Pymaceuticals_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis\n",
    "* Overall, it is clear that Capomulin outperforms all other treatment options in the screen.\n",
    "* Capomulin was the only treatment to reduce tumor volume. It held to a 19% reduction in tumor volume over the course of trial, whereas all other drugs were correlated with an increase in tumor volume by roughly 40-50%.\n",
    "* Capomulin greatly limited the spread of the tumor compared to other treatment options. By study end, the average mouse on Capomulin had only 1 new metastatic site, as opposed to the average 2-3 found in mice of other treatment options.\n",
    "* Lastly, mice on the Capomulin treatment had the highest survival rate of any treatment in the screen. Over 90% of mice treated by Capomulin survived the full duration of the trial, compared to only 35-45% of mice on other treatment options. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f932</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites       Drug\n",
       "0     b128          0                45.0                 0  Capomulin\n",
       "1     f932          0                45.0                 0   Ketapril\n",
       "2     g107          0                45.0                 0   Ketapril\n",
       "3     a457          0                45.0                 0   Ketapril\n",
       "4     c819          0                45.0                 0   Ketapril"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Hide warning messages in notebook\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "mouse_drug_data_to_load = \"data/mouse_drug_data.csv\"\n",
    "clinical_trial_data_to_load = \"data/clinicaltrial_data.csv\"\n",
    "\n",
    "# Read the Mouse and Drug Data and the Clinical Trial Data\n",
    "mouse_drug_data = pd.read_csv(mouse_drug_data_to_load)\n",
    "clinical_data = pd.read_csv(clinical_trial_data_to_load)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "clinical_data_complete = pd.merge(clinical_data, mouse_drug_data, how=\"left\", on=[\"Mouse ID\", \"Mouse ID\"])\n",
    "\n",
    "# Display the data table for preview\n",
    "clinical_data_complete.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tumor Response to Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>5</td>\n",
       "      <td>44.266086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>10</td>\n",
       "      <td>43.084291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>15</td>\n",
       "      <td>42.064317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>20</td>\n",
       "      <td>40.716325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>25</td>\n",
       "      <td>39.939528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>30</td>\n",
       "      <td>38.769339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>35</td>\n",
       "      <td>37.816839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>40</td>\n",
       "      <td>36.958001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>45</td>\n",
       "      <td>36.236114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>5</td>\n",
       "      <td>46.503051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>10</td>\n",
       "      <td>48.285125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>15</td>\n",
       "      <td>50.094055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>20</td>\n",
       "      <td>52.157049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>25</td>\n",
       "      <td>54.287674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>30</td>\n",
       "      <td>56.769517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>35</td>\n",
       "      <td>58.827548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>40</td>\n",
       "      <td>61.467895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>45</td>\n",
       "      <td>64.132421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>5</td>\n",
       "      <td>47.062001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>10</td>\n",
       "      <td>49.403909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>15</td>\n",
       "      <td>51.296397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>20</td>\n",
       "      <td>53.197691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>25</td>\n",
       "      <td>55.715252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>30</td>\n",
       "      <td>58.299397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>35</td>\n",
       "      <td>60.742461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>40</td>\n",
       "      <td>63.162824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Infubinol</td>\n",
       "      <td>45</td>\n",
       "      <td>65.755562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>5</td>\n",
       "      <td>43.944859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>10</td>\n",
       "      <td>42.531957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>15</td>\n",
       "      <td>41.495061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>20</td>\n",
       "      <td>40.238325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>25</td>\n",
       "      <td>38.974300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>30</td>\n",
       "      <td>38.703137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>35</td>\n",
       "      <td>37.451996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>40</td>\n",
       "      <td>36.574081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Ramicane</td>\n",
       "      <td>45</td>\n",
       "      <td>34.955595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>5</td>\n",
       "      <td>47.527452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>10</td>\n",
       "      <td>49.463844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>15</td>\n",
       "      <td>51.529409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>20</td>\n",
       "      <td>54.067395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>25</td>\n",
       "      <td>56.166123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>30</td>\n",
       "      <td>59.826738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>35</td>\n",
       "      <td>62.440699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>40</td>\n",
       "      <td>65.356386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>45</td>\n",
       "      <td>68.438310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>5</td>\n",
       "      <td>46.851818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>10</td>\n",
       "      <td>48.689881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>15</td>\n",
