pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations and Insights "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "Pymaceuticals Observations and Results\n",
    "\n",
    "Upon cleaning the data by removing the duplicated mouse with the same timepoint, the data is presented with all the mice test subjects which has been given different drug regimen in order identify the effectiveness of the drugs against the tumor. \n",
    "\n",
    "<img src=\"images/mice_treatment.png\" />\n",
    "Capomulin and Ramicane are the top 2 drugs with the most test subjects, with over 200 tests. \n",
    "\n",
    "<img src=\"images/mice_genderdist.png\" />\n",
    "According to the Gender pie chart, the mice gender are not skewed to one gender, hence there is no gender bias in the test results.\n",
    "\n",
    "<img src=\"images/mice_capomulin.png\" />\n",
    "Looking at the box plot, Capomulin and Ramicane are more effective agaisnt tumor due to the closeness to the median. Whereas there is an outlier for Infubinol and Ceftamin may not be as effective due to the lower and higher quartile are close to the extremes.  \n",
    "\n",
    "<img src=\"images/miceweight_avgtum.png\" />\n",
    "\n",
    "$r = frac{Nsum{XY}-(sum{X}sum{Y})}{sqrt{ [N sum{x^2}-(sum{x})^2 ][N sum{y^2}-(sum{y})^2 }]}$\n",
    "                                            (Pearson's R)\n",
    "\n",
    "The correlation coefficient of mouse weight vs. average tumor volume is 0.84. Since it is close to 1, we can conclude that the variables are positively linearly related.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </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",
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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",
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       "      <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>z969</td>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>z969</td>\n",
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       "      <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",
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       "  </tbody>\n",
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       "<p>1893 rows × 8 columns</p>\n",
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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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as sts\n",
    "import numpy as np\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "mouse_results = pd.merge(mouse_metadata, study_results, on=\"Mouse ID\", how=\"outer\")\n",
    "# Display the data table for preview\n",
    "mouse_results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 249 mice.\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "total_mice = mouse_results.nunique()['Mouse ID']\n",
    "\n",
    "print(f'There are a total of {total_mice} mice.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    Mouse ID  Timepoint\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "mouse_results['Duplicated'] = mouse_results.duplicated(subset=['Mouse ID','Timepoint'])\n",
    "\n",
    "duplicated_mice = mouse_results[mouse_results['Duplicated']==True] \n",
    "duplicated_mice[['Mouse ID','Timepoint']] #showing only the duplicated mice by their ID and Timepoint\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <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",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <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",
       "      <td>True</td>\n",
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      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  Duplicated  \n",
       "909           45.000000                 0        True  \n",
       "911           47.570392                 0        True  \n",
       "913           49.880528                 0        True  \n",
       "915           53.442020                 0        True  \n",
