GroupingCustomerSupportProblems.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping Similar Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to group customer support problems into problem groups based on their attributes. Then we will analyze the groups to see similarities and differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PROBLEM_TYPE             object\n",
       "COUNT                     int64\n",
       "AVG_CALLS_TO_RESOLVE    float64\n",
       "AVG_RESOLUTION_TIME       int64\n",
       "REOCCUR_RATE            float64\n",
       "REPLACEMENT_RATE        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "import sklearn.metrics\n",
    "\n",
    "raw_data = pd.read_csv(\"issues.csv\")\n",
    "raw_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains one record for each unique problem type. It has metrics for each type like count, average calls to resolve, average resolution time etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>PROBLEM_TYPE</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>AVG_CALLS_TO_RESOLVE</th>\n",
       "      <th>AVG_RESOLUTION_TIME</th>\n",
       "      <th>REOCCUR_RATE</th>\n",
       "      <th>REPLACEMENT_RATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Admin Password Lost</td>\n",
       "      <td>45</td>\n",
       "      <td>2.3</td>\n",
       "      <td>54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows Reboots automatically</td>\n",
       "      <td>47</td>\n",
       "      <td>3.1</td>\n",
       "      <td>132</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>System not coming up after reboot</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Slow system</td>\n",
       "      <td>165</td>\n",
       "      <td>1.2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Internet Connectivity loss</td>\n",
       "      <td>321</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        PROBLEM_TYPE  COUNT  AVG_CALLS_TO_RESOLVE  \\\n",
       "0                Admin Password Lost     45                   2.3   \n",
       "1      Windows Reboots automatically     47                   3.1   \n",
       "2  System not coming up after reboot     12                   4.0   \n",
       "3                        Slow system    165                   1.2   \n",
       "4         Internet Connectivity loss    321                   1.0   \n",
       "\n",
       "   AVG_RESOLUTION_TIME  REOCCUR_RATE  REPLACEMENT_RATE  \n",
       "0                   54          0.15              0.00  \n",
       "1                  132          0.30              0.03  \n",
       "2                  154          0.02              0.05  \n",
       "3                   32          0.03              0.00  \n",
       "4                    5          0.21              0.00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Data into similar clusters\n",
    "Now, we will use K-Means clustering to group data based on their attribute. First, we need to determine the optimal number of groups. For that we conduct the knee test to see where the knee happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Selecting k with the Elbow Method')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clust_data = raw_data.drop(\"PROBLEM_TYPE\",axis=1)\n",
    "\n",
    "#Finding optimal no. of clusters\n",
    "from scipy.spatial.distance import cdist\n",
    "clusters=range(1,10)\n",
    "meanDistortions=[]\n",
    "\n",
    "for k in clusters:\n",
    "    model=KMeans(n_clusters=k)\n",
    "    model.fit(clust_data)\n",
    "    prediction=model.predict(clust_data)\n",
    "    meanDistortions.append(sum(np.min(cdist(clust_data, model.cluster_centers_, 'euclidean'), axis=1)) / clust_data.shape[0])\n",
    "\n",
    "#plt.cla()\n",
    "plt.plot(clusters, meanDistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average distortion')\n",
    "plt.title('Selecting k with the Elbow Method')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the plot, we see that the knee happens at cluster=3. That is the ideal number of clusters. We now perform the actual clustering for 3. Then we add the cluster ID to the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups Assigned : \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GROUP</th>\n",
       "      <th>PROBLEM_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Admin Password Lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Windows Reboots automatically</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>System not coming up after reboot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Slow system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet Connectivity loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>New Installation hangs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Intermittent Blank Screen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Too many popups in Browser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Cannot find printer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Missing peripheral driver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Cannot detect keyboard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Cannot detect mouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Head phone jack not working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>DVD read error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>Cannot recover using restore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>WIFI not functioning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>Laptop not charging</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>Laptop loses charge very fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>Dark areas on screen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>anti-virus not working</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GROUP                       PROBLEM_TYPE\n",
       "0       1                Admin Password Lost\n",
       "1       1      Windows Reboots automatically\n",
       "2       1  System not coming up after reboot\n",
       "3       0                        Slow system\n",
       "4       2         Internet Connectivity loss\n",
       "5       1             New Installation hangs\n",
       "6       1          Intermittent Blank Screen\n",
       "7       0         Too many popups in Browser\n",
       "8       0                Cannot find printer\n",
       "9       1          Missing peripheral driver\n",
       "10      2             Cannot detect keyboard\n",
       "11      2                Cannot detect mouse\n",
       "12      2        Head phone jack not working\n",
       "13      0                     DVD read error\n",
       "14      1       Cannot recover using restore\n",
       "15      0               WIFI not functioning\n",
       "16      1                Laptop not charging\n",