       "      <td>50.779059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>20</td>\n",
       "      <td>53.170334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>25</td>\n",
       "      <td>55.432935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>30</td>\n",
       "      <td>57.713531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>35</td>\n",
       "      <td>60.089372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>40</td>\n",
       "      <td>62.916692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Zoniferol</td>\n",
       "      <td>45</td>\n",
       "      <td>65.960888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Drug  Timepoint  Tumor Volume (mm3)\n",
       "0   Capomulin          0           45.000000\n",
       "1   Capomulin          5           44.266086\n",
       "2   Capomulin         10           43.084291\n",
       "3   Capomulin         15           42.064317\n",
       "4   Capomulin         20           40.716325\n",
       "5   Capomulin         25           39.939528\n",
       "6   Capomulin         30           38.769339\n",
       "7   Capomulin         35           37.816839\n",
       "8   Capomulin         40           36.958001\n",
       "9   Capomulin         45           36.236114\n",
       "10   Ceftamin          0           45.000000\n",
       "11   Ceftamin          5           46.503051\n",
       "12   Ceftamin         10           48.285125\n",
       "13   Ceftamin         15           50.094055\n",
       "14   Ceftamin         20           52.157049\n",
       "15   Ceftamin         25           54.287674\n",
       "16   Ceftamin         30           56.769517\n",
       "17   Ceftamin         35           58.827548\n",
       "18   Ceftamin         40           61.467895\n",
       "19   Ceftamin         45           64.132421\n",
       "20  Infubinol          0           45.000000\n",
       "21  Infubinol          5           47.062001\n",
       "22  Infubinol         10           49.403909\n",
       "23  Infubinol         15           51.296397\n",
       "24  Infubinol         20           53.197691\n",
       "25  Infubinol         25           55.715252\n",
       "26  Infubinol         30           58.299397\n",
       "27  Infubinol         35           60.742461\n",
       "28  Infubinol         40           63.162824\n",
       "29  Infubinol         45           65.755562\n",
       "..        ...        ...                 ...\n",
       "70   Ramicane          0           45.000000\n",
       "71   Ramicane          5           43.944859\n",
       "72   Ramicane         10           42.531957\n",
       "73   Ramicane         15           41.495061\n",
       "74   Ramicane         20           40.238325\n",
       "75   Ramicane         25           38.974300\n",
       "76   Ramicane         30           38.703137\n",
       "77   Ramicane         35           37.451996\n",
       "78   Ramicane         40           36.574081\n",
       "79   Ramicane         45           34.955595\n",
       "80   Stelasyn          0           45.000000\n",
       "81   Stelasyn          5           47.527452\n",
       "82   Stelasyn         10           49.463844\n",
       "83   Stelasyn         15           51.529409\n",
       "84   Stelasyn         20           54.067395\n",
       "85   Stelasyn         25           56.166123\n",
       "86   Stelasyn         30           59.826738\n",
       "87   Stelasyn         35           62.440699\n",
       "88   Stelasyn         40           65.356386\n",
       "89   Stelasyn         45           68.438310\n",
       "90  Zoniferol          0           45.000000\n",
       "91  Zoniferol          5           46.851818\n",
       "92  Zoniferol         10           48.689881\n",
       "93  Zoniferol         15           50.779059\n",
       "94  Zoniferol         20           53.170334\n",
       "95  Zoniferol         25           55.432935\n",
       "96  Zoniferol         30           57.713531\n",
       "97  Zoniferol         35           60.089372\n",
       "98  Zoniferol         40           62.916692\n",
       "99  Zoniferol         45           65.960888\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Tumor Volume Data Grouped by Drug and Timepoint \n",
    "tumor_vols_mean = clinical_data_complete.groupby([\"Drug\", \"Timepoint\"]).mean()[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Convert to DataFrame\n",
    "tumor_vols_mean = pd.DataFrame(tumor_vols_mean)\n",
    "\n",
    "# Flatten the column headings\n",
    "tumor_vols_mean = tumor_vols_mean.reset_index()\n",
    "\n",
    "# Preview DataFrame\n",
    "tumor_vols_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>5</td>\n",
       "      <td>0.448593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>10</td>\n",
       "      <td>0.702684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>15</td>\n",
       "      <td>0.838617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>20</td>\n",
       "      <td>0.909731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Drug  Timepoint  Tumor Volume (mm3)\n",
       "0  Capomulin          0            0.000000\n",
       "1  Capomulin          5            0.448593\n",
       "2  Capomulin         10            0.702684\n",
       "3  Capomulin         15            0.838617\n",
       "4  Capomulin         20            0.909731"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Standard Error of Tumor Volumes Grouped by Drug and Timepoint\n",
    "tumor_vols_sem = clinical_data_complete.groupby([\"Drug\", \"Timepoint\"]).sem()[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Convert to DataFrame\n",
    "tumor_vols_sem = pd.DataFrame(tumor_vols_sem)\n",
    "\n",
    "# Preview DataFrame\n",
    "tumor_vols_sem.head().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Drug</th>\n",
       "      <th>Capomulin</th>\n",
       "      <th>Ceftamin</th>\n",
       "      <th>Infubinol</th>\n",
       "      <th>Ketapril</th>\n",
       "      <th>Naftisol</th>\n",
       "      <th>Placebo</th>\n",
       "      <th>Propriva</th>\n",
       "      <th>Ramicane</th>\n",
       "      <th>Stelasyn</th>\n",
       "      <th>Zoniferol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timepoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44.266086</td>\n",
       "      <td>46.503051</td>\n",
       "      <td>47.062001</td>\n",
       "      <td>47.389175</td>\n",
       "      <td>46.796098</td>\n",
       "      <td>47.125589</td>\n",
       "      <td>47.248967</td>\n",
       "      <td>43.944859</td>\n",
       "      <td>47.527452</td>\n",
       "      <td>46.851818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43.084291</td>\n",
