_code/2018-01-29-queue/Queuing_stats_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=false; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "The raw code for this IPython notebook is by default hidden for easier reading.\n",
       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('''<script>\n",
    "code_show=false; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "The raw code for this IPython notebook is by default hidden for easier reading.\n",
    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import *\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from numpy.random import *\n",
    "import pandas as pd\n",
    "# plt.switch_backend('agg')\n",
    "# fig, ax = plt.subplots(1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration of sharing toilet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_consumers = 25\n",
    "time_range = 180.0\n",
    "unit_service_time = 12.0\n",
    "num_service_booths = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gen_collection(arrival_rate, count=1000000):\n",
    "    exponential_col = exponential(1/arrival_rate, count)\n",
    "    return exponential_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def simulate(arrival_collection, num_consumers, num_service_booths, unit_service_time):\n",
    "    arrival_times = np.cumsum(np.random.choice(arrival_collection, size=num_consumers, replace=True))\n",
    "    complete_times = [0 for i in arrival_times]\n",
    "    for k in range(0, num_consumers):\n",
    "        if k>=0 and (k+1)<=num_service_booths:\n",
    "            complete_times[k] = unit_service_time + arrival_times[k]\n",
    "        elif (k+1)>num_service_booths:\n",
    "            complete_times[k] = unit_service_time + max(arrival_times[k], complete_times[k-num_service_booths])\n",
    "\n",
    "    wait_times = [0 for i in arrival_times]\n",
    "    for k in range(0, num_consumers):\n",
    "        if (k+1)<=num_service_booths:\n",
    "            wait_times[k] = 0\n",
    "        else:\n",
    "            wait_times[k] = max(0, complete_times[k-num_service_booths] - arrival_times[k])\n",
    "    return arrival_times, complete_times, wait_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_experiment(exp_iter, num_consumers, time_range, unit_service_time, num_service_booths):\n",
    "    arrival_collection = gen_collection(num_consumers/time_range, num_consumers*100)\n",
    "    observation = {'max_wait_time': np.zeros(exp_iter), 'avg_wait_time': np.zeros(exp_iter)}\n",
    "    for j in range(exp_iter):\n",
    "        arrival_times, complete_times, wait_times = simulate(arrival_collection,\n",
    "                                                             num_consumers,\n",
    "                                                             num_service_booths,\n",
    "                                                             unit_service_time)\n",
    "        data_df = pd.DataFrame({'complete_times': complete_times, 'wait_times': wait_times, 'arrival_times': arrival_times})\n",
    "        observation['max_wait_time'][j] = np.max(wait_times)\n",
    "        observation['avg_wait_time'][j] = np.mean(wait_times)\n",
    "#     print 'max_wait_time is ', np.mean(observation['max_wait_time'])\n",
    "#     print 'avg_wait_time is ', np.mean(observation['avg_wait_time'])\n",
    "    return {'service_time': unit_service_time,\n",
    "            'max': np.mean(observation['max_wait_time']),\n",
    "            'avg_wait_time': np.mean(observation['avg_wait_time']),\n",
    "            'num_consumers': num_consumers,\n",
    "            'num_service_booths': num_service_booths}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delhi-gurgaon toll plaza configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'avg_wait_time': 28.37672319599437,\n",
       " 'max': 57.86945368502836,\n",
       " 'num_consumers': 800,\n",
       " 'num_service_booths': 18,\n",
       " 'service_time': 4}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_iter = 1000\n",
    "num_consumers = 800\n",
    "time_range = 120.0\n",
    "unit_service_time = 4\n",
    "num_service_booths = 18\n",
    "run_experiment(exp_iter, num_consumers, time_range, unit_service_time, num_service_booths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Estimating waiting time over a grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "toll_data = [run_experiment(exp_iter, num_consumers, time_range, unit_service_time, num_service_booths)\n",
    "             for num_service_booths in range(18, 37, 3)\n",
    "             for num_consumers in range(400, 1000, 100)\n",
    "             for unit_service_time in range(1, 9, 1)\n",
    "            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "toll_data_df = pd.DataFrame(toll_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, 38)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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K92EoPaC0nCvirQ9lQMRfANie9hml1myFFamouAdqMHAjNBqNazJ506ZNOBwO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c8136df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "toll_data_df['num_booth'] = toll_data_df['num_service_booths']\n",
    "ax = sns.pointplot(x='num_booth',\n",
    "         y='avg_wait_time',\n",
    "         hue='num_consumers', \n",
    "         data=toll_data_df.query('service_time==5'))\n",
    "ax.set_ylim((-1,38))\n",
    "#          color='b',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def quant(x):\n",
    "    if x<1:\n",
    "        return '<1'\n",
    "    elif x<4:\n",
    "        return '<4'\n",
    "    elif x<10:\n",
    "        return '<10'\n",
    "    else:\n",
    "        return '>10'\n",
    "toll_data_df['wait_time'] = toll_data_df['avg_wait_time'].apply(quant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_wait_time</th>\n",
       "      <th>max</th>\n",
       "      <th>num_consumers</th>\n",
       "      <th>num_service_booths</th>\n",
       "      <th>service_time</th>\n",
       "      <th>num_booth</th>\n",
       "      <th>wait_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.209223e-03</td>\n",
       "      <td>0.329276</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.022970e+00</td>\n",
       "      <td>4.031123</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.649541e+01</td>\n",
       "      <td>34.753452</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.151380e-02</td>\n",
       "      <td>0.902544</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.552419e+00</td>\n",
       "      <td>20.009788</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.586393e+01</td>\n",
       "      <td>73.102397</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.178708e-07</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.852620e-01</td>\n",
       "      <td>1.671332</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.258931e+01</td>\n",
       "      <td>46.336197</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.502962e+01</td>\n",
       "      <td>112.608944</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.974543e-06</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>700</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.203397e+00</td>\n",
       "      <td>3.844944</td>\n",
       "      <td>700</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.655315e+01</td>\n",
       "      <td>73.742864</td>\n",
       "      <td>700</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.478805e+01</td>\n",
       "      <td>150.438829</td>\n",
       "      <td>700</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.056128e-05</td>\n",
       "      <td>0.004260</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.089391e+00</td>\n",
       "      <td>14.742352</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.997524e+01</td>\n",
       "      <td>101.456827</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9.302944e+01</td>\n",
       "      <td>188.691063</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.272540e-05</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>900</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.497179e+01</td>\n",
       "      <td>30.455940</td>\n",
       "      <td>900</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.330653e+01</td>\n",
       "      <td>127.208979</td>\n",
       "      <td>900</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.126444e+02</td>\n",
       "      <td>225.887042</td>\n",
       "      <td>900</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.111302e-04</td>\n",
       "      <td>0.088584</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.327677e-01</td>\n",
       "      <td>2.144267</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.638456e+00</td>\n",
       "      <td>20.510376</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.024742e-03</td>\n",
       "      <td>0.503638</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1.614244e+01</td>\n",
       "      <td>33.161258</td>\n",
       "      <td>800</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>4.631001e+01</td>\n",
       "      <td>93.938572</td>\n",
       "      <td>800</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.558643e-01</td>\n",
       "      <td>1.426383</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2.522328e+01</td>\n",
       "      <td>51.399370</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>5.960571e+01</td>\n",
       "      <td>121.012557</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.529860e-03</td>\n",
       "      <td>0.180697</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.932794e-01</td>\n",
       "      <td>2.081975</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>9.695793e-06</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>3.768420e-02</td>\n",
       "      <td>1.033498</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>3.559907e+00</td>\n",
       "      <td>8.570454</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3.758291e-04</td>\n",
       "      <td>0.057867</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3.504729e-01</td>\n",
       "      <td>2.291680</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1.436440e+01</td>\n",