       "917           54.657650                 1        True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "duplicated_mice\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    </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",
       "      <td>False</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",
       "      <td>False</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",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1893 rows × 9 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  Duplicated  \n",
       "0              45.000000                 0       False  \n",
       "1              38.825898                 0       False  \n",
       "2              35.014271                 1       False  \n",
       "3              34.223992                 1       False  \n",
       "4              32.997729                 1       False  \n",
       "...                  ...               ...         ...  \n",
       "1888           63.145652                 2       False  \n",
       "1889           65.841013                 3       False  \n",
       "1890           69.176246                 4       False  \n",
       "1891           70.314904                 4       False  \n",
       "1892           73.867845                 4       False  \n",
       "\n",
       "[1893 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "cleaned_mice = mouse_results[mouse_results['Mouse ID'].isin(duplicated_mice)==False] #dropping the duplicates\n",
    "cleaned_mice.reset_index(drop= True, inplace=True) #reset the index without having the index in the column\n",
    "\n",
    "cleaned_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After cleaning the data, the total of mice is 249.\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "cleaned_total = len(cleaned_mice['Mouse ID'].unique())\n",
    "\n",
    "print(f'After cleaning the data, the total of mice is {cleaned_total}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trail Count</th>\n",
       "      <th>Tumor Mean</th>\n",
       "      <th>Tumor Median</th>\n",
       "      <th>Tumor Variance</th>\n",
       "      <th>Tumor Std Dev</th>\n",
       "      <th>SEM of Tumor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>230</td>\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>178</td>\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>178</td>\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>188</td>\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>186</td>\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>181</td>\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>161</td>\n",
       "      <td>52.322552</td>\n",
       "      <td>50.854632</td>\n",
       "      <td>42.351070</td>\n",
       "      <td>6.507770</td>\n",
       "      <td>0.512884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>228</td>\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>181</td>\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>182</td>\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": [
       "              Trail Count  Tumor Mean  Tumor Median  Tumor Variance  \\\n",
       "Drug Regimen                                                          \n",
       "Capomulin             230   40.675741     41.557809       24.947764   \n",
       "Ceftamin              178   52.591172     51.776157       39.290177   \n",
       "Infubinol             178   52.884795     51.820584       43.128684   \n",
       "Ketapril              188   55.235638     53.698743       68.553577   \n",
       "Naftisol              186   54.331565     52.509285       66.173479   \n",
       "Placebo               181   54.033581     52.288934       61.168083   \n",
       "Propriva              161   52.322552     50.854632       42.351070   \n",
       "Ramicane              228   40.216745     40.673236       23.486704   \n",
       "Stelasyn              181   54.233149     52.431737       59.450562   \n",
       "Zoniferol             182   53.236507     51.818479       48.533355   \n",
