pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 105,
     "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",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "# Hide warning messages in notebook\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "mouseData = \"mouse_drug_data.csv\"\n",
    "clinicalData = \"clinicaltrial_data.csv\"\n",
    "\n",
    "# Read the Mouse and Drug Data and the Clinical Trial Data\n",
    "mouseDF = pd.read_csv(mouseData)\n",
    "clinicalDF = pd.read_csv(clinicalData)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "fullDF = clinicalDF.merge(mouseDF, on=\"Mouse ID\", how=\"left\")\n",
    "\n",
    "# Display the data table for preview\n",
    "fullDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tumor Response to Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Tumor Volume Data Grouped by Drug and Timepoint \n",
    "# .mean() calculates the average\n",
    "# .reset_index() turn grouped indexes back to columns\n",
    "# .drop() remove unwanted column\n",
    "drugsGrouped = fullDF.groupby([\"Drug\", \"Timepoint\"]).mean().reset_index().drop(columns=[\"Metastatic Sites\"]) \n",
    "\n",
    "# Preview DataFrame\n",
    "drugsGrouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Standard Error of Tumor Volumes Grouped by Drug and Timepoint\n",
    "# .sem() calculates standard error of the mean \n",
    "# .reset_index() turn grouped indexes back to columns\n",
    "# .drop() remove unwanted column\n",
    "drugsGrouped_ste = fullDF.groupby([\"Drug\", \"Timepoint\"]).sem().reset_index()\\\n",
    "                                            .drop(columns=[\"Metastatic Sites\", \"Mouse ID\"])\n",
    "# Flatten sem for plotting\n",
    "drugsGroupedFlat_ste = fullDF.groupby([\"Drug\", \"Timepoint\"]).sem()\\\n",
    "                                            .drop(columns=[\"Metastatic Sites\", \"Mouse ID\"]).unstack(level=0)\n",
    "\n",
    "# Remove \"Tumor Volume (mm3)\" from the hierarchical columns index\n",
    "drugsGroupedFlat_ste.columns = flat_sem_drugsGrouped.columns.droplevel() \n",
    "\n",
    "# Preview DataFrame\n",
    "drugsGrouped_ste.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "# .mean() calculates the average\n",
    "# .unstack() flattens data removing hierarchical indexing\n",
    "# .drop() remove unwanted column\n",
    "DrugsGroupedFlat = fullDF.groupby([\"Drug\", \"Timepoint\"]).mean()\\\n",
    "                                            .drop(columns=[\"Metastatic Sites\"]).unstack(level=0)\n",
    "\n",
    "# Remove \"Tumor Volume (mm3)\" from the hierarchical columns index\n",
    "DrugsGroupedFlat.columns = DrugsGroupedFlat.columns.droplevel()\n",
    "\n",
    "# Preview that Reformatting worked\n",
    "DrugsGroupedFlat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the Plot (with Error Bars)\n",
    "x_axis = DrugsGroupedFlat.index.tolist() # Create the abcissa\n",
    "\n",
    "# Create function that plots same configuration for all graphs\n",
    "def std_plot_config(abcissa, capo_data, capo_err, infu_data, infu_err, keta_data, keta_err, plac_data, plac_err, \n",
    "                    x_label, y_label, plot_title, output_fig, fig_dpi):\n",
    "    plt.errorbar(x=abcissa, y=capo_data, yerr=capo_err, marker=\"o\", color=\"red\", ls=\":\",linewidth=1,label=\"Capomulin\")\n",
    "    plt.errorbar(x=abcissa,y=infu_data,yerr=infu_err,marker=\"^\",color=\"blue\",ls=\":\",linewidth=1,label=\"Infubinol\")\n",
    "    plt.errorbar(x=abcissa, y=keta_data, yerr=keta_err, marker=\"s\", color=\"green\",ls=\":\",linewidth=1,label=\"Ketapril\")\n",
    "    plt.errorbar(x=abcissa,y=plac_data,yerr=plac_err,marker=\"d\",color=\"black\",ls=\":\",linewidth=1,label=\"Placebo\")\n",
    "    \n",
    "    plt.xlabel(x_label) \n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(plot_title) \n",
    "    plt.grid() \n",
    "    plt.legend() \n",
    "    plt.savefig(output_fig, dpi=fig_dpi)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "# Call function to generate plot    \n",
    "std_plot_config(x_axis, flatDrugsGrouped[\"Capomulin\"], flat_sem_drugsGrouped[\"Capomulin\"],\n",
    "               flatDrugsGrouped[\"Infubinol\"], flat_sem_drugsGrouped[\"Infubinol\"],\n",
    "               flatDrugsGrouped[\"Ketapril\"], flat_sem_drugsGrouped[\"Ketapril\"],\n",
    "               flatDrugsGrouped[\"Placebo\"], flat_sem_drugsGrouped[\"Placebo\"],\n",
    "               \"Time (Days)\", \"Tumor Volume (mm3)\", \"Tumor Response to Treatment\", \"Tumor_Response_to_Treatment\", 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metastatic Response to Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Met. Site Data Grouped by Drug and Timepoint \n",
    "# .mean() calculates the average\n",
    "# .drop() remove unwanted column\n",