       "      <td>29.937263</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2.653391e-03</td>\n",
       "      <td>0.269793</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3.304563e+00</td>\n",
       "      <td>7.754921</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2.641683e+01</td>\n",
       "      <td>53.773135</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.487055e-02</td>\n",
       "      <td>0.629278</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.104803e+01</td>\n",
       "      <td>22.906158</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3.839924e+01</td>\n",
       "      <td>78.510565</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.136862e-02</td>\n",
       "      <td>1.016073</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.957160e+01</td>\n",
       "      <td>40.537968</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.074915e+01</td>\n",
       "      <td>104.512620</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&gt;10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     avg_wait_time         max  num_consumers  num_service_booths  \\\n",
       "0     0.000000e+00    0.000000            400                  18   \n",
       "1     4.209223e-03    0.329276            400                  18   \n",
       "2     1.022970e+00    4.031123            400                  18   \n",
       "3     1.649541e+01   34.753452            400                  18   \n",
       "4     0.000000e+00    0.000000            500                  18   \n",
       "5     3.151380e-02    0.902544            500                  18   \n",
       "6     9.552419e+00   20.009788            500                  18   \n",
       "7     3.586393e+01   73.102397            500                  18   \n",
       "8     4.178708e-07    0.000183            600                  18   \n",
       "9     1.852620e-01    1.671332            600                  18   \n",
       "10    2.258931e+01   46.336197            600                  18   \n",
       "11    5.502962e+01  112.608944            600                  18   \n",
       "12    1.974543e-06    0.000634            700                  18   \n",
       "13    1.203397e+00    3.844944            700                  18   \n",
       "14    3.655315e+01   73.742864            700                  18   \n",
       "15    7.478805e+01  150.438829            700                  18   \n",
       "16    1.056128e-05    0.004260            800                  18   \n",
       "17    7.089391e+00   14.742352            800                  18   \n",
       "18    4.997524e+01  101.456827            800                  18   \n",
       "19    9.302944e+01  188.691063            800                  18   \n",
       "20    6.272540e-05    0.022141            900                  18   \n",
       "21    1.497179e+01   30.455940            900                  18   \n",
       "22    6.330653e+01  127.208979            900                  18   \n",
       "23    1.126444e+02  225.887042            900                  18   \n",
       "24    0.000000e+00    0.000000            400                  20   \n",
       "25    7.111302e-04    0.088584            400                  20   \n",
       "26    2.327677e-01    2.144267            400                  20   \n",
       "27    9.638456e+00   20.510376            400                  20   \n",
       "28    0.000000e+00    0.000000            500                  20   \n",
       "29    8.024742e-03    0.503638            500                  20   \n",
       "..             ...         ...            ...                 ...   \n",
       "114   1.614244e+01   33.161258            800                  26   \n",
       "115   4.631001e+01   93.938572            800                  26   \n",
       "116   0.000000e+00    0.000000            900                  26   \n",
       "117   1.558643e-01    1.426383            900                  26   \n",
       "118   2.522328e+01   51.399370            900                  26   \n",
       "119   5.960571e+01  121.012557            900                  26   \n",
       "120   0.000000e+00    0.000000            400                  28   \n",
       "121   0.000000e+00    0.000000            400                  28   \n",
       "122   2.529860e-03    0.180697            400                  28   \n",
       "123   1.932794e-01    2.081975            400                  28   \n",
       "124   0.000000e+00    0.000000            500                  28   \n",
       "125   9.695793e-06    0.001942            500                  28   \n",
       "126   3.768420e-02    1.033498            500                  28   \n",
       "127   3.559907e+00    8.570454            500                  28   \n",
       "128   0.000000e+00    0.000000            600                  28   \n",
       "129   3.758291e-04    0.057867            600                  28   \n",
       "130   3.504729e-01    2.291680            600                  28   \n",
       "131   1.436440e+01   29.937263            600                  28   \n",
       "132   0.000000e+00    0.000000            700                  28   \n",
