911_Emergency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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 17:10:52</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 17:29:21</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 14:39:21</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 16:47:36</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 16:56:52</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     EMS: BACK PAINS/INJURY\n",
       "1    EMS: DIABETIC EMERGENCY\n",
       "2        Fire: GAS-ODOR/LEAK\n",
       "3     EMS: CARDIAC EMERGENCY\n",
       "4             EMS: DIZZINESS\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def title_sep(x):\n",
    "    x=x.split(':')\n",
    "    return x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['type_of_call']=data['title'].apply(title_sep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>type_of_call</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 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</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 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e type_of_call  \n",
       "0      REINDEER CT & DEAD END  1          EMS  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1          EMS  \n",
       "2                    HAWS AVE  1         Fire  \n",
       "3          AIRY ST & SWEDE ST  1          EMS  \n",
       "4    CHERRYWOOD CT & DEAD END  1          EMS  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['EMS', 'Fire', 'Traffic'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['type_of_call'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_type=data['type_of_call'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "font={\n",
    "    'size':20\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decimal import Decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "px=call_type.plot(kind='bar')\n",
    "for p in px.patches:\n",
    "    px.annotate(Decimal(str(p.get_height())), (p.get_x(), p.get_height()))\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig(\"moh0.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for extracting time and date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 423909 entries, 0 to 423908\n",
      "Data columns (total 10 columns):\n",
      "lat             423909 non-null float64\n",
      "lng             423909 non-null float64\n",
      "desc            423909 non-null object\n",
      "zip             371780 non-null float64\n",
      "title           423909 non-null object\n",
      "timeStamp       423909 non-null object\n",
      "twp             423750 non-null object\n",
      "addr            423909 non-null object\n",
      "e               423909 non-null int64\n",
      "type_of_call    423909 non-null object\n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 32.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['timeStamp']=pd.to_datetime(data['timeStamp'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 423909 entries, 0 to 423908\n",
      "Data columns (total 10 columns):\n",
      "lat             423909 non-null float64\n",
      "lng             423909 non-null float64\n",
      "desc            423909 non-null object\n",
      "zip             371780 non-null float64\n",
      "title           423909 non-null object\n",
      "timeStamp       423909 non-null datetime64[ns]\n",
      "twp             423750 non-null object\n",
      "addr            423909 non-null object\n",
      "e               423909 non-null int64\n",
      "type_of_call    423909 non-null object\n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(5)\n",
      "memory usage: 32.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Year']=data['timeStamp'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Month']=data['timeStamp'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Day']=data['timeStamp'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Hour']=data['timeStamp'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>type_of_call</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</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 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</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 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e type_of_call  Year     Month       Day  Hour  \n",
       "0      REINDEER CT & DEAD END  1          EMS  2015  December  Thursday    17  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1          EMS  2015  December  Thursday    17  \n",
       "2                    HAWS AVE  1         Fire  2015  December  Thursday    14  \n",
       "3          AIRY ST & SWEDE ST  1          EMS  2015  December  Thursday    16  \n",
       "4    CHERRYWOOD CT & DEAD END  1          EMS  2015  December  Thursday    16  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At what time of month I can expect which type of call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_month = data.groupby(['Month', 'type_of_call'])['type_of_call'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month      type_of_call\n",
       "April      EMS             17255\n",
       "           Fire             4983\n",