    "drugTimepoinrMeta = fullDF.groupby([\"Drug\", \"Timepoint\"]).mean().drop(columns=[\"Tumor Volume (mm3)\"]) \n",
    "\n",
    "# Preview DataFrame\n",
    "drugTimepoinrMeta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Standard Error associated with Met. Sites Grouped by Drug and Timepoint \n",
    "drugTimepoinrMeta_ste = fullDF.groupby([\"Drug\", \"Timepoint\"]).sem().drop(columns=[\"Tumor Volume (mm3)\", \"Mouse ID\"])\n",
    "\n",
    "# Preview DataFrame\n",
    "drugTimepoinrMeta_ste.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.652174</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.210526</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>1.294118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug       Capomulin  Ceftamin  Infubinol  Ketapril  Naftisol   Placebo  \\\n",
       "Timepoint                                                                 \n",
       "0           0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "5           0.160000  0.380952   0.280000  0.304348  0.260870  0.375000   \n",
       "10          0.320000  0.600000   0.666667  0.590909  0.523810  0.833333   \n",
       "15          0.375000  0.789474   0.904762  0.842105  0.857143  1.250000   \n",
       "20          0.652174  1.111111   1.050000  1.210526  1.150000  1.526316   \n",
       "\n",
       "Drug       Propriva  Ramicane  Stelasyn  Zoniferol  \n",
       "Timepoint                                           \n",
       "0          0.000000  0.000000  0.000000   0.000000  \n",
       "5          0.320000  0.120000  0.240000   0.166667  \n",
       "10         0.565217  0.250000  0.478261   0.500000  \n",
       "15         0.764706  0.333333  0.782609   0.809524  \n",
       "20         1.000000  0.347826  0.952381   1.294118  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.074833</td>\n",
       "      <td>0.108588</td>\n",
       "      <td>0.091652</td>\n",
       "      <td>0.098100</td>\n",
       "      <td>0.093618</td>\n",
       "      <td>0.100947</td>\n",
       "      <td>0.095219</td>\n",
       "      <td>0.066332</td>\n",
       "      <td>0.087178</td>\n",
       "      <td>0.077709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.125433</td>\n",
       "      <td>0.152177</td>\n",
       "      <td>0.159364</td>\n",
       "      <td>0.142018</td>\n",
       "      <td>0.163577</td>\n",
       "      <td>0.115261</td>\n",
       "      <td>0.105690</td>\n",
       "      <td>0.090289</td>\n",
       "      <td>0.123672</td>\n",
       "      <td>0.109109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.132048</td>\n",
       "      <td>0.180625</td>\n",
       "      <td>0.194015</td>\n",
       "      <td>0.191381</td>\n",
       "      <td>0.158651</td>\n",
       "      <td>0.190221</td>\n",
       "      <td>0.136377</td>\n",
       "      <td>0.115261</td>\n",
       "      <td>0.153439</td>\n",
       "      <td>0.111677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.161621</td>\n",
       "      <td>0.241034</td>\n",
       "      <td>0.234801</td>\n",
       "      <td>0.236680</td>\n",
       "      <td>0.181731</td>\n",
       "      <td>0.234064</td>\n",
       "      <td>0.171499</td>\n",
       "      <td>0.119430</td>\n",
       "      <td>0.200905</td>\n",
       "      <td>0.166378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug       Capomulin  Ceftamin  Infubinol  Ketapril  Naftisol   Placebo  \\\n",
       "Timepoint                                                                 \n",
       "0           0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "5           0.074833  0.108588   0.091652  0.098100  0.093618  0.100947   \n",
       "10          0.125433  0.152177   0.159364  0.142018  0.163577  0.115261   \n",
       "15          0.132048  0.180625   0.194015  0.191381  0.158651  0.190221   \n",
       "20          0.161621  0.241034   0.234801  0.236680  0.181731  0.234064   \n",
       "\n",
       "Drug       Propriva  Ramicane  Stelasyn  Zoniferol  \n",
       "Timepoint                                           \n",
       "0          0.000000  0.000000  0.000000   0.000000  \n",
       "5          0.095219  0.066332  0.087178   0.077709  \n",
       "10         0.105690  0.090289  0.123672   0.109109  \n",
       "15         0.136377  0.115261  0.153439   0.111677  \n",
       "20         0.171499  0.119430  0.200905   0.166378  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "# Flattens the data\n",
    "drugTimepoinrMetaFlat = drugTimepoinrMeta.unstack(level=0)\n",
    "# Remove hierarchical column\n",
    "drugTimepoinrMetaFlat.columns = drugTimepoinrMetaFlat.columns.droplevel()\n",
    "# Flattens the data\n",
    "drugTimepoinrMetaFlat_ste = drugTimepoinrMeta_ste.unstack(level=0)\n",
    "# Remove hierarchical column \n",
