Emergency Calls Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emergency Calls Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This dataset conatins 911 Emergency calls in Montgomery County Pennsylvania.\n",
    "\n",
    "Information about the columns\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Data Source - Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read the csv file into a pandas dataframe as df\n",
    "calls = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the head (first 5 rows) of the dataframe\n",
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "calls.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing the 'title' column, there are words, before what seems to be the main title. Those words looks like umbrella to explain the departments that callled 911. Hence, we will split the title column into 'reason' and 'title'.\n",
    "\n",
    "For instance, if the title column value is EMS: DIABETIC EMERGENCY, the Reason column value would be EMS\n",
    "\n",
    "**Split title column to 'title' and reason'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "calls['Reason'] = calls['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert the timeStamp to Datetime object using the pandas pd.to_datetime**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "calls['timeStamp'] = pd.to_datetime(calls['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**With the timestamp column now Datetime object, we can use .apply() to create 3 new columns called Hour, Month and Day of week based off of the timeStamp column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['Hour'] = calls['timeStamp'].apply(lambda time: time.hour)\n",
    "calls['Month'] = calls['timeStamp'].apply(lambda time: time.month)\n",
    "calls['Day of Week'] = calls['timeStamp'].apply(lambda time: time.dayofweek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The 'Day of week' column has integers 0-6 which would actually stand for the 7days of the week. We can create a dictionary and use the .map() function to rename the integers to the normal names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create a dictionary\n",
    "dic = {0:'Mon', 1:'Tue', 2:'Wed', 3:'Thur', 4:'Fri', 5:'Sat', 6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#use the .map() to rename the column with the dictionary created\n",
    "calls['Day of Week'] = calls['Day of Week'].map(dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Highest Reason for 911 Calls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18aca537d68>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=calls['Reason'], data=calls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rate of the calls per each day of the week**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4cdk3dM899/Dwww/jdrvp6OigqKiIm266iWXLllFWVkZqaip5eXnExsbi8Xhwu90YhkFR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\nERExRQUiIiKm/C/Oi50ZWCDKXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=calls);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Which reason is more prominent in each days calls were made?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RU0RExDCGHMFGRUUxYsQIhgwZwldffcVDDz2Ez+fDZDIBYLVaqa2txePx+E8jnxr3eDxN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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=calls, hue='Reason', palette='Set1')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can do the same for the month**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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333yTX/7yl5SUlAQ6Ci0tLcybN48PP/yQ5uZmMjMz+c53vhPQTK2treTl5XHg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\nqGBFREQMUMGKiIgYoIIVEREx4P8DIKGQVVuBu4UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month', data=calls, hue='Reason', palette='magma')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Something to note in the plot above is that, the month 9, 10 and 11 are missing. Let's look for a way to fix that.**\n",
    "\n",
    "**We will create a line plot that takes the missing months into account maybe we can fill the missing data points**\n",
    "\n",
    "**First, create a groupby object called bymonth where you group the DataFrame by the month column and use the count() method for aggregation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "byMonth = calls.groupby('Month').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**we can create a simple line plot to visualize the dataframe indicating the count of calls per month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18acc92a7f0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+pE8fLRUR/3LLxEQmXzCEP+86zPtfVfNuWTVR4cFcPTqOazLjGZ9g7d4nJIOT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\nICIiwP8H4wGGb4LPwBUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Could be any column\n",
    "byMonth['zip'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The line plot above make it sort of clearer than the bar plot to visualize the missing months**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top 5 towns that called**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top_5_town:  LOWER MERION    8443\n",
      "ABINGTON        5977\n",
      "NORRISTOWN      5890\n",
      "UPPER MERION    5227\n",
      "CHELTENHAM      4575\n",
      "Name: twp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "top_5_town = calls['twp'].value_counts().head(5)\n",
    "\n",
    "print('top_5_town: ', top_5_town)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+/fHK6+8IndpDnerKc8NZs2aJWNljjdr1izrrKaG5bobdJblujmr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\nhsFPRCQYBj8RkWAY/EREgvl/nMctOiPFK+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_town.plot(kind='bar', colormap='Accent');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "From the analysis above, the following insights were drawn:\n",
    "* Most 911 calls were due to Health condition\n",
    "* Days of the week did not influence emergency calls placed\n",
    "* Lower Merion township placed more emergency calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}