       "      <td>48.285125</td>\n",
       "      <td>49.403909</td>\n",
       "      <td>49.582269</td>\n",
       "      <td>48.694210</td>\n",
       "      <td>49.423329</td>\n",
       "      <td>49.101541</td>\n",
       "      <td>42.531957</td>\n",
       "      <td>49.463844</td>\n",
       "      <td>48.689881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42.064317</td>\n",
       "      <td>50.094055</td>\n",
       "      <td>51.296397</td>\n",
       "      <td>52.399974</td>\n",
       "      <td>50.933018</td>\n",
       "      <td>51.359742</td>\n",
       "      <td>51.067318</td>\n",
       "      <td>41.495061</td>\n",
       "      <td>51.529409</td>\n",
       "      <td>50.779059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.716325</td>\n",
       "      <td>52.157049</td>\n",
       "      <td>53.197691</td>\n",
       "      <td>54.920935</td>\n",
       "      <td>53.644087</td>\n",
       "      <td>54.364417</td>\n",
       "      <td>53.346737</td>\n",
       "      <td>40.238325</td>\n",
       "      <td>54.067395</td>\n",
       "      <td>53.170334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug       Capomulin   Ceftamin  Infubinol   Ketapril   Naftisol    Placebo  \\\n",
       "Timepoint                                                                     \n",
       "0          45.000000  45.000000  45.000000  45.000000  45.000000  45.000000   \n",
       "5          44.266086  46.503051  47.062001  47.389175  46.796098  47.125589   \n",
       "10         43.084291  48.285125  49.403909  49.582269  48.694210  49.423329   \n",
       "15         42.064317  50.094055  51.296397  52.399974  50.933018  51.359742   \n",
       "20         40.716325  52.157049  53.197691  54.920935  53.644087  54.364417   \n",
       "\n",
       "Drug        Propriva   Ramicane   Stelasyn  Zoniferol  \n",
       "Timepoint                                              \n",
       "0          45.000000  45.000000  45.000000  45.000000  \n",
       "5          47.248967  43.944859  47.527452  46.851818  \n",
       "10         49.101541  42.531957  49.463844  48.689881  \n",
       "15         51.067318  41.495061  51.529409  50.779059  \n",
       "20         53.346737  40.238325  54.067395  53.170334  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "tumor_vols_mean = tumor_vols_mean.reset_index()\n",
    "tumor_vols_pivot_mean = tumor_vols_mean.pivot(index=\"Timepoint\", columns=\"Drug\")[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "tumor_vols_sem = tumor_vols_sem.reset_index()\n",
    "tumor_vols_pivot_sem = tumor_vols_sem.pivot(index=\"Timepoint\", columns=\"Drug\")[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Preview that Reformatting worked\n",
    "tumor_vols_pivot_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vW7eOSVY3FgkJCQwaNIhWrVrx008/pdcVFxdHYGAgffv2pXv37rRp0ybdXUVmzuo0Gk3hQER478/3qFiqIm1rtLUbadHZOHVnsVKqNNAFsH0MTwdWKKWeB84A/W6njax8zi1YYPiWmz3bBRHYsgVeeAHq1IFnn3XY59wt9O3bl7lz5/LQQw9x5coVZs6cmb6RLDAwMNMyZ86c4e+//6ZkyZL4+fnRu3fG9fHy5cuzePFiFi5cyKpVq+jfv3+mzuo0Gs3dz4oDK3B3cWd65+kOKYCw0DDwc65MTlUEIpIAVLwpLRrDiihPyMrn3KRJxsM/Kcno6OvXYe1aOHkS3Nwc9jl3C2lO3mrUqEFsbGyGa1n5/bnnnnuoUKECAK6urqSmpmZaZ82aNdmxY0eWzuo0Gs3dS1xyHJFxkXi6edKlbhfHC4Y5TaR0CvXO4rlzISHhxvmVKzB9+u3VmZ2TtzRncUCG6ZywsDBiY2OJj48nKSmJEiUy6t+b68zKWZ1Go7k7OXf1HO9sfAd3F/ecKQHAu7y3c4SyoVArgiNHQOTGQzYhAdascV57nTt35p9//qFr164cOXIkPb1mzZqMHDmShx9+mICAALv12DqrGzhwINNvV3tpNJoCwSIWPvn3EyxiYW7XuVR1r5qj8hEREQQGBqZ7P3YajqwoF/TndqyGrl69KkXZGEJbDWWN7ouM6P7IyO32x6X4S7Lz3E4JPhec6zpefvllUUrJyJEjc1WeO8hqqMDRLoA0Gk1+sjlsMx9u+5DmVZrTulrrXNURERHBl19+iYjw5ZdfOnVUUCQUgUaj0eQHSalJzN85n1amVkzvPB3XEq65rmvy5MlYLBbA2Bg7derUvBLzFrQi0Gg0mjwgKTWJlQdX0q1eN8q6ls11Pampqbz//vt8/fXXJCcbHhGSk5P59ttv80rUWyi8Eco0Go0mn/hmzzdcSbrCq/e/mus6Tp48ycKFCxkyZAgTJkzg0qVLfPHFFyQnJ+Pi4sLAgQPzUOKMFIkRQVBYUEGLoNFoCiGx12NZe2wtft5+uVYCISEhTJs2jTJlyjBx4kQaNWpEqVKlCAgIoFgx4xFdvHhxhywOc4tWBA4QFhbGU089BcCRI0fw8fEhPDz81naCgjh69OhttbVu3Tp+/PFHAHx87PuK0mg0BYNFLMz5Zw5tq7elZrmaOS6/ZcsWNmzYQEJCAuPHj8fLyws3N7f06yaTiaFDh6KUYujQoVStmjPT05xQqKeGbGN+Tt48Gch5zE9bwsPDGThwIEuXLqVGjRq3XA8KCsLHx4cGDRrkqn6LxULXrl1zVVaj0eQtobGh+GXi28FsMTNn+xxaerVkcsfJOapTRFi3bh116tTh8uXLPPHEE+lv/ZkREBDA2rVrnToagEKuCPIy5ufly5d56qmn+Pzzz2nQoAHBwcG89dZbpKam0qtXL1555RUCAwP5/vvv+f7775k+fTrPPfccycnJVKlSheXLl3P27FkGDRpElSpVOHnyJPPnz6ddu3bcd999tG/fnitXrvDII48QFxfHq6/mfq5Ro9HcPqGxobekhV8NJzohmsfrP06Tyk0crstsNnPgwAFOnjxJmTJlaNCgAQ0bNrRbzmQy4e/v79TRABQCRRAUFkRQWBBPNn6SoLAgohOjGd56OAtCFmRZJiw2jMDQQFqbDPteD1cP/Lz9sm1n165ddOjQgdZWT3Xjx4/nhx9+wNPTkz59+jBo0CD8/f3x8fGhR48eJCcns27dOkqUKMHo0aP5888/qV+/PuHh4WzcuJHIyEiGDRvGH3/8QUxMDG+88Qb16tXL0nGdRqMpWE7FnOLL0C+Z8NAESpV0LI5ISkoK8fHxzJo1ix49etzicNIRwsJyXCTH3PWKwM/bL/0h3tzrhte5SX6T0qeDbsa7vDeT/CblqJ3OnTtjMpmYNGkSkyZNYt++ffTp0weAmJiYdB9DaVy+fJkRI0YQExNDREQELVu2pH79+jRr1gwXFxdq1aqV7kzO09PT4UA7Go0mf0lISeC/f/2XV+9/lSkdpzhUJjU1lX379rF8+XJGjBjBBx98cBsS+N1GWce46xVBfjJ37lx69+7NN998Q8uWLVm1ahXlypXDbDZTrFgx1q9fj9lsBmDJkiU8+uijjBw5ktGjR6c7qDtw4AApKSlERUVRrlw5gGznCDUaTf5iu7b41WQjvHrl0pWZ2smxDV2LFy/m9OnTTJo0Kd2z8O3gbWe2Ii8o1IrAq4zXLWsCtxPzs3jx4nz33Xd07tyZGTNm0LdvXywWCy4uLvz000906tSJ8ePHs3nzZgYPHsygQYNYv349ZcqUoUWLFgBUr16dfv36cfr0aebPn39b96fRaPKezNYRLyZczLZMXFwcn3zyCU2bNuXZZ5/NYP1zN1CoFUHk2EiuXbtGSHSI3TWA7PD29mbVqlUAuLu7s337dgA2btyYIV+7du3YsmVL+vm+ffsyXA8LC8PLyyu9rjSCg4PTj/39/TNN12g0dx7nzp1j0aJFPP/88wwbNowqVarkaf3R0UYQLX9/8PbO06ozUKgVQRq3owQ0Gk3h58r1K4YBiYMO4o4fP87mzZvx8fHhjTfeSJ/mzWsWLyY91O769U5pAigiiuBOwXZkodFoCp59Ufv4/tD3vHjfi/Rp3Ida5WoZF64Bq4CnAA+b/Pv2YbFYCA0NZcCAAZQuXTrPZUpNhdWroWlTmDkTRODvv+G336BbtzxvDnB+zOLywCKgGSDAMOAx4EUgbdLtHRFZ60w5NBqNJo1USyo/H/4Zl+IuVC5Tmbcfehu3Ejfm9L3KeBG1OsqIqL4Z6AEVYiuwf/9+du7cycCBA2nZsmWey3XuHBw4AHFxULUqnD9vhNgFI6jW88/fCLWb1zh7RDAPWCciTymlXIDSGIpgjojMcnLbGo1Gk86lhEt8FfoVfRr3oZpHNR6o8UCmweN3P7ebOgF1uC7XcdvvxrQB06jWsBpNmjShWbNmeSqTxQIbN0KdOvDrrzBwIFSqZFzr2xfi42/kTQu1m9t469nhNLtFpVRZoAPwBYCIJItIbPal8p7IyEhatmzp/FBvGo3mjiTkfAjf7fuOAxcO8GzzZ6njWYd2NdtlqgQgYxyA1NRUTpw4wTPPPJOnZt7R0UbY3N9/N87vuQfeeOOGEoC0ULs3zp0ZaleJiP1cualYqXuBBcBBoCUQAowC3gL8gatAMDBGRGIyKT8cGA7g5eXVetmyZenXypUr5/AGrDfeeIPFixfzwgsv8NFHH+XqXk6fPo2fnx+NGzcmJSWFgQMHcvToUaZNm5ar+tLw9fVl8+bNt1WHPY4fP56+cQ0MMzd3d3entnm3oPsiI4WpP1IsKWy+uJkGHg04Hnechys9TMliJbMvk5LCrl27CAgIICUlJT29dOnSrMmDJ7AI7N9flvLlU/jnn4p07HiBypWT7ZYLDPTG3z8sV2127NgxRETse690JJ5lbj6AD5AKtLWezwOmAl5AcYzRyDRgsb26chuz+Pz58+Lm5iaAlCpVSiIiIhwqdzOnTp2SJ598UkREVq1aJfXq1ZMxY8bkqi5bbr4vZ6BjFmeN7ouMFIb+OH/1vCzZu0T+Ov2XhJwPsZvfYrFIUlKSTJgwQRYvXiwWi0VefvllcXFxEUBcXFxyHS84jStXRH78UeSvv0R++EEkJSVn5SdOzH3b3AExi8OBcBHZYT1fBdwnIlEiYhYRC7AQuN9ZAkydOjXPQ701a9YsgwvqsWPH4ufnx/33309oqOGk6t9//+Whhx7C19eX2bNnA/Cf//wHX19fOnTokL6/wGKx8Morr3D//ffz6aefArB3714efPBB2rdvf9sjDo2mKCAibDu7jb1Re/n16K88WvdRHqr1EPeZ7suyjNlsZtu2bYwbN44zZ84wbdq0dJfPeRUHIDQU9u6Fr76C5s3hoYegTx8okcOV2XyJue6ItsjtB/gLaGg9ngR8CJhsrr8JLLNXT3Yjgk2bNsnEiRNl79698vHHH8vEiRPl3LlzMnHiRClVqpRgWCsJIK6urjJt2jQ5deqUTJw4UX755Rf55Zdf7L4J2Y4IFi5cKC1btkwfEcTHx4uIyJ49e+TZZ58VEZH27dtLeHi4iIiYzWbZu3evDB48WEREIiIipHfv3iIi4u3tLUeOHJGUlBRp3bq1xMfHS/fu3eXgwYNisVjkkUcekVOnTmUrmz30iCBrdF9k5E7pj02nNjmULzElUVYeWCm7zu+S7w9+Lylm+6/aZrNZpk+fLh999JGYzeYs87388ssC5Hg0kJAgsnKlSGioyDffiCQm5qh4noODIwJnWw29BiyxWgydBIYCH1vXDwQIA166nQb8/Pzws6rM5s1tnM5NmsSFCxcyhHp7/vnneeedd9Kv54TNmzfj5+dHhQoVmDBhAjt37gRg9uzZrF+/nmLFilG8eHHAiC9avXp1wPAjdOjQIbZt25YuZ1o+d3f39NgF9evXJyIigqioKBo3bgwYgWlOnDiBtzO3FGo0dxhBYUHZbgI9c+UMF+MvEhIRQrsa7Wju1ZxWpqx9+lgsFo4cOcKXX35J//79GTt2bPr/YFYEBASwbNlPDo8Gjh6F2FjYswceeMAYATjBwtRpOFURiEgoxlqBLYOc2aYtAQEBfPnll8Dth3rz9fVN3wwWFBQEQHR0NKtXr2b79u3s27eP119/HQBXV1ciIiIwmUxYLBYaNWqEr68vixYtAkhfiIqLi+PYsWPUqVOHEydOYDKZ8PLy4tChQzRq1Ijg4GBGjBiRa5k1msKCiPDXmb8wuZv47fhvDGoxyO4uYBFh4cKFnDt3joCAAGbOnOlweyaTiVq1+mcbByAlxdj41aQJbNkCAwbA/U6b6HYuhXpnsclk4rnnnmPx4sVOCfXm6emJl5cXHTt2pH379unpH330EU899RQuLi707NmT0aNHU79+fXx9fSlWrBhdunThnXfewdPTk9mzZ7Nr1y6GDBlC6dKlmTZtGi+88AIWi4UePXro0YCmSBOfHM/xy8fZG7WXCqUq8FCth3i97etZ5hcRIiIimDdvHl26dGHo0KGULJm9tVBOOXMGTp82PvXrQ4MG4ECMmTsbR+aPCvqTW6shEZGjR49K7dq1c20xdLej1wiyRvdFRgq6P7w+9BImkeFTZloZOXLpiN2yFotFli9fLuPGjZO4uLhs5/8d5bHHvks/NptF1q4VOXZMZN48kejo264+X+AOWSMocKpWrUpgYKDTQ71pNJrbIzP3z/Ep8TSomHUM8KtXrzJz5kxatWrFE088Qf/+/fNEluhoCA3tTXCwMfd/7RqULw9168LrWQ9I7lrsKgLrQu/jwMNANSAR2A+sFZHDzhUvb/DLF/srjUaTGw5cOEB5t/I5KvPHH3+wfv16xo4dS0BAAK6urnkmjwgEBEBUlCvPPgubN4PJlGfV35FkqwiUUu8BTwJbMHYGbwDcgAbAHGXs0R4rIvudLejNiEiWW8Q1BsbIUKO584hOiObfc/9SsnhJzl87z1NNnjIuZOH1Ewx3D9OnT6d69er079+fzp0756lMZ89CcDCUKgUrVgAozp0z9gMUaUUA7BORrIJtzlRKmYCaeSyTXdzc3IiOjqZixYpaGWSBiBAdHX3XRUrSFF5SzCmsP7Ge+hXqs+bYGvo27ot3ee+MmTaTwesnwK5du/juu+946aWXGDVqFB4eN2mI2+D6dfj5Z7j3XvjzT+jXDw4ehKQk47qzvX7eKWSrCETkZzvXI4CIPJXIAWrUqEF4eDgXL2YfPg7g+vXrRfZh6ObmRo0aNQpaDE0RZ1/UPnae30nDig0p51qOBhUb0LDSrWY2lcyVuBR6ydhhFAqUBfcy7tR7sx4zZ87Ms5c+Edi9G3btMkw/b7b8mTs3/7x+3inYmxqqAgQAFoydwa9gTBUdAkaLSIG49CxZsiT33HOPQ3mDgoLyJIC0RqNxnOiEaJbtX4aftx+HLx3m6aZPU8alTLZl+l3qxyK1iBRSKKlKMviewel7b/JEpmj47jvo1MkwAR04MPO3/Ky8fhZmRWDP19BXGDuCLwCbMHT1k8BO4DPniqbRaO42Vh9dzZx/5nAx4SI9GvSgaZWm9Gvaz64SWLJkCYsXL07fbJnjrotsAAAgAElEQVSSksLKlSvzRKbffoM5c+DiRejVyxgF9O6d9VTPgQOGIhgyJAwR49jqSKDQYm+NwCQicwCUUiNEJM0L2hyl1BDniqbRaO4GwmLD+Cr0K3o16kV1j+p0r9/doWmcyMhIPvvsMzp27Mijjz7KsGHDMriEGThwYK5lOnvWCPreowdUqQKPPQZ5GE6g0GFPEdj+Nb+96ZruVo2mCLMwZCGXEy8ztNVQxrYfa/etP421a9eyefNmJkyYwLhx4yhTxiiXFy5hvv0WwsPB3x9GjYKyZXNcRTr33pvvcbQKDHuKYLVSyl1E4kRkQlqiUqoecMK5omk0mvwkNDYUP/yyvC4iHL50mMDQQJ5q8hR9G/elYumKDtV95coVPv30U5o3b06bNm3o1q3bLaMGk8nE0KFD+fzzz3PkEubkSfjiC3jiCWP+v1o1h4rZRSsCKyLybhbpx4E+TpFIo9EUCKGxoZmmp5hT+HTnp8QnxzOm/Rimd57usAXP1q1b+fnnnxkzZgwjRoygQoUK2eYPCAjghx/W2h0NiMCCBRAZCWPGwOTJOffzr7mBQ11njT88EPC2LSMio50jlkajyS+qzqqa7t7hq8lfAVClTBX+HPwn3+z9hqeaPMVr979G8WLZu25OIykpic8++4zKlSvTsWNHpk+f7nC8X5PJRKNG/pmOBlJTISzMUAA9esDgwcbmL83t46gOXQvsAvZhmJJqNJpCQmY+fi7EX6BJ5SZM7zzd4Xr27dvH0qVLefnll+nXr196TI7bJTHRiPIVEQHvvQczZoDeR5q3OKoISotIIXS1pNFossKR6R8RYdGiRaSmptKrVy+mTJlyW26fo6Ph8OE32LMHypWD+fONuf+XXtIPf2fiqCJYqpQaCqwGktISReSqU6TSaDT5wqqDq3JV7tSpUwQGBvL000/TpUuXPImbYTbD//0fREWVp18/w54/B7FkNLeBo4ogDpgLTMXYVIb1u5YzhNJoNM7DIhYW716MQjG45eAclV25ciUnT55kyJAhjB8/ntKlS9+2PCdPQsmS8Mknhu0/wLlz8Mcf0K3bbVevcQBHFcFbQH0RuZCTypVS5YFFQDMMxTEMOAIsx1h4DgP6i0hMTurVaDQ5xyIWlu1fRuNKjfGt7Uv9ivUB8CrjRVRkVAavn15lvNLLRUVFsWjRIjp16kSrVq3o16/fbcuSmGhE+Nq1C65ehaFDoXt3YyoIio6ztzsFRzeFHQRyMw00D1gnIo2Alhg+it4GNopIfWCj9Vyj0TgJEWHtsbUcuHCAup51aWVqla4EACLHRvJywstwGkYmjkQmCpFjI/njjz8ICAhAKcXIkSNp164d9erVuw05DDfPZ84YC74WCzz7LIwYAa6uWTt70zgfR0cEycBupdSfZFwjyNJ81Gpy2gHwt+ZNBpKVUr0gfdfKV0AQMD6Hcms0GjuICFHxUfx+4ndqlK1Bc6/mmeaLiIhI39G7ePFiqlSpQrt27ahduzZTpky5ba+f0dFw6BBERRnrAC1aZO7ArSg6e7tTUI4EL1FKPZ9Zuoh8kU2Ze4EFGKOJlhiBbUYB50SkvE2+GBHxzKT8cGA4gJeXV+tly5bZlTMz4uLicHd3z1XZwojujxsU5r5IsaSw6NQi2ni2waeCT7Z558yZw5o1azCbzZQoUYJOnToxYcKEbMvYw2yGkBBPatRIZPPmynTqdAEvryT7BYHAQG/8/cNuq/28oDD8Pjp27BgiItn/AMB5wesBHyAVaGs9n4ex2Bx7U74Ye3XdHLw+JxR0QO47Dd0fNyiMfXEs+piMXjdaDl44KBaLJdu8MTEx8sEHH0ipUqUEYw1PAClbtmyu2z9xQuSHH4xA7xs3GkHfc8qd8mcpDL8PHAxe79AagVKqq1Jqp1LqglLqslIqRil12U6xcCBcRHZYz1cB9wFR1shmWL9ztACt0Whu5Wj0USYFTaJiqYrM6DKDxpUbZzqlY7FY+PHHHxk/fjxms5lXX30Vf39/XFxcAHLl9TMxEZYuhWPHYMMG8PMzrH06dcqdx08dYjz/cXSN4BOgPznYWSwikUqps0qphiJyBHgEY5roIDAEmG79zjYKmkajyZozV86w/8J+SpcszdsPvY1biVtNbJKSkrh27RqzZs2ibdu2dO3alT59brgKy43XTxHD4mfvXiO6V9OmRqSv+vXtFtXcgTiqCMKBUBHJqXuJ14AlSikXjAA3QzEslVZY1x3OALdvi6bRFDGi4qJINiez6uAqhrcejrtLxrlsEeHSpUts2rSJffv2MXbsWP773/9mOkpI8/r52Wef2fX6mRbl65FHDOufAQO0eWdhwFFFMA74VSkVREaroY+zKyQioRhrBTfziKMCajSaGySlJhESEcKmU5t45f5XGN0uo+FeWFgYrq6uzJs3j8cee4z+/fvTv39/u/UGBASwbNlPmY4GRIwpn8OHDQXQsyfUrg2NG+fZbWkKGEcVwWQgBSiPdjqn0RQIC0MWciH+Au88/A7ta7ZPT7969SrHjh3j9OnTREVFMWTIEKbn0ADfZDJRq1b/DKOB8+cNZ2+PP24EeHn1VR3lq7DiqCKoIiKtnSqJRqO5haTUJBbtWkSNsjUY2GIgpUoafpdTU1PZsGEDjRo1YunSpfTr14++ffvmup3oaDhz5h3CwuDff40dvQMHGpu9PG8x7tYUNhxVBBuVUp1E5E+nSqPRFEHmLpvLG8+8kSEt1ZLK13u+5uFaD9O5TmcaVmoIwJ49ezhx4gQuLi54eHhQu3Zt3n030/hROWLOHIiJqcwzzxhrAA7MJmkKEY4qgheBsUqpBIxdxgoQEck+3JBGo8mWiIgIJr0xiWf8nqFq1apYxEJQWBAli5XkPtN91K9Yn4iICObOnUvPnj05cOAAvXv3zhNnbwDLl8PBg7B4MYBi3z5jLeCee/Kkes1dgqOKoJJTpdBoiiBVZ1UlankUXABTTxP0AA8XD9YNXEebqm1YsWIFoSmhNGvWjKeffhqTyUTdunVvu90LFwznbh06GDb7JhN89JFxTTt7K5pkqwiUUjVF5KyImLO4rgCTiJx3inQaTSEmKjIKQjH284YCvnAt7hprPl9D9eHVadasGS1btsyz9tauhc2b4e23YexYSPOe8PLLmTt70z5+ig72RgTzlFIpGJu+QoCLgBtQD+gIPApMAbQi0GhyymZuRPcwA98DT8LkyZMpUaIEtWvXvu0mrlwx/Pw3awYPPGBYAN2MdvamyVYRiEhfpVQL4DlgJGACEjDcSa8FOotIotOl1GgKCWaLmY/++QjPUp6wB0MBgKEQIgAPKFHC0RnbrNmyBVavNt78X3kFypfPOu+BA8Z3UFAQftq/Q5HE7i9ORPYCe/NBFo2m0HL88nEW717M4JaDaXq9KXu27IGmGE5bzEBxIHMv0Q5z/Tp8/DFUrw5dusDDD+s4vxrH0NtDNBon8uuRX5n+93Q8XDzww49ze87hWdqT8ePHU6lnJcP+Doxv34yRwRwlJATGjzcWgYcOheeegypVtBLQOM7tj0E1Gk0GriZdJTA0kPtM91HHsw5uZ9yICY8hMS6Rnj17Usy6PffilIuMvDSSzz77jJHDR/LprE8dbsNiMQK9u7rCk0/Cf/+rd/1qco9WBBpNHnE0+ijL9y9naKuhPNHgCVKjU9m/az8uLi40bNiQRo0a3VImICCAtWvXOuTxE4xIX19/DYMHGw7fqlTJ67vQFEUcVgRKqWeAuiIyTSlVE8PtRIjzRNNo7nxEhA0nN5CYkohnKU/efOBNiluK88GMD+jUqRO9e/fOtrzJZMLf3z9bj58AixZBbCwMGwZTp0IerCdrNOk49HNSSn0ClMSIQTwNiAc+B9o4TzSN5s4lISWBJXuX4Ofth4jQs2FPDh08xPsT3ufFF19k2rRpDteVlaXO6dOwcCE88YTx0W//Gmfh6HtFexG5Tym1G0BELltjDGg0RYozV86w89xOPFw96FC7A/Ur1ufwtsO8+8W7TJkyhY/StujmCL8MZ8uWGVNAo0dDQICxDqDROBNHFUGKUqoY1u0vSqmKaHfUmiKCiLDt7DYqla7EHyf/4LkWz+FR0oPly5ez33U/Dz74ID179sxV3dHR4O8PK1cadv8dOhg+/595Jm/vQaPJDkcVwacY+x4rK6UmY4StnOw0qTSaO4BkczKbwzZTqmQpLsRfoG2NttQtX5clS5bQrl076tevT5s2tzc7+tZbxhTQ+PGGIsgjX3IaTY5wSBGIyNdKqRCgM4bFcz8R2W+vnFIqDLiGsWUmVUR8lFKTMLyZXrRme0dE1uZCdo3mtgiNDcXvpmkZgAvxF7iadJVfjvxCp3s6cW/Ve0lOTub3db/j7u5OixYtaNCgQa7bjYmBefOgVStYt85I27HD8APUrVuuq9Vock1ObA/OAhusZYoppVpYdx3bo6OIXLopbY6IzMpB2xpNnhMaG5rh/MCFAxRTxfj16K/43+vP6HajSUpKIjw8nBUrVtClSxeaN8/99t+1ayEoyHj7f+stYyPYtWvGNe31U1OQOGo1NBEYDpzihpsswbAi0mjuKqrOqkpUfBQAX03+CoCyLmX5tPunPNv8WRpXbsz169c5ffo08+fP54UXXmD06NHZVZkl8fEwYwY0aQLdu2d0+jZ3rvb6qbkzUGLrdjCrTEodAVqISJLdzBnLnQJiMJTG/0RkgXVqyB+4CgQDY0QkJpOywzGUD15eXq2XLVuWk6bTiYuLwz3N365G9wfQcXNHY8JyFfAU4GGkb/LdBMBff/3F4cOHGTRoEG65eD0XgZAQT4KDPenXLxx391RcXW+1rfD3b8Pp02UypDVseJXPP9+V4zbzAv3byEhh6I+OHTuGiIiPvXyOKoIfgOGZTPHYK1dNRM4rpapgTCu9BhwBLmEoh6kY8QyGZVePj4+PBAcH56TpdLRHxYwU5f4QEc5dO0fNOTVhNYZj9dZAD8ACczzn4OHhwZAhQ3LlATQ52djsdc89hr+fu83ssyj/NjKjMPSHUsohReDor30asFsptRdIHxWISLbRstMC1ojIBaXUj8D9IrLFRsiFGP+SGo3TuBB/AYVi1rZZPF7/cWM0YBsQpgzQBJ4eYUQBywlmM/z9t+G//+WXDbt/F73DRnOX4agi+AqYg+E016H9A0qpMkAxEblmPX4UmKKUMolIhDVbH8Cu9ZFGk1MsYuFkzEmORh/l4MWDvHjfi8zoMsO4aBsQxoIxeemFw0pABFJTYdo0Y7fv8OHg6+uEm9Bo8glHFcFlEcnplkkv4EcjmiUlgKUisk4p9Y1S6l6Mf8Uw4KUc1qvRZEnEtQiKqWJ8vONjutTtwuP1HzdGAVbOnz+fMSCMBTjimPvn5GTYvRu+/97w+fP++9rjp6Zw4Kgi2KmUmgr8QsapoSzNR0XkJHBLwFURGZRTITWa7DBbzOy/sJ+Y6zGEnA9hWKthTHsko6+fEydOUKJECVasWMGwQcP49ttvSU5OxsXFhReef4FPx2buAloEkpIMCx83Nxg1Ctq2zY+70mjyD0cVwf3Wbz+bNG0+qilQzl09h0UsBIYG8nDth/Hz9sPP2y9DnkuXLrFjxw7CwsIYNmwYb731FhERESxduhSA4sWLZ+oCOj4eTpyAb7+FgQMN238d6EVTWHF0Z/HDzhZEo3EEs8XM9vDtuBR3YevZrQxuOZgA31sf5CkpKUyZMoVmzZrx9NNPZ7hmMpkYOnQon332GUOHDk13AS1ibPAKDDRGAWPHwsyZ+XFXGk3B4uiGsncySxeR/+StOBpN5py5cobElETWHFuDTzUf2lRvQ5vqt/r5OXz4MAsWLGDIkCFMnTo1y/peeSWABQt24+8fwOXLcPkyLFgATz8Nr7/uzDvRaO48HJ0aMtscuwHdgQN5L45Gc4MUcwobT22kZtma/HnqT55r8Ryj22W+w/eXX37hn3/+YdKkScyePRtlZx5nzRoTZvM2/P0V/v7w5pv67V9TdHF0amiG7blSagbwk1Mk0hR5wmLDiE6IJvh8MM29mtOkchOaVml6S77k5GS++eYbKlasyAMPPMATTzyRbb0xMfD77+DjAx98AKAIC4NmzXTEL03RJrc/f1egbl4KoilaBIUFZVjYTTGnsProappVacaGkxsY0GwArau1zrRsXFwcixcvpkePHrRt25ZmzZpl2U5UFCxdCq1bQ2QkNG0K4eHGegBoZ28aDTi+RrCbG1twigMmQK8PaHKMrcO3NDzdPPm428fUr1CfehXqUb9i/UzLXr58mQ0bNlC9enV69uzJPffcc0uelBRITIQvvjAsf15+2bD6qVz5Rp6+fbWzN43GFkdHBE/ZHKcCkTl1QKfRALcoAYCY6zEMbDEwyzLnzp0jLi6O33//nX79+t0S6P3yZdi5EypWhF9/Nfz8jBqV9WavI0dujAjAGBWsWaMVgabokq0iUEqVtR5evOmSq1LKVUSuOkcsTWHkQvyFHOU/ffo08fHxrF27lhdeeIHXXnvN5hqsWmUEcgkKMlw8NG1qzP/b44DVzKEwOBXTaPICeyOCAxhTQpmZYAhQK88l0hQ6Dlw4QBmXMqw4sMKh/AkJCSxbtoykpCSGDx9OkyZNSE42pnxmzACTCR59FAYPNqZ8mjRx8g1oNIWcbBWBiNTML0E0hQsRYd+FfUQnRBMWG8aA5gMY9+A4xv8xPtNYAADXrl1j0qRJPPLIIwwbNozoaAgONqZ+/vnHmO9/910oWbKAbkqjKaQ4bDWklHqcGy4lgkRknXNE0tzNXE+9ztWkqywMWUjbGm3pXKdzhuteZbyIWh0FZzC8gPaACtEVGD16NGPGjOGllz7k11+L0bSpMfXTo4fh20fH8tVonIdDvhOVUtOAccBJ62ecUuoDZwqmubuISYxh/4X9fLDlA+KT43m3w7u3KAGA3c/txm2/GwiU3OPCe6kTCRl9ijJlZrN9e3USE4vh7w+1a8OYMdCwYf7fi0ZT1HB0RNATaCUiZgCl1GJgF/CeswTT3B0kpSYRGBrIlaQrjG0/lg86Zf9+8O6775KUlAJASoqwbt1Fxo0rqwO6aDQFSE42lJXFCOEBGWZ2NUUNEeFSwiVmbJ3BY3Uf4yWfrENKiAjJyclMmbKAXbuu8NdfK7G+TwApHDz4LR4embuA1mg0+YOjYTVmAruUUouUUl9gBJ2fYaeMphCy9thaxm0YR4liJfiwy4d0qdvlljzJycns2BHCTz/t5OGH32Xp0qP4+r7IkiXv0bnzICDt1d8FGMj16/l5BxqN5mYc9TX0rVJqE9AWw5T0fRE551TJNHcMqZZUPtz6IV7uXjzb/NkMEb/SiI2NZenStTRpci+zZv1KtWoPMHeuL927t8lg5ZOcHAB8aT0rjkiA3tWr0RQw9jaU7QGWAstE5DTwQ75IpbkjOH75OIt3L2ZIyyGM8BmBZynPDNdTU81MmPAJkZGuPP54e86caUHv3o1ZvTprw/7Tp03AUOB/wFASE6vqXb0aTQFjb0QwDHgG2KyUOgd8B6wQEYe2iCqlwjCsxs1Aqoj4KKUqAMsBb4yYxf1FJCarOjT5z7rj69gTuYch9w5hou9EXEu4AmCxWDh79hxr1hxkyZItTJ78Ii1aDOC996pQrhwMGGC/7gMHICIigGeeOcDy5QHc5C1Co9EUAPY2lIUAIcBbSqkHMZTCLqXUQeA7Efkyu/JWOorIJZvzt4GNIjJdKfW29Xx87sTX5BWJKYl8u/dbGlVqRDWPajxW9zGUUiQkJBD47a+0bt2SESMW8MADnQgIeIyXXnqM4sVz15bJZGLz5s15ewMajSbXOGw1JCJbga1KqRXAxxhje0cUwc304kbs46+AILQiyBciIiIYNWoU69evT3fcFhkXydd7vqZfk348VOshGlduTFxcAgEBi0lK8qBs2XLExlaiZ8/qbN2qt45oNIURJbZuGLPKpFQrYADQDzgPLMOYIrrVlWTGcqcwTE4F+J+ILFBKxYpIeZs8MSLimUnZ4cBwAC8vr9bLli1z/K5siIuLw93dPVdlCxN9t/Ul5scYY3zXGugB7sXdCWgSQAP3BrikurJixV6Cg8/y2mvdOHxY8eijHpQqZbZX9V2L/m1kRPdHRgpDf3Ts2DFEROy7YhSRLD/AFOAIEIoxhVM7u/yZlK9m/a4C7MFwURF7U54Ye/W0bt1acsumTZtyXbYwwRiEEghYv99EGIL8++9pad/+HZkyZZOEh6eI2VzQkuYf+reREd0fGSkM/QEEiwPPantTQwroIyIHc6ONROS89fuCUupH4H4gSillEpEIpZQJyJlvYk2OEBH2X9hv+PVJG/xZgB+Bh6Bp0+ps3Tqt4ATUaDQFTrYbykQkILdKQClVRinlkXYMPArsB34BhlizDQF+zk39muw5FXOKGX/P4FTsKeYtWgq7MWy3wFAEEUA9KF06lyu+Go2m0ODMkN1ewI9KqbR2lorIOqXUTmCFUup5DB+U/ZwoQ5EiKTWJz4I/w9PNk4qJtfl96iWk4ykea/A8X9w73ZjgM2MEG21ewMJqNJo7BruKQBlP8qoiEpGTikXkJNAyk/Ro4JGc1KXJmhRzCn+e+pNNYZsYcd8IDi4V9gcf5dtv+/K/Tx+gXj0jIrvL35VI3mO14lWAL5QRr4ITXKPR3DHYVQQiIkqp1Ri2Jpo7gNjrsYgIM7bOoF7p+jQt/jBb3t9EuZ6neG/sq9SsWRJ1U0y5en9c5GDqSOB/kDoCZn9KYx9gUgHcgEajuaNwdGroX6XUfSKyy6nSaLIkPjme45ePc+bKGULPhfJEtb5EL6/H9mMX+eGHOgQFTc/WjXPajt6uXbeyfr3e0avRaG7gqCJ4CHhRKXUCiMeYXBARuc9pkmlISk1i/Yn1tPBqwVehX1E9ui7NazzIb+/tpOJA4fPPX8jR7l6TycS8efPSN5NpNBoNOK4IejtVCk06ZouZP0/9SXRiNOVcyxF3PpHdh84Rsa42R6+V5enPa7Ft25SCFlOj0RQiHIpHICIngFJAF+vHzZqmySNCI0N5f9P7hF8N52rMVfaviMYjqgHffngND/emfP65P0uWPIGHhzb31Gg0eYujMYtfBVYAtayfFUqpkc4UrDARERGBr68vkZGR6WkiQlxyHJODJrN031JKqVJUP96YlYv28e9KV8qX7U6bNnVZu/YFOncun03tGo1Gc3s4OjU0HLhfROIAlFL/AbYB850lWGGh6qyqRC2PghAw9TRBDyjvWp4RPiN4s92btLP48r9JG6g9ugMH99dnzJjW1Kql7Fes0Wg0eYSjikABKTbnKdY0jR2iIqOMjVyC8e0LsYmxXF5ZhtkbtlO3biPGjJlEu3YlefDBGgUsrUajKYo4qgi+AbYrpb63nvfBcCGtyYKElASORh+91cfPEmAwPPTQ6/TvXxZX14KTUaPRaMDxxeKZGNNDCUAiMEJEZjlTsLsREWHb2W2cuXKGmVtnEn8lHvaS0cdPDFAaBg3SSkCj0dwZ5MTX0BGMPQQlAJRSLURkr1OkussIvxrOnsg9AMRcjqHkhdLEr6lHQMg2aIbhgFv7+NFoNHcoDikCpdREjBHBKW5MdAhGfIEiSWJKIj8d/ok21dvwfcj3JAaXonvnnsyasJJ+/dozbdpAXFyg/NszubI3o48frzLax49Go7lzcHRE8CxQR0SSnCnMnY6IsOPcDg5fOkxVl6r8s/IoZ93LE32pEaVLN6JJk3vYtGlchjKdjl7kRxsfP6U/+5QRbxWM/BqNRpMZjiqCA4AHUCQVQWRcJEv3LaV7/e58ufhnDv9p5sOZ3ahcsjZDhzamcuWsyx45AhCA0YUBJCTAmjUwaVK+iK7RaDR2cVQRTAN2K6X2YqMMRKSvU6S6AxARVh5cyZkLZ6lDS1ZODCOu8xn6tR+Lz+sVKV8e7r/f/hTPgQMAJgzzIY1Go7nzcFQRfAXMAfZh2L4UWo5GH2XhtkW0L/coSxYcIDbclR++9+PRtZ25y+NYazQaTaY4qggui8hHTpWkgJmxcSabfj7AC11HsGO2Bw+8dg8/fdP5Fr/+Go1GU9hwVBHsVEpNxYg3bDs1dEebj0ZERDBq1CjWr19/i+tlEeGvo9sZN+s/+NbrzfkjZXnC5y16P9qUpx5vV0ASazQaTf7jqCK43/rtZ5PmkPmoUqo4EAycE5EeSqlAwBe4Ys3iLyKhDsrhOGYzU59/nn179zL1+ef59JdfkGLFSLGk0P2NFzixL4JFU+YzrM0HDB3agpIl9au/RqMpmjikCETk4dtoYxRwCChrk/aWiKy6jTqzx2ym8uhSXNpguEea//ta5r9YgmJHSzCu10Le7jMF3zm1KVFC0anI7oTQaDQaA0c3lL2TWbqI/MdOuRpAdwyro9E5li63/PYbl7al3Nj6Zgb2gqVHKv99+Sn0qq9Go9HcwNGpIbPNsRvGw/2AA+XmAuMw9iDYMk0p9T6wEXg7s41qSqnhGLuZ8fLyIigoyEFRofYPPxj2TWlSC7Af6Al7588HEcoePEjko49SJSgIs5sbFzt2pFR4OAk1a5JSvjyFdZU4Li4uR31ZmNF9kRHdHxkpSv2hRMR+rpsLKeUG/CQiXbPJ0wN4XERGKqX8gLHWNQITEAm4AAuAEyKSbexFHx8fCQ4OdlzA1atRk3vCbm74+GkF9ACZmMn9JiVBYiIEB0PFinDsmLEBYPhwWLAATCbw9TV2h7VpA2XLGqMKR5SF2Qy//QY//AB9+0K3buQo0HAeExQUhJ+fX4G1fyeh+yIjuj8yUhj6QykVIiI+9vLlxOmcLa5AXTt5HgSeUEo9jjGKKKuU+lZEBlqvJymlvgTG5lKGrOnWDYIw/P9Duo+fLHF1NT6dOxvnrVrduDZ5svF97Rpcv2482NesgcOH4cUXDUXRsCHUqGEokK5dITkZqlQBNzd47DHYsQPi4mDlSmjbFtavL/XFHuoAABM7SURBVFBloNFoNLZkqwiUUiVEJFUptZsbM+7FMbbKZrs+ICITgAnWevwwRgQDlVImEYlQSimgN8akTd5SvDjFS3hhvteIDMa9gAeUuH4bzt48PG4oiFq1bqSnKYrUVPD2hjJl4N9/4dQpKFcOtmyBFGtMn7g42L4dVq+GXr1yL4tGo9HkIfZGBP8C9wFP2aSlApG34YBuiVKqMsZ7eigwIpf1ZEvq9EgiIiLo2rUr63+9dR9BnlOixA0F0aeP8T11qqEgbElIgI8/hvPnwccH/vkHevY0pp/c3Jwro0aj0WSCPUWgAETkxO00IiJBGJM1iEin26krJ5hMJubN+//27j06qvpa4Ph3JwSIBhtATHkJehWFKwIawusKhEcXIkVasV6utWppkfa2RcVr9SJosFW7WtEudXmLtYJLG0SxWnGxiq+oaAWCJqLGq0ivWuWlGHUiEEn2/WOfYSYhISHJzIQ5+7PWrJlz5uTMj98is/N77d/vEx8EGjNsmLUQIpHYuaOPhiuugGnT7LhfP3suLoatW21c4v77YcIEGDzYrk/TgWvnXPvQVCDoISKNTvtM97QTrXb22TYmsH49VFXZl/qIEXY+6rjj7PnSS2Pn5s+Hzz6DjRvhuedg9mxYtgwGDoSpUyEri5Zub5ZbVgZH+ACYc65tNRUIMoEcfKP6lsnMtIHhNWugrAyGDm3erKHOna2rqGdPKCy0c4sWwc6d1qX08MN2/s034Ysv4LLLbCD72GMbv2cwe6nf8uVw0kkpn73knGs/mgoE25qa2umakJlp3UDRrqCWEoG8PHssWGDnRo+2lsP+/fDggxY4cnJsGuwPf2iD1P36geqB2Uu5kQjMmuWzl5xzBzRrjMC1Y1272vO8ebFzkyfbl//KlfDEExZEgtlLAjZmsX69tVRaG6Ccc0e8pgLBxKSUwrWtrCx7/n6wZKOh2UtVVXDrrfD553DqqbB7NxQU2JRX51yoHDIQqOruZBXEJVBjs5fmz7cWQWWlTWuNROCee6xLaeZMWw09ebJ1L+Xmtm03UkmJD1o71060dGWxO5LEzV7SSATJyak7eyk3F84KEsxeFbfQ+6c/tQDx2mvw8ss2e2npUlsvMWmStSZOOeXwZjBFU27ceqsFHh+0di7lPBCEQdzspcqFC+l6443N+wLu0sUeeXmx9BtFRVBbCzt2WO4lEXjxRdi+HebOhbVrLcgMGHDw/Wtq6qbcKC31QWvn2gEPBGERzF56f8sWurZ2gDgjw2YonX++HQ8ebM+1tTB2rE1lXbvWVk1feik88IC1Ijp1shQbVVV2vQ9aO9cueCAImcqhQxN384wMOPFEez1oUKzr6brrbJrrTTfFgkBUVRWsW+eBwLkUykh1AVwIiEC3bjY4XH9ToKOOssHqhx6ylsGSJdbttGdPSorqXBh5i8AlT3zKjUjEgsKIEXDXXbExgpEjbfrrHXdAx462D8SmTZbeu3dvz7vkXAJ4IHDJE59yY8kSuPLKgwetowvkrr7anlWtNVFba1NbP/wQfvITm8U0ejT06tXi4njeJeeMBwKXXNGUG6WlzRsXEIllaJ0zx57374eTT4b337f7bNxo773zDpx5pk2HbYbcsrKmL3IuBDwQuNRozV/iHTrAkCGx4+nTrcWwdatt+jNggKXWmDHDthXt18+6maKCtQzfKC+3630tgws5DwQuNdq6SyYjw8YTogoK7Av/6adtIPqii+Deey1ra1ERbNrkCficC/isIZe+MjNtAdt111mr4IYbbEZSMFh9IAHfunW238NLL9k0V9VD37etlZQk9/OcqyfhgUBEMkXkNRFZHRyfICLrReRdEXlIRDo2dQ/n2kRGBrz7Luyrt8tqdTVs2WJrGnbssJbD9dfDRx/Zc3ExvPGGbRJUWWktjbbkgcClWDK6huYBFcAxwfFvgNtUdYWI/A8wG7g7CeVwrvEEfGPGwLe+Zcennhp7L5pS45NPYNcu+7nly+HLLy276333wfDhNrMpErGZTB06NG//6Wjepeef97EKl1IJDQQi0gc4B/g1cKWICDAB+I/gkuXADXggcMnSVAK+hmRk2Jai0W1F4/d+KCqy5507bdbS11/DnXfaQrlx4yyD68SJFiRUbYyipsbe97xLrp0QTWB/qIg8AtwMdAGuAi4BXlHVk4L3+wJrVPW0Bn52DjAHIC8v78wVK1a0qAyRSISc+qtZQ8zrA6ipofuGDfQsLmbbrFl8WlCQ8C/frMpKOm/bxp7evem5Zg0dd+2i1xNPkFldHStWp068tWgRn44endCyNMb/b9SVDvVRWFi4SVXzm7ouYS0CEZkG7FTVTSIyPnq6gUsbjESquhRYCpCfn6/jWzjLpKSkhJb+bDry+ghMnEhZdjZDL788+Z89fbptFvToo3VOZ1ZXM3j3bnj2WZv1dNRR9saoUZCdnfBi+f+NusJUH4nsGhoDTBeRqUBnbIzgdiBXRDqo6n6gD/BxAsvgXKMSmoCvKY2NVYwfH1tot3s3lJfbNTffbCk2hg+3ge1x46yrqg1TbvhK6/BKWCBQ1WuBawGCFsFVqnqhiDwMzARWABcDjyeqDM61W43lXYofq+jWzcYUABYvtuevv7ZcTPv2wd132yynH/3IFtCNGwcDB9qYRgv4SuvwSsU6gl9iA8dbgO7AvSkog3OpFc27VFxsX/bFxc0bKM7Ksv0fjj/edpArKoI+feA737HV008+CYsWwQcfwC232L4Q1dWHXhtRUwOrV8dWWrf19FjX7iVlZbGqlgAlweutQEEyPte5di2adyknp3VdMiK2URBYDqZvf9tez5tnSfrefhtWrbLWxuuv2/TWWbPs57KyDsxe8pXW4eUpJpxLtUT1y2dnW94lgNNPt+cRIyxZX1UV/OEP1i310kuwd29spfUrr6R217iSEh+rSDJPMeFcmIhA//5w7LGwYIHNTKq/0rqqyloEixbZl/LTT8MLLzTdxdRWfKV10nmLwLkwa2j2Uk6OdRdFWwQffwxvvWXpNe6807YjPfFEePFFuOACG1Po3fvg3efcEcMDgXNh1pyV1r16xTYAis5eAlvrUFtrf8GvXg0zZ1qepvx86NLFths96yxrdTRnvMFTbqSMBwLnwixu17jKhQvpeuONzf8CjuZTmjrVHhALFJWVNki9fz/cdJPtPDdmDDz2mC2oy86G7t1jayFqatpdyo0wravwQOBc2AWzlz5/5BG6ttUAcW6u7T8NsHBh7PyQITYmUV5u2VxnzLBB66++su1H9+yx6yIRCwopHLQO07oKDwTOOSBJK60zMqw1MHJkLFAsXmwpN/burXttJGLpNjZssGtzc23AuqDAWiMtXDjnDuaBwDkHtMOUGzk5MGFCrEWwfTtUVFjA+O1vbebTqFHw+OPWnSVi9xg40McWDpMHAudc6jUn5cY3v2kPsA2Dos44w8YYKipsdlP37pZ+45RTbKHdyy/bQrlIxI6j6cQb0972tE7CugoPBM651IsbtGbJErjyysP7As7MhNNOswfUnd00Zoytf1i3zgLN5Mm2oVBBgeVu2r4dvvc9G+Du3dtaIO1ppbUHAudcaERTbpSWtu0AcadO9jxpUuxcNFCoWiDIzrY8TevXHxi0rrOn9apVluOpb9+0XC/hgcA5F17xeZp+8ANLv1F/0Lq6GjZvtjUTFRU2NvHMM/Dd79qK60gE5s61mVADBtj92iI9eBLXVXggcM61L6mcu9/YPhEjRtRtpYwda89DhljgqKmx1sWHH0JZmbUsLr7Yssp26gTnnWezn/LzbQwjJ8eS/zUmyesqPBA459qXVAaCluxpHV1YF907AmIL7BYssOe9e61rKSPDkvpt2ACzZ8PSpbZq+4wz4O9/ty//6DXRIAAJX1fhgcA556Jas9L6UDp3hkGD7PUJJ8CUKfa6qMieVS1/U2amtShWrrTkf/Gqquy9BAQCX5HhnHPxoiuthwyxL91kzBYSsS6j3FxrEV12mXVJxTv6aEjQWg8PBM45195Eu6iiM5Sa00XVCh4InHOuASldad3SrUxbKGGBQEQ6i8gGESkXkTdFpCg4v0xE/iEiZcEjhbXtnHMNS2kggNi6irFjE95FlcjB4n3ABFWNiEgWsE5E1gTv/ZeqPpLAz3bOOddMCQsEqqpAdDJuVvBIwj53zjnnDodoAvcgFZFMYBNwEnCXqv5SRJYBo7AWwzPANaq6r4GfnQPMAcjLyztzxYoVLSpDJBIhJw2XhLeU10eM10VdXh91tZf6yC0ra3E3VWFh4SZVzW/quoQGggMfIpIL/AX4OfApsB3oCCwF3lPVxYf4cfLz87W0tLRFn11SUsL4kOwy1BxeHzFeF3V5fdSVDvUhIs0KBEmZNaSqlUAJMEVVt6nZB9wHFCSjDM455xqWyFlDPYKWACKSDUwC3haRnsE5AWYAbySqDM4555qWyFlDPYHlwThBBrBSVVeLyLMi0gMQoAyYm8AyOOeca0IiZw29Dgxr4PyERH2mc865w+cri51zLuQ8EDjnXMh5IHDOuZDzQOCccyGXlAVlrSUiu4D3W/jjxwKftGFxjnReHzFeF3V5fdSVDvXRT1V7NHXREREIWkNESpuzsi4svD5ivC7q8vqoK0z14V1DzjkXch4InHMu5MIQCJamugDtjNdHjNdFXV4fdYWmPtJ+jMA559yhhaFF4Jxz7hA8EDjnXMildSAQkSki8r8iskVErkl1eZJNRP4kIjtF5I24c91E5CkReTd47prKMiaLiPQVkedEpEJE3hSRecH5sNZHZxHZICLlQX0UBedPEJH1QX08JCIdU13WZBGRTBF5TURWB8ehqYu0DQRB+uu7gLOBQcAsERmU2lIl3TJgSr1z1wDPqOrJBFuFJrtQKbIfmK+qA4GRwH8G/x/CWh/7gAmqOgQYCkwRkZHAb4Dbgvr4DJidwjIm2zygIu44NHWRtoEA2/lsi6puVdVqYAVwborLlFSq+gKwu97pc4Hlwevl2OZAaS/YGe/V4PWX2C98b8JbH6qqkeAwK3goMAF4JDgfmvoQkT7AOcAfg2MhRHWRzoGgN/Bh3PE/g3Nhl6eq28C+HIHjUlyepBOR/theGesJcX0EXSFlwE7gKeA9oFJV9weXhOl35nbgaqA2OO5OiOoinQOBNHDO58qGnIjkAKuAy1X1i1SXJ5VUtUZVhwJ9sBb0wIYuS26pkk9EpgE7VXVT/OkGLk3bukjkVpWp9k+gb9xxH+DjFJWlPdkhIj1VdVuwf/TOVBcoWUQkCwsCD6rqo8Hp0NZHlKpWikgJNnaSKyIdgr+Ew/I7MwaYLiJTgc7AMVgLITR1kc4tgo3AycHIf0fg34G/prhM7cFfgYuD1xcDj6ewLEkT9PneC1So6pK4t8JaHz1EJDd4nQ1MwsZNngNmBpeFoj5U9VpV7aOq/bHviWdV9UJCVBdpvbI4iPC3A5nAn1T11ykuUlKJSDEwHkunuwO4HngMWAkcD3wAnK+q9QeU046I/BvwIrCZWD/wf2PjBGGsj9OxAdBM7A/Claq6WEROxCZWdANeA76vqvtSV9LkEpHxwFWqOi1MdZHWgcA551zT0rlryDnnXDN4IHDOuZDzQOCccyHngcA550LOA4FzzoWcBwKXFkSku4iUBY/tIvJR3PHLCfrMYSISzU1ziYjsCrJXvisifxOR0W38edOiWUKda0s+fdSlHRG5AYio6u8S/DkPA79S1XIRuQTIV9WfBe8VAsVAoapWHOI2h/N5ArwKjFHVr9rins6BtwhcCIhIJHgeLyLPi8hKEXlHRG4RkQuDvPybReRfgut6iMgqEdkYPMY0cM8uwOmqWt7QZ6rqc9iet3OC638c3Ks8uPdRItJFRP4RpL5ARI4Rkf8TkSwR+YWIvCUir4vIiuCeCpQA09q+llyYeSBwYTMEyzs/GLgIGKCqBVj64Z8H1/wey0M/HDgveK++fOCNBs7HexU4NXj9qKoOD/L/VwCzg3TYJVj6Y7D0BqtU9WtsX4Rhqno6MDfunqXAWc38tzrXLOmcdM65hmyMpp0WkfeAtcH5zUBh8HoSMMh6YgA4RkS6BF/cUT2BXU18VnwGy9NE5FdALpAD/C04/0cs/fFjwKXAj4PzrwMPishjwXtRO4FeTf0jnTscHghc2MTniqmNO64l9vuQAYxS1T2HuM8eLFPloQwjtuPVMmBG3HjCeABVfUlE+ovIOCBTVaOtjHOAscB0YKGI/GuQBbNz8NnOtRnvGnLuYGuBn0UPRGRoA9dUACc1doPgi30OcE9wqguwLRgPuLDe5fdjA8v3BT+bAfQNxhmuJtaKABhA011Szh0WDwTOHewXQH4wUPsWdfvoAVDVt4FvBIPGURcE01XfwTKbnhc3Y2ghlun0KeDterd7EOiKBQOwjKAPiMhmLOvlbapaGbxXCDzZ6n+hc3F8+qhzLSQiVwBfqmpDg8mHc5+ZwLmqelET1+UBf1bVia35POfq8zEC51rubuD81txARO4AzgamNuPy44H5rfk85xriLQLnnAs5HyNwzrmQ80DgnHMh54HAOedCzgOBc86FnAcC55wLuf8HWbuhV+OV+4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the Plot (with Error Bars)\n",
    "plt.errorbar(tumor_vols_pivot_mean.index, tumor_vols_pivot_mean[\"Capomulin\"], yerr=tumor_vols_pivot_sem[\"Capomulin\"], color=\"r\", marker=\"o\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(tumor_vols_pivot_mean.index, tumor_vols_pivot_mean[\"Infubinol\"], yerr=tumor_vols_pivot_sem[\"Infubinol\"], color=\"b\", marker=\"^\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(tumor_vols_pivot_mean.index, tumor_vols_pivot_mean[\"Ketapril\"], yerr=tumor_vols_pivot_sem[\"Ketapril\"], color=\"g\", marker=\"s\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(tumor_vols_pivot_mean.index, tumor_vols_pivot_mean[\"Placebo\"], yerr=tumor_vols_pivot_sem[\"Placebo\"], color=\"k\", marker=\"d\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "\n",
