Big Data and Policing - HW2 - Q1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from gmplot import gmplot\n",
    "from matplotlib.pyplot import figure\n",
    "import datetime\n",
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>23:58:00</td>\n",
       "      <td>4C</td>\n",
       "      <td>600 S PACA ST</td>\n",
       "      <td>AGG. ASSAULT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>941.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Ridgely's Delight</td>\n",
       "      <td>(39.283010000000, -76.624750000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>23:36:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>1000 EVANS WAY</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433.0</td>\n",
       "      <td>NORTHEASTERN</td>\n",
       "      <td>Armistead Gardens</td>\n",
       "      <td>(39.304860000000, -76.545180000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>4B</td>\n",
       "      <td>300 W CAMDEN</td>\n",
       "      <td>AGG. ASSAULT</td>\n",
       "      <td>KNIFE</td>\n",
       "      <td>941.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Stadium Area</td>\n",
       "      <td>(39.285230000000, -76.619700000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>1100 RACE ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>942.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Sharp-Leadenhall</td>\n",
       "      <td>(39.276100000000, -76.616710000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>22:25:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>1000 WASHINGTON BLVD</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>932.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Washington Village/Pigtow</td>\n",
       "      <td>(39.282900000000, -76.631890000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>22:00:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>1400 W FAYETTE ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Franklin Square</td>\n",
       "      <td>(39.289740000000, -76.639730000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>22:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>500 DOLPHIN ST</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Upton</td>\n",
       "      <td>(39.300540000000, -76.628280000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>21:30:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>3700 TOONE ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Canton</td>\n",
       "      <td>(39.278500000000, -76.566770000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>500 S CHESTER ST</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Canton</td>\n",
       "      <td>(39.285520000000, -76.587000000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>2900 WILKENS AVE</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Wilhelm Park</td>\n",
       "      <td>(39.275870000000, -76.665120000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>20:30:00</td>\n",
       "      <td>6G</td>\n",
       "      <td>300 E MADISON ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Penn-Fallsway</td>\n",
       "      <td>(39.298830000000, -76.610420000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>19:15:00</td>\n",
       "      <td>3K</td>\n",
       "      <td>1100 N GILMOR ST</td>\n",
       "      <td>ROBBERY - RESIDENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>743.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Sandtown-Winchester</td>\n",
       "      <td>(39.301240000000, -76.643120000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>6800 EASTBROOK AVE</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Eastwood</td>\n",
       "      <td>(39.291270000000, -76.531140000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>FULTON AV &amp; EAGLE ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>935.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Carrollton Ridge</td>\n",
       "      <td>(39.281070000000, -76.644780000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>18:15:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>4700 WILLISTON ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>833.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Beechfield</td>\n",
       "      <td>(39.274500000000, -76.695790000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>18:13:00</td>\n",
       "      <td>6C</td>\n",
       "      <td>500 RUSSELL ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>941.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Stadium Area</td>\n",
       "      <td>(39.283040000000, -76.623170000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>3P</td>\n",
       "      <td>900 N BROADWAY</td>\n",
       "      <td>ROBBERY - STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>(39.301110000000, -76.594280000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>1000 S ROBINSON ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Canton</td>\n",
       "      <td>(39.280970000000, -76.571930000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>300 DOLPHON ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Bolton Hill</td>\n",
       "      <td>(39.303760000000, -76.624360000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>17:30:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>100 N MONASTERY AVE</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Allendale</td>\n",
       "      <td>(39.288530000000, -76.675780000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>2500 MARBOURNE AVE</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>923.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Lakeland</td>\n",
       "      <td>(39.253890000000, -76.649410000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>1600 PATAPSCO ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>942.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>South Baltimore</td>\n",
       "      <td>(39.271550000000, -76.613100000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>15:45:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>2000 MCHENRY ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>934.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Carrollton Ridge</td>\n",
       "      <td>(39.284190000000, -76.648870000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>15:20:00</td>\n",
       "      <td>6C</td>\n",
       "      <td>4000 EASTERN AVE</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Highlandtown</td>\n",
       "      <td>(39.286810000000, -76.563080000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>200 S AUGUSTA AVE</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Irvington</td>\n",
       "      <td>(39.283270000000, -76.684740000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>4F</td>\n",
       "      <td>1400 N ELLAMONT ST</td>\n",
       "      <td>ASSAULT BY THREAT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>813.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Rosemont</td>\n",
       "      <td>(39.303110000000, -76.670630000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>14:00:00</td>\n",
       "      <td>5D</td>\n",
       "      <td>1500 CHARLOTTE AVE</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Broening Manor</td>\n",
       "      <td>(39.275320000000, -76.540480000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>13:06:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>500 N PATTERSON PARK AVE</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>CARE</td>\n",
       "      <td>(39.297030000000, -76.585050000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>2000 HOLLINS ST</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>842.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Boyd-Booth</td>\n",
       "      <td>(39.287030000000, -76.649050000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>300 S GILMOR ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>935.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>New Southwest/Mount Clare</td>\n",
       "      <td>(39.284110000000, -76.642170000000)</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9611</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>400 MCMECHEN ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Madison Park</td>\n",
       "      <td>(39.305610000000, -76.630180000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9612</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>400 W LEXINGTON ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>(39.291310000000, -76.610100000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9613</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>500 N CAROLINE ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Dunbar-Broadway</td>\n",
       "      <td>(39.296580000000, -76.597520000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9614</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>6B</td>\n",
       "      <td>1000 HOLLINS ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>931.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Hollins Market</td>\n",
