pymaceutical drug testing version2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "IPython提供了许多魔法命令，使得在IPython环境中的操作更加得心应手。\n",
    "魔法命令都以%或者%%开头，以%开头的成为行命令，%%开头的称为单元命令。\n",
    "行命令只对命令所在的行有效，而单元命令则必须出现在单元的第一行，对整个单元的代码进行处理。\n",
    "'''\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x402</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a492</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>w540</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>v764</td>\n",
       "      <td>Stelasyn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID      Drug\n",
       "0     f234  Stelasyn\n",
       "1     x402  Stelasyn\n",
       "2     a492  Stelasyn\n",
       "3     w540  Stelasyn\n",
       "4     v764  Stelasyn"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pwd   path\n",
    "fmouse_path = \"data/mouse_drug_data.csv\"\n",
    "fdrug_path =  \"data/clinicaltrial_data.csv\"\n",
    "df_mouse = pd.read_csv(fmouse_path)\n",
    "df_mouse.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites\n",
       "0     b128          0                45.0                 0\n",
       "1     f932          0                45.0                 0\n",
       "2     g107          0                45.0                 0\n",
       "3     a457          0                45.0                 0\n",
       "4     c819          0                45.0                 0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drug = pd.read_csv(fdrug_path)\n",
    "df_drug.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1906\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>5</td>\n",
       "      <td>47.313491</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>10</td>\n",
       "      <td>47.904324</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>15</td>\n",
       "      <td>48.735197</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f234</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>20</td>\n",
       "      <td>51.112713</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  Mouse ID      Drug  Timepoint  Tumor Volume (mm3)  Metastatic Sites\n",
       "0     f234  Stelasyn          0           45.000000                 0\n",
       "1     f234  Stelasyn          5           47.313491                 0\n",
       "2     f234  Stelasyn         10           47.904324                 0\n",
       "3     f234  Stelasyn         15           48.735197                 1\n",
       "4     f234  Stelasyn         20           51.112713                 2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge = pd.merge(df_mouse,df_drug,on='Mouse ID',how = 'inner')\n",
    "print(len(df_merge))\n",
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tumor Response to Treatment  (mean, sd, lines chart, errorbar chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Tumor Volume Data Grouped by Drug and Timepoint \n",
    "group_by = df_merge.groupby(['Drug','Timepoint'])\n",
    "# Convert to DataFrame \n",
    "group= group_by[['Tumor Volume (mm3)']].mean()  #one bracket come to a series, double come to a df\n",
    "group.reset_index(inplace=True)\n",
    "group.head()\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Standard Error of Tumor Volumes Grouped by Drug and Timepoint # Convert to DataFrame [[]]\n",
    "std_er = group_by[['Tumor Volume (mm3)']].sem()\n",
    "# Preview DataFrame\n",
    "std_er =std_er.reset_index()\n",
    "std_er.head()\n",
    "#std_er[std_er['Drug']=='Capomulin']['Tumor Volume (mm3)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "format_group = group.pivot(index = 'Timepoint',columns = 'Drug',values = 'Tumor Volume (mm3)')\n",
    "format_group.head()\n",
    "# Preview that Reformatting worked\n",
    "#format_group.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Tumor Volumes(mm3)'),\n",
       " Text(0.5,0,'Time(Days)'),\n",
       " Text(0.5,1,'Tumor response to treatment')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots() # if it was plt.subplot, then an error occur\n",
    "a= plt.errorbar(format_group.index,format_group[\"Capomulin\"],\n",
    "                yerr =std_er[std_er['Drug']=='Capomulin']['Tumor Volume (mm3)'],\n",
    "                marker='o',linestyle='dashed',c='r',linewidth=0.5)\n",
    "# Don't know how to plot errorbar with muti-categories.\n",
    "# resource:https://stackoverflow.com/search?q=Plotting+error+bars+with+multi-categories+in+matplotlib%2C+using+pandas+data+frame\n",