       "\n",
       "              Tumor Std Dev  SEM of Tumor  \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           6.507770      0.512884  \n",
       "Ramicane           4.846308      0.320955  \n",
       "Stelasyn           7.710419      0.573111  \n",
       "Zoniferol          6.966589      0.516398  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "tumor_mean = cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].mean()\n",
    "tumor_median = cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].median()\n",
    "tumor_var = cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].var()\n",
    "tumor_sd = cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].std()\n",
    "tumor_sem = cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].sem()\n",
    "trail_count = cleaned_mice.groupby(['Drug Regimen'])['Mouse ID'].count()\n",
    "\n",
    "# This method is the most straighforward, creating multiple series and putting them all together at the end.\n",
    "tumor_stats_df = pd.DataFrame({'Trail Count': trail_count, 'Tumor Mean': tumor_mean, 'Tumor Median': tumor_median,'Tumor Variance': tumor_var, \n",
    "                               'Tumor Std Dev': tumor_sd, 'SEM of Tumor': tumor_sem})\n",
    "\n",
    "tumor_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capomulin</th>\n",
       "      <td>230</td>\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>178</td>\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>178</td>\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>188</td>\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>186</td>\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>181</td>\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>161</td>\n",
       "      <td>52.322552</td>\n",
       "      <td>50.854632</td>\n",
       "      <td>42.351070</td>\n",
       "      <td>6.507770</td>\n",
       "      <td>0.512884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>228</td>\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>181</td>\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>182</td>\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": [
       "             Tumor Volume (mm3)                                             \\\n",
       "                          count       mean     median        var       std   \n",
       "Drug Regimen                                                                 \n",
       "Capomulin                   230  40.675741  41.557809  24.947764  4.994774   \n",
       "Ceftamin                    178  52.591172  51.776157  39.290177  6.268188   \n",
       "Infubinol                   178  52.884795  51.820584  43.128684  6.567243   \n",
       "Ketapril                    188  55.235638  53.698743  68.553577  8.279709   \n",
       "Naftisol                    186  54.331565  52.509285  66.173479  8.134708   \n",
       "Placebo                     181  54.033581  52.288934  61.168083  7.821003   \n",
       "Propriva                    161  52.322552  50.854632  42.351070  6.507770   \n",
       "Ramicane                    228  40.216745  40.673236  23.486704  4.846308   \n",
       "Stelasyn                    181  54.233149  52.431737  59.450562  7.710419   \n",
       "Zoniferol                   182  53.236507  51.818479  48.533355  6.966589   \n",
       "\n",
       "                        \n",
       "                   sem  \n",
       "Drug Regimen            \n",
       "Capomulin     0.329346  \n",
       "Ceftamin      0.469821  \n",
       "Infubinol     0.492236  \n",
       "Ketapril      0.603860  \n",
       "Naftisol      0.596466  \n",
       "Placebo       0.581331  \n",
       "Propriva      0.512884  \n",
       "Ramicane      0.320955  \n",
       "Stelasyn      0.573111  \n",
       "Zoniferol     0.516398  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "\n",
    "# cleaned_mice.groupby(['Drug Regimen'])['Tumor Volume (mm3)'].describe(include='all')\n",
