRAaQv4l36ybXIPz8s2HHWrFCbqpo3/5pIgPIzSrffguUKQMsXQrcu2e08E1q0CC5KeXy5eId54cfgHfe0U5kALkWbMgQ+d/r1um+btYs4OOP5etZoULxYiCzYTJDRLbt2DH55vfyywXvM3WqvM/nn5svroIkJsr9Ylxd5RtrfnnNEdu2GXa8Eyfkn61b627z8gJCQoDsbGDnziKFq3HqlJwgeXoCN27obh88WL7Gw4cX7zzm8MIL8s+bNy0bBxkNm5mIyLY1biz37zh4EDh7FqhbV3u7Wi0369jba9dcWEpef4zQUMDRUXd7XrOGof02MjLknwXVInh4PD3egAG62/fskROVzEygShXglVf095epX1/ugzNunNw/Z/duOXkB5CazFSvkjrKzZxsWtyX984/808fHPOfLyJCv3dWrck1ZgwbAq6/q7w/zrLwaNTs7uUN3jx5y8xnpEiVcSkqKACBSUlIsHQoRmcqiRUIAQowapbvtl1/kbZGRhh8vPFx+jZJHTIxhx54/X96/Z0/92x8+lLd7eBh2vL595f0nTNC/PThY3v6vf2mXT5lS8Hv517+ESEvTPZZaLUSbNvI+c+bIZdevy7E6OgoRH29YzIV58035+ImJxT+WPg8eCOHlJZ9j48bC901MlPcLDi76+Qq6xp6eQmzfrv81Bf39OToKMW1a0WOxMUru36yZISLb17cv8OGHwOrVwIwZcnNIniVL5J///rfhx+vUSXnHS0O/5aenyz/LlNG/Pa+PR95+zxMeLvcPWbcOmDYNcHJ6uu3wYeD8efnfaWnar6tRQ65FeeUVICAAePAA2LtX7iy8caPcoXjzZu3XSBKwciUQFib3K2nbFhg7Frh/X77uDRoYFrMlDRsG3LkDvPQS0LOn6c83cKD891mvHqBSARcvyqOoVq+Wa2f27QOaNNF+TatWcsfs5s0BX1/g2jW59uvzz4FPPwXKlZNHXZGGJIQQlg7ClFJTU6FSqZCSkoJy5cpZOhwiMpX33pNH7KxbB/TuLZclJ8tNJ15echV/YcNgzWX6dHn+l/795Rtafjk5cvOTk5M8Iul5MjLkfjFXr8pJ2OzZclPEgQNyAnfrlnzMTp2AX355/vGSkuQb77178jGaN9fd56ef5BE/5coBqalyQrVnj9wcYqi//pIToPz275c7QOsblly7dvHmv5kxA5g4UW56O3ZMnn+mMJcvA4GBQHCwHK8xffwxEBUldzTetcuw1+zaBXTsKCdFSUlyv6sSTNH92+T1RBbGZiaiUuKPP+Sq+DZtnpbNnCmXffKJ5eLKz9jNTEIIkZAgRNWqus0SVasKMX68/O8+fQw/3ocfyq/59NOC9+nZU97HzU2IK1cMP3aemBjlTXnh4crPk2fFCiEkSY730CHDXmOMZqaCPHwohL293HSUlWX46158UY5pzx7jx2Rl2MxERKVPWJjcdBATI3+zDwqSO1BKEvDWW8qONWOG8m/iH31k2EyxeR04r1/Xvz2vXElHz3r15Hh/+kmeLC8nRx6x07fv0xFc+TtGF6ZmTflnUpL+7UlJcvMIIM8w/Ndfyjumtm4tpyj5DRokN2UlJhpvjpWff5b/BhwdgU2b5L8TS1OpAG9v+VrevQv4+Rn2upo15d9xQb+bUorJDBGVHO++K/cTWbpUro6/cEGeBE3pTTE6Wp7WXolBgwxLZvKGBZ85Iw+Zzj+iKT5e/hkWpuz8rq5y/4yBA7XLf/tN/qlv6HZBHjyQf+obbSOE/F7v3pWTpR9/lJ8nJMiTw1mb2Fh5tmVAnik5/yR5lqJWy010wPNHNT2rsN9NKcZ5Zoio5Hj9dXmI8ooVT2e8VdLxN09srNIGEMOThcBAoE4d4PFjeTba/DZskH927ao87vzi4uTkqG7dwufheZYQTzv+6hui/dVXct+Nli3lPj8TJsi1BEW5zqZ24oQ8CeGTJ3JH8IKWFLCE6Gi5v1ONGnLfI0PcufO0RswYMxOXIExmiKjkyKudSEqSawy8vOTFF63NmDHyz/Hj5U7KeTZtkhdBDAyU5xR51ubNcs1P/poXQJ4nJidHuyw+Xq45kSR5wcVn3b0LrFql28E4PV0e7XPkiDw6K/9onzNn5Oa0cuXkRMbOTl6wslEjYMuWpyPHrMH58/JIrbQ0YP580y7ZMHGi/Lv55hvt8g0b5NrB/OLiniZ/772nve3wYbmpNH8T3OXL8u8jI0NO0KpUMVr4JQGbmYioZHnnHfnmBcg3MH0T01nakCHyjLx5CUrbtnKCERcnr3i9Zo1u3Ckp8g1a3xDwUaPkWYXr15ebei5flhMSOzvg+++BiAjt/dPT5Ynv3n9friWqWhV4+FBOgO7dkxe/3LBBe/h4Vpa8ZlRmJrBokTycG5DjXLNGrikYNUquodK3cKW59e4t12R4eck1NPqSGX2jo5YseZqU5SV7V65o97P57jvtmpGkJPl3c/eu9rG2b5dXQq9ZU05Q84Zmnzr1NMb8Q6z/+kue3NHXV54oz8dH7kd14oR87evWBRYvVno1SjwmM2TVctUCRxPvIzktE97uLmgS6AF7O8nSYZE1q1NH7kx586Y8V4c1srOTO+vOny8vDrl9uzy/TM+e8lwxSjrrAvIw7zVr5Jvkw4fyDbx3b3m23vr1dff39JSbhw4fBv7+W36dvb18wx00SF5ROv/KzBMnyv1iXntNdybhvJl/hw+XY9m/37BFMk0pr2/JnTtyh2J9wsN1k5nr1+VE8FmZmdpleX1dnueNN+QasxMngKNH5STSw0OuMRoyRH+zV9OmT2vHzp2Th8e7ucm/x9dek7eV8CHZRcF5ZshqRZ9JwtRt55CUkqkp81W5YEpkCDqF+lowMrJqBw/K/UPCw+W+L0Rkk5Tcv9lnhqxS9JkkDFsTr5XIAMCtlEwMWxOP6DMclkgF+OIL+eeIEZaNg4jMhskMWZ1ctcDUbeegr8owr2zqtnPIVZfoSkVS4uBBeR6Rpk3lEUKNGslTxRNRqcBkhqzO0cT7OjUyzxIAklIycTTxvvmCIut24YLc9+TPP4HISHlUkJKp9YnIprEDMFmd5LSCE5mi7EelwKBBph16S0RWjV9dyOp4u7sYdT8iIirZmMyQ1WkS6AFflQsKGoAtQR7V1CTQw5xhERGRlWIyQ1bH3k7ClMgQANBJaPKeT4kM4XwzREQEgMkMWalOob5Y0L8hKpVz1iqvVM4ZC/o35DwzRESkwWSGrFxBdTNEREQyJjNklfImzbuVqj1i6XYqJ80jIiJtTGbI6nDSPCIiUoLJDFkdTppHRERKMJkhq8NJ84iISAkmM2R1OGkeEREpwWSGrA4nzSMiIiWYzJDV4aR5RESkBJMZskp5k+b5qLSbknxULpw0j4iItHDVbLJanUJ90T7EB0cT7yM5LRPe7nLTEmtkiIjoWUxmyKrZ20loFuRp6TCIiMiKsZmJiIiIbBqTGSIiIrJpTGaIiIjIpjGZISIiIpvGZIaIiIhsmkWTmQULFiAsLAzlypVDuXLl0KxZM/zyyy96933nnXcgSRLmzZtn3iCJiIjIqlk0malSpQpmzJiB48eP4/jx42jTpg26d++Os2fPau23ZcsWHDlyBH5+fhaKlIiIiKyVRZOZyMhIdO7cGbVq1UKtWrUwffp0lC1bFocPH9bsc+PGDYwYMQJr166Fo6OjBaMlIiIia2Q1k+bl5ubip59+QkZGBpo1awYAUKvVGDBgAMaNG4e6detaOEIiIiKyRhZPZk6fPo1mzZohMzMTZcuWxebNmxESIi8yOHPmTDg4OGDkyJEGHy8rKwtZWVma5ykpKQCA1NRU4wZOREREJpN33xZCPHdfiyczwcHBOHXqFB4+fIiNGzfizTffRFxcHB4/foz58+cjPj4ekmT4WjxRUVGYOnWqTrm/v78xwyYiIiIzSEtLg0qlKnQfSRiS8phRu3btEBQUhDp16mDMmDGws3varSc3Nxd2dnbw9/fH5cuX9b4+f82MWq3G/fv34enpqSgpKqlSU1Ph7++Pa9euoVy5cpYOp8TidTYPXmfz4HU2D15nbUIIpKWlwc/PTysX0MfiNTP5CSGQlZWFAQMGoF27dlrbOnbsiAEDBmDw4MEFvt7Z2RnOzs5aZeXLlzdFqDYtbzg8mRavs3nwOpsHr7N58Do/9bwamTwWTWY+/vhjvPLKK/D390daWhrWr1+P2NhYREdHw9PTE56e2qslOzo6wsfHB8HBwRaKmIiIiKyNRZOZ27dvY8CAAUhKSoJKpUJYWBiio6PRvn17S4ZFRERENsSiyczSpUsV7V9QPxkynLOzM6ZMmaLTFEfGxetsHrzO5sHrbB68zkVndR2AiYiIiJTgQpNERERk05jMEBERkU1jMkNEREQ2jckMERER2TQmMyXQ3r17ERkZCT8/P0iShC1btujs8+eff6Jbt25QqVRwd3fHSy+9hKtXr5o/WBv3vGudnp6OESNGoEqVKnB1dUWdOnWwYMECywRro6KiotC4cWO4u7vD29sbPXr0wPnz57X2EULgs88+g5+fH1xdXdG6dWucPXvWQhHbpudd5+zsbEyYMAH16tWDm5sb/Pz8MHDgQNy8edOCUdsmQ/6mn/XOO+9AkiTMmzfPfEHaGCYzJVBGRgZeeOEFfPPNN3q3X7p0CS1atEDt2rURGxuLP/74A5MnT4aLi4uZI7V9z7vWo0ePRnR0NNasWYM///wTo0ePxvvvv4+ff/7ZzJHarri4OAwfPhyHDx/G7t27kZOTgw4dOiAjI0Ozz6xZszB37lx88803OHbsGHx8fNC+fXukpaVZMHLb8rzr/OjRI8THx2Py5MmIj4/Hpk2bcOHCBXTr1s3CkdseQ/6m82zZsgVHjhyBn5+fBSK1IYJKNABi8+bNWmVvvPGG6N+/v2UCKsH0Xeu6deuKadOmaZU1bNhQfPLJJ2aMrGRJTk4WAERcXJwQQgi1Wi18fHzEjBkzNPtkZmYKlUolFi5caKkwbV7+66zP0aNHBQBx5coVM0ZW8hR0ra9fvy4qV64szpw5IwICAsR///tfywRoA1gzU8qo1Wrs2LEDtWrVQseOHeHt7Y2mTZvqbYqi4mvRogW2bt2KGzduQAiBmJgYXLhwAR07drR0aDYrJSUFAODh4QEASExMxK1bt9ChQwfNPs7OzggPD8fBgwctEmNJkP86F7SPJElc/66Y9F1rtVqNAQMGYNy4cahbt66lQrMZTGZKmeTkZKSnp2PGjBno1KkTdu3ahZ49e+LVV19FXFycpcMrcb766iuEhISgSpUqcHJyQqdOnfDdd9+hRYsWlg7NJgkhMGbMGLRo0QKhoaEAgFu3bgEAKlWqpLVvpUqVNNtIGX3XOb/MzEx89NFH6Nu3LxdFLIaCrvXMmTPh4OCAkSNHWjA622F1q2aTaanVagBA9+7dMXr0aABA/fr1cfDgQSxcuBDh4eGWDK/E+eqrr3D48GFs3boVAQEB2Lt3L9577z34+vrqrApPzzdixAgkJCRg//79OtskSdJ6LoTQKSPDFHadAbkzcO/evaFWq/Hdd9+ZObqSRd+1PnHiBObPn4/4+Hj+DRuINTOlTMWKFeHg4ICQkBCt8jp16nA0k5E9fvwYH3/8MebOnYvIyEiEhYVhxIgReOONNzB79mxLh2dz3n//fWzduhUxMTGoUqWKptzHxwcAdGphkpOTdWpr6PkKus55srOz8frrryMxMRG7d+9mrUwxFHSt9+3bh+TkZFStWhUODg5wcHDAlStXMHbsWFSrVs1yAVsxJjOljJOTExo3bqwzDPDChQsICAiwUFQlU3Z2NrKzs2Fnp/3fzN7eXlNDRs8nhMCIESOwadMm7NmzB4GBgVrbAwMD4ePjg927d2vKnjx5gri4ODRv3tzc4dqs511n4Gkic/HiRfz222/w9PS0QKS273nXesCAAUhISMCpU6c0Dz8/P4wbNw6//vqrhaK2bmxmKoHS09Px999/a54nJibi1KlT8PDwQNWqVTFu3Di88cYbaNWqFSIiIhAdHY1t27YhNjbWckHbqOdd6/DwcIwbNw6urq4ICAhAXFwcVq1ahblz51owatsyfPhw/PDDD/j555/h7u6uqYFRqVRwdXWFJEkYNWoUvvjiC9SsWRM1a9bEF198gTJlyqBv374Wjt52PO865+TkoFevXoiPj8f27duRm5ur2cfDwwNOTk6WDN+mPO9ae3p66iSKjo6O8PHxQXBwsCVCtn6WG0hFphITEyMA6DzefPNNzT5Lly4VNWrUEC4uLuKFF14QW7ZssVzANux51zopKUkMGjRI+Pn5CRcXFxEcHCzmzJkj1Gq1ZQO3IfquLwCxfPlyzT5qtVpMmTJF+Pj4CGdnZ9GqVStx+vRpywVtg553nRMTEwvcJyYmxqKx2xpD/qbz49DswklCCGHqhImIiIjIVNhnhoiIiGwakxkiIiKyaUxmiIiIyKYxmSEiIiKbxmSGiIiIbBqTGSIiIrJpTGaIiIjIpjGZISKrsGLFCpQvX17RawYNGoQePXoYLYY9e/agdu3aipab+PDDD7myMZGFMZkhIkUWLlwId3d35OTkaMrS09Ph6OiIli1bau27b98+SJKECxcuPPe4b7zxhkH7KVWtWjXMmzfPoH3Hjx+PSZMm6ayn9bzXLF++HImJiUWMkIiKi8kMESkSERGB9PR0HD9+XFO2b98++Pj44NixY3j06JGmPDY2Fn5+fqhVq9Zzj+vq6gpvb2+TxGyIgwcP4uLFi3jttdcUvc7b2xsdOnTAwoULTRQZET0PkxkiUiQ4OBh+fn5aC5PGxsaie/fuCAoKwsGDB7XKIyIiAMgrWY8fPx6VK1eGm5sbmjZtqnUMfc1Mn3/+Oby9veHu7o63334bH330EerXr68T0+zZs+Hr6wtPT08MHz4c2dnZAIDWrVvjypUrGD16NCRJgiRJBb6v9evXo0OHDnBxcVEcQ7du3bBu3bpCrhoRmRKTGSJSrHXr1oiJidE8j4mJQevWrREeHq4pf/LkCQ4dOqRJZgYPHowDBw5g/fr1SEhIwGuvvYZOnTrh4sWLes+xdu1aTJ8+HTNnzsSJEydQtWpVLFiwQGe/mJgYXLp0CTExMVi5ciVWrFiBFStWAAA2bdqEKlWqYNq0aUhKSkJSUlKB72nv3r148cUXixRDkyZNcO3aNVy5cqXwC0dEpmHplS6JyPYsWrRIuLm5iezsbJGamiocHBzE7du3xfr160Xz5s2FEELExcUJAOLSpUvi77//FpIkiRs3bmgdp23btmLixIlCCCGWL18uVCqVZlvTpk3F8OHDtfZ/+eWXxQsvvKB5/uabb4qAgACRk5OjKXvttdfEG2+8oXlu6GrDKpVKrFq1SqvMkBiEECIlJUUAELGxsc89DxEZH2tmiEixiIgIZGRk4NixY9i3bx9q1aoFb29vhIeH49ixY8jIyEBsbCyqVq2K6tWrIz4+HkII1KpVC2XLltU84uLicOnSJb3nOH/+PJo0aaJVlv85ANStWxf29vaa576+vkhOTlb8nh4/fqzTxGRoDK6urgCg1V+IiMzHwdIBEJHtqVGjBqpUqYKYmBg8ePAA4eHhAAAfHx8EBgbiwIEDiImJQZs2bQAAarUa9vb2OHHihFbiAQBly5Yt8Dz5+7gIIXT2cXR01HmNkqHVeSpWrIgHDx4UKYb79+8DALy8vBSfl4iKjzUzRFQkERERiI2NRWxsLFq3bq0pDw8Px6+//orDhw9r+ss0aNAAubm5SE5ORo0aNbQePj4+eo8fHByMo0ePapU9O4LKUE5OTsjNzX3ufg