    "d= plt.errorbar(format_group.index,format_group[\"Placebo\"],\n",
    "                yerr =std_er[std_er['Drug']=='Placebo']['Tumor Volume (mm3)'],\n",
    "                marker='d',c='k',linestyle='dashed',linewidth=0.5)\n",
    "b=plt.errorbar(format_group.index,format_group[\"Infubinol\"],\n",
    "               yerr =std_er[std_er['Drug']=='Infubinol']['Tumor Volume (mm3)']\n",
    "               ,marker='^',c='b',linestyle='dashed',linewidth=0.5)\n",
    "b=plt.errorbar(format_group.index,format_group[\"Ketapril\"],\n",
    "               yerr =std_er[std_er['Drug']=='Ketapril']['Tumor Volume (mm3)']\n",
    "               ,marker='s',c='g',linestyle='dashed',linewidth=0.5)\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "# plt.xlabel('Time(days)')\n",
    "# plt.ylabel('Tumor Volume (mm3)')\n",
    "# plt.title('Tumor Response to Treatment')\n",
    "ax.set(xlabel='Time(Days)',ylabel='Tumor Volumes(mm3)',title='Tumor response to treatment')\n",
    "\n",
    "# solution 2\n",
    "# format_group.loc[:,'Capomulin':'Ketapril'].plot(yerr = yerr,marker = 'd')\n",
    "# ax.set(xlabel='Time(Days)',ylabel='Tumor Volumes(mm3)',title='Tumor response to treatment')\n",
    "# The resulting graph didn't have the error bar.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yerr = format_group.loc[:,'Capomulin':'Ketapril'].std()\n",
    "data =format_group.loc[:,'Capomulin':'Ketapril'].mean()\n",
    "fig,ax = plt.subplots()\n",
    "data.plot.bar(ax = ax,yerr = yerr)#before coding the bar(),should put plot.in front of it.\n",
    "#if I didn't put ax=ax, there is no graph.\n",
    "#Instead of using mean(),if i just use format_group.loc[:,'Capomulin':'Ketapril'] the error bar didn't show up.\n",
    " # Generate the Plot (with Error Bars)\n",
    "# Save the Figure\n",
    "# Show the Figure\n",
    "ax.set( ylabel='Tumor Volumes(mm3)',title='Tumor response to treatment')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metastatic Response to Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Metastatic Sites</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>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>10</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>15</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>20</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Drug  Timepoint  Metastatic Sites\n",
       "0  Capomulin          0          0.000000\n",
       "1  Capomulin          5          0.160000\n",
       "2  Capomulin         10          0.320000\n",
       "3  Capomulin         15          0.375000\n",
       "4  Capomulin         20          0.652174"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Mean Met. Site Data Grouped by Drug and Timepoint \n",
    "Metastatic = group_by[['Metastatic Sites']].mean()\n",
    "# Convert to DataFrame\n",
    "Metastatic.reset_index(inplace = True)\n",
    "\n",
    "# Preview DataFrame\n",
    "Metastatic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Metastatic Sites</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>0.074833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>10</td>\n",
       "      <td>0.125433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>15</td>\n",
       "      <td>0.132048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Capomulin</td>\n",
       "      <td>20</td>\n",
       "      <td>0.161621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Drug  Timepoint  Metastatic Sites\n",
       "0  Capomulin          0          0.000000\n",
       "1  Capomulin          5          0.074833\n",
       "2  Capomulin         10          0.125433\n",
       "3  Capomulin         15          0.132048\n",
       "4  Capomulin         20          0.161621"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the standard deviation error Met. Site Data Grouped by Drug and Timepoint \n",
    "Met_std = group_by[['Metastatic Sites']].sem()\n",
    "# Convert to DataFrame\n",
    "Met_std.reset_index(inplace = True)\n",
    "# Preview DataFrame\n",
    "Met_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "Met_2 = Metastatic.pivot(index='Timepoint',columns = 'Drug',values = 'Metastatic Sites')\n",
    "# Preview that Reformatting worked\n",
    "Met_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "fig,ax1 = plt.subplots()\n",
    "plt.errorbar(Met_2.index,Met_2[\"Capomulin\"],\n",
    "                yerr =Met_std[Met_std['Drug']=='Capomulin']['Metastatic Sites'],\n",
    "                marker='o',linestyle='dashed',c='r',ms=5,linewidth=0.5)\n",
    "\n",
    "plt.errorbar(Met_2.index,Met_2[\"Placebo\"],\n",
    "                yerr =Met_std[Met_std['Drug']=='Placebo']['Metastatic Sites'],\n",
    "                marker='d',c='k',linestyle='dashed',ms=5,linewidth=0.5)\n",
    "plt.errorbar(Met_2.index,Met_2[\"Infubinol\"],\n",
    "               yerr =Met_std[Met_std['Drug']=='Infubinol']['Metastatic Sites']\n",
    "               ,marker='^',c='b',linestyle='dashed',ms=5,linewidth=0.5)\n",
    "plt.errorbar(Met_2.index,Met_2[\"Ketapril\"],\n",
    "               yerr =Met_std[Met_std['Drug']=='Ketapril']['Metastatic Sites']\n",
    "               ,marker='s',c='g',linestyle='dashed',ms=5,linewidth=0.5)\n",
    "plt.legend()\n",
    "# plt.title(\"Metastatic Spred During Treatment\")\n",
    "# plt.xlabel(\"Treament Duration(Days)\")\n",
    "# plt.ylabel(\"Met.Sites\")\n",
    "ax1.set(xlabel='Treament Duration(Days)',ylabel='Met.Sites',title='Metastatic Spred During Treatment')\n",
    "plt.grid()\n",
    "# Save the Figure\n",
    "plt.savefig('met.png', bbox_inches='tight')\n",
    "\n",
    "# Show the Figure\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the Count of Mice Grouped by Drug and Timepoint (W can pass any metric)\n",
    "Mice = group_by[['Mouse ID']].count()\n",
    "Mice.columns =['Mouse Count']\n",
    "# Convert to DataFrame\n",
    "Mice.reset_index(inplace = True)\n",
    "# Preview DataFrame\n",
    "Mice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minor Data Munging to Re-Format the Data Frames\n",
    "Mice_2 = Mice.pivot(index = 'Timepoint',columns = 'Drug',values = 'Mouse Count')\n",
    "# Preview the Data Frame\n",
    "Mice_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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 (Accounting for percentages)\n",
    "from itertools import cycle\n",
    "mrk = ['o', '^', 's', 'd']\n",
    "color =['r', 'blue', 'green', 'k']\n",
    "x = Mice_2.index\n",
    "y = Mice_2.loc[:,['Capomulin','Infubinol','Ketapril', 'Placebo']].values.T\n",
    "names =['Capomulin','Infubinol','Ketapril', 'Placebo']\n",
    "\n",
    "Mice_2.loc[:,['Capomulin','Infubinol','Ketapril', 'Placebo']\n",
    "          ].plot(linestyle = 'dashed',linewidth = 0.5,marker='o')\n",
    " \n",
    "# Save the Figure\n",
    "\n",
    "# Show the Figure\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28328b04390>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_4drugs=Mice_2[['Capomulin','Infubinol','Ketapril','Placebo']]\n",
    "survival_4drugs.head()\n",
    "survival_4drugs=survival_4drugs.apply(lambda x:x/25*100)   \n",
    "ax=survival_4drugs.plot(style=['ro-', 'b^--', 'gs-.', 'kD-'],markersize=8,grid=True,legend=True)# no need color again colors=['r','b','g','k'],\n",
    "ax.set(xlabel='Time(Days)',ylabel='Survival Rate(%)',title='Survival during treatment')\n",
    "ax.legend()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Bar Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "[-19.47530267  42.51649186  46.12347173  57.02879469  53.92334713\n",
      "  51.29796048  47.24117486 -22.32090046  52.08513429  46.57975087]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percent changes for each drug\n",
    "percent_change = (format_group.iloc[9,:]-format_group.iloc[0,:])/format_group.iloc[0,:]*100\n",
    "# Display the data to confirm\n",
    "print(percent_change)\n",
    "print(percent_change.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Capomulin', 'Ceftamin', 'Infubinol', 'Ketapril', 'Naftisol', 'Placebo',\n",
       "       'Propriva', 'Ramicane', 'Stelasyn', 'Zoniferol'],\n",
       "      dtype='object', name='Drug')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_change.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "colors = []\n",
    "for i in np.arange(len(percent_change.values)):\n",
    "    if i>0:\n",
    "        colors.append('red')\n",
    "    else:\n",
    "        colors.append('green')\n",
    "plt.bar(percent_change.index,percent_change.values,color=colors,width=0.5,align='edge')#np.arange(len(data_list))\n",
    "plt.gca()\n",
    "ax.grid(which='major',alpha=0.5)\n",
    "ax.set(xlabel='Drug',ylabel='Tumor Volume Change(%)',title='Tumor Change Over 45 Day Treatment')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlim(-0.25, len(percent_change.index))\n",
    "plt.ylim(-30, max(percent_change.values) + 10)\n",
    "plt.show()"
   ]
  },
  {
   "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
}