       "      <td>(39.287780000000, -76.633840000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9615</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>14:52:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>1000 E PRATT ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Little Italy</td>\n",
       "      <td>(39.287960000000, -76.601380000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9616</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>14:45:00</td>\n",
       "      <td>3AF</td>\n",
       "      <td>2400 MOSHER ST</td>\n",
       "      <td>ROBBERY - STREET</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>723.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Bridgeview/Greenlawn</td>\n",
       "      <td>(39.299570000000, -76.655290000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9617</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>14:30:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>300 SAINT PAUL ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>(39.293750000000, -76.613600000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9618</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>14:20:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>600 W LEXINGTON ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>University Of Maryland</td>\n",
       "      <td>(39.291420000000, -76.623870000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9619</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>14:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>3000 HURON ST</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>923.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Lakeland</td>\n",
       "      <td>(39.254770000000, -76.650350000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9620</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>13:30:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>2700 E BALTIMORE ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Patterson Park Neighborho</td>\n",
       "      <td>(39.292180000000, -76.578130000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9621</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>100 N PAYSON ST</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Penrose/Fayette Street Ou</td>\n",
       "      <td>(39.290210000000, -76.648820000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9622</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>12:43:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>800 BRADDISH AVE</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>721.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Evergreen Lawn</td>\n",
       "      <td>(39.297390000000, -76.659510000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9623</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>11:50:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>3900 E LOMBARD ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Baltimore Highlands</td>\n",
       "      <td>(39.291490000000, -76.564290000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9624</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>10:40:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>500 N WASHINGTON ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Dunbar-Broadway</td>\n",
       "      <td>(39.296590000000, -76.589510000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9625</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>800 E EAGER ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Johnston Square</td>\n",
       "      <td>(39.301420000000, -76.606240000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9626</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>2800 WILKENS AVE</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Gwynns Falls</td>\n",
       "      <td>(39.276650000000, -76.662970000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9627</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>09:49:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>1100 N WARWICK AVE</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>723.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Bridgeview/Greenlawn</td>\n",
       "      <td>(39.300740000000, -76.656690000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9628</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>600 WINANS WAY</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>822.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Hunting Ridge</td>\n",
       "      <td>(39.293410000000, -76.698120000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9629</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>09:15:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>500 RANDOM RD</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>833.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Beechfield</td>\n",
       "      <td>(39.279040000000, -76.701160000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9630</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>08:45:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>3000 ARUNAH AVE</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>814.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Franklintown Road</td>\n",
       "      <td>(39.295060000000, -76.667440000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9631</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>08:30:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>500 S POTOMAC ST</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Canton</td>\n",
       "      <td>(39.285870000000, -76.574380000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9632</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>08:10:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>1300 WILDWOOD PKWY</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>844.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Edmondson Village</td>\n",
       "      <td>(39.300840000000, -76.683880000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9633</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>1600 E CHASE ST</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Gay Street</td>\n",
       "      <td>(39.303230000000, -76.595390000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9634</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>100 CLAY ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>(39.292360000000, -76.617010000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9635</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>1500 N ELLAMONT ST</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>813.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Rosemont</td>\n",
       "      <td>(39.304790000000, -76.670710000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9636</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>02:27:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>2200 E LOMBARD ST</td>\n",
       "      <td>LARCENY FROM AUTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.0</td>\n",
       "      <td>SOUTHEASTERN</td>\n",
       "      <td>Butcher's Hill</td>\n",
       "      <td>(39.290680000000, -76.585210000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9637</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>3500 POTEE ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>912.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>(39.239530000000, -76.610720000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9638</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>3500 POTEE ST</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>912.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>(39.239530000000, -76.610720000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>00:31:00</td>\n",
       "      <td>8H</td>\n",
       "      <td>3500 4TH ST</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>913.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>(39.239230000000, -76.604450000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9640</th>\n",
       "      <td>01/12/2016</td>\n",
       "      <td>00:30:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>100 N FULTON AVE</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Franklin Square</td>\n",
       "      <td>(39.290100000000, -76.645270000000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9641 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CrimeDate CrimeTime CrimeCode                  Location  \\\n",
       "0     09/01/2015  23:58:00        4C             600 S PACA ST   \n",
       "1     09/01/2015  23:36:00        6J            1000 EVANS WAY   \n",
       "2     09/01/2015  23:30:00        4B              300 W CAMDEN   \n",
       "3     09/01/2015  23:30:00        6D              1100 RACE ST   \n",
       "4     09/01/2015  22:25:00        4E      1000 WASHINGTON BLVD   \n",
       "5     09/01/2015  22:00:00        6E         1400 W FAYETTE ST   \n",
       "6     09/01/2015  22:00:00        7A            500 DOLPHIN ST   \n",
       "7     09/01/2015  21:30:00        6D             3700 TOONE ST   \n",