    "# this only shows count, mean, std, min, 25, 50, 75 and Max, no Variance and SEM\n",
    "\n",
    "# This method produces everything in a single groupby function\n",
    "another_method = cleaned_mice.groupby('Drug Regimen').agg({'Tumor Volume (mm3)':['count','mean','median','var','std','sem']})\n",
    "another_method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total number of Mice for each treatment'}, xlabel='Drug Regimen', ylabel='Trail Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pandas. \n",
    "mice_treatment = tumor_stats_df[['Trail Count']]\n",
    "mice_treatment.plot(kind='bar', title='Total number of Mice for each treatment', \n",
    "                    color='green',width=0.5, ylabel=\"Trail Count\",legend=False )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pyplot.\n",
    "\n",
    "type_of_drugs = tumor_stats_df.index\n",
    "trial_count = tumor_stats_df['Trail Count']\n",
    "plt.title('Total number of Mice for each treatment')\n",
    "plt.xlabel('Drug Regimen')\n",
    "plt.ylabel('Trail Count')\n",
    "plt.bar(type_of_drugs, trail_count, color='green', width=0.5)\n",
    "tick_locations = [value for value in type_of_drugs]\n",
    "plt.xticks(tick_locations, rotation= 90)\n",
    "plt.savefig(\"images/mice_treatment.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gender Distribution'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "mice_gend = cleaned_mice.groupby(['Sex'])['Mouse ID'].count()\n",
    "mice_gend.plot(kind='pie',colors=['red','blue'], startangle=90, autopct = \"%.1f%%\", ylabel='', title='Gender Distribution')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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12efbgGnA7GQfbwN2qqNWCIGUmX8B/oP1m1x7su5J/gx4toFtxxNO4f164O+AicBs4B+zKriybiP8H/tj8vM3CC+becnlNwbY7tPAO4E/ETqSU4jQ3rO6VzRzM7u65ucRZvaKmd04yHYzBltnIM2c/dgIb/7pA9y/Ornsqbne+/MIM5sBHAxMd/cVZjaLDWf8NuAqd/9iE/Vl9onNjYRR0GmEJlKvy4FTga8C7wFGNbAtwOeSBULX5avAZcD/EsYqTs+i+Mq7nnW/+WOAGcB5fdbpAO4gvPkh/CVHAZ3AGsAJ4TCS0Kk8lRycW3k5sKeZjXb3lYT5O55v5Q6beUM9CWxuZtMBzGykmTVy7PlEYHESELvR/7kfbgWOMLMtkn1sYmHi33rUncqDuYvQYtgROBL4PXA0sBvhDT0HOArYuYFtaz2YXE4G/hv4H0Lfa15WTyBDaxnRFbuGgRlwCCGS/yu5bQGhp0ty+XI/2z1NGDM8ltC2O47wHmx7e88bXP83wKHJ9aOAa3rvMLM3mdndZvZgcrlr343NbKyZXW5ms5P10p9YHWMSy/q5bSohgh8CHgOOrxmTeGNyfQZwY802s4A3EroDvyG05X6W3D6jdkyiZqxjbrLeHODNdY5JdLei03gb68YVFiSX3eAfAf9hA9vWLoeCPw++GHz/5LajwOfG7+RvsLzMZgtzUMYAy/PJ5QKHvR1ud5jYZ51J/Ww322G4w73Jz6c6nN7Pep9weMDhBw4fcDg76+ewpN7xAWAZocH5c0ILfG7tew2YAIxIrh8MXNv3/QicAxydXJ8E/BkYO9A+B+1uuPu4fm6bC7yln9tn1FyfRU1Lu/Y+BjiVuLvvWHP9p8BPB6svhmuAi5Lr7yf8H4IwSnsccFMdj/FLYF9gm+Tn3o9U9yafZyFZxrjVg68VS+9vcQvC5033E6a1eJHQiniR/g+f2S5Zes+ZfAQbjl08mFxOJoxf3EFoG85jw6G0pjX0u3X3h5PjlY5iw5fbROAqM3sd4PTfPzoEeI+ZfTb5eWPCQYhPDLTDci0DD2NrGcLyMHs+nYMy+lmWOXTUXJ/u8BuHzzqcm9x+rsO/DbD9AQ5/Sq7/e7Jd7f2HemipLHbYP7ntKIe5WT6P+Y20JJLLMwkz1O3F+q2EK4FTk+s7AvP7aUnMAXatd59lPCz71dgFlFEn49fErqF/C4ADCO2vNxG66u8EvgDcTPhvf3PyM4T2Xu00nBcAHya04eYCX6q575esa+9NYl17z8i4vbeyiW0uB77q7o/0uX0i6wYyPzbAtr8D/tnMDMDM0mcSi/6fP+sF7srBv7fSLb/lkIdzUEZZlzmNtiT63DaDda2E6YQxhruAs+m/JTEauJTw2e6j1Iwd9reUcQJYtSRaoIMJa2PXUGKL6l3R+x8jnEUy/ufu97D+OV7P6GedlcCJ9e5T3Q2pSwcTcvwRaOH9NXYBaRQSUpelTOyOXUOJPRO7gDRlDIlXYhdQRh1MUEi0jloSbaaWRAt0MKEndg0lppBos1z/wouqgwkeu4YSU3ejzR6OXUAZdTI+dgll5Qz8ReJcKF9IuL8KvBS7jLJRSLTMS+7k9EC1oHwhEfQ9Ck2GaBkbfDwv2Zgfu4DBlDUk1OXI2ArG1Hv6QGnMnNgFDKasIaGWRMaWM7asr5XY7oldwGDK+odXSyJjKxldxkP480AhEckThJMTSkZWsbFCInsL3Pm/2EUMppwh4b4KeDx2GWWyhlEKiezdG7uAepQzJIKbYxdQJl2M7O98vzI0ue9qQLlD4rexCygThURLKCQiuwNYEbuIsuhmeKbzmQhrWTdJSK6VNyTChEC3xy6jLBQSmbvHvRj/xMobEsHvYhdQFo5lPsdqxf08dgH1suScd+UUJib5U+wyysDo6QEr+z+VdnFge/fWzryVlXL/0d2fJOdfwy2ClWy8WgGRqXuLEhBQ9pAIfh27gKJbzthmTvkuAytMVwOqERJXxi6g6FYwRiGRLYVErrjPRl/4GpL8TsxTSLPdi3X2tPKHRPDD2AUUWSfjczwPaOEUqhUB1QmJmZDvs//kmVoSmekhp5Ngp6lGSLgvBK6PXUZRdTJeE/Nk40b34n3aVo2QCNTlaJKm+MvMBbELaEaVQuJmdLr9pixlokJi6P7kzi2xi2hGdULCvYcwk7I0SBPzZOJ7sQtoVnVCIriABmZwlkDzgA7Zy8AVsYtoVrVCwr0T+HbsMoqmk/El/oJPW1zgzqrYRTSrWiERfA9YGLuIItHEPEOyHLgodhFDUb2QUGuiYR1MiF1CkV3gzuLYRQxF9UIiuAC1Juq2