       "           Traffic         11345\n",
       "August     EMS             17965\n",
       "           Fire             5717\n",
       "           Traffic         12290\n",
       "December   EMS             16131\n",
       "           Fire             4717\n",
       "           Traffic         12171\n",
       "February   EMS             16587\n",
       "           Fire             4894\n",
       "           Traffic         11347\n",
       "January    EMS             18403\n",
       "           Fire             5637\n",
       "           Traffic         13784\n",
       "July       EMS             18096\n",
       "           Fire             5725\n",
       "           Traffic         12584\n",
       "June       EMS             17644\n",
       "           Fire             5618\n",
       "           Traffic         12909\n",
       "March      EMS             17883\n",
       "           Fire             5752\n",
       "           Traffic         14031\n",
       "May        EMS             17819\n",
       "           Fire             5168\n",
       "           Traffic         12657\n",
       "November   EMS             14669\n",
       "           Fire             4456\n",
       "           Traffic         12518\n",
       "October    EMS             18565\n",
       "           Fire             5636\n",
       "           Traffic         13614\n",
       "September  EMS             17659\n",
       "           Fire             5472\n",
       "           Traffic         12208\n",
       "Name: type_of_call, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_month_percentage = calls_month.groupby(level=0).apply(lambda x:round(100*x/float(x.sum())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month      type_of_call\n",
       "April      EMS             51.0\n",
       "           Fire            15.0\n",
       "           Traffic         34.0\n",
       "August     EMS             50.0\n",
       "           Fire            16.0\n",
       "           Traffic         34.0\n",
       "December   EMS             49.0\n",
       "           Fire            14.0\n",
       "           Traffic         37.0\n",
       "February   EMS             51.0\n",
       "           Fire            15.0\n",
       "           Traffic         35.0\n",
       "January    EMS             49.0\n",
       "           Fire            15.0\n",
       "           Traffic         36.0\n",
       "July       EMS             50.0\n",
       "           Fire            16.0\n",
       "           Traffic         35.0\n",
       "June       EMS             49.0\n",
       "           Fire            16.0\n",
       "           Traffic         36.0\n",
       "March      EMS             47.0\n",
       "           Fire            15.0\n",
       "           Traffic         37.0\n",
       "May        EMS             50.0\n",
       "           Fire            14.0\n",
       "           Traffic         36.0\n",
       "November   EMS             46.0\n",
       "           Fire            14.0\n",
       "           Traffic         40.0\n",
       "October    EMS             49.0\n",
       "           Fire            15.0\n",
       "           Traffic         36.0\n",
       "September  EMS             50.0\n",
       "           Fire            15.0\n",
       "           Traffic         35.0\n",
       "Name: type_of_call, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_month_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_month_percentage = calls_month_percentage.reindex(month_order, level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month      type_of_call\n",
       "January    EMS             49.0\n",
       "           Fire            15.0\n",
       "           Traffic         36.0\n",
       "February   EMS             51.0\n",
       "           Fire            15.0\n",
       "           Traffic         35.0\n",
       "March      EMS             47.0\n",
       "           Fire            15.0\n",
       "           Traffic         37.0\n",
       "April      EMS             51.0\n",
       "           Fire            15.0\n",
       "           Traffic         34.0\n",
       "May        EMS             50.0\n",
       "           Fire            14.0\n",
       "           Traffic         36.0\n",
       "June       EMS             49.0\n",
       "           Fire            16.0\n",
       "           Traffic         36.0\n",
       "July       EMS             50.0\n",
       "           Fire            16.0\n",
       "           Traffic         35.0\n",
       "August     EMS             50.0\n",
       "           Fire            16.0\n",
       "           Traffic         34.0\n",
       "September  EMS             50.0\n",
       "           Fire            15.0\n",
       "           Traffic         35.0\n",
       "October    EMS             49.0\n",
       "           Fire            15.0\n",
       "           Traffic         36.0\n",
       "November   EMS             46.0\n",
       "           Fire            14.0\n",
       "           Traffic         40.0\n",
       "December   EMS             49.0\n",
       "           Fire            14.0\n",
       "           Traffic         37.0\n",