       "8     09/01/2015  21:00:00        5A          500 S CHESTER ST   \n",
       "9     09/01/2015  21:00:00        6E          2900 WILKENS AVE   \n",
       "10    09/01/2015  20:30:00        6G          300 E MADISON ST   \n",
       "11    09/01/2015  19:15:00        3K          1100 N GILMOR ST   \n",
       "12    09/01/2015  19:00:00        6D        6800 EASTBROOK AVE   \n",
       "13    09/01/2015  19:00:00        6J      FULTON AV & EAGLE ST   \n",
       "14    09/01/2015  18:15:00        6D         4700 WILLISTON ST   \n",
       "15    09/01/2015  18:13:00        6C            500 RUSSELL ST   \n",
       "16    09/01/2015  18:00:00        3P            900 N BROADWAY   \n",
       "17    09/01/2015  18:00:00        6D        1000 S ROBINSON ST   \n",
       "18    09/01/2015  18:00:00        6D            300 DOLPHON ST   \n",
       "19    09/01/2015  17:30:00        6E       100 N MONASTERY AVE   \n",
       "20    09/01/2015  17:00:00        5A        2500 MARBOURNE AVE   \n",
       "21    09/01/2015  17:00:00        6D          1600 PATAPSCO ST   \n",
       "22    09/01/2015  15:45:00        6D           2000 MCHENRY ST   \n",
       "23    09/01/2015  15:20:00        6C          4000 EASTERN AVE   \n",
       "24    09/01/2015  15:00:00        4E         200 S AUGUSTA AVE   \n",
       "25    09/01/2015  15:00:00        4F        1400 N ELLAMONT ST   \n",
       "26    09/01/2015  14:00:00        5D        1500 CHARLOTTE AVE   \n",
       "27    09/01/2015  13:06:00        5A  500 N PATTERSON PARK AVE   \n",
       "28    09/01/2015  13:00:00        5A           2000 HOLLINS ST   \n",
       "29    09/01/2015  13:00:00        6J           300 S GILMOR ST   \n",
       "...          ...       ...       ...                       ...   \n",
       "9611  01/12/2016  15:30:00        4E           400 MCMECHEN ST   \n",
       "9612  01/12/2016  15:30:00        4E        400 W LEXINGTON ST   \n",
       "9613  01/12/2016  15:00:00        4E         500 N CAROLINE ST   \n",
       "9614  01/12/2016  15:00:00        6B           1000 HOLLINS ST   \n",
       "9615  01/12/2016  14:52:00        6D           1000 E PRATT ST   \n",
       "9616  01/12/2016  14:45:00       3AF            2400 MOSHER ST   \n",
       "9617  01/12/2016  14:30:00        4E         300 SAINT PAUL ST   \n",
       "9618  01/12/2016  14:20:00        6D        600 W LEXINGTON ST   \n",
       "9619  01/12/2016  14:00:00        7A             3000 HURON ST   \n",
       "9620  01/12/2016  13:30:00        6D       2700 E BALTIMORE ST   \n",
       "9621  01/12/2016  13:00:00        5A           100 N PAYSON ST   \n",
       "9622  01/12/2016  12:43:00        4E          800 BRADDISH AVE   \n",
       "9623  01/12/2016  11:50:00        6J         3900 E LOMBARD ST   \n",
       "9624  01/12/2016  10:40:00        4E       500 N WASHINGTON ST   \n",
       "9625  01/12/2016  10:00:00        6E            800 E EAGER ST   \n",
       "9626  01/12/2016  10:00:00        6J          2800 WILKENS AVE   \n",
       "9627  01/12/2016  09:49:00        4E        1100 N WARWICK AVE   \n",
       "9628  01/12/2016  09:30:00        5A            600 WINANS WAY   \n",
       "9629  01/12/2016  09:15:00        5A             500 RANDOM RD   \n",
       "9630  01/12/2016  08:45:00        7A           3000 ARUNAH AVE   \n",
       "9631  01/12/2016  08:30:00        5A          500 S POTOMAC ST   \n",
       "9632  01/12/2016  08:10:00        6J        1300 WILDWOOD PKWY   \n",
       "9633  01/12/2016  07:00:00        7A           1600 E CHASE ST   \n",
       "9634  01/12/2016  06:00:00        6D               100 CLAY ST   \n",
       "9635  01/12/2016  06:00:00        7A        1500 N ELLAMONT ST   \n",
       "9636  01/12/2016  02:27:00        6D         2200 E LOMBARD ST   \n",
       "9637  01/12/2016  01:40:00        6J             3500 POTEE ST   \n",
       "9638  01/12/2016  01:40:00        6J             3500 POTEE ST   \n",
       "9639  01/12/2016  00:31:00        8H               3500 4TH ST   \n",
       "9640  01/12/2016  00:30:00        7A          100 N FULTON AVE   \n",
       "\n",
       "              Description   Weapon   Post      District  \\\n",
       "0            AGG. ASSAULT    OTHER  941.0      SOUTHERN   \n",
       "1                 LARCENY      NaN  433.0  NORTHEASTERN   \n",
       "2            AGG. ASSAULT    KNIFE  941.0       CENTRAL   \n",
       "3       LARCENY FROM AUTO      NaN  942.0      SOUTHERN   \n",
       "4          COMMON ASSAULT      NaN  932.0      SOUTHERN   \n",
       "5                 LARCENY      NaN  711.0       WESTERN   \n",
       "6              AUTO THEFT      NaN  123.0       CENTRAL   \n",
       "7       LARCENY FROM AUTO      NaN  232.0  SOUTHEASTERN   \n",
       "8                BURGLARY      NaN  232.0  SOUTHEASTERN   \n",
       "9                 LARCENY      NaN  832.0  SOUTHWESTERN   \n",
       "10                LARCENY      NaN  312.0       EASTERN   \n",
       "11    ROBBERY - RESIDENCE      NaN  743.0       WESTERN   \n",
       "12      LARCENY FROM AUTO      NaN  241.0  SOUTHEASTERN   \n",
       "13                LARCENY      NaN  935.0      SOUTHERN   \n",
       "14      LARCENY FROM AUTO      NaN  833.0  SOUTHWESTERN   \n",
       "15                LARCENY      NaN  941.0      SOUTHERN   \n",
       "16       ROBBERY - STREET      NaN  321.0       EASTERN   \n",
       "17      LARCENY FROM AUTO      NaN  232.0  SOUTHEASTERN   \n",
       "18      LARCENY FROM AUTO      NaN  132.0       CENTRAL   \n",
       "19                LARCENY      NaN  843.0  SOUTHWESTERN   \n",
       "20               BURGLARY      NaN  923.0      SOUTHERN   \n",
       "21      LARCENY FROM AUTO      NaN  942.0      SOUTHERN   \n",
       "22      LARCENY FROM AUTO      NaN  934.0      SOUTHERN   \n",
       "23                LARCENY      NaN  231.0  SOUTHEASTERN   \n",
       "24         COMMON ASSAULT      NaN  843.0  SOUTHWESTERN   \n",
       "25      ASSAULT BY THREAT      NaN  813.0  SOUTHWESTERN   \n",
       "26               BURGLARY      NaN  243.0  SOUTHEASTERN   \n",
       "27               BURGLARY      NaN  321.0       EASTERN   \n",
       "28               BURGLARY      NaN  842.0  SOUTHWESTERN   \n",
       "29                LARCENY      NaN  935.0      SOUTHERN   \n",
       "...                   ...      ...    ...           ...   \n",
       "9611       COMMON ASSAULT      NaN  132.0       CENTRAL   \n",
       "9612       COMMON ASSAULT      NaN  111.0       CENTRAL   \n",
       "9613       COMMON ASSAULT      NaN  321.0       EASTERN   \n",
       "9614              LARCENY      NaN  931.0      SOUTHERN   \n",
       "9615    LARCENY FROM AUTO      NaN  211.0  SOUTHEASTERN   \n",
       "9616     ROBBERY - STREET  FIREARM  723.0       WESTERN   \n",
       "9617       COMMON ASSAULT      NaN  111.0       CENTRAL   \n",
       "9618    LARCENY FROM AUTO      NaN  121.0       CENTRAL   \n",
       "9619           AUTO THEFT      NaN  923.0      SOUTHERN   \n",
       "9620    LARCENY FROM AUTO      NaN  222.0  SOUTHEASTERN   \n",
       "9621             BURGLARY      NaN  714.0       WESTERN   \n",
       "9622       COMMON ASSAULT      NaN  721.0       WESTERN   \n",
       "9623              LARCENY      NaN  223.0  SOUTHEASTERN   \n",
       "9624       COMMON ASSAULT      NaN  321.0       EASTERN   \n",
       "9625              LARCENY      NaN  311.0       EASTERN   \n",
       "9626              LARCENY      NaN  832.0  SOUTHWESTERN   \n",
       "9627       COMMON ASSAULT      NaN  723.0       WESTERN   \n",
       "9628             BURGLARY      NaN  822.0  SOUTHWESTERN   \n",
       "9629             BURGLARY      NaN  833.0  SOUTHWESTERN   \n",
       "9630           AUTO THEFT      NaN  814.0  SOUTHWESTERN   \n",
       "9631             BURGLARY      NaN  232.0  SOUTHEASTERN   \n",
       "9632              LARCENY      NaN  844.0  SOUTHWESTERN   \n",
       "9633           AUTO THEFT      NaN  313.0       EASTERN   \n",
       "9634    LARCENY FROM AUTO      NaN  111.0       CENTRAL   \n",
       "9635           AUTO THEFT      NaN  813.0  SOUTHWESTERN   \n",
       "9636    LARCENY FROM AUTO      NaN  212.0  SOUTHEASTERN   \n",
       "9637              LARCENY      NaN  912.0      SOUTHERN   \n",
       "9638              LARCENY      NaN  912.0      SOUTHERN   \n",
       "9639                ARSON     FIRE  913.0      SOUTHERN   \n",
       "9640           AUTO THEFT      NaN  711.0       WESTERN   \n",
       "\n",
       "                   Neighborhood                           Location 1  \\\n",
       "0             Ridgely's Delight  (39.283010000000, -76.624750000000)   \n",
       "1             Armistead Gardens  (39.304860000000, -76.545180000000)   \n",
       "2                  Stadium Area  (39.285230000000, -76.619700000000)   \n",
       "3              Sharp-Leadenhall  (39.276100000000, -76.616710000000)   \n",
       "4     Washington Village/Pigtow  (39.282900000000, -76.631890000000)   \n",