jHEWu4aCWgCcE7uIoapmSITWxLdil1EUyxmrkGjOGe50xi5iqKoZEsGFwCuxiygCTfHXlIcoyQF81Q0J92XA52OXUQQrGa2QaNxp7pTi+JLqhgSA+xXArNhl5J1m72rYDe78PnYRWal2SAQnAvoqdIrVbDQydg0F0gV8NnYRWVJIuP+ZEoxAt5Km+GvIhe7Mi11Elsp9tux6mY0C5gJTIleSS6NZ8ZdVjN45dh0F8CTwhqLMp1EvtSQA3NcAJxBOdS59rGWEJuYZXBfw4bIFBCgk1nH/A3BZ7DLyqIdhG8euoQDOdGdO7CJaQd2NWmbjgfuB3WKXkidGzzKwcbHryLHbgYPK8pFnXwqJvsx2JwTF2Nil5IXR0w2mYyX6twTYp2hnwG6Euht9uT8OHB+7jLxYzaguBUSqU8ocEKCQ6J/7NRT0fIRZ08Q8qWa6c03sIlpNITGwzwD3xC4itmWMU0j0717CJ2Klp5AYiHsX8AEq/iWw5Ywt7BmVWuhp4D3uVCJAFRJp3J8HjgQqe7ZoTcyzgUXAu9yr889DITEY998Dx1LRA60UEutZDbzXnSdjF9JOCol6uM8ETotdRgwdTNDsXYEDx7pzZ+xC2k0hUS/37wLnxi6j3ZYyUSERnFGFTzL6o5BohPuXKPAkK83oZHxlx2NqXODO12MXEYtColHunwa+H7uMdtHsXXzbnVNjFxGTQqI5n6QiXwZbysQqh8S57nwmdhGxKSSaEb7wcgIVGKOocEviTHe+FLuIPFBINMvdkzGKEyjxcRQVnOKvBzjZnbNjF5IXComhcv8B8G4o/vwK/elkfJXm3FgNfMidS2IXkicKiSy4/xY4EHg+dilZq9A8oC8Bh7jz89iF5I1CIivuDwFvBh6JXUqWljO2Cq+ROwnnprwjdiF5VIUXQPu4PwccAPwidilZqUBI/CfhrFIvxi4kr8r+Amg/9w7c308Y0Cz8SVFLPHtXB3CEO591L+/AcxYUEq0SBjTfAMU+OWpJZ+96BHijO9fGLqQIFBKt5P4kMB04D4p5ktSSzd7lhIPg3ly2CXRaSSHRau5duH8BOBh4LnY5jVrDqLKExBPADHeOL+PcGK2kkGgX99uAvYH/okCtirWMKHpIrALOAKbq04vmKCTayX0x7icSxipmRa6mLgWfvesWYC93vuaOTp7TJIVEDO4P4f4PwBHA/MjVpOpmeBFn73oF+Ig7b3fnqdjFFJ1CIib3awmTFJ8OLI9cTb8KNsXfEuArwGR3ZsYtpTw0g1demG0DnAkcA+TmjWn0rAXL+8egi4DvAN9zpyN2MWWjkMgbsy2ATwGnAJvGLKWLEWtH0ZXngHgV+DZwoXs5v2CXBwqJvDIbA3wM+FdglxglLGVC5ySW5vEbXi8D3wIuds9nN61MNCaRV+4rcL8Y2BU4nAiziS1jXJ4m5nHgVsI8KNu7800FRHvkuSkpAO49wHXAdZhNIbxJPkQIj5bKSUg8A/wIuNydv8QuporU3Sgqs6msC4wdW7GLB3j9U9N4IEZXZxHwP4RwuMu9mhMj5YVaEkXlPheYC3wBs/0IYfF2YA8gk7NJdTChXQcgrQHuJhz8dAvwR3e627RvGYRCogzc7wPuA8BsE+DvCWfKOhCYBjR1aHULZ+9y4CHWhcKd+j5FfikkysZ9EfCrZAGz0cB+hMCYCuycLOMGe6iMQmIh4ctVj9dcPlilCXeLTiFRdu4rCd8TmbXe7eF4jJ37LDsCk4DxwISlTFw9wKN2E07asiRZltZcXwQ8RRIK7ryczRORWDRwKanMGEboroxKljXAMg0mVodCQkRS6WAqEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUmlkBCRVAoJEUn1/wG/54V2wpjwuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "\n",
    "mice_gender = cleaned_mice.groupby(['Sex'])['Mouse ID'].count()\n",
    "labels = ['Female', 'Male']\n",
    "colors = ['red','blue']\n",
    "\n",
    "plt.pie(mice_gender, labels=labels, colors=colors,\n",
    "        autopct=\"%.1f%%\", shadow=False, startangle=90)\n",
    "plt.title('Gender Distribution')\n",
    "plt.savefig(\"images/mice_genderdist.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>Last Timepoint</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</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",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1893 rows × 9 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  Last Timepoint  \n",
       "0              45.000000                 0              45  \n",
       "1              38.825898                 0              45  \n",
       "2              35.014271                 1              45  \n",
       "3              34.223992                 1              45  \n",
       "4              32.997729                 1              45  \n",
       "...                  ...               ...             ...  \n",
       "1888           63.145652                 2              45  \n",
       "1889           65.841013                 3              45  \n",
       "1890           69.176246                 4              45  \n",
       "1891           70.314904                 4              45  \n",
       "1892           73.867845                 4              45  \n",
       "\n",
       "[1893 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "# Start by getting the last (greatest) timepoint for each mouse\n",
    "\n",
    "last_timepoint = pd.DataFrame(cleaned_mice.groupby('Mouse ID')['Timepoint'].max()).rename(columns={'Timepoint': 'Last Timepoint'})\n",
    "\n",
    "last_timepoint.reset_index(inplace=True)\n",
    "\n",
    "# print(last_timepoint) # to test output\n",
    "\n",
    "merged_results = pd.merge(cleaned_mice, last_timepoint, on='Mouse ID')\n",
    "merged_results.pop('Duplicated') #for a cleaner look as Column Duplicated no longer required\n",