       "Name: type_of_call, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_month_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_month_percentage = calls_month_percentage.reindex(['EMS','Traffic','Fire'], level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month      type_of_call\n",
       "January    EMS             49.0\n",
       "           Traffic         36.0\n",
       "           Fire            15.0\n",
       "February   EMS             51.0\n",
       "           Traffic         35.0\n",
       "           Fire            15.0\n",
       "March      EMS             47.0\n",
       "           Traffic         37.0\n",
       "           Fire            15.0\n",
       "April      EMS             51.0\n",
       "           Traffic         34.0\n",
       "           Fire            15.0\n",
       "May        EMS             50.0\n",
       "           Traffic         36.0\n",
       "           Fire            14.0\n",
       "June       EMS             49.0\n",
       "           Traffic         36.0\n",
       "           Fire            16.0\n",
       "July       EMS             50.0\n",
       "           Traffic         35.0\n",
       "           Fire            16.0\n",
       "August     EMS             50.0\n",
       "           Traffic         34.0\n",
       "           Fire            16.0\n",
       "September  EMS             50.0\n",
       "           Traffic         35.0\n",
       "           Fire            15.0\n",
       "October    EMS             49.0\n",
       "           Traffic         36.0\n",
       "           Fire            15.0\n",
       "November   EMS             46.0\n",
       "           Traffic         40.0\n",
       "           Fire            14.0\n",
       "December   EMS             49.0\n",
       "           Traffic         37.0\n",
       "           Fire            14.0\n",
       "Name: type_of_call, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_month_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12, 8)})\n",
    "calls_month_percentage.unstack().plot(kind='bar')\n",
    "plt.xlabel('Name of the Month', fontdict=font)\n",
    "plt.ylabel('Percentage of Calls', fontdict=font)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title('Calls/Month', fontdict=font)\n",
    "plt.savefig(\"moh1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " At what time of hour I can expect which type of call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_hour = data.groupby(['Hour', 'type_of_call'])['type_of_call'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour  type_of_call\n",
       "0     EMS              5263\n",
       "      Fire             1341\n",
       "      Traffic          2046\n",
       "1     EMS              4711\n",
       "      Fire             1104\n",
       "      Traffic          1566\n",
       "2     EMS              4425\n",
       "      Fire              981\n",
       "      Traffic          1390\n",
       "3     EMS              3956\n",
       "      Fire              930\n",
       "      Traffic          1128\n",
       "4     EMS              3840\n",
       "      Fire              905\n",
       "      Traffic          1156\n",
       "5     EMS              4227\n",
       "      Fire             1184\n",
       "      Traffic          1896\n",
       "6     EMS              5600\n",
       "      Fire             1557\n",
       "      Traffic          3967\n",
       "7     EMS              7629\n",
       "      Fire             2307\n",
       "      Traffic          7675\n",
       "8     EMS              9812\n",
       "      Fire             2941\n",
       "      Traffic          8848\n",
       "9     EMS             11708\n",
       "      Fire             3266\n",
       "      Traffic          7741\n",
       "                      ...  \n",
       "14    EMS             11763\n",
       "      Fire             3797\n",
       "      Traffic         10276\n",
       "15    EMS             11448\n",
       "      Fire             3768\n",
       "      Traffic         11760\n",
       "16    EMS             11527\n",
       "      Fire             3922\n",
       "      Traffic         12289\n",
       "17    EMS             11212\n",
       "      Fire             4064\n",
       "      Traffic         12657\n",
       "18    EMS             10651\n",
       "      Fire             4077\n",
       "      Traffic          9343\n",
       "19    EMS             10196\n",
       "      Fire             3572\n",
       "      Traffic          6832\n",
       "20    EMS              9296\n",
       "      Fire             3151\n",
       "      Traffic          5513\n",
       "21    EMS              8307\n",
       "      Fire             2614\n",
       "      Traffic          4568\n",
       "22    EMS              7129\n",
       "      Fire             2082\n",
       "      Traffic          3685\n",
       "23    EMS              6099\n",
       "      Fire             1723\n",
       "      Traffic          2945\n",
       "Name: type_of_call, Length: 72, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_hour_percentage = calls_hour.groupby(level=0).apply(lambda x:round(100*x/float(x.sum())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour  type_of_call\n",