       "17      1      Laptop loses charge very fast\n",
       "18      1               Dark areas on screen\n",
       "19      0             anti-virus not working"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optimal clusters is 3\n",
    "final_model=KMeans(3)\n",
    "final_model.fit(clust_data)\n",
    "prediction=final_model.predict(clust_data)\n",
    "\n",
    "#Join predicted clusters back to raw data\n",
    "raw_data[\"GROUP\"] = prediction\n",
    "print(\"Groups Assigned : \\n\")\n",
    "raw_data[[\"GROUP\",\"PROBLEM_TYPE\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the groups\n",
    "We now do a set of boxplots to see how the groups differ for various feature attributes.\n",
    "We start off with Count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-a884ffb7c475>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m               \u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"COUNT\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGROUP\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                 [raw_data[\"COUNT\"][raw_data.GROUP==2]]],\n\u001b[0;32m----> 5\u001b[0;31m             labels = ('GROUP 1','GROUP 2','GROUP 3'))\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder, data)\u001b[0m\n\u001b[1;32m   2478\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNFFT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNFFT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         \u001b[0mnoverlap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoverlap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msides\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2480\u001b[0;31m         scale_by_freq=scale_by_freq, **({\"data\": data} if data is not\n\u001b[0m\u001b[1;32m   2481\u001b[0m         None else {}), **kwargs)\n\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_deprecated_parameter_class\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"<deprecated parameter>\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib version %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'interactive is %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'platform is %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplatform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loaded modules: %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3667\u001b[0m         \u001b[0;31m# on the flierprops dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3668\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msym\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3669\u001b[0;31m             \u001b[0;31m# no-flier case, which should really be done with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3670\u001b[0m             \u001b[0;31m# 'showfliers=False' but none-the-less deal with it to keep back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3671\u001b[0m             \u001b[0;31m# compatibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m   1290\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m         \u001b[0midx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1292\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1477\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1478\u001b[0m         raise ValueError(\n\u001b[0;32m-> 1479\u001b[0;31m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1480\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1481\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.cla()\n",
    "plt.boxplot([[raw_data[\"COUNT\"][raw_data.GROUP==0]],\n",
    "              [raw_data[\"COUNT\"][raw_data.GROUP==1]],\n",
    "                [raw_data[\"COUNT\"][raw_data.GROUP==2]]],\n",
    "            labels = ('GROUP 1','GROUP 2','GROUP 3'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the count of incidents range differently for different groups.\n",
    "\n",
    "Next we see avg. calls to resolve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-981c83106267>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m               \u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"AVG_CALLS_TO_RESOLVE\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGROUP\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                 [raw_data[\"AVG_CALLS_TO_RESOLVE\"][raw_data.GROUP==2]]],\n\u001b[0;32m----> 6\u001b[0;31m             labels=('GROUP 1','GROUP 2','GROUP 3'))\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder, data)\u001b[0m\n\u001b[1;32m   2478\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNFFT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNFFT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         \u001b[0mnoverlap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoverlap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msides\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2480\u001b[0;31m         scale_by_freq=scale_by_freq, **({\"data\": data} if data is not\n\u001b[0m\u001b[1;32m   2481\u001b[0m         None else {}), **kwargs)\n\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_deprecated_parameter_class\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"<deprecated parameter>\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib version %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'interactive is %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'platform is %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplatform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loaded modules: %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3667\u001b[0m         \u001b[0;31m# on the flierprops dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3668\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msym\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3669\u001b[0;31m             \u001b[0;31m# no-flier case, which should really be done with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3670\u001b[0m             \u001b[0;31m# 'showfliers=False' but none-the-less deal with it to keep back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3671\u001b[0m             \u001b[0;31m# compatibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m   1290\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m         \u001b[0midx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1292\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1477\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1478\u001b[0m         raise ValueError(\n\u001b[0;32m-> 1479\u001b[0;31m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1480\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1481\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "#Now for Avg. Calls to resolve\n",
    "plt.cla()\n",
    "plt.boxplot([[raw_data[\"AVG_CALLS_TO_RESOLVE\"][raw_data.GROUP==0]],\n",
    "              [raw_data[\"AVG_CALLS_TO_RESOLVE\"][raw_data.GROUP==1]] ,\n",