       "5               Franklin Square  (39.289740000000, -76.639730000000)   \n",
       "6                         Upton  (39.300540000000, -76.628280000000)   \n",
       "7                        Canton  (39.278500000000, -76.566770000000)   \n",
       "8                        Canton  (39.285520000000, -76.587000000000)   \n",
       "9                  Wilhelm Park  (39.275870000000, -76.665120000000)   \n",
       "10                Penn-Fallsway  (39.298830000000, -76.610420000000)   \n",
       "11          Sandtown-Winchester  (39.301240000000, -76.643120000000)   \n",
       "12                     Eastwood  (39.291270000000, -76.531140000000)   \n",
       "13             Carrollton Ridge  (39.281070000000, -76.644780000000)   \n",
       "14                   Beechfield  (39.274500000000, -76.695790000000)   \n",
       "15                 Stadium Area  (39.283040000000, -76.623170000000)   \n",
       "16                  Middle East  (39.301110000000, -76.594280000000)   \n",
       "17                       Canton  (39.280970000000, -76.571930000000)   \n",
       "18                  Bolton Hill  (39.303760000000, -76.624360000000)   \n",
       "19                    Allendale  (39.288530000000, -76.675780000000)   \n",
       "20                     Lakeland  (39.253890000000, -76.649410000000)   \n",
       "21              South Baltimore  (39.271550000000, -76.613100000000)   \n",
       "22             Carrollton Ridge  (39.284190000000, -76.648870000000)   \n",
       "23                 Highlandtown  (39.286810000000, -76.563080000000)   \n",
       "24                    Irvington  (39.283270000000, -76.684740000000)   \n",
       "25                     Rosemont  (39.303110000000, -76.670630000000)   \n",
       "26               Broening Manor  (39.275320000000, -76.540480000000)   \n",
       "27                         CARE  (39.297030000000, -76.585050000000)   \n",
       "28                   Boyd-Booth  (39.287030000000, -76.649050000000)   \n",
       "29    New Southwest/Mount Clare  (39.284110000000, -76.642170000000)   \n",
       "...                         ...                                  ...   \n",
       "9611               Madison Park  (39.305610000000, -76.630180000000)   \n",
       "9612                   Downtown  (39.291310000000, -76.610100000000)   \n",
       "9613            Dunbar-Broadway  (39.296580000000, -76.597520000000)   \n",
       "9614             Hollins Market  (39.287780000000, -76.633840000000)   \n",
       "9615               Little Italy  (39.287960000000, -76.601380000000)   \n",
       "9616       Bridgeview/Greenlawn  (39.299570000000, -76.655290000000)   \n",
       "9617                   Downtown  (39.293750000000, -76.613600000000)   \n",
       "9618     University Of Maryland  (39.291420000000, -76.623870000000)   \n",
       "9619                   Lakeland  (39.254770000000, -76.650350000000)   \n",
       "9620  Patterson Park Neighborho  (39.292180000000, -76.578130000000)   \n",
       "9621  Penrose/Fayette Street Ou  (39.290210000000, -76.648820000000)   \n",
       "9622             Evergreen Lawn  (39.297390000000, -76.659510000000)   \n",
       "9623        Baltimore Highlands  (39.291490000000, -76.564290000000)   \n",
       "9624            Dunbar-Broadway  (39.296590000000, -76.589510000000)   \n",
       "9625            Johnston Square  (39.301420000000, -76.606240000000)   \n",
       "9626               Gwynns Falls  (39.276650000000, -76.662970000000)   \n",
       "9627       Bridgeview/Greenlawn  (39.300740000000, -76.656690000000)   \n",
       "9628              Hunting Ridge  (39.293410000000, -76.698120000000)   \n",
       "9629                 Beechfield  (39.279040000000, -76.701160000000)   \n",
       "9630          Franklintown Road  (39.295060000000, -76.667440000000)   \n",
       "9631                     Canton  (39.285870000000, -76.574380000000)   \n",
       "9632          Edmondson Village  (39.300840000000, -76.683880000000)   \n",
       "9633                 Gay Street  (39.303230000000, -76.595390000000)   \n",
       "9634                   Downtown  (39.292360000000, -76.617010000000)   \n",
       "9635                   Rosemont  (39.304790000000, -76.670710000000)   \n",
       "9636             Butcher's Hill  (39.290680000000, -76.585210000000)   \n",
       "9637                   Brooklyn  (39.239530000000, -76.610720000000)   \n",
       "9638                   Brooklyn  (39.239530000000, -76.610720000000)   \n",
       "9639                   Brooklyn  (39.239230000000, -76.604450000000)   \n",
       "9640            Franklin Square  (39.290100000000, -76.645270000000)   \n",
       "\n",
       "      Total Incidents  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  \n",
       "5                   1  \n",
       "6                   1  \n",
       "7                   1  \n",
       "8                   1  \n",
       "9                   1  \n",
       "10                  1  \n",
       "11                  1  \n",
       "12                  1  \n",
       "13                  1  \n",
       "14                  1  \n",
       "15                  1  \n",
       "16                  1  \n",
       "17                  1  \n",
       "18                  1  \n",
       "19                  1  \n",
       "20                  1  \n",
       "21                  1  \n",
       "22                  1  \n",
       "23                  1  \n",
       "24                  1  \n",
       "25                  1  \n",
       "26                  1  \n",
       "27                  1  \n",
       "28                  1  \n",
       "29                  1  \n",
       "...               ...  \n",
       "9611                1  \n",
       "9612                1  \n",
       "9613                1  \n",
       "9614                1  \n",
       "9615                1  \n",
       "9616                1  \n",
       "9617                1  \n",
       "9618                1  \n",
       "9619                1  \n",
       "9620                1  \n",
       "9621                1  \n",
       "9622                1  \n",
       "9623                1  \n",
       "9624                1  \n",
       "9625                1  \n",
       "9626                1  \n",
       "9627                1  \n",
       "9628                1  \n",
       "9629                1  \n",
       "9630                1  \n",
       "9631                1  \n",
       "9632                1  \n",
       "9633                1  \n",
       "9634                1  \n",
       "9635                1  \n",
       "9636                1  \n",
       "9637                1  \n",
       "9638                1  \n",
       "9639                1  \n",
       "9640                1  \n",
       "\n",
       "[9641 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore = pd.read_csv('./baltimore_crimes.csv')\n",
    "baltimore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CENTRAL': 1593,\n",
       " 'EASTERN': 800,\n",
       " 'NORTHEASTERN': 51,\n",
       " 'SOUTHEASTERN': 2828,\n",
       " 'SOUTHERN': 1920,\n",
       " 'SOUTHWESTERN': 1551,\n",
       " 'WESTERN': 898}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_count = {}\n",
    "for value in baltimore['District']:\n",
    "    if value not in district_count.keys():\n",
    "        district_count[value] = 1\n",
    "    else:\n",
    "        district_count[value] += 1\n",
    "district_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.bar(district_count.keys(), district_count.values())\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Crime Count')\n",
    "plt.title('Count of Crimes v/s District')\n",
    "for x, y in zip(district_count.keys(), district_count.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most Number of Incidents - `Southeastern`\n",
    "### Least Number of Incidents - `Northeastern`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"index.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f8de9f478d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location = baltimore['Location 1']\n",
    "lats, longs = [], []\n",
    "for item in location:\n",
    "    lat, long = item.strip('()').split(',')\n",
    "    lats.append(float(lat))\n",
    "    longs.append(float(long.strip()))\n",
    "\n",
    "gmap = gmplot.GoogleMapPlotter(39.299236, -76.609383, 13)\n",
    "gmap.heatmap(lats, longs)\n",
    "gmap.draw('./index.html')\n",
    "\n",
    "IFrame(src = \"index.html\", width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The crime is spread unevenly across the city. Near Gordon Ramsy Steak, there is a red spot indicating more crime density at that spot. We can alse see a bit of redness near Royal Farms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'00:00-00:59': 381,\n",
       "  '01:00-01:59': 403,\n",
       "  '02:00-02:59': 263,\n",
       "  '03:00-03:59': 189,\n",
       "  '04:00-04:59': 136,\n",
       "  '05:00-05:59': 132,\n",
       "  '06:00-06:59': 144,\n",
       "  '07:00-07:59': 247,\n",
       "  '08:00-08:59': 344,\n",