       "133   2.653391e-03    0.269793            700                  28   \n",
       "134   3.304563e+00    7.754921            700                  28   \n",
       "135   2.641683e+01   53.773135            700                  28   \n",
       "136   0.000000e+00    0.000000            800                  28   \n",
       "137   1.487055e-02    0.629278            800                  28   \n",
       "138   1.104803e+01   22.906158            800                  28   \n",
       "139   3.839924e+01   78.510565            800                  28   \n",
       "140   0.000000e+00    0.000000            900                  28   \n",
       "141   6.136862e-02    1.016073            900                  28   \n",
       "142   1.957160e+01   40.537968            900                  28   \n",
       "143   5.074915e+01  104.512620            900                  28   \n",
       "\n",
       "     service_time  num_booth wait_time  \n",
       "0               1         18        <1  \n",
       "1               3         18        <1  \n",
       "2               5         18        <4  \n",
       "3               7         18       >10  \n",
       "4               1         18        <1  \n",
       "5               3         18        <1  \n",
       "6               5         18       <10  \n",
       "7               7         18       >10  \n",
       "8               1         18        <1  \n",
       "9               3         18        <1  \n",
       "10              5         18       >10  \n",
       "11              7         18       >10  \n",
       "12              1         18        <1  \n",
       "13              3         18        <4  \n",
       "14              5         18       >10  \n",
       "15              7         18       >10  \n",
       "16              1         18        <1  \n",
       "17              3         18       <10  \n",
       "18              5         18       >10  \n",
       "19              7         18       >10  \n",
       "20              1         18        <1  \n",
       "21              3         18       >10  \n",
       "22              5         18       >10  \n",
       "23              7         18       >10  \n",
       "24              1         20        <1  \n",
       "25              3         20        <1  \n",
       "26              5         20        <1  \n",
       "27              7         20       <10  \n",
       "28              1         20        <1  \n",
       "29              3         20        <1  \n",
       "..            ...        ...       ...  \n",
       "114             5         26       >10  \n",
       "115             7         26       >10  \n",
       "116             1         26        <1  \n",
       "117             3         26        <1  \n",
       "118             5         26       >10  \n",
       "119             7         26       >10  \n",
       "120             1         28        <1  \n",
       "121             3         28        <1  \n",
       "122             5         28        <1  \n",
       "123             7         28        <1  \n",
       "124             1         28        <1  \n",
       "125             3         28        <1  \n",
       "126             5         28        <1  \n",
       "127             7         28        <4  \n",
       "128             1         28        <1  \n",
       "129             3         28        <1  \n",
       "130             5         28        <1  \n",
       "131             7         28       >10  \n",
       "132             1         28        <1  \n",
       "133             3         28        <1  \n",
       "134             5         28        <4  \n",
       "135             7         28       >10  \n",
       "136             1         28        <1  \n",
       "137             3         28        <1  \n",
       "138             5         28       >10  \n",
       "139             7         28       >10  \n",
       "140             1         28        <1  \n",
       "141             3         28        <1  \n",
       "142             5         28       >10  \n",
       "143             7         28       >10  \n",
       "\n",
       "[144 rows x 7 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toll_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3YVZh28kCjmSX8EJfy+9wVpGBzen51tebVTCaVL45ksvoHtEAGEwq//rlLFtOWmasvD7M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\nDQaD+uKLL6qnT5921uHrZM2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c81af5490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.pointplot(x='num_booth',\n",
    "             y='service_time',\n",
    "             hue='wait_time',\n",
    "             data=toll_data_df.query('num_consumers==600'),\n",
    "              fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_wait_time</th>\n",
       "      <th>max</th>\n",
       "      <th>num_consumers</th>\n",
       "      <th>num_service_booths</th>\n",
       "      <th>service_time</th>\n",
       "      <th>num_booth</th>\n",
       "      <th>wait_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.209223e-03</td>\n",