    "drugTimepoinrMetaFlat_ste.columns = drugTimepoinrMetaFlat_ste.columns.droplevel()\n",
    "\n",
    "# Preview that Reformatting worked\n",
    "drugTimepoinrMetaFlat.head()\n",
    "drugTimepoinrMetaFlat_ste.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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 the Plot (with Error Bars)\n",
    "# Save the Figure\n",
    "# Show the Figure\n",
    "std_plot_config(x_axis, drugTimepoinrMetaFlat[\"Capomulin\"], drugTimepoinrMetaFlat_ste[\"Capomulin\"],\n",
    "               drugTimepoinrMetaFlat[\"Infubinol\"], drugTimepoinrMetaFlat_ste[\"Infubinol\"],\n",
    "               drugTimepoinrMetaFlat[\"Ketapril\"], drugTimepoinrMetaFlat_ste[\"Ketapril\"],\n",
    "               drugTimepoinrMetaFlat[\"Placebo\"], drugTimepoinrMetaFlat_ste[\"Placebo\"],\n",
    "               \"Treatment Duration (Days)\", \"Met. Sites\", \"Metastatic Spread During Treatment\", \n",
    "                \"Metastatic_Spread_During_Treatment\", 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Count of Mice Grouped by Drug and Timepoint (W can pass any metric)\n",
    "drugsGroupedMice = fullDF.groupby([\"Drug\", \"Timepoint\"]).count().reset_index()\\\n",
    "                                            .drop(columns=[\"Tumor Volume (mm3)\", \"Metastatic Sites\"])\n",
    "drugsGroupedMice = drugsGroupedMice.rename(columns={\"Mouse ID\": \"Mouse Count\"})\n",
    "\n",
    "# Preview DataFrame\n",
    "drugsGroupedMice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>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": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "# .count() counts the number of instances\n",
    "# .unstack() flattens data removing hierarchical indexing\n",
    "# .drop() remove unwanted column\n",
    "miceDrugFlat = fullDF.groupby([\"Drug\", \"Timepoint\"]).count()\\\n",
    "                                            .drop(columns=[\"Tumor Volume (mm3)\", \"Metastatic Sites\"])\\\n",
    "                                            .unstack(level=0)\n",
    "# Remove hierarchical column\n",
    "miceDrugFlat.columns = miceDrugFlat.columns.droplevel()\n",
    "\n",
    "# Preview the Data Frame\n",
    "miceDrugFlat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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 the Plot (Accounting for percentages)\n",
    "# Save the Figure\n",
    "# Show the Figure\n",
    "std_plot_config(x_axis, flat_mice_drug_timepoint[\"Capomulin\"], 0,\n",
    "               flat_mice_drug_timepoint[\"Infubinol\"], 0,\n",
    "               flat_mice_drug_timepoint[\"Ketapril\"], 0,\n",
    "               flat_mice_drug_timepoint[\"Placebo\"], 0,\n",
    "               \"Time (Days)\", \"Survival Rate (%)\", \"Survival During Treatment\", \n",
    "                \"Survival_During_Treatment\", 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Bar Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percent changes for each drug\n",
    "changeTumorPercentage = (flatDrugsGrouped.loc[45,:]-flatDrugsGrouped.loc[0, :])/\\\n",
    "                        flatDrugsGrouped.loc[0, :]*100\n",
    "\n",
    "# Display the data to confirm\n",
    "changeTumorPercentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'percent_change_tumor' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-105e1200018a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Store all Relevant Percent Changes into a Tuple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m percent_change_clean = [percent_change_tumor[\"Capomulin\"], percent_change_tumor[\"Infubinol\"],\n\u001b[0m\u001b[1;32m      3\u001b[0m                         percent_change_tumor[\"Ketapril\"], percent_change_tumor[\"Placebo\"]]\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Splice the data between passing and failing drugs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'percent_change_tumor' is not defined"
     ]
    }
   ],
   "source": [
    "# Store all Relevant Percent Changes into a Tuple\n",
    "percent_change_clean = [percent_change_tumor[\"Capomulin\"], percent_change_tumor[\"Infubinol\"],\n",
    "                        percent_change_tumor[\"Ketapril\"], percent_change_tumor[\"Placebo\"]]\n",
    "\n",
    "# Splice the data between passing and failing drugs\n",
    "\n",
    "###I don't understand this part, failing is < 0?\n",
    "\n",
    "# Orient widths. Add labels, tick marks, etc. \n",
    "\n",
    "\n",
    "# Use functions to label the percentages of changes\n",
    "\n",
    "\n",
    "# Call functions to implement the function calls\n",
    "\n",
    "\n",
    "# Save the Figure\n",
    "\n",
    "\n",
    "# Show the Figure\n"
   ]
  },
  {
   "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",
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 },
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