    "                [raw_data[\"AVG_CALLS_TO_RESOLVE\"][raw_data.GROUP==2]]],\n",
    "            labels=('GROUP 1','GROUP 2','GROUP 3'))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group 2 has hardly any time needed to resolve. This points to problems that are simple and straight forward. The business need to look at these incidents and provide a self-service path (product help, online help) for the customer instead of wasting agent's time\n",
    "\n",
    "Next we see Reoccurance Rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-431c88be111b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m               \u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"REOCCUR_RATE\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGROUP\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                 [raw_data[\"REOCCUR_RATE\"][raw_data.GROUP==2]] ],\n\u001b[0;32m----> 5\u001b[0;31m             labels=('GROUP 1','GROUP 2','GROUP 3'))\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder, data)\u001b[0m\n\u001b[1;32m   2478\u001b[0m         \u001b[0mwhiskerprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhiskerprops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmanage_ticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanage_ticks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         autorange=autorange, zorder=zorder, **({\"data\": data} if data\n\u001b[0;32m-> 2480\u001b[0;31m         is not None else {}))\n\u001b[0m\u001b[1;32m   2481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    306\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3668\u001b[0m         bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,\n\u001b[0;32m-> 3669\u001b[0;31m                                        labels=labels, autorange=autorange)\n\u001b[0m\u001b[1;32m   3670\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnotch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3671\u001b[0m             \u001b[0mnotch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.notch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m   1290\u001b[0m         \u001b[0;31m# interquartile range\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m         \u001b[0mstats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'iqr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq3\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mq1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1292\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'iqr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mautorange\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m             \u001b[0mwhis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'range'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1477\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1478\u001b[0m         raise ValueError(\n\u001b[0;32m-> 1479\u001b[0;31m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1480\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1481\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "plt.cla()\n",
    "plt.boxplot([[raw_data[\"REOCCUR_RATE\"][raw_data.GROUP==0]],\n",
    "              [raw_data[\"REOCCUR_RATE\"][raw_data.GROUP==1]] ,\n",
    "                [raw_data[\"REOCCUR_RATE\"][raw_data.GROUP==2]] ],\n",
    "            labels=('GROUP 1','GROUP 2','GROUP 3'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group 2 has really high reoccurance rate. This set of incidents need to be analyze to see how the product quality can be improved to prevent these from happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-81edb3572482>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m               \u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"REPLACEMENT_RATE\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGROUP\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                 [raw_data[\"REPLACEMENT_RATE\"][raw_data.GROUP==2]] ],\n\u001b[0;32m----> 5\u001b[0;31m             labels=('GROUP 1','GROUP 2','GROUP 3'))\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder, data)\u001b[0m\n\u001b[1;32m   2478\u001b[0m         \u001b[0mwhiskerprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhiskerprops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmanage_ticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanage_ticks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         autorange=autorange, zorder=zorder, **({\"data\": data} if data\n\u001b[0;32m-> 2480\u001b[0;31m         is not None else {}))\n\u001b[0m\u001b[1;32m   2481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 f\"for the old name will be dropped %(removal)s.\")\n\u001b[1;32m    306\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;31m# wrapper() must keep the same documented signature as func(): if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_ticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3668\u001b[0m         bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,\n\u001b[0;32m-> 3669\u001b[0;31m                                        labels=labels, autorange=autorange)\n\u001b[0m\u001b[1;32m   3670\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnotch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3671\u001b[0m             \u001b[0mnotch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.notch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m   1290\u001b[0m         \u001b[0;31m# interquartile range\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m         \u001b[0mstats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'iqr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq3\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mq1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1292\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'iqr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mautorange\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m             \u001b[0mwhis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'range'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1477\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1478\u001b[0m         raise ValueError(\n\u001b[0;32m-> 1479\u001b[0;31m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1480\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1481\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "plt.cla()\n",
    "plt.boxplot([[raw_data[\"REPLACEMENT_RATE\"][raw_data.GROUP==0]],\n",
    "              [raw_data[\"REPLACEMENT_RATE\"][raw_data.GROUP==1]] ,\n",
    "                [raw_data[\"REPLACEMENT_RATE\"][raw_data.GROUP==2]] ],\n",
    "            labels=('GROUP 1','GROUP 2','GROUP 3'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacement rates vary widely for Group 1. It does not provide any significant pattern to act upon."
   ]
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}