       "      <td>0.329276</td>\n",
       "      <td>400</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.151380e-02</td>\n",
       "      <td>0.902544</td>\n",
       "      <td>500</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.178708e-07</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.852620e-01</td>\n",
       "      <td>1.671332</td>\n",
       "      <td>600</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.974543e-06</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>700</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.056128e-05</td>\n",
       "      <td>0.004260</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.272540e-05</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>900</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.111302e-04</td>\n",
       "      <td>0.088584</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.327677e-01</td>\n",
       "      <td>2.144267</td>\n",
       "      <td>400</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.024742e-03</td>\n",
       "      <td>0.503638</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4.810796e-02</td>\n",
       "      <td>1.024207</td>\n",
       "      <td>600</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7.550504e-07</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>700</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.460960e-01</td>\n",
       "      <td>1.810744</td>\n",
       "      <td>700</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.420494e-06</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>800</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.080777e-05</td>\n",
       "      <td>0.003219</td>\n",
       "      <td>900</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.993495e-04</td>\n",
       "      <td>0.024351</td>\n",
       "      <td>400</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>8.261656e-02</td>\n",
       "      <td>1.451857</td>\n",
       "      <td>400</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2.425256e-03</td>\n",
       "      <td>0.238231</td>\n",
       "      <td>500</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.712633e-02</td>\n",
       "      <td>0.695939</td>\n",
       "      <td>600</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>8.139068e-02</td>\n",
       "      <td>1.183611</td>\n",
       "      <td>700</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.257218e-06</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7.930201e-03</td>\n",
       "      <td>0.477171</td>\n",
       "      <td>400</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6.446948e-01</td>\n",
       "      <td>3.332604</td>\n",
       "      <td>400</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.788197e-05</td>\n",
       "      <td>0.011962</td>\n",
       "      <td>500</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>9.453157e-02</td>\n",
       "      <td>1.447792</td>\n",
       "      <td>500</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1.379929e-03</td>\n",
       "      <td>0.165124</td>\n",
       "      <td>600</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>8.790425e-03</td>\n",
       "      <td>0.510698</td>\n",
       "      <td>700</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>4.340358e-02</td>\n",
       "      <td>0.919923</td>\n",
       "      <td>800</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.558643e-01</td>\n",
       "      <td>1.426383</td>\n",
       "      <td>900</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.529860e-03</td>\n",
       "      <td>0.180697</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.932794e-01</td>\n",
       "      <td>2.081975</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>9.695793e-06</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>3.768420e-02</td>\n",
       "      <td>1.033498</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3.758291e-04</td>\n",
       "      <td>0.057867</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3.504729e-01</td>\n",
       "      <td>2.291680</td>\n",
       "      <td>600</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2.653391e-03</td>\n",
       "      <td>0.269793</td>\n",
       "      <td>700</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.487055e-02</td>\n",
       "      <td>0.629278</td>\n",
       "      <td>800</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.136862e-02</td>\n",
       "      <td>1.016073</td>\n",
       "      <td>900</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     avg_wait_time       max  num_consumers  num_service_booths  service_time  \\\n",
       "0     0.000000e+00  0.000000            400                  18             1   \n",
       "1     4.209223e-03  0.329276            400                  18             3   \n",
       "4     0.000000e+00  0.000000            500                  18             1   \n",
       "5     3.151380e-02  0.902544            500                  18             3   \n",
       "8     4.178708e-07  0.000183            600                  18             1   \n",