       "0     EMS             61.0\n",
       "      Fire            16.0\n",
       "      Traffic         24.0\n",
       "1     EMS             64.0\n",
       "      Fire            15.0\n",
       "      Traffic         21.0\n",
       "2     EMS             65.0\n",
       "      Fire            14.0\n",
       "      Traffic         20.0\n",
       "3     EMS             66.0\n",
       "      Fire            15.0\n",
       "      Traffic         19.0\n",
       "4     EMS             65.0\n",
       "      Fire            15.0\n",
       "      Traffic         20.0\n",
       "5     EMS             58.0\n",
       "      Fire            16.0\n",
       "      Traffic         26.0\n",
       "6     EMS             50.0\n",
       "      Fire            14.0\n",
       "      Traffic         36.0\n",
       "7     EMS             43.0\n",
       "      Fire            13.0\n",
       "      Traffic         44.0\n",
       "8     EMS             45.0\n",
       "      Fire            14.0\n",
       "      Traffic         41.0\n",
       "9     EMS             52.0\n",
       "      Fire            14.0\n",
       "      Traffic         34.0\n",
       "                      ... \n",
       "14    EMS             46.0\n",
       "      Fire            15.0\n",
       "      Traffic         40.0\n",
       "15    EMS             42.0\n",
       "      Fire            14.0\n",
       "      Traffic         44.0\n",
       "16    EMS             42.0\n",
       "      Fire            14.0\n",
       "      Traffic         44.0\n",
       "17    EMS             40.0\n",
       "      Fire            15.0\n",
       "      Traffic         45.0\n",
       "18    EMS             44.0\n",
       "      Fire            17.0\n",
       "      Traffic         39.0\n",
       "19    EMS             49.0\n",
       "      Fire            17.0\n",
       "      Traffic         33.0\n",
       "20    EMS             52.0\n",
       "      Fire            18.0\n",
       "      Traffic         31.0\n",
       "21    EMS             54.0\n",
       "      Fire            17.0\n",
       "      Traffic         29.0\n",
       "22    EMS             55.0\n",
       "      Fire            16.0\n",
       "      Traffic         29.0\n",
       "23    EMS             57.0\n",
       "      Fire            16.0\n",
       "      Traffic         27.0\n",
       "Name: type_of_call, Length: 72, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_hour_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_hour_percentage = calls_hour_percentage.reindex(['EMS','Traffic','Fire'], level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour  type_of_call\n",
       "0     EMS             61.0\n",
       "      Traffic         24.0\n",
       "      Fire            16.0\n",
       "1     EMS             64.0\n",
       "      Traffic         21.0\n",
       "      Fire            15.0\n",
       "2     EMS             65.0\n",
       "      Traffic         20.0\n",
       "      Fire            14.0\n",
       "3     EMS             66.0\n",
       "      Traffic         19.0\n",
       "      Fire            15.0\n",
       "4     EMS             65.0\n",
       "      Traffic         20.0\n",
       "      Fire            15.0\n",
       "5     EMS             58.0\n",
       "      Traffic         26.0\n",
       "      Fire            16.0\n",
       "6     EMS             50.0\n",
       "      Traffic         36.0\n",
       "      Fire            14.0\n",
       "7     EMS             43.0\n",
       "      Traffic         44.0\n",
       "      Fire            13.0\n",
       "8     EMS             45.0\n",
       "      Traffic         41.0\n",
       "      Fire            14.0\n",
       "9     EMS             52.0\n",
       "      Traffic         34.0\n",
       "      Fire            14.0\n",
       "                      ... \n",
       "14    EMS             46.0\n",
       "      Traffic         40.0\n",
       "      Fire            15.0\n",
       "15    EMS             42.0\n",
       "      Traffic         44.0\n",
       "      Fire            14.0\n",
       "16    EMS             42.0\n",
       "      Traffic         44.0\n",
       "      Fire            14.0\n",
       "17    EMS             40.0\n",
       "      Traffic         45.0\n",
       "      Fire            15.0\n",
       "18    EMS             44.0\n",
       "      Traffic         39.0\n",
       "      Fire            17.0\n",
       "19    EMS             49.0\n",
       "      Traffic         33.0\n",
       "      Fire            17.0\n",
       "20    EMS             52.0\n",
       "      Traffic         31.0\n",
       "      Fire            18.0\n",
       "21    EMS             54.0\n",
       "      Traffic         29.0\n",
       "      Fire            17.0\n",
       "22    EMS             55.0\n",
       "      Traffic         29.0\n",
       "      Fire            16.0\n",
       "23    EMS             57.0\n",
       "      Traffic         27.0\n",
       "      Fire            16.0\n",
       "Name: type_of_call, Length: 72, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_hour_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12, 8)})\n",