    "merged_results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile for Capomulin is: 32.37735684\n",
      "The upper quartile for Capomulin is: 40.1592203\n",
      "The interquartile range for Capomulin is: 7.781863460000004\n",
      "The the median result for Capomulin is: 38.125164399999996 \n",
      "Values below 20.70456164999999 for Capomulin could be outliers.\n",
      "Values above 51.83201549 for Capomulin could be outliers.\n",
      "---------------------------------------------\n",
      "The lower quartile for Ramicane is: 31.56046955\n",
      "The upper quartile for Ramicane is: 40.65900627\n",
      "The interquartile range for Ramicane is: 9.098536719999998\n",
      "The the median result for Ramicane is: 36.56165229 \n",
      "Values below 17.912664470000003 for Ramicane could be outliers.\n",
      "Values above 54.30681135 for Ramicane could be outliers.\n",
      "---------------------------------------------\n",
      "The lower quartile for Infubinol is: 54.04860769\n",
      "The upper quartile for Infubinol is: 65.52574285\n",
      "The interquartile range for Infubinol is: 11.477135160000003\n",
      "The the median result for Infubinol is: 60.16518046 \n",
      "Values below 36.83290494999999 for Infubinol could be outliers.\n",
      "Values above 82.74144559000001 for Infubinol could be outliers.\n",
      "---------------------------------------------\n",
      "The lower quartile for Ceftamin is: 48.72207785\n",
      "The upper quartile for Ceftamin is: 64.29983003\n",
      "The interquartile range for Ceftamin is: 15.577752179999997\n",
      "The the median result for Ceftamin is: 59.85195552 \n",
      "Values below 25.355449580000002 for Ceftamin could be outliers.\n",
      "Values above 87.66645829999999 for Ceftamin could be outliers.\n",
      "---------------------------------------------\n",
      "\n",
      "\n",
      "These are the Outlier Mice\n"
     ]
    },
    {
     "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",
       "      <th>Last Timepoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>24.365505</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>22.050126</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>s185</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>23.343598</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "8      k403     Ramicane    Male          21          16         40   \n",
       "9      k403     Ramicane    Male          21          16         45   \n",
       "19     s185    Capomulin  Female           3          17         45   \n",
       "\n",
       "    Tumor Volume (mm3)  Metastatic Sites  Last Timepoint  \n",
       "8            24.365505                 1              45  \n",
       "9            22.050126                 1              45  \n",
       "19           23.343598                 1              45  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "four_drugs = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "treatments = []\n",
    "\n",
    "i = 0 #use this to as a row counter\n",
    "\n",
    "for drug in four_drugs:\n",
    "    temporary = merged_results.loc[merged_results['Drug Regimen'] == drug]\n",
    "    final_results = temporary.loc[temporary['Timepoint'] == temporary['Last Timepoint']] # to get rows with only the Last Timepoint\n",
    "    # Create a series that consists of all final volume values for each regimen\n",
    "    values = final_results['Tumor Volume (mm3)']\n",
    "    treatments.append(values)\n",
    "    quartiles = values.quantile([.25,.5,.75])\n",
    "    \n",
    "    lowerq = quartiles[0.25]\n",
    "    print(f'The lower quartile for {drug} is: {lowerq}')\n",
    "    \n",
    "    upperq = quartiles[0.75]\n",
    "    print(f'The upper quartile for {drug} is: {upperq}')\n",
    "    # Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "    iqr = upperq-lowerq\n",
    "    print(f'The interquartile range for {drug} is: {iqr}')\n",