       "9     1.852620e-01  1.671332            600                  18             3   \n",
       "12    1.974543e-06  0.000634            700                  18             1   \n",
       "16    1.056128e-05  0.004260            800                  18             1   \n",
       "20    6.272540e-05  0.022141            900                  18             1   \n",
       "24    0.000000e+00  0.000000            400                  20             1   \n",
       "25    7.111302e-04  0.088584            400                  20             3   \n",
       "26    2.327677e-01  2.144267            400                  20             5   \n",
       "28    0.000000e+00  0.000000            500                  20             1   \n",
       "29    8.024742e-03  0.503638            500                  20             3   \n",
       "32    0.000000e+00  0.000000            600                  20             1   \n",
       "33    4.810796e-02  1.024207            600                  20             3   \n",
       "36    7.550504e-07  0.000184            700                  20             1   \n",
       "37    2.460960e-01  1.810744            700                  20             3   \n",
       "40    1.420494e-06  0.000666            800                  20             1   \n",
       "44    1.080777e-05  0.003219            900                  20             1   \n",
       "48    0.000000e+00  0.000000            400                  22             1   \n",
       "49    1.993495e-04  0.024351            400                  22             3   \n",
       "50    8.261656e-02  1.451857            400                  22             5   \n",
       "52    0.000000e+00  0.000000            500                  22             1   \n",
       "53    2.425256e-03  0.238231            500                  22             3   \n",
       "56    0.000000e+00  0.000000            600                  22             1   \n",
       "57    1.712633e-02  0.695939            600                  22             3   \n",
       "60    0.000000e+00  0.000000            700                  22             1   \n",
       "61    8.139068e-02  1.183611            700                  22             3   \n",
       "64    0.000000e+00  0.000000            800                  22             1   \n",
       "..             ...       ...            ...                 ...           ...   \n",
       "97    1.257218e-06  0.000415            400                  26             3   \n",
       "98    7.930201e-03  0.477171            400                  26             5   \n",
       "99    6.446948e-01  3.332604            400                  26             7   \n",
       "100   0.000000e+00  0.000000            500                  26             1   \n",
       "101   5.788197e-05  0.011962            500                  26             3   \n",
       "102   9.453157e-02  1.447792            500                  26             5   \n",
       "104   0.000000e+00  0.000000            600                  26             1   \n",
       "105   1.379929e-03  0.165124            600                  26             3   \n",
       "108   0.000000e+00  0.000000            700                  26             1   \n",
       "109   8.790425e-03  0.510698            700                  26             3   \n",
       "112   0.000000e+00  0.000000            800                  26             1   \n",
       "113   4.340358e-02  0.919923            800                  26             3   \n",
       "116   0.000000e+00  0.000000            900                  26             1   \n",
       "117   1.558643e-01  1.426383            900                  26             3   \n",
       "120   0.000000e+00  0.000000            400                  28             1   \n",
       "121   0.000000e+00  0.000000            400                  28             3   \n",
       "122   2.529860e-03  0.180697            400                  28             5   \n",
       "123   1.932794e-01  2.081975            400                  28             7   \n",
       "124   0.000000e+00  0.000000            500                  28             1   \n",
       "125   9.695793e-06  0.001942            500                  28             3   \n",
       "126   3.768420e-02  1.033498            500                  28             5   \n",
       "128   0.000000e+00  0.000000            600                  28             1   \n",
       "129   3.758291e-04  0.057867            600                  28             3   \n",
       "130   3.504729e-01  2.291680            600                  28             5   \n",
       "132   0.000000e+00  0.000000            700                  28             1   \n",
       "133   2.653391e-03  0.269793            700                  28             3   \n",
       "136   0.000000e+00  0.000000            800                  28             1   \n",
       "137   1.487055e-02  0.629278            800                  28             3   \n",
       "140   0.000000e+00  0.000000            900                  28             1   \n",
       "141   6.136862e-02  1.016073            900                  28             3   \n",