    "calls_hour_percentage.unstack().plot(kind='bar')\n",
    "plt.xlabel('Hour of the day', fontdict=font)\n",
    "plt.ylabel('Percentage of Calls', fontdict=font)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title('Calls/Month', fontdict=font)\n",
    "plt.savefig(\"moh2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the percentage share of emergency type for each call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>type_of_call</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</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 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</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 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e type_of_call  Year     Month       Day  Hour  \n",
       "0      REINDEER CT & DEAD END  1          EMS  2015  December  Thursday    17  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1          EMS  2015  December  Thursday    17  \n",
       "2                    HAWS AVE  1         Fire  2015  December  Thursday    14  \n",
       "3          AIRY ST & SWEDE ST  1          EMS  2015  December  Thursday    16  \n",
       "4    CHERRYWOOD CT & DEAD END  1          EMS  2015  December  Thursday    16  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spliter(x):\n",
    "    x=x.split(':')\n",
    "    return x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['emergency_call']=data['title'].apply(spliter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>type_of_call</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>emergency_call</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 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>BACK PAINS/INJURY</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 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>DIABETIC EMERGENCY</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</td>\n",
       "      <td>GAS-ODOR/LEAK</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</td>\n",
       "      <td>CARDIAC EMERGENCY</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</td>\n",
       "      <td>DIZZINESS</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e type_of_call  Year     Month       Day  Hour  \\\n",
       "0      REINDEER CT & DEAD END  1          EMS  2015  December  Thursday    17   \n",
       "1  BRIAR PATH & WHITEMARSH LN  1          EMS  2015  December  Thursday    17   \n",
       "2                    HAWS AVE  1         Fire  2015  December  Thursday    14   \n",
       "3          AIRY ST & SWEDE ST  1          EMS  2015  December  Thursday    16   \n",
       "4    CHERRYWOOD CT & DEAD END  1          EMS  2015  December  Thursday    16   \n",
       "\n",
       "        emergency_call  \n",
       "0    BACK PAINS/INJURY  \n",
       "1   DIABETIC EMERGENCY  \n",
       "2        GAS-ODOR/LEAK  \n",
       "3    CARDIAC EMERGENCY  \n",
       "4            DIZZINESS  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' BACK PAINS/INJURY', ' DIABETIC EMERGENCY', ' GAS-ODOR/LEAK',\n",
       "       ' CARDIAC EMERGENCY', ' DIZZINESS', ' HEAD INJURY',\n",
       "       ' NAUSEA/VOMITING', ' RESPIRATORY EMERGENCY', ' SYNCOPAL EPISODE',\n",
       "       ' VEHICLE ACCIDENT -', ' VEHICLE ACCIDENT', ' DISABLED VEHICLE -',\n",
       "       ' APPLIANCE FIRE', ' GENERAL WEAKNESS',\n",
       "       ' CARBON MONOXIDE DETECTOR', ' UNKNOWN MEDICAL EMERGENCY',\n",
       "       ' UNRESPONSIVE SUBJECT', ' ALTERED MENTAL STATUS', ' FIRE ALARM',\n",
       "       ' CVA/STROKE', ' ROAD OBSTRUCTION -', ' SUBJECT IN PAIN',\n",
       "       ' HEMORRHAGING', ' FALL VICTIM', ' ASSAULT VICTIM', ' SEIZURES',\n",
       "       ' MEDICAL ALERT ALARM', ' ABDOMINAL PAINS', ' PUMP DETAIL',\n",
       "       ' FIRE INVESTIGATION', ' OVERDOSE', ' MATERNITY',\n",
       "       ' UNCONSCIOUS SUBJECT', ' CHOKING', ' LACERATIONS',\n",
       "       ' TRASH/DUMPSTER FIRE', ' UNKNOWN TYPE FIRE', ' BUILDING FIRE',\n",
       "       ' ELECTRICAL FIRE OUTSIDE', ' DEBRIS/FLUIDS ON HIGHWAY',\n",
       "       ' DEBRIS/FLUIDS ON HIGHWAY -', ' FEVER', ' ALLERGIC REACTION',\n",
       "       ' VEHICLE LEAKING FUEL -', ' FRACTURE', ' BURN VICTIM',\n",
       "       ' RESCUE - GENERAL', ' WOODS/FIELD FIRE', ' FIRE SPECIAL SERVICE',\n",
       "       ' VEHICLE FIRE', ' VEHICLE FIRE -', ' WARRANT SERVICE',\n",
       "       ' S/B AT HELICOPTER LANDING', ' EMS SPECIAL SERVICE',\n",
       "       ' HAZARDOUS ROAD CONDITIONS -', ' RESCUE - ELEVATOR',\n",
       "       ' DEHYDRATION', ' SHOOTING', ' POISONING', ' TRANSFERRED CALL',\n",
       "       ' RESCUE - TECHNICAL', ' VEHICLE LEAKING FUEL', ' EYE INJURY',\n",
       "       ' ELECTROCUTION', ' STABBING', ' FIRE POLICE NEEDED',\n",
       "       ' AMPUTATION', ' ANIMAL BITE', ' HAZARDOUS MATERIALS INCIDENT',\n",
       "       ' TRAIN CRASH', ' RESCUE - WATER', ' CARDIAC ARREST',\n",
       "       ' PLANE CRASH', ' HEAT EXHAUSTION', ' ACTIVE SHOOTER',\n",
       "       ' DISABLED VEHICLE', ' POLICE INFORMATION', ' BOMB DEVICE FOUND',\n",
       "       ' INDUSTRIAL ACCIDENT', ' DROWNING', ' SUSPICIOUS',\n",
       "       ' ARMED SUBJECT', ' STANDBY FOR ANOTHER CO', ' HIT + RUN',\n",