    "plt.title(\"Tumor Response to Treatment\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "plt.xlabel(\"Time (Days)\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metastatic Response to Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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></th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Capomulin</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Metastatic Sites\n",
       "Drug      Timepoint                  \n",
       "Capomulin 0                  0.000000\n",
       "          5                  0.160000\n",
       "          10                 0.320000\n",
       "          15                 0.375000\n",
       "          20                 0.652174"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Met. Site Data Grouped by Drug and Timepoint \n",
    "met_sites_mean = clinical_data_complete.groupby([\"Drug\", \"Timepoint\"]).mean()[\"Metastatic Sites\"]\n",
    "\n",
    "# Convert to DataFrame\n",
    "met_sites_mean = pd.DataFrame(met_sites_mean)\n",
    "\n",
    "# Preview DataFrame\n",
    "met_sites_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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></th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Capomulin</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.074833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.125433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.132048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.161621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Metastatic Sites\n",
       "Drug      Timepoint                  \n",
       "Capomulin 0                  0.000000\n",
       "          5                  0.074833\n",
       "          10                 0.125433\n",
       "          15                 0.132048\n",
       "          20                 0.161621"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Standard Error associated with Met. Sites Grouped by Drug and Timepoint \n",
    "met_sites_sem = clinical_data_complete.groupby([\"Drug\", \"Timepoint\"]).sem()[\"Metastatic Sites\"]\n",
    "\n",
    "# Convert to DataFrame\n",
    "met_sites_sem = pd.DataFrame(met_sites_sem)\n",
    "\n",
    "# Preview DataFrame\n",
    "met_sites_sem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Drug</th>\n",
       "      <th>Capomulin</th>\n",
       "      <th>Ceftamin</th>\n",
       "      <th>Infubinol</th>\n",
       "      <th>Ketapril</th>\n",
       "      <th>Naftisol</th>\n",
       "      <th>Placebo</th>\n",
       "      <th>Propriva</th>\n",
       "      <th>Ramicane</th>\n",
       "      <th>Stelasyn</th>\n",
       "      <th>Zoniferol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timepoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44.266086</td>\n",
       "      <td>46.503051</td>\n",
       "      <td>47.062001</td>\n",
       "      <td>47.389175</td>\n",
       "      <td>46.796098</td>\n",
       "      <td>47.125589</td>\n",
       "      <td>47.248967</td>\n",
       "      <td>43.944859</td>\n",
       "      <td>47.527452</td>\n",
       "      <td>46.851818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43.084291</td>\n",
       "      <td>48.285125</td>\n",
       "      <td>49.403909</td>\n",
       "      <td>49.582269</td>\n",
       "      <td>48.694210</td>\n",
       "      <td>49.423329</td>\n",
       "      <td>49.101541</td>\n",
       "      <td>42.531957</td>\n",
       "      <td>49.463844</td>\n",
       "      <td>48.689881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42.064317</td>\n",
       "      <td>50.094055</td>\n",
       "      <td>51.296397</td>\n",
       "      <td>52.399974</td>\n",
       "      <td>50.933018</td>\n",
       "      <td>51.359742</td>\n",
       "      <td>51.067318</td>\n",
       "      <td>41.495061</td>\n",
       "      <td>51.529409</td>\n",
       "      <td>50.779059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.716325</td>\n",
       "      <td>52.157049</td>\n",
       "      <td>53.197691</td>\n",
       "      <td>54.920935</td>\n",
       "      <td>53.644087</td>\n",
       "      <td>54.364417</td>\n",
       "      <td>53.346737</td>\n",
       "      <td>40.238325</td>\n",
       "      <td>54.067395</td>\n",
       "      <td>53.170334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug       Capomulin   Ceftamin  Infubinol   Ketapril   Naftisol    Placebo  \\\n",
       "Timepoint                                                                     \n",
       "0          45.000000  45.000000  45.000000  45.000000  45.000000  45.000000   \n",
       "5          44.266086  46.503051  47.062001  47.389175  46.796098  47.125589   \n",
       "10         43.084291  48.285125  49.403909  49.582269  48.694210  49.423329   \n",
       "15         42.064317  50.094055  51.296397  52.399974  50.933018  51.359742   \n",
       "20         40.716325  52.157049  53.197691  54.920935  53.644087  54.364417   \n",
       "\n",
       "Drug        Propriva   Ramicane   Stelasyn  Zoniferol  \n",
       "Timepoint                                              \n",
       "0          45.000000  45.000000  45.000000  45.000000  \n",
       "5          47.248967  43.944859  47.527452  46.851818  \n",
       "10         49.101541  42.531957  49.463844  48.689881  \n",
       "15         51.067318  41.495061  51.529409  50.779059  \n",
       "20         53.346737  40.238325  54.067395  53.170334  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "met_sites_mean = met_sites_mean.reset_index()\n",
    "met_sites_pivot_mean = met_sites_mean.pivot(index=\"Timepoint\", columns=\"Drug\")[\"Metastatic Sites\"]\n",
    "\n",
    "met_sites_sem = met_sites_sem.reset_index()\n",
    "met_sites_pivot_sem = met_sites_sem.pivot(index=\"Timepoint\", columns=\"Drug\")[\"Metastatic Sites\"]\n",
    "\n",
    "# Preview that Reformatting worked\n",
    "tumor_vols_pivot_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the Plot (with Error Bars)\n",
    "plt.errorbar(met_sites_pivot_mean.index, met_sites_pivot_mean[\"Capomulin\"], yerr=met_sites_pivot_sem[\"Capomulin\"], color=\"r\", marker=\"o\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(met_sites_pivot_mean.index, met_sites_pivot_mean[\"Infubinol\"], yerr=met_sites_pivot_sem[\"Infubinol\"], color=\"b\", marker=\"^\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(met_sites_pivot_mean.index, met_sites_pivot_mean[\"Ketapril\"], yerr=met_sites_pivot_sem[\"Ketapril\"], color=\"g\", marker=\"s\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "plt.errorbar(met_sites_pivot_mean.index, met_sites_pivot_mean[\"Placebo\"], yerr=met_sites_pivot_sem[\"Placebo\"], color=\"k\", marker=\"d\", markersize=5, linestyle=\"dashed\", linewidth=0.50)\n",