       "  '09:00-09:59': 329,\n",
       "  '10:00-10:59': 396,\n",
       "  '11:00-11:59': 413,\n",
       "  '12:00-12:59': 444,\n",
       "  '13:00-13:59': 422,\n",
       "  '14:00-14:59': 487,\n",
       "  '15:00-15:59': 521,\n",
       "  '16:00-16:59': 558,\n",
       "  '17:00-17:59': 603,\n",
       "  '18:00-18:59': 611,\n",
       "  '19:00-19:59': 588,\n",
       "  '20:00-20:59': 554,\n",
       "  '21:00-21:59': 593,\n",
       "  '22:00-22:59': 472,\n",
       "  '23:00-23:59': 411},\n",
       " {'00:00-00:59': {'AGG. ASSAULT': 61,\n",
       "   'ARSON': 6,\n",
       "   'ASSAULT BY THREAT': 1,\n",
       "   'AUTO THEFT': 47,\n",
       "   'BURGLARY': 43,\n",
       "   'COMMON ASSAULT': 68,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 50,\n",
       "   'LARCENY FROM AUTO': 40,\n",
       "   'RAPE': 4,\n",
       "   'ROBBERY - CARJACKING': 2,\n",
       "   'ROBBERY - COMMERCIAL': 4,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 45,\n",
       "   'SHOOTING': 4},\n",
       "  '01:00-01:59': {'AGG. ASSAULT': 59,\n",
       "   'ARSON': 5,\n",
       "   'ASSAULT BY THREAT': 4,\n",
       "   'AUTO THEFT': 45,\n",
       "   'BURGLARY': 53,\n",
       "   'COMMON ASSAULT': 85,\n",
       "   'HOMICIDE': 7,\n",
       "   'LARCENY': 67,\n",
       "   'LARCENY FROM AUTO': 24,\n",
       "   'ROBBERY - CARJACKING': 1,\n",
       "   'ROBBERY - COMMERCIAL': 6,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 38,\n",
       "   'SHOOTING': 4},\n",
       "  '02:00-02:59': {'AGG. ASSAULT': 45,\n",
       "   'ARSON': 1,\n",
       "   'AUTO THEFT': 30,\n",
       "   'BURGLARY': 40,\n",
       "   'COMMON ASSAULT': 51,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 26,\n",
       "   'LARCENY FROM AUTO': 27,\n",
       "   'ROBBERY - CARJACKING': 5,\n",
       "   'ROBBERY - COMMERCIAL': 8,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 20,\n",
       "   'SHOOTING': 4},\n",
       "  '03:00-03:59': {'AGG. ASSAULT': 20,\n",
       "   'ARSON': 4,\n",
       "   'ASSAULT BY THREAT': 1,\n",
       "   'AUTO THEFT': 19,\n",
       "   'BURGLARY': 32,\n",
       "   'COMMON ASSAULT': 42,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 21,\n",
       "   'LARCENY FROM AUTO': 14,\n",
       "   'RAPE': 2,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 7,\n",
       "   'ROBBERY - RESIDENCE': 1,\n",
       "   'ROBBERY - STREET': 17,\n",
       "   'SHOOTING': 5},\n",
       "  '04:00-04:59': {'AGG. ASSAULT': 15,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 2,\n",
       "   'AUTO THEFT': 12,\n",
       "   'BURGLARY': 35,\n",
       "   'COMMON ASSAULT': 19,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 25,\n",
       "   'LARCENY FROM AUTO': 5,\n",
       "   'ROBBERY - COMMERCIAL': 1,\n",
       "   'ROBBERY - RESIDENCE': 1,\n",
       "   'ROBBERY - STREET': 14,\n",
       "   'SHOOTING': 4},\n",
       "  '05:00-05:59': {'AGG. ASSAULT': 8,\n",
       "   'ARSON': 2,\n",
       "   'AUTO THEFT': 13,\n",
       "   'BURGLARY': 36,\n",
       "   'COMMON ASSAULT': 21,\n",
       "   'LARCENY': 24,\n",
       "   'LARCENY FROM AUTO': 11,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 1,\n",
       "   'ROBBERY - COMMERCIAL': 3,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 9,\n",
       "   'SHOOTING': 1},\n",
       "  '06:00-06:59': {'AGG. ASSAULT': 5,\n",
       "   'AUTO THEFT': 18,\n",
       "   'BURGLARY': 46,\n",
       "   'COMMON ASSAULT': 14,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 24,\n",
       "   'LARCENY FROM AUTO': 20,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 2,\n",
       "   'ROBBERY - COMMERCIAL': 1,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 10},\n",
       "  '07:00-07:59': {'AGG. ASSAULT': 14,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 2,\n",
       "   'AUTO THEFT': 25,\n",
       "   'BURGLARY': 65,\n",
       "   'COMMON ASSAULT': 32,\n",
       "   'LARCENY': 50,\n",
       "   'LARCENY FROM AUTO': 45,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - COMMERCIAL': 3,\n",
       "   'ROBBERY - RESIDENCE': 3,\n",
       "   'ROBBERY - STREET': 6},\n",
       "  '08:00-08:59': {'AGG. ASSAULT': 19,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 5,\n",
       "   'AUTO THEFT': 34,\n",
       "   'BURGLARY': 88,\n",
       "   'COMMON ASSAULT': 40,\n",
       "   'LARCENY': 73,\n",
       "   'LARCENY FROM AUTO': 63,\n",
       "   'RAPE': 2,\n",
       "   'ROBBERY - COMMERCIAL': 3,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 13,\n",
       "   'SHOOTING': 1},\n",
       "  '09:00-09:59': {'AGG. ASSAULT': 31,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 6,\n",
       "   'AUTO THEFT': 23,\n",
       "   'BURGLARY': 70,\n",
       "   'COMMON ASSAULT': 48,\n",
       "   'LARCENY': 92,\n",
       "   'LARCENY FROM AUTO': 36,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - COMMERCIAL': 9,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 10},\n",
       "  '10:00-10:59': {'AGG. ASSAULT': 29,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 8,\n",
       "   'AUTO THEFT': 21,\n",
       "   'BURGLARY': 74,\n",
       "   'COMMON ASSAULT': 52,\n",
       "   'HOMICIDE': 3,\n",
       "   'LARCENY': 116,\n",
       "   'LARCENY FROM AUTO': 52,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 1,\n",
       "   'ROBBERY - COMMERCIAL': 12,\n",
       "   'ROBBERY - RESIDENCE': 1,\n",
       "   'ROBBERY - STREET': 20,\n",
       "   'SHOOTING': 5},\n",
       "  '11:00-11:59': {'AGG. ASSAULT': 26,\n",
       "   'ASSAULT BY THREAT': 6,\n",
       "   'AUTO THEFT': 28,\n",
       "   'BURGLARY': 70,\n",
       "   'COMMON ASSAULT': 64,\n",
       "   'HOMICIDE': 2,\n",
       "   'LARCENY': 137,\n",
       "   'LARCENY FROM AUTO': 46,\n",
       "   'RAPE': 2,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 10,\n",
       "   'ROBBERY - RESIDENCE': 1,\n",
       "   'ROBBERY - STREET': 17,\n",
       "   'SHOOTING': 1},\n",
       "  '12:00-12:59': {'AGG. ASSAULT': 25,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 8,\n",
       "   'AUTO THEFT': 32,\n",
       "   'BURGLARY': 92,\n",
       "   'COMMON ASSAULT': 60,\n",
       "   'HOMICIDE': 3,\n",
       "   'LARCENY': 134,\n",
       "   'LARCENY FROM AUTO': 55,\n",
       "   'RAPE': 5,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 6,\n",
       "   'ROBBERY - RESIDENCE': 4,\n",
       "   'ROBBERY - STREET': 13,\n",
       "   'SHOOTING': 2},\n",
       "  '13:00-13:59': {'AGG. ASSAULT': 27,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 4,\n",
       "   'AUTO THEFT': 30,\n",
       "   'BURGLARY': 57,\n",
       "   'COMMON ASSAULT': 72,\n",
       "   'HOMICIDE': 3,\n",
       "   'LARCENY': 119,\n",
       "   'LARCENY FROM AUTO': 66,\n",
       "   'RAPE': 5,\n",
       "   'ROBBERY - COMMERCIAL': 9,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 24,\n",
       "   'SHOOTING': 2},\n",
       "  '14:00-14:59': {'AGG. ASSAULT': 43,\n",
       "   'ARSON': 3,\n",
       "   'ASSAULT BY THREAT': 9,\n",
       "   'AUTO THEFT': 27,\n",
       "   'BURGLARY': 82,\n",
       "   'COMMON ASSAULT': 75,\n",
       "   'LARCENY': 127,\n",
       "   'LARCENY FROM AUTO': 72,\n",
       "   'RAPE': 3,\n",
       "   'ROBBERY - CARJACKING': 1,\n",
       "   'ROBBERY - COMMERCIAL': 8,\n",
       "   'ROBBERY - RESIDENCE': 3,\n",
       "   'ROBBERY - STREET': 27,\n",
       "   'SHOOTING': 7},\n",
       "  '15:00-15:59': {'AGG. ASSAULT': 47,\n",
       "   'ARSON': 3,\n",
       "   'ASSAULT BY THREAT': 8,\n",
       "   'AUTO THEFT': 33,\n",
       "   'BURGLARY': 62,\n",
       "   'COMMON ASSAULT': 86,\n",
       "   'HOMICIDE': 6,\n",
       "   'LARCENY': 145,\n",
       "   'LARCENY FROM AUTO': 75,\n",
       "   'ROBBERY - CARJACKING': 1,\n",
       "   'ROBBERY - COMMERCIAL': 15,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 29,\n",
       "   'SHOOTING': 9},\n",
       "  '16:00-16:59': {'AGG. ASSAULT': 51,\n",
       "   'ASSAULT BY THREAT': 7,\n",
       "   'AUTO THEFT': 42,\n",
       "   'BURGLARY': 77,\n",
       "   'COMMON ASSAULT': 61,\n",
       "   'HOMICIDE': 6,\n",
       "   'LARCENY': 160,\n",
       "   'LARCENY FROM AUTO': 89,\n",
       "   'RAPE': 5,\n",
       "   'ROBBERY - CARJACKING': 6,\n",
       "   'ROBBERY - COMMERCIAL': 7,\n",
       "   'ROBBERY - RESIDENCE': 3,\n",
       "   'ROBBERY - STREET': 39,\n",
       "   'SHOOTING': 5},\n",
       "  '17:00-17:59': {'AGG. ASSAULT': 55,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 6,\n",
       "   'AUTO THEFT': 56,\n",
       "   'BURGLARY': 87,\n",
       "   'COMMON ASSAULT': 71,\n",
       "   'HOMICIDE': 4,\n",
       "   'LARCENY': 159,\n",
       "   'LARCENY FROM AUTO': 103,\n",
       "   'RAPE': 4,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 7,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 34,\n",
       "   'SHOOTING': 7},\n",
       "  '18:00-18:59': {'AGG. ASSAULT': 51,\n",
       "   'ARSON': 4,\n",
       "   'ASSAULT BY THREAT': 8,\n",
       "   'AUTO THEFT': 61,\n",