       "\n",
       "     num_booth wait_time  \n",
       "0           18        <1  \n",
       "1           18        <1  \n",
       "4           18        <1  \n",
       "5           18        <1  \n",
       "8           18        <1  \n",
       "9           18        <1  \n",
       "12          18        <1  \n",
       "16          18        <1  \n",
       "20          18        <1  \n",
       "24          20        <1  \n",
       "25          20        <1  \n",
       "26          20        <1  \n",
       "28          20        <1  \n",
       "29          20        <1  \n",
       "32          20        <1  \n",
       "33          20        <1  \n",
       "36          20        <1  \n",
       "37          20        <1  \n",
       "40          20        <1  \n",
       "44          20        <1  \n",
       "48          22        <1  \n",
       "49          22        <1  \n",
       "50          22        <1  \n",
       "52          22        <1  \n",
       "53          22        <1  \n",
       "56          22        <1  \n",
       "57          22        <1  \n",
       "60          22        <1  \n",
       "61          22        <1  \n",
       "64          22        <1  \n",
       "..         ...       ...  \n",
       "97          26        <1  \n",
       "98          26        <1  \n",
       "99          26        <1  \n",
       "100         26        <1  \n",
       "101         26        <1  \n",
       "102         26        <1  \n",
       "104         26        <1  \n",
       "105         26        <1  \n",
       "108         26        <1  \n",
       "109         26        <1  \n",
       "112         26        <1  \n",
       "113         26        <1  \n",
       "116         26        <1  \n",
       "117         26        <1  \n",
       "120         28        <1  \n",
       "121         28        <1  \n",
       "122         28        <1  \n",
       "123         28        <1  \n",
       "124         28        <1  \n",
       "125         28        <1  \n",
       "126         28        <1  \n",
       "128         28        <1  \n",
       "129         28        <1  \n",
       "130         28        <1  \n",
       "132         28        <1  \n",
       "133         28        <1  \n",
       "136         28        <1  \n",
       "137         28        <1  \n",
       "140         28        <1  \n",
       "141         28        <1  \n",
       "\n",
       "[77 rows x 7 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toll_data_df.query('avg_wait_time<1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XKCD plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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txaCTk5MbvAetVqthMBigUCigVCrDKtZgMKCiogKlpaUAAJPJBJlMhueeey7o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\nWkJCQuIxRRLQEhISEo8p/w+v+dML3/z34gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e4e5f1b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Turn off ggplot in matplotlib and install humor sans as font\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('TkAgg')\n",
    "import numpy as np\n",
    "\n",
    "with plt.xkcd():\n",
    "#     # Based on \"Stove Ownership\" from XKCD by Randall Monroe\n",
    "#     # http://xkcd.com/418/\n",
    "\n",
    "#     fig = plt.figure()\n",
    "#     ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
    "#     ax.spines['right'].set_color('none')\n",
    "#     ax.spines['top'].set_color('none')\n",
    "#     plt.xticks([])\n",
    "#     plt.yticks([])\n",
    "#     ax.set_ylim([-30, 10])\n",
    "\n",
    "#     data = np.ones(100)\n",
    "#     data[70:] -= np.arange(30)\n",
    "\n",
    "#     plt.annotate(\n",
    "#         'THE DAY I REALIZED\\nI COULD COOK BACON\\nWHENEVER I WANTED',\n",
    "#         xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))\n",
    "\n",
    "#     plt.plot(data)\n",
    "\n",
    "#     plt.xlabel('time')\n",
    "#     plt.ylabel('my overall health')\n",
    "#     fig.text(\n",
    "#         0.5, 0.05,\n",
    "#         '\"Stove Ownership\" from xkcd by Randall Monroe',\n",
    "#         ha='center')\n",
    "\n",
    "#     # Based on \"The Data So Far\" from XKCD by Randall Monroe\n",
    "#     # http://xkcd.com/373/\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
    "    ax.bar([-0.05, 0.85], [55, 53], 0.25, color='none')\n",
    "    ax.spines['right'].set_color('none')\n",
    "    ax.spines['top'].set_color('none')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.set_xticks([0.08, 0.97])\n",
    "    ax.set_xlim([-0.3, 1.5])\n",
    "    ax.set_ylim([0, 70])\n",
    "    ax.set_xticklabels(['BAD BY\\nDESIGN', 'OTHERS'], size='x-large')\n",
    "#     ax.text()\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.title(\"REASON BEHIND POOR SERVICE QUALITY\", size=17)\n",
    "    ax.text(-0.02, 59, '51%', fontsize=40)\n",
    "    ax.text(0.89, 56, '49%', fontsize=20)\n",
    "    fig.text(\n",
    "        0.5, 0.02,\n",
    "        'SOURCE: RANDOM NUMBER GENERATION',\n",
    "        ha='center')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}