    "    print(f'The the median result for {drug} is: {quartiles[0.5]} ')\n",
    "    \n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "    print(f'Values below {lower_bound} for {drug} could be outliers.')\n",
    "    print(f'Values above {upper_bound} for {drug} could be outliers.')\n",
    "   \n",
    "    print(f'---------------------------------------------')\n",
    "    \n",
    "    # Locate the rows which contain mice on each drug and get the tumor volumes\n",
    "    # Determine outliers using upper and lower bounds\n",
    "outlier_mice = merged_results.loc[(merged_results['Tumor Volume (mm3)'] < lower_bound) | (merged_results['Tumor Volume (mm3)'] > upper_bound)]\n",
    "print(f'\\n')\n",
    "print(f'These are the Outlier Mice')\n",
    "outlier_mice\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "\n",
    "# Plot boxplot\n",
    "plt.boxplot(treatments)\n",
    "\n",
    "# Add title and labels to boxplot\n",
    "plt.title('Results for Tumor Volume (mm3) by Drugs')\n",
    "plt.ylabel('Tumor Volume (mm3)')\n",
    "plt.xticks([1, 2, 3, 4], ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin'])\n",
    "plt.savefig(\"images/mice_4drugs.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following are the mice treated with Capomulin: \n",
      " ['s185' 'x401' 'm601' 'f966' 'u364' 'y793' 'r554' 'm957' 't565' 'i738'\n",
      " 'w914' 'g288' 'l897' 'b742' 'b128' 'j246' 'j119' 'w150' 'v923' 'g316'\n",
      " 's710' 'l509' 'r944' 'i557' 'r157']\n",
      "Please enter Mouse ID to view treatment data:f966\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin\n",
    "capomulin = merged_results[merged_results['Drug Regimen'] =='Capomulin']\n",
    "capo_mouse = capomulin['Mouse ID']\n",
    "print(f'The following are the mice treated with Capomulin: \\n {capo_mouse.unique()}') \n",
    "#Displays the list of Mouse ID treated with Capomulin \n",
    "\n",
    "mouseinput= input('Please enter Mouse ID to view treatment data:') #get user input\n",
    "\n",
    "mouseoutput = capomulin[capomulin['Mouse ID'] == mouseinput]\n",
    "mouse_time = mouseoutput['Timepoint']\n",
    "mouse_tumvol = mouseoutput['Tumor Volume (mm3)']\n",
    "\n",
    "plt.title(f'Mouse ID: {mouseinput} Treatment Data')\n",
    "plt.xlabel('Timepoint')\n",
    "plt.ylabel('Tumor Volume(mm3)')\n",
    "plt.plot(mouse_time, mouse_tumvol, marker = 'p')\n",
    "plt.savefig(\"images/mice_capomulin.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen\n",
    "\n",
    "avg_tumvol = pd.DataFrame(capomulin.groupby('Mouse ID')['Tumor Volume (mm3)'].mean()) #getting average tumor volume\n",
    "mouse_weight = capomulin[['Mouse ID','Weight (g)']].drop_duplicates()\n",
    "avg_tumvol = pd.merge(avg_tumvol, mouse_weight, on='Mouse ID') #merging into one with weight corresponding to average tumor volume\n",
    "\n",
    "x_axis = avg_tumvol['Weight (g)']\n",
    "y_axis = avg_tumvol['Tumor Volume (mm3)']\n",
    "\n",
    "# Create the scatter plot \n",
    "plt.scatter(x_axis, y_axis)\n",
    "plt.xlabel('Weight (g)')\n",
    "plt.ylabel('Average Tumor Volume (mm3)')\n",
    "plt.title('Average Tumor Volume by Weight')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "correlation = sts.pearsonr(x_axis,y_axis)\n",
    "print(f'The correlation between both factors is {round(correlation[0],2)}')\n",
    "\n",
    "mt_slope, mt_int, mt_r, mt_p, mt_std_err = sts.linregress(x_axis, y_axis)\n",
    "mt_fit = mt_slope * x_axis + mt_int\n",
    "line_equation = f'y = {str(round(mt_slope,2))}x + {str(round(mt_int,2))}'\n",
    "\n",
    "\n",
    "plt.scatter(x_axis, y_axis)\n",
    "plt.xlabel('Weight (g)')\n",
    "plt.ylabel('Average Tumor Volume (mm3)')\n",
    "plt.title('Average Tumor Volume by Weight (Capomulin)')\n",
    "plt.annotate(line_equation,(18,38),fontsize=13,color='black')\n",
    "plt.plot(x_axis,mt_fit,':r')\n",
    "plt.savefig(\"images/miceweight_avgtum.png\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}