0aNMC5c+eKFMOZM2fg6OiIunXrKo6PiIqPyQwRFUlERAT279+PU6dOaWpmADmZWbx4MTIzMzXJTK1atdCvXz8MHDgQmzZtQmJiIo4dO4aZM2di586deo///vvvY+nSpVi5ciUuXryIzz//HAkJCYWOSNKnWrVq2Lt3L27cuIG7d+8WuF/Hjh2xf//+IsWwb98+tGzZUtPcRETmxWSGiIokIiICjx8/Ro0aNVCpUiVNeXh4ONLS0hAUFAR/f39N+fLlyzFw4ECMHTsWwcHB6NatG44cOaK1z7P69euHiRMn4sMPP0TDhg2RmJiIQYMG6fRreZ5p06bh8uXLCAoKKrQZqH///jh37hzOnz+vOIZ169bh3//+t6K4iMh4JKGvAZiIyAq1b98ePj4+WL16tUmOP378eKSkpOD77783OIYdO3Zg3LhxSEhIgIMDuyESWQL/5xGRVXr06BEWLlyIjh07wt7eHuvWrcNvv/2G3bt3m+yckyZNwrfffovc3FzY29sbFENGRgaWL1/ORIbIglgzQ0RW6fHjx4iMjER8fDyysrIQHByMTz75BK+++mqpioGIno/JDBEREdk0dgAmIiIim8ZkhoiIiGwakxkiIiKyaUxmiIiIyKYxmSEiIiKbxmSGiIiIbBqTGSIiIrJpTGaIiIjIpjGZISIiIpv2f0qT+mEZ0U2BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "\n",
    "# Same code from scatter plot above:\n",
    "cap = clean_pydf.loc[clean_pydf[\"Drug Regimen\"] == \"Capomulin\"]\n",
    "cap_df = cap.groupby(['Mouse ID']).mean()\n",
    "x_cap = cap_df['Weight (g)']\n",
    "y_cap = cap_df['Tumor Volume (mm3)']\n",
    "\n",
    "\n",
    "# Calculate correction coefficeint between mouse weight and average tumor volume\n",
    "correlation = st.pearsonr(x_cap,y_cap)\n",
    "print(f\"The correlation between mouse weight and the average tumor volume is {round(correlation[0],2)}.\")\n",
    "\n",
    "\n",
    "# Calculate linear regression \n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_cap, y_cap)\n",
    "regress_values = x_cap * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "\n",
    "# Create scatter plot and labels\n",
    "plt.scatter(x_cap, y_cap)\n",
    "plt.plot(x_cap,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(20,35),fontsize=15,color=\"red\")\n",
    "\n",
    "plt.title('Average Tumor Volume versus Mouse Weight')\n",
    "plt.xlabel('Weight (g)')\n",
    "plt.ylabel('Average Tumor Volume (mm3)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b0515f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}