    "\n",
    "plt.title(\"Metastatic Spread During Treatment\")\n",
    "plt.ylabel(\"Met. Sites\")\n",
    "plt.xlabel(\"Treatment Duration (Days)\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"analysis/Fig2.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Mouse Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Drug  Timepoint  Mouse Count\n",
       "0  Capomulin          0           25\n",
       "1  Capomulin          5           25\n",
       "2  Capomulin         10           25\n",
       "3  Capomulin         15           24\n",
       "4  Capomulin         20           23"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Count of Mice Grouped by Drug and Timepoint (W can pass any metric)\n",
    "survival_count = clinical_data_complete.groupby([\"Drug\", \"Timepoint\"]).count()[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Convert to DataFrame\n",
    "survival_count = pd.DataFrame({\"Mouse Count\": survival_count})\n",
    "\n",
    "# Preview DataFrame\n",
    "survival_count.head().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Drug</th>\n",
       "      <th>Capomulin</th>\n",
       "      <th>Ceftamin</th>\n",
       "      <th>Infubinol</th>\n",
       "      <th>Ketapril</th>\n",
       "      <th>Naftisol</th>\n",
       "      <th>Placebo</th>\n",
       "      <th>Propriva</th>\n",
       "      <th>Ramicane</th>\n",
       "      <th>Stelasyn</th>\n",
       "      <th>Zoniferol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timepoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug       Capomulin  Ceftamin  Infubinol  Ketapril  Naftisol  Placebo  \\\n",
       "Timepoint                                                                \n",
       "0                 25        25         25        25        25       25   \n",
       "5                 25        21         25        23        23       24   \n",
       "10                25        20         21        22        21       24   \n",
       "15                24        19         21        19        21       20   \n",
       "20                23        18         20        19        20       19   \n",
       "\n",
       "Drug       Propriva  Ramicane  Stelasyn  Zoniferol  \n",
       "Timepoint                                           \n",
       "0                26        25        26         25  \n",
       "5                25        25        25         24  \n",
       "10               23        24        23         22  \n",
       "15               17        24        23         21  \n",
       "20               17        23        21         17  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "survival_count = survival_count.reset_index()\n",
    "survival_count_pivot = survival_count.pivot(index=\"Timepoint\", columns=\"Drug\")[\"Mouse Count\"]\n",
    "\n",
    "# Preview the Data Frame\n",
    "survival_count_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Plot (Accounting for percentages)\n",
    "plt.plot(100 * survival_count_pivot[\"Capomulin\"] / 25, \"ro\", linestyle=\"dashed\", markersize=5, linewidth=0.50)\n",
    "plt.plot(100 * survival_count_pivot[\"Infubinol\"] / 25, \"b^\", linestyle=\"dashed\", markersize=5, linewidth=0.50)\n",
    "plt.plot(100 * survival_count_pivot[\"Ketapril\"] / 25, \"gs\", linestyle=\"dashed\", markersize=5, linewidth=0.50)\n",
    "plt.plot(100 * survival_count_pivot[\"Placebo\"] / 25 , \"kd\", linestyle=\"dashed\", markersize=6, linewidth=0.50)\n",
    "plt.title(\"Survival During Treatment\")\n",
    "plt.ylabel(\"Survival Rate (%)\")\n",
    "plt.xlabel(\"Time (Days)\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"best\", fontsize=\"small\", fancybox=True)\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Bar Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drug\n",
       "Capomulin   -19.475303\n",
       "Ceftamin     42.516492\n",
       "Infubinol    46.123472\n",
       "Ketapril     57.028795\n",
       "Naftisol     53.923347\n",
       "Placebo      51.297960\n",
       "Propriva     47.241175\n",
       "Ramicane    -22.320900\n",
       "Stelasyn     52.085134\n",
       "Zoniferol    46.579751\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percent changes for each drug\n",
    "tumor_pct_change =  100 * (tumor_vols_pivot_mean.iloc[-1] - tumor_vols_pivot_mean.iloc[0]) / tumor_vols_pivot_mean.iloc[0]\n",
    "tumor_pct_change_sem =  100 * (tumor_vols_pivot_sem.iloc[-1] - tumor_vols_pivot_sem.iloc[0]) / tumor_vols_pivot_sem.iloc[0]\n",
    "\n",
    "# Display the data to confirm\n",
    "tumor_pct_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Store all Relevant Percent Changes into a Tuple\n",
    "pct_changes = (tumor_pct_change[\"Capomulin\"], \n",
    "               tumor_pct_change[\"Infubinol\"], \n",
    "               tumor_pct_change[\"Ketapril\"], \n",
    "               tumor_pct_change[\"Placebo\"])\n",
    "\n",
    "# Splice the data between passing and failing drugs\n",
    "fig, ax = plt.subplots()\n",
    "ind = np.arange(len(pct_changes))  \n",
    "width = 1\n",
    "rectsPass = ax.bar(ind[0], pct_changes[0], width, color='green')\n",
    "rectsFail = ax.bar(ind[1:], pct_changes[1:], width, color='red')\n",
    "\n",
    "# Orient widths. Add labels, tick marks, etc. \n",
    "ax.set_ylabel('% Tumor Volume Change')\n",
    "ax.set_title('Tumor Change Over 45 Day Treatment')\n",
    "ax.set_xticks(ind + 0.5)\n",
    "ax.set_xticklabels(('Capomulin', 'Infubinol', 'Ketapril', 'Placebo'))\n",
    "ax.set_autoscaley_on(False)\n",
    "ax.set_ylim([-30,70])\n",
    "ax.grid(True)\n",
    "\n",
    "# Use functions to label the percentages of changes\n",
    "def autolabelFail(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., 3,\n",
    "                '%d%%' % int(height),\n",
    "                ha='center', va='bottom', color=\"white\")\n",
    "\n",
    "def autolabelPass(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., -8,\n",
    "                '-%d%% ' % int(height),\n",
    "                ha='center', va='bottom', color=\"white\")\n",
    "\n",
    "# Call functions to implement the function calls\n",
    "autolabelPass(rectsPass)\n",
    "autolabelFail(rectsFail)\n",
    "\n",
    "# Save the Figure\n",
    "fig.savefig(\"analysis/Fig4.png\")\n",
    "\n",
    "# Show the Figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}