       "       ' SUICIDE THREAT', ' ANIMAL COMPLAINT', ' PRISONER IN CUSTODY',\n",
       "       ' SUICIDE ATTEMPT', ' PUBLIC SERVICE', ' BOMB THREAT',\n",
       "       ' ELEVATOR EMERGENCY', ' HAZARDOUS ROAD CONDITIONS'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['emergency_call'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "emergency_call=data['emergency_call'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " VEHICLE ACCIDENT -              98401\n",
       " DISABLED VEHICLE -              31871\n",
       " FIRE ALARM                      24459\n",
       " VEHICLE ACCIDENT                24081\n",
       " FALL VICTIM                     21258\n",
       " RESPIRATORY EMERGENCY           21159\n",
       " CARDIAC EMERGENCY               20620\n",
       " ROAD OBSTRUCTION -              14134\n",
       " SUBJECT IN PAIN                 12004\n",
       " HEAD INJURY                     11105\n",
       " UNKNOWN MEDICAL EMERGENCY        7171\n",
       " SYNCOPAL EPISODE                 7087\n",
       " GENERAL WEAKNESS                 7070\n",
       " SEIZURES                         6946\n",
       " ALTERED MENTAL STATUS            6184\n",
       " FIRE INVESTIGATION               6036\n",
       " MEDICAL ALERT ALARM              6022\n",
       " ABDOMINAL PAINS                  5802\n",
       " UNCONSCIOUS SUBJECT              5374\n",
       " HEMORRHAGING                     5264\n",
       " OVERDOSE                         5242\n",
       " CVA/STROKE                       5234\n",
       " NAUSEA/VOMITING                  4920\n",
       " GAS-ODOR/LEAK                    4468\n",
       " HAZARDOUS ROAD CONDITIONS -      4395\n",
       " CARDIAC ARREST                   3986\n",
       " BUILDING FIRE                    3888\n",
       " DIABETIC EMERGENCY               3737\n",
       " DIZZINESS                        3348\n",
       " BACK PAINS/INJURY                3142\n",
       "                                 ...  \n",
       " SHOOTING                          141\n",
       " DEBRIS/FLUIDS ON HIGHWAY -        139\n",
       " POISONING                         138\n",
       " STABBING                          122\n",
       " PUMP DETAIL                       108\n",
       " TRANSFERRED CALL                   86\n",
       " AMPUTATION                         65\n",
       " HAZARDOUS MATERIALS INCIDENT       64\n",
       " RESCUE - TECHNICAL                 52\n",
       " DROWNING                           24\n",
       " ELECTROCUTION                      22\n",
       " INDUSTRIAL ACCIDENT                21\n",
       " TRAIN CRASH                        18\n",
       " STANDBY FOR ANOTHER CO             14\n",
       " PLANE CRASH                         9\n",
       " BOMB DEVICE FOUND                   9\n",
       " DISABLED VEHICLE                    6\n",
       " WARRANT SERVICE                     4\n",
       " POLICE INFORMATION                  4\n",
       " SUSPICIOUS                          4\n",
       " ACTIVE SHOOTER                      3\n",
       " ARMED SUBJECT                       2\n",
       " SUICIDE THREAT                      2\n",
       " SUICIDE ATTEMPT                     2\n",
       " PUBLIC SERVICE                      2\n",
       " BOMB THREAT                         2\n",
       " ANIMAL COMPLAINT                    1\n",
       " HAZARDOUS ROAD CONDITIONS           1\n",
       " HIT + RUN                           1\n",
       " PRISONER IN CUSTODY                 1\n",
       "Name: emergency_call, Length: 92, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emergency_call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "emergency_call_percentage = emergency_call.groupby(level=0).apply(lambda x:round(100*x/float(emergency_call.sum())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "emergency_call_percentage=emergency_call_percentage.head(38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10, 6)})\n",
    "emergency_call_percentage.plot(kind='bar')\n",
    "plt.xlabel('type_of_emergency_calls', fontdict=font)\n",
    "plt.ylabel('Percentage of Calls', fontdict=font)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Calls/types_of_emergency_calls', fontdict=font)\n",
    "plt.savefig(\"moh3.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualize percentage share of emergency type for each call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "ems_data=data[data['type_of_call']=='EMS']['emergency_call'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "fire_data=data[data['type_of_call']=='Fire']['emergency_call'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_data=data[data['type_of_call']=='Traffic']['emergency_call'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(ems_data,labels=ems_data.index,autopct=\"%.2f\")\n",
    "plt.savefig(\"moh4.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(fire_data,labels=fire_data.index,autopct=\"%.2f\")\n",
    "plt.savefig(\"moh5.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(traffic_data,labels=traffic_data.index,autopct=\"%.2f\")\n",
    "plt.savefig(\"moh6.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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