       "   'BURGLARY': 85,\n",
       "   'COMMON ASSAULT': 85,\n",
       "   'HOMICIDE': 3,\n",
       "   'LARCENY': 140,\n",
       "   'LARCENY FROM AUTO': 101,\n",
       "   'ROBBERY - CARJACKING': 6,\n",
       "   'ROBBERY - COMMERCIAL': 13,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 49,\n",
       "   'SHOOTING': 3},\n",
       "  '19:00-19:59': {'AGG. ASSAULT': 55,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 6,\n",
       "   'AUTO THEFT': 67,\n",
       "   'BURGLARY': 74,\n",
       "   'COMMON ASSAULT': 87,\n",
       "   'HOMICIDE': 4,\n",
       "   'LARCENY': 114,\n",
       "   'LARCENY FROM AUTO': 92,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 12,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 56,\n",
       "   'SHOOTING': 10},\n",
       "  '20:00-20:59': {'AGG. ASSAULT': 61,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 3,\n",
       "   'AUTO THEFT': 64,\n",
       "   'BURGLARY': 60,\n",
       "   'COMMON ASSAULT': 74,\n",
       "   'HOMICIDE': 3,\n",
       "   'LARCENY': 106,\n",
       "   'LARCENY FROM AUTO': 95,\n",
       "   'RAPE': 3,\n",
       "   'ROBBERY - CARJACKING': 13,\n",
       "   'ROBBERY - COMMERCIAL': 12,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 48,\n",
       "   'SHOOTING': 9},\n",
       "  '21:00-21:59': {'AGG. ASSAULT': 66,\n",
       "   'ARSON': 3,\n",
       "   'ASSAULT BY THREAT': 4,\n",
       "   'AUTO THEFT': 63,\n",
       "   'BURGLARY': 72,\n",
       "   'COMMON ASSAULT': 71,\n",
       "   'HOMICIDE': 9,\n",
       "   'LARCENY': 109,\n",
       "   'LARCENY FROM AUTO': 117,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 3,\n",
       "   'ROBBERY - COMMERCIAL': 7,\n",
       "   'ROBBERY - RESIDENCE': 6,\n",
       "   'ROBBERY - STREET': 53,\n",
       "   'SHOOTING': 9},\n",
       "  '22:00-22:59': {'AGG. ASSAULT': 44,\n",
       "   'ARSON': 2,\n",
       "   'ASSAULT BY THREAT': 3,\n",
       "   'AUTO THEFT': 61,\n",
       "   'BURGLARY': 55,\n",
       "   'COMMON ASSAULT': 78,\n",
       "   'HOMICIDE': 1,\n",
       "   'LARCENY': 75,\n",
       "   'LARCENY FROM AUTO': 86,\n",
       "   'RAPE': 1,\n",
       "   'ROBBERY - CARJACKING': 2,\n",
       "   'ROBBERY - COMMERCIAL': 6,\n",
       "   'ROBBERY - RESIDENCE': 2,\n",
       "   'ROBBERY - STREET': 49,\n",
       "   'SHOOTING': 7},\n",
       "  '23:00-23:59': {'AGG. ASSAULT': 45,\n",
       "   'ARSON': 1,\n",
       "   'ASSAULT BY THREAT': 3,\n",
       "   'AUTO THEFT': 53,\n",
       "   'BURGLARY': 51,\n",
       "   'COMMON ASSAULT': 68,\n",
       "   'HOMICIDE': 5,\n",
       "   'LARCENY': 60,\n",
       "   'LARCENY FROM AUTO': 60,\n",
       "   'RAPE': 4,\n",
       "   'ROBBERY - CARJACKING': 4,\n",
       "   'ROBBERY - COMMERCIAL': 5,\n",
       "   'ROBBERY - RESIDENCE': 5,\n",
       "   'ROBBERY - STREET': 37,\n",
       "   'SHOOTING': 10}})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dict = {}\n",
    "crime_type = {}\n",
    "for index, row in baltimore.iterrows():\n",
    "    code = row['Description']\n",
    "    hour = row['CrimeTime'].split(':')[0]\n",
    "    if len(hour) > 2:\n",
    "        hour = hour[:2]\n",
    "    hour_key = hour + ':00-' + hour + ':59'\n",
    "    if hour_key not in crime_type.keys():\n",
    "        crime_type[hour_key] = {}\n",
    "        crime_type[hour_key][code] = 1\n",
    "    else:\n",
    "        if code not in crime_type[hour_key].keys():\n",
    "            crime_type[hour_key][code] = 1\n",
    "        else:\n",
    "            crime_type[hour_key][code] += 1\n",
    "    if hour_key not in time_dict.keys():\n",
    "        time_dict[hour_key] = 1\n",
    "    else:\n",
    "        time_dict[hour_key] += 1\n",
    "time_dict, crime_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.bar(time_dict.keys(), time_dict.values())\n",
    "plt.xlabel('Time (24Hr Format)')\n",
    "plt.ylabel('Crime Count')\n",
    "plt.title('Count of Crimes v/s Time Of The Day')\n",
    "for x, y in zip(time_dict.keys(), time_dict.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AGG. ASSAULT': 216,\n",
       " 'ARSON': 7,\n",
       " 'ASSAULT BY THREAT': 13,\n",
       " 'AUTO THEFT': 241,\n",
       " 'BURGLARY': 238,\n",
       " 'COMMON ASSAULT': 291,\n",
       " 'HOMICIDE': 18,\n",
       " 'LARCENY': 350,\n",
       " 'LARCENY FROM AUTO': 358,\n",
       " 'RAPE': 9,\n",
       " 'ROBBERY - CARJACKING': 22,\n",
       " 'ROBBERY - COMMERCIAL': 30,\n",
       " 'ROBBERY - RESIDENCE': 15,\n",
       " 'ROBBERY - STREET': 187,\n",
       " 'SHOOTING': 35}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_count = {}\n",
    "for key in crime_type:\n",
    "    if key in ['20:00-20:59', '21:00-21:59', '22:00-22:59', '23:00-23:59']:\n",
    "        for crime in crime_type[key]:\n",
    "            if crime not in crime_count.keys():\n",
    "                crime_count[crime] = crime_type[key][crime]\n",
    "            else:\n",
    "                crime_count[crime] += crime_type[key][crime]\n",
    "crime_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.bar(crime_count.keys(), crime_count.values())\n",
    "plt.xlabel('Crime Type')\n",
    "plt.ylabel('Crime Count')\n",
    "plt.title('Count of Crimes between 2000 Hrs and 2359 Hrs')\n",
    "for x, y in zip(crime_count.keys(), crime_count.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It can be seen from the above plot that `Larceny From Auto` has the maximum count.\n",
    "### Thus, `Larceny From Auto` is most likely to occur between 2000 Hrs to 2359 Hrs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Friday': 1433,\n",
       " 'Monday': 1366,\n",
       " 'Saturday': 1375,\n",
       " 'Sunday': 1369,\n",
       " 'Thursday': 1331,\n",
       " 'Tuesday': 1459,\n",
       " 'Wednesday': 1308}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_dict = {}\n",
    "for index, row in baltimore.iterrows():\n",
    "    date = row['CrimeDate']\n",
    "    mm, dd, yyyy = [int(x) for x in date.split('/')]\n",
    "    day = datetime.date(yyyy, mm, dd).strftime('%A')\n",
    "    if day not in day_dict.keys():\n",
    "        day_dict[day] = 1\n",
    "    else:\n",
    "        day_dict[day] += 1\n",
    "day_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "plt.bar(day_dict.keys(), day_dict.values())\n",
    "plt.xlabel('Day Of The Week')\n",
    "plt.ylabel('Crime Count')\n",
    "plt.title('Count of Crimes v/s Day Of The Week')\n",
    "for x, y in zip(day_dict.keys(), day_dict.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It can be seen from the above graph, most number of crimes occur on `Tuesday`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AGG. ASSAULT': 902,\n",
       " 'ARSON': 49,\n",
       " 'ASSAULT BY THREAT': 104,\n",
       " 'AUTO THEFT': 904,\n",
       " 'BURGLARY': 1506,\n",
       " 'COMMON ASSAULT': 1424,\n",
       " 'HOMICIDE': 64,\n",
       " 'LARCENY': 2153,\n",
       " 'LARCENY FROM AUTO': 1394,\n",
       " 'RAPE': 47,\n",
       " 'ROBBERY - CARJACKING': 63,\n",
       " 'ROBBERY - COMMERCIAL': 174,\n",
       " 'ROBBERY - RESIDENCE': 71,\n",
       " 'ROBBERY - STREET': 677,\n",
       " 'SHOOTING': 109}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_freq = {}\n",
    "for key in crime_type:\n",
    "    for crime in crime_type[key]:\n",
    "        if crime not in crime_freq.keys():\n",
    "            crime_freq[crime] = crime_type[key][crime]\n",
    "        else:\n",
    "            crime_freq[crime] += crime_type[key][crime]\n",
    "crime_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WClNKi1NKs7P77wAvAl2BE4Fx2WrjgJOy+ycCt6aCPwO7RkQX4BjgwZTSWyml5cCDwLGlqluSJEmS8qQs5xRGRDXQF/gL0CmltDhb9DrQKbvfFVhYtFlt1tZQuyRJkiTpEyp5KIyInYDfAxemlN4uXpZSSkDaSs8zKiJmRcSspUuXbo1dSpIkSVKzV9JQGBGfohAIb08pTcyal2TDQsl+vpG1LwK6FW1elbU11L6elNIvUkr9U0r9O3TosHUPRJIkSZKaqVLOPhrATcCLKaUfFi2aAqybQXQ4MLmo/YxsFtJDgZXZMNP7gaMjom02wczRWZskSZIk6RNqVcJ9/y/gP4BnI2JO1vZtYCxwV0ScDbwGfDFbNg04DngFeA84EyCl9FZEfBd4IlvvipTSWyWsW5IkSZJyo2ShMKX0GBANLD6qnvUTcF4D+7oZuHnrVSdJkiRJgjLNPipJkiRJapoMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSY1y1lln0bFjR3r16lXXNmbMGLp27UpNTQ01NTVMmzatbtn3v/999tlnH/bbbz/uv//+uvYVK1YwZMgQevToQc+ePXn88cfLehySJElan6FQUqOMGDGC++67b6P2iy66iDlz5jBnzhyOO+44AF544QXGjx/P888/z3333cdXv/pV1q5dC8AFF1zAsccey0svvcTTTz9Nz549y3ockiRJWp+hUFKjDBgwgN12261R606ePJmhQ4ey3Xbbsddee7HPPvswc+ZMVq5cyaOPPsrZZ58NQOvWrdl1111LWbYkSZI2w1Ao6RO58cYb6d27N2eddRbLly8HYNGiRXTr1q1unaqqKhYtWsS8efPo0KEDZ555Jn379mXkyJGsWrWqUqVLkiQJQ6GkT+Dcc8/lb3/7G3PmzKFLly5cfPHFm1x/zZo1zJ49m3PPPZennnqKHXfckbFjx5apWkmSJNXHUCjpY+vUqRMtW7akRYsW/Od//iczZ84EoGvXrixcuLBuvdraWrp27UpVVRVVVVUccsghAAwZMoTZs2dXpHZJkiQVlCwURsTNEfFGRDxX1DYmIhZFxJzsdlzRsv+OiFci4uWIOKao/dis7ZWIGF2qeiVtucWLF9fdnzRpUt3MpCeccALjx4/nww8/ZN68ecydO5eDDz6Yzp07061bN15++WUApk+fzv7771+R2iVJklTQqoT7/jVwI3DrBu0/Sin9oLghIvYHhgIHALsDf4iIfbPFPwYGArXAExExJaX0QgnrllSP008/nUceeYRly5ZRVVXFd77zHR555BHmzJlDRFBdXc3Pf/5zAA444AC++MUvsv/++9OqVSt+/OMf07JlSwBuuOEGhg0bxurVq9l777255ZZbKnlYkiRJuRcppdLtPKIauCel1Ct7PAZ4t55Q+N8AKaXvZ4/vB8Zki8eklI6pb72G9O/fP82aNWtrHYa0zakePbXSJWzW/LHHV7oEfUJ+ziRJaroi4smUUv/GrFuJcwrPj4hnsuGlbbO2rsDConVqs7aG2jcSEaMiYlZEzFq6dGkp6pYkSZKkZqfcofCnwGeAGmAxcM3W2nFK6Rcppf4ppf4dOnTYWruVJEmSpGatlOcUbiSltGTd/Yj4JXBP9nAR0K1o1aqsjU20S5IkSZI+obL2FEZEl6KHJwPrZiadAgyNiO0iYi+gOzATeALoHhF7RURrCpPRTClnzZIkSZLUnJWspzAi7gAOB9pHRC1wOXB4RNQACZgPfAUgpfR8RNwFvACsAc5LKa3N9nM+cD/QErg5pfR8qWqWJEmSpLwpWShMKZ1eT/NNm1j/e8D36mmfBkzbiqVJkiRJkjKVmH1UkiRJktREGAqbgbPOOouOHTvSq1evjZZdc801RATLli0D4Pbbb6d3794ceOCBHHbYYTz99NPrrb927Vr69u3L4MGDy1K7JEmSpMoyFDYDI0aM4L777tuofeHChTzwwAPssccedW177bUXf/zjH3n22We57LLLGDVq1HrbXHfddfTs2bPkNUuSJElqGgyFzcCAAQPYbbfdNmq/6KKLuPrqq4mIurbDDjuMtm3bAnDooYdSW1tbt6y2tpapU6cycuTI0hctSZIkqUkwFDZTkydPpmvXrvTp06fBdW666SYGDRpU9/jCCy/k6quvpkULPxaSJElSXpT14vUqj/fee48rr7ySBx54oMF1Hn74YW666SYee+wxAO655x46duzIQQcdxCOPPFKmSiVJkiRVml1CzdDf/vY35s2bR58+faiurqa2tpZ+/frx+uuvA/DMM88wcuRIJk+eTLt27QD405/+xJQpU6iurmbo0KE89NBDfPnLX67kYUiSJEkqA0NhM3TggQfyxhtvMH/+fObPn09VVRWzZ8+mc+fOLFiwgFNOOYXf/OY37LvvvnXbfP/736e2tpb58+czfvx4jjzySG677bYKHoUkbfvqmx36sssuo3fv3tTU1HD00Ufz97//HYDly5dz8skn07t3bw4++GCee+659fbl7NCSpFJx+GgZVY+eWpL9Lp1yNR8ueJa1779Nq53bs8vnhrFzn6Prltcuf5++VzxAyx124c17r+e92tc5/ORCL2C0aEmX4deut79fH7tjSeqUpLwZMWIE559/PmeccUZd2ze/+U2++93vAnD99ddzxRVX8LOf/Ywrr7ySmpoaJk2axEsvvcR5553H9OnT67ZbNzv022+/XfbjkCQ1b4bCZqDDCd/a5PKqc2+uu99u0NdoN+hrm1z/8MMP5/DDD98apUlSrg0YMID58+ev19amTZu6+6tWraqbIfqFF15g9OjRAPTo0YP58+ezZMkSOnXqVDc79KWXXsoPf/jDstUvScoHh49KklRml156Kd26deP222/niiuuAKBPnz5MnDgRgJkzZ/Laa6/VXTbI2aElSaXk/y6SJJXZ9773PRYuXMiwYcO48cYbARg9ejQrVqygpqaGG264gb59+9KyZcv1ZoeWJKkUDIWSJFXIsGHD+P3vfw8UhpXecsstzJkzh1tvvZWlS5ey9957Ozu0JKnkDIWSJJXR3Llz6+5PnjyZHj16ALBixQpWr14NwK9+9SsGDBhAmzZtnB1aklRyTjQjScq9cs4O/cGrs/jHW7UQLWjVpgO7HXMe1aOn8uGiF1k29UcQwafa70G7QRdsVJezQ0uSSsFQKElSidQ3O3TxJYOKbde1J11H/WKT+3N2aElSKTh8VJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScqxzYbCiGhZjkIkSZIkSeXXmJ7CuRHxfyNi/5JXI0mSJEkqq8aEwj7AX4FfRcSfI2JURLQpcV2SJEmSpDLYbChMKb2TUvplSukw4BLgcmBxRIyLiH1KXqEkSZIkqWQadU5hRJwQEZOAa4FrgL2B/wdMK3F9kiRJkqQSatWIdeYCDwP/N6X0P0XtEyJiQGnKkiRJkiSVQ2NCYe+U0rv1LUgpfW0r1yNJkiRJKqPGTDTz44jYdd2DiGgbETeXsCZJkiRJUpk0JhT2TimtWPcgpbQc6Fu6kiRJkiRJ5dKYUNgiItquexARu9G4YaeSJEmSpCauMeHuGuDxiPgdEMAQ4HslrUqSJEmSVBabDYUppVsj4kngiKzplJTSC6UtS5IkSZJUDo0dBvoSsHzd+hGxR0ppQcmqkiRJkiSVxWZDYUT8F3A5sARYS2EIaQJ6l7Y0SZIkSVKpNaan8AJgv5TSm6UuRpIkSZJUXo2ZfXQhsLLUhUiSJEmSyq8xPYWvAo9ExFTgw3WNKaUflqwqSZIkSVJZNCYULshurbObJEmSJKmZaMwlKb4DEBE7pJTeK31JkiRJkqRy2ew5hRHx2Yh4gcJlKYiIPhHxk5JXJkmSJEkqucZMNHMtcAzwJkBK6WlgQCmLkiRJkiSVR2NCISmlhRs0rS1BLZIkSZKkMmvMRDMLI+IwIEXEpyhct/DF0pYlSZIkSSqHxvQUngOcB3QFFgE12WNJkiRJ0jauMbOPLgOGlaEWSZIkSVKZbTYURsQtQNqwPaV0VkkqkiRJkiSVTWPOKbyn6P72wMnA30tTjiRJkiSpnBozfPT3xY8j4g7gsZJVJEmSJEkqm0ZdkmID3YGOW7sQSZIkSVL5NeacwnconFMY2c/XgUtKXJckSZIkqQwaM3x053IUIkmSJEkqv8b0FPbb1PKU0uytV44kSZIkqZwaM/voT4B+wDMUhpD2BmYBH1AYTnpkyaqTJEmSJJVUYyaa+TtwUEqpf0rpIKAvsCildERKyUAoSZIkSduwxoTC/VJKz657kFJ6DuhZupIkSZIkSeXSmOGjz0TEr4DbssfDKAwllSRJkiRt4xoTCs8EzgUuyB4/Cvy0ZBVJkiRJksqmMZek+CAifgZMSym9XIaaJEmSJEllstlzCiPiBGAOcF/2uCYippS6MEmSJElS6TVmopnLgYOBFQAppTnAXqUsSpIkSZJUHo0Jhf9IKa3coC2VohhJkiRJUnk1ZqKZ5yPiS0DLiOgOfA34n9KWJUmSJEkqh8b0FP4XcADwIfBbYCVwYSmLkiRJkiSVxyZ7CiOiJXBFSukbwKXlKUmSJEmSVC6b7ClMKa0FPlemWiRJkiRJZdaYcwqfyi5B8Ttg1brGlNLEklUlSZIkSSqLxoTC7YE3gSOL2hJgKJQkSZKkbdxmQ2FK6cxyFCJJkiRJKr8GzymMiAeK7v93ecqRJEmSJJXTpiaa6VB0/wulLkSSJEmSVH6bCoWpbFVIkiRJkipiU+cU7p3NOhpF9+uklE4oaWWSJEmSpJLbVCg8sej+D0pdiCRJkiSp/BoMhSmlP5azEEmSJElS+W3qnEJJkiRJUjNnKJQkSZKkHGt0KIyIHUpZiCRJkiSp/DYbCiPisIh4AXgpe9wnIn5S8sokSZIkSSXXmJ7CHwHHAG8CpJSeBgaUsihJkiRJUnk0avhoSmnhBk1rS1CLJEmSJKnMNnWdwnUWRsRhQIqITwEXAC+WtixJkiRJUjk0pqfwHOA8oCuwCKjJHkuSJEmStnGb7SlMKS0DhpWhFkmSJElSmW02FEbEXsB/AdXF66eUTihdWZIkSZKkcmjM8NG7gfnADcA1RbdNioibI+KNiHiuqG23iHgwIuZmP9tm7RER10fEKxHxTET0K9pmeLb+3IgYvoXHJ0mSJEnahMaEwg9SStenlB5OKf1x3a0R2/0aOHaDttHA9JRSd2B69hhgENA9u40CfgqFEAlcDhwCHAxcvi5ISpIkSZI+ucaEwusi4vKI+Gwnkd9AAAAgAElEQVRE9Ft329xGKaVHgbc2aD4RGJfdHwecVNR+ayr4M7BrRHShcH3EB1NKb6WUlgMPsnHQlCRJkiR9TI25JMWBwH8ARwIfZW0pe7ylOqWUFmf3Xwc6Zfe7AsXXQqzN2hpq30hEjKLQy8gee+zxMUqTJEmSpPxpTCj8ArB3Smn11nzilFKKiLQV9/cL4BcA/fv332r7lSRJkqTmrDHDR58Ddt1Kz7ckGxZK9vONrH0R0K1ovaqsraF2SZIkSdJW0JhQuCvwUkTcHxFT1t0+5vNNAdbNIDocmFzUfkY2C+mhwMpsmOn9wNER0TabYOborE2SJEmStBU0Zvjo5R9nxxFxB3A40D4iarP9jAXuioizgdeAL2arTwOOA14B3gPOBEgpvRUR3wWeyNa7IqW04eQ1kiRJkqSPabOhsJGXn6hvu9MbWHRUPesm4LwG9nMzcPPHqUGSJEmStGkNhsKIeCyl9LmIeIfCbKN1iyjkuDYlr06SJEmSVFINhsKU0ueynzuXrxxJkiRJUjltcqKZiGgZES+VqxhJkiRJUnltMhSmlNYCL0eEV4OXJEmSpGaoMbOPtgWej4iZwKp1jSmlE0pWlSRJkiSpLBpzncLLgMHAFcA1RTdJkiQ1Addddx29evXigAMO4NprrwXgrbfeYuDAgXTv3p2BAweyfPny9bZ54oknaNWqFRMmTKhEyZKakAZDYUTsExH/K6X0x+IbsBaoLV+JkiRJashzzz3HL3/5S2bOnMnTTz/NPffcwyuvvMLYsWM56qijmDt3LkcddRRjx46t22bt2rVccsklHH300RWsXKqcLflDyu23307v3r058MADOeyww3j66acrWXpJbKqn8Frg7XraV2bLJEmSVGEvvvgihxxyCDvssAOtWrXi85//PBMnTmTy5MkMHz4cgOHDh3P33XfXbXPDDTdw6qmn0rFjx0qVLVXMlv4hZa+99uKPf/wjzz77LJdddhmjRo2q8BFsfZsKhZ1SSs9u2Ji1VZesIkmSJDVar169mDFjBm+++Sbvvfce06ZNY+HChSxZsoQuXboA0LlzZ5YsWQLAokWLmDRpEueee24ly5YqZkv/kHLYYYfRtm1bAA499FBqa5vfoMlNhcJdN7Hs01u7EEmSJG25nj171g0FPfbYY6mpqaFly5brrRMRRAQAF154IVdddRUtWjRmagmp+dnSP6QUu+mmmxg0aFC5Sy65Tc0+Oisi/jOl9MvixogYCTxZ2rIkSZLUWGeffTZnn302AN/+9repqqqiU6dOLF68mC5durB48eK6oaKzZs1i6NChACxbtoxp06bRqlUrTjrppIrVL5VT8R9Sdtxxx83+IWWdhx9+mJtuuonHHnusnOWWxaZC4YXApIgYxj9DYH+gNXByqQuTJElS47zxxht07NiRBQsWMHHiRP785z8zb948xo0bx+jRoxk3bhwnnngiAPPmzavbbsSIEQwePNhAqNzZkj+kADzzzDOMHDmSe++9l3bt2lWq7JJpMBSmlJYAh0XEEUCvrHlqSumhslQmSZLUzFSPnlqS/b5++7f46P13oEVL2h45kpqxf2Lt6n4s+9lYLrv6elq16Uj7E0dz1wbPv+zJWqaueJJvzPrnmUHzxx5fkhqlpmRL/pCyYMECTjnlFH7zm9+w7777Vrjy0tjsxetTSg8DD5ehFkmSJH0MnYddvVFby0+3odPQKze5XfvjLypVSdJW0RT+kPLmvdfzXu3rHH7ylwGIFi3pMvyfF2NoDn9I2WwolCRJkqTmZEv+kNJu0NdoN+hr5SirYpx2SpIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJSkZmDFihUMGTKEHj160LNnTx5//HFOO+00ampqqKmpobq6mpqaGgBuv/32uvaamhpatGjBnDlzKnwEkiSpUlpVugBJ0id3wQUXcOyxxzJhwgRWr17Ne++9x5133lm3/OKLL2aXXXYBYNiwYQwbNgyAZ599lpNOOqkuMEqSpPwxFErSNm7lypU8+uij/PrXvwagdevWtG7dum55Som77rqLhx56aKNt77jjDoYOHVquUiVJUhPk8FFJ2sbNmzePDh06cOaZZ9K3b19GjhzJqlWr6pbPmDGDTp060b179422vfPOOzn99NPLWa4kSWpiDIWStI1bs2YNs2fP5txzz+Wpp55ixx13ZOzYsXXL77jjjnqD31/+8hd22GEHevXqVc5yJUlSE2MolKRtXFVVFVVVVRxyyCEADBkyhNmzZwOFwDhx4kROO+20jbYbP368vYSSJMlQKEnbus6dO9OtWzdefvllAKZPn87+++8PwB/+8Ad69OhBVVXVett89NFH3HXXXZ5PKEmSnGhGksqpevTUkux39T5fpPfhg0lr19Bq1860O+5Cfjd6Ksum/ojtdu+10fN+sOAZlqedOfIXLwIvrrds/tjjS1KjJElqmgyFktQMtO60N12GX7tRe/vjL6p3/e336E2XM64pdVmSJGkb4PBRSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZIkKccMhZIkSZKUY4ZCSZIkScoxQ6EkSZIk5ZihUJIkSZJyzFAoSZIkSTlmKJQkSZKkHKtIKIyI+RHxbETMiYhZWdtuEfFgRMzNfrbN2iMiro+IVyLimYjoV4maJUmSJKk5qmRP4REppZqUUv/s8WhgekqpOzA9ewwwCOie3UYBPy17pZIkSZLUTDWl4aMnAuOy++OAk4rab00FfwZ2jYgulShQkiRJkpqbSoXCBDwQEU9GxKisrVNKaXF2/3WgU3a/K7CwaNvarG09ETEqImZFxKylS5eWqm5JkiRJalZaVeh5P5dSWhQRHYEHI+Kl4oUppRQRaUt2mFL6BfALgP79+2/RtpIkSZKUVxXpKUwpLcp+vgFMAg4GlqwbFpr9fCNbfRHQrWjzqqxNkiRJkvQJlT0URsSOEbHzuvvA0cBzwBRgeLbacGBydn8KcEY2C+mhwMqiYaaSJEmSpE+gEsNHOwGTImLd8/82pXRfRDwB3BURZwOvAV/M1p8GHAe8ArwHnFn+kiVJkiSpeSp7KEwpvQr0qaf9TeCoetoTcF4ZSpMkSZKk3GlKl6SQJEmSJJWZoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKEmSJEk5ZiiUJEmSpBwzFEqSJElSjhkKJUmSJCnHDIWSJEmSlGOGQkmSJEnKMUOhJEmSJOWYoVCSJEmScsxQKJXQWWedRceOHenVq1dd22mnnUZNTQ01NTVUV1dTU1Oz3jYLFixgp5124gc/+EG5y5UkSVIOtap0AVJzNmLECM4//3zOOOOMurY777yz7v7FF1/MLrvsst42X//61xk0aFDZapQkSVK+GQqlEhowYADz58+vd1lKibvuuouHHnqoru3uu+9mr732YscddyxThZIkSco7h49KFTJjxgw6depE9+7dAXj33Xe56qqruPzyyytcmSRJkvLEUChVyB133MHpp59e93jMmDFcdNFF7LTTThWsSpIkSXnj8FGpAtasWcPEiRN58skn69r+8pe/MGHCBL71rW+xYsUKWrRowfbbb8/5559fwUolSZLU3BkKpQr4wx/+QI8ePaiqqqprmzFjRt39MWPGsNNOOxkIJUmSVHKGQgmoHj21JPtdOuVqPlzwLGvff5tWO7dnl88NY+c+R7Ns6o/YbvdeDT7visf+Snzq09y47J/L5489viQ1SpIkKd8MhVIJdTjhW/W2tz/+ok1ut+vnhpWiHEmSJGkjTjQjSZIkSTlmKJQkSZKkHDMUSpIkSVKOGQolSZKkEjjrrLPo2LEjvXr1qmt76623GDhwIN27d2fgwIEsX74cgOXLl3PyySfTu3dvDj74YJ577rlKla0cMhRKkiSpydiSILXOE088QatWrZgwYUK5y92kESNGcN99963XNnbsWI466ijmzp3LUUcdxdixYwG48sorqamp4ZlnnuHWW2/lggsuqETJyilDoSRJkpqMLQlSAGvXruWSSy7h6KOPLnepmzVgwAB222239domT57M8OHDARg+fDh33303AC+88AJHHnkkAD169GD+/PksWbKkvAUrtwyFkiRJajK2JEgB3HDDDZx66ql07NixrHV+XEuWLKFLly4AdO7cuS749enTh4kTJwIwc+ZMXnvtNWpraytWp/LFUChJkqQmraEgtWjRIiZNmsS5555byfI+toggIgAYPXo0K1asoKamhhtuuIG+ffvSsmXLCleovPDi9ZIkSdpmFAepCy+8kKuuuooWLbadfo5OnTqxePFiunTpwuLFi+t6ONu0acMtt9wCQEqJvfbai7333ruSpSpHtp1/QZIkScqldUEKWC9IzZo1i6FDh1JdXc2ECRP46le/ut7Q0qbohBNOYNy4cQCMGzeOE088EYAVK1awevVqAH71q18xYMAA2rRpU7E6lS/2FEqSpM16+eWXOe200+oev/rqq1xxxRV07dqVMWPG8OKLLzJz5kz69+9fwSrVXK0LUqNHj14vSM2bN69unREjRjB48GBOOumkLd5/9eipW63WYkunXM2HC55l7ftv02rn9uzyuWHssO+hLPvZWC67+npatelI+xNHc9foqXy46EWWTf0RRPCp9nvQbtAFG9U1f+zxJalTMhRKkqTN2m+//ZgzZw5QmO2xa9eunHzyybz33ntMnDiRr3zlKxWuUOXWFIJUsWVP1jJ1xZN8Y9an69oqHaI6nPCtets7Db1yo7btuvak66hflLokqV6GQjU5K1asYOTIkTz33HNEBDfffDPTpk1j8uTJtGjRgo4dO/LrX/+a3XffvdKlSlIuTZ8+nc985jPsueeelS5FzdCWBKli7Y+/qBTlqEh939E++9nPAnDNNdfwjW98g6VLl9K+ffsKV6ot5TmFanIuuOACjj32WF566SWefvppevbsyTe/+U2eeeYZ5syZw+DBg7niiisqXaYk5db48eM5/fTTK12GpDKr7zsawMKFC3nggQfYY489KlyhPi5DoZqUlStX8uijj3L22WcD0Lp1a3bdddf1TrRetWpV3axjkqTyWr16NVOmTOELX/hCpUuRVEYNfUcDuOiii7j66qv9frYNMxSqSZk3bx4dOnTgzDPPpG/fvowcOZJVq1YBcOmll9KtWzduv/12ewq1Vaxdu5a+ffsyePBgAP71X/+Vmpoaampq2H333T/WZAVSc3fvvffSr18/OnXqVOlSPrENfwc89NBD9OvXj169ejF8+HDWrFlT4QqlpqOh72iTJ0+ma9eu9OnTp9Il6hMwFKpJWbNmDbNnz+bcc8/lqaeeYscdd2Ts2LEAfO9732PhwoUMGzaMG2+8scKVqjm47rrr6oa+AMyYMYM5c+YwZ84cPvvZz3LKKadUsDqpabrjjjuazdDR4t8BH330EcOHD2f8+PE899xz7LnnnnWXDZBU/3e0MWPGcOWVV/rH+mbAUKgmpaqqiqqqKg455BAAhgwZwuzZs9dbZ9iwYfz+97+vRHlqRmpra5k6dSojR47caNnbb7/NQw89ZE+htIFVq1bx4IMPrvcHk0mTJlFVVcXjjz/O8ccfzzHHHFPBChtvw98Bb775Jq1bt2bfffcFYODAgf5fIxVp6DvavHnz6NOnD9XV1dTW1tKvXz9ef/31ClerLeXso2pSOnfuTLdu3Xj55ZfZb7/9mD59Ovvvvz9z586le/fuAEyePJkePXpUuFJt6y688EKuvvpq3nnnnY2W3X333Rx11FFeNFjbrFJdKgBg5/+8lT7ff6yopTWtvvxzumSPXm7k81f6UgEb/g5o3749a9asYdasWfTv358JEyawcOHCitYoNSX1fUfr168f06dPr1unurqaWbNmOfvoNshQqI+tVF86Vu/zRXofPpi0dg2tdu1Mu+Mu5CeDR/CPt2ohWtCqTQd2O+a8beJLh5qme+65h44dO3LQQQfxyCOPbLT8jjvuqLcHUVLzUN/vgIhg/PjxXHTRRXz44YccffTRtGzZsrKFSh9TOb+j/a7ouWqXv0/fKx6g5Q67bHZffkdrWgyFanJad9qbLsOvXa+tw8nfrlA1ao7+9Kc/MWXKFKZNm8YHH3zA22+/zZe//GVuu+02li1bxsyZM5k0aVKly5RUIpv6HTBjxgwAHnjgAf76179WuFKpaanvO1qxqnNvLmM12po8p1BS7nz/+9+ntraW+fPnM378eI488khuu+02ACZMmMDgwYPZfvvtK1ylpFJp6HfAG2+8AcCHH37IVVddxTnnnFPhSiWpPOwplNSklfLcKIAPFjzD2y+9Ufc8r//2x+xy6Be26HkdAiOVTjl/Byx/+Gbee2UmkNi55jjOeuB9eMBTFSQ1f4ZCSbm2/R692X6P3nWPO39pbAWrkVRuxb8D2h5xFm2POKvCFUlS+Tl8VJIkSZJyzFAoSZIkSTlmKJQkSZKkHDMUSpIkSVKObTOhMCKOjYiXI+KViBhd6XokSZIkqTnYJkJhRLQEfgwMAvYHTo+I/StblSRJkiRt+7aJUAgcDLySUno1pbQaGA+cWOGaJEmSJGmbFymlStewWRExBDg2pTQye/wfwCEppfOL1hkFjMoe7ge8XPZCK6M9sKzSRWwlzelYoHkdT3M6Fmhex9OcjgWa1/F4LE1Xczqe5nQs0LyOx2Npuv7/9u47TtKi2v/45wso4MISFESSEkQEJAiiAioS9YqXdMkGFPWqqCCSzBlERECSIkqUdBVE1B9yCRIURUByEARJykUwEUQQvr8/qnqnt+mZ3e6Z7ep6+rxfr3nxhB33tDvT/VTVqXOa9nrG82Lbi83OH2xM83rbxwLHlo5j0CRdZXud0nFMhSa9FmjW62nSa4FmvZ4mvRZo1uuJ1zK8mvR6mvRaoFmvJ17L8Gra65kKtaSP3g8s03a+dL4WQgghhBBCCGESahkU/gZ4qaTlJD0X2BH4UeGYQgghhBBCCKF6VaSP2v63pA8BPwPmBr5r+6bCYQ2LJqXMNum1QLNeT5NeCzTr9TTptUCzXk+8luHVpNfTpNcCzXo98VqGV9Nez6RVUWgmhBBCCCGEEMKcUUv6aAghhBBCCCGEOSAGhSGEEEIIIYQwwmJQWAlJW5aOIYRQjqQq9oCHEEIIoT4xKKzHZ0sHMFUkTZf00rbz7SS9I3+9sGRsU03SnqVj6JWkuUvHELq6snQAYXySnivpXZK+lr/eJWne0nH1StK+bcfbddw7YPARhZam/f8v6TulYwjNJ+n/lY6hFjEoDCV8DVi/7fxA4FXA64HPF4loztmrdAB9uFrSa0sHMVUkbdR2vFzHvW0GH1HfVDqAqSbpGEnTS8cxWZJWAW4GNgTuyV8bAjflezXZse344x333jTIQKaCpNdP9FU6vh5V9///LKxVOoCpJGlpSRu0ne8l6TP5a8WSsY24JUoHUItIR6rHypKu6XJdgG2/ctABTcKrgP9uO3/E9ocBJF1eJqQ5psYH+f8GjpB0HbCv7b+WDmiSvga0fj9+0HYM8CngrIFH1J/FJI07yWD764MMZorcSZqE+KztU0sHMwlHAB+w/b/tFyVtAhwFvLFIVP3ROMfdzmuwT5drBlYHliG1uarF3JIWYZx/B9t/GXA8k/U8Sa9g/Ndz/YDjmayDge+1nf83qe3B80gT3ruUCKofks60vX0+Psj2fm33zre9WbnoeraQpP8c76bt6HuexaCwHncB283yT9VhHs/cC+XtbccLDzqYOay6ni+2fy3p1cD7gaty6sUzbfc/Uiy4/jTlIXduYAHqinlCtg+WdCrwdUm7Accw889aLQP2pToHhAC2L5B0RImAJsHjHHc7H3q239p+Lml90mTQA8CHiwTVv5WBq+n+HmBg+cGGM2lLkSZNxns9ta3kvsz2j9vOH7d9CICkywrF1K+Xth1vCuzXdr7YgGOZrIWA/2L8n7MYFGYxKKzHk7Z/XzqIKfKMpCVsPwBg+0YASUvR9kBYC0mP0P1hSaQZwhotSlrR/TPpIaS6f5c2TXnI/ZPtL5QOYqrZvl/ST4AvA29l7GfN1LOKO5ekeW3/q/2ipPmo73N2DUn/IL1/zZ+PyefzlQtrciRtDHya9HN1QLdBfAVutt2klMs7bNc28JtI5+/Hxm3HLxhkIFNgos/Gmj43Ae62/Y7SQdSgtg+rUfar8W5I2tb2DwYZzCQdDJwr6WPAb/O1V5LS/A4uFlWfbC9YOoapJOn9pJSrg4HdOlZ1a7S8pB+RHmpbx+Tz5cb/tqHTmBXCFkmrklYH/wisa/tPhUPq10nADyTtbvtuAEkvAb4BnFwwrp7ZrimdcpYkvQX4JPB34FO2m7ZFIQyPRyStZPt3MJbOK2ll4JGikfXueZLWItUemT8fK3/NXzSy3jXus3NOUf3Pe0HSPbaXLR1HLyS9CfgEsGq+dCPwFduNqBIlaRqwNbCT7beUjqcXkk4B9rL9YOlYpoKkN0x03/Ylg4plMiRtb/vMfLyc7bva7m1TUarlDJJuAfa0/bPSsUyWpA8B+zKWHfAY8DXbVaWPSnoV8ILO92JJbwYetH11mcj6I+kZ4D7gOrqscNged6/RsJH0PtvHlo5jqkh6c7fPfElLAjvYPrRAWH3LzzXfIGU9tGpArE161tmjpucbSRdPdN92NfukJa1h+7px7v3C9vrd7o2iGBQ2gKR7bS9TOo5RJ+m5wFuAnYHNSUVNzrJ9btHApoiklYB9bL+3dCy9krQmsCJwk+1bSsfTD0nXtApKtR93O69Ft5TLGrUPyiUtCGC7tpUBACRdBLyrteLZdv3FwPG2N+r+ncOpKZNC8Kz3gCNaBdqaQNKipH1fO5EyOH5ou8aWTquRJofaJ7wPbm2TCcMlnp9nFumjzVDVyF7SZya4bdtfHFgwU0DSZqQPss2Ai0mpZK+y/a6igfVJ0uqkVN4lgR+SCgEcCbwaOKRgaH3JP29vI+2N/KqkA21/u3BY/WhKwZx2t0pqf/8SY+9ntr1CgZj6MaOKba2DwTYLdg4IAWzfLam2fVEzBn15f2erLcAdtp8oF1Xf2n/Pq1/dyBk1W5EmUlcFzgFWsr1U0cAmIQ/+GrF/TdLzSf82K+dLtwCnVljldiJVPT/PaTEorISkGxi/mEltDd8f63JtGrAb8HygqkEhcB5wGbBBK6VP0uFlQ5qUb5P2eV1B6ot1LXAisEulD1I7AGvafjx/yJ1Heo21aUrBnHbrdJzPBWwP7M3YfuMwWItMcK+6wlmS5gEOAN4N3E36zFxG0vHAJ20/VTK+HtX6ez6eB4GrgM8Bl9h+ZqLWAcMu/0yN929k27sNMp7JkPRy4CLgZ6T3YpGKz31C0ka2by0ZXy8m6Edc4/7IOSrSRyuRU3fG1W1mtwY51WoP0oDwTOCQ2vay5dTEHUktQ+4ETgc+Y3vCf7NhJela22u2nd9pu7ZS5zN0SbW82vbaJWPqh6S/AZeSPshel4/J5xvYnuhhfqhJmovUmmYf0iTEAbZvLhvV7JP0OHBHt1ukh8HVBxxS3yR9E3iYVJTF+ZpIfdaWsP2+kvH1StKhwILAR1uruJKmk7Ih/ml7j5Lx9aLt50zACoz9zFX3cwYgaW/SZ+fcwGnAGcDFtX7eSNq2y+VlgI8Cc9teesAh9U3S94EzW/vY265vC+xsu9trHUp5sD6uWrO65oQYFIYi8v6BvUjNXE8EDnf9TdKRtB4plXRbUmGDs2srDCDpVtJraKUqfY+UQiIA29eM861DqW0wBc8eUFVTaKJJe6NaJD2HtILzUeByUrGpboOroSbpJuA/xrtf06RdTuk7DliXNEAHWIO0ovMe24+Wiq0fkm4npSS64/rcwK22X9r9O4dPgyeHX0r6zNmR1Gvxk6TPzjuLBjYJkpYnFZh5PXAo8B3bT5aNavZJus32y3q9F+oWg8JKdOmFZ+Ah0h62/Ww/XCSwPkg6GNgGOBY4qraHjNmRVz42AXa0/e7S8fRiFlXHHIUmypA03fY/xrm3rO17Bh3TZEm6D/g3cBjwrPhrqWLHd34AACAASURBVKgq6bcN6x/XeqhtFcu4qdYHdEm/s71Sr/eGkaSVW2l7nUWaJL3G9ritq2qRM292Ara3XVPLIGBG+4lPAWuR2jqdYvvfZaPq3UTFy2orbCZpr45Lrefny9ureIcYFFZN0iLArsB6trcrHM5syyXC/0V6GHxWkQnb04sE1idJE7451rayNhFJz6lsD864JC1DGrRX0Ruzo/LghbY37navJpJOYOI9OFVMqEg60vaHSscxFSRN2N6otskHST8kVYE+qeP620gDjyoyBaB5FYglvdT27fl4nvbBk6R1bV9ZLrreSfofUguKQ0jbYZ5uv19TgZY8Yff1brdIbYSqqdgp6bNdLi9KqhL/OdunDzikoRWDwgao8cOgSZq2stYp7yfaiJRCuoXt2gobzSBpMdLez51I1VXPtr132ahmT/tqVOfKVBNXqmoi6WN0z+Sobia6rahZe6VLA4sBi7uy5vaSliJVhv0nqQIxpAJH8wNb276/VGy9atp7QAMHuX+grXoyHb9DNe2VHGcgNYPtzw8qljklb2O6oLafszkpqo9WLu/JqerfMf8ijqum2TSoq4lrLyS9hjQQ3Io0q7Y7qSpkVXIxo21Ir2Ul0gPicjVt+s8aV31U0mHOvcgk7WH78LZ7J9jetVhwvVmgy7WXAJ+UVNVMtO1XtJ9LegmwHykd/oACIU1KHvS9WtJGjKXD/tT2hQXD6lfT3gMa1WbH9ktKxzBVmjDomxXbf8mT3iGrajAxysYpqbsIqdz+9wcczmRdzbNn0VpM2mhejZyGJNsnd1x/O/C07VPLRNYfSQeQVtPuIVWE+zxwle0TiwbWvweBK0n7PC63bUlbF46pH4vnvRFqOyafL1YurEl5fdvxO4H2Vi7VVFIc7wGqNRNNqkhclVz845OM9Sf9SI2p45KeBzxl+yLgIkkvA/4j79E9u3B4vVpa0jdIv/OtY/J5jb39GjXIbdJWkrafra5sf2RQscwpkt4IVF/gcCrFoLAeb+04N6ls+OG2f1Ignr5NtHl8VquIQ+rDwMZdrp9FqnJZ1aAQeA/wO1KvwnNt/0szNxivzcdJVe2OBk6TdEbhePr1bVJp/c5jSNUiazTRSkH1apyJlrQaaTC4KvBVYDfbT0/8XUPtPFLLo9slrUjqv/o9YIu8b+3jRaPrzT5tx1d13Os8r8HSkr7O2CC3tYet1kHuIRPcM2kbRi2unvUfqYO69/leFPgjaTIyZLGnsAEkvcr2b0rHMbskHWf7PV2uLw2cZ3u1AmH1bRZVuq6vsHfU3MCmpH13G5Mq3G4CLFNjFbWWXE1xR9LreinwWdKewt8VDWwKSJpm+7HScfRK0nXAhqSm9Rfl49Yg6mLba5SJbGrkmehP17SvWNLTwL3AT+golAH1rRBIuqGVEivpi8CitneX9Fzg6s502TA4kiZs5m77O4OKZSpIeq3tK0rHMRUkzQcsaPvPHdcXAx6x/USZyHrXpZWLgYdr/Myc02KlsFKSViE93O4E/I20cb4Wz5F0CvAO28/AjNfzY+ALRSPrz/zdHsrzXrbnFoqpb3lV4DzgPEnzAluQijLcn6te7lw0wD7lkvoHAAfk1ZCdgZ8CKxYNrAe5aMaLgOttPylpcWBPUhXiJUvG1qeFSDPSrYFge3pVNTOWDZuJ3o2K/r+fDe2vZSNSmwDy788zZULqj6RzmeDfpqZKqjDxoC+/19XmKKApRUu+QXoO6GwLtAGwGfCBgUfUJ3fp3ylpWt76s5PttxQIayjFSmFF8ob/1kDwKeDFwDq2/1Auqt7ldKpvkfZE7kjas3IG8AHbPy4ZWz8k7U1aUXt/680n/1sdBfy8lpYHsyJpOrBl597JWuUV0Z1sn1I6ltkhaU9SWt8dwLykdNiDgJOAr9r+U8HwRtqozER3tg2oQZ6AfAC4H9ifVGTqcUkLA5fUtBqtsZ6rIqWQz5Rx40p6rraT9CpSqujlth+StCqpsNFGtRUDq7EC7HgkXW177XHu3WR71W73hlnODngLaUJ4c+AHpHY15xYNbIjEoLASkq4AppMKFpxu+3ZJd020P2/Y5Y3Ma5EGt9u74sa7kt5P2rvWqkL4KPAV28eUi2rqSbrH9oR9zIZNHszuTnrw+BHwv8CHgL1IK25bFgxvtkm6Gdgg71NblrTvc33b1e79kLQ5KUXp+x3XtwX+Yft/y0Q2eZKmAVtT2Uy0pMttb5CPT7b99rZ7NbYJmB/Yg7TC/l3b1+Xr6wEr1DrJ1YQBiKQDgW2B64DlSNlCHyRNdh1j+/GC4fVM0t9IdQS6qmklV9Ittl/e671hJGkz0mLKZqTtMGcARzSpWuxUifTRevwf6aH2haRKg7dTaYqPpCMYqz66CillbGdJO0N9e1YAbH8T+GZOGcX2I4VDmlOqKpqRnUyqMHYFaWb9E6TXsbXta0sG1qMnWu1abN8j6baaB4TZZ0gtTzpdApxLGsBXY5yZ6G8WDap309qOO1cDqvv9t/1P4Ctdrv+ytiJAHar8/O+wJbCG7X/mInP3Aq/Iqf41+jMTF5upyYO5ENOV7Rfzyu6fx/meYXUecBlpUvUuAEmHT/wtoykGhZWwvZWkhUj91j6Xy4Uv3O2XtgJXjXNcrZyKuIjth/L5c0n7vD5a04zabKjxQWT5tkITxwF/ApataaN81l6CHuBF7ec1TqYA83YWMgDIaWTTun3DMOoyE30S8Crb7yoaWH8m+h2v7vc/vzdvT5pUPc/2jZK2IE0OzU/KVqlCR3XuuSUtQttA3ZX1+CVNdP0TZlTq/V3FA0JIBViqS+Edxz7AmZJOYKwS6TrAO0jbfmrySlLMF0i6k5RxN3fZkIZTDAorYvvvwPHA8ZJeSPqgO1TSsraXKRtdT85ggqpWZULqn6QdSXskH5N0O/Bl4LvAb4BdSsbWj3GKZkB6+HjhgMOZCjN6q9l+WtJ9FQ4IYeZy9NCMkuHTu+1Tk/Qc0gN7LZo0E72wUh/PufJxq0euSIWBavMdYBlSr9JvSPoj6eF2f9s/LBpZ7zp7/HYWZqqqxy+wvKRWIRMBy7WdY7tbf+Zh9ofSAUwV21dKejUpnXfXfPkm4NW2HywWWB9yRtC1wP45bXwnUsHD/0eqQH5s0QCHSOwprFieJfwbadXjWdWVhpWkY0kztmd1XN8a2Mx2NVWtACTdCGxl+w6l5rVXAP9V6+blLkUzZlLTzxrMKLHfKvgh0mDj8Xxs29NLxdYLSQfY/kTpOKaSpK+QJho+1CrKImkBUhP7h2zvVzK+2SVpTdJM9HZAayb6M7Yn/F0aRpKOn+h+bauf+f15ddvP5DL7D5D2Ej5cOLSeSXpxbe+/E5HUrb/vDLYvHFQsUyVXhN6dsdTrm4Cjbf9fuahCJ0lzkQoE7mT73aXjGRYxKKyEpM8AZ9q+NbcJOA9YA/g3sLPtC4oG2IOmVbXqLL4g6UZX1msxDL8ai3zMiqR5gC+R9nq2HnaXJa3ufNr2U+N977Bqm4luFdCImeiCurw/V/t7VHPso0DS+sCpwAmMZXKsTWpLs4vtXxQKrWezyBiyK+q/nCe6/5az7Vr9Y7cifeYcafvJkvENkxgUVkLSTcBqti3pfaSHjk2AlYATba9bNMAeNKmqFYCk+4Cvt13aq/3c9tef9U1DTKmh8KKtVhqS7gcWJH0Y7JOL6oQB01ij967FMSrcTzRDrhDZ6hd5R2ufUc3yTPQmwI61zURLehnwPmDlfOkW4FjbvysXVX8kPU5q4wLpd2eFfC7gmcpaUlRfcbSdpN8ycd/FqgbAkn5Faq31247rawLfsv3qMpH1rkkZQ5J+TSos98f8b3EBcCCwOvCk7fcWDXCIxJ7CejzpsRH85qS2FE8Dt+T9NzVpUlUrSP2iFpzgvDbvB97Udv6g7aVy6tXPqK+aYlOszMyN3tvVuJ9ohjwIvKF1LmlTYF/bm5aLanJsPwOcn7+qIem1pIbVx+YvkYqx/FzSNq6vdVC3SUaR9hl+fMCxTNZSHcWmZlJhsan/yv8VcA5QTcuGcUzvHBBC2tPWqkxei5oGfbNhftt/zMdvI7WmOSRP3NVUgXyOi0FhPf4laTVSa4o3Anu33aupIAM0q6oVtj9fOoYppo79Nv8DYPuJvKITyri5SasEAJI2Ik0yLAn8kNSf7HjSQ+KXC4Y2yj5D2mfz87ZrP5R0EfBZ4M1FoupT+8OtpLVI7UK2A+4itQypyT9pRoEpAGz/vnUs6V/t55WSpEVs/7Xj4qKkwk3VkPQIM6/iirEiR9Xsxc/aJ1I3Ik8G5X3GZSIaUjEorMeewPdJPQoPbatw9x/As2amhlmuarUuaTP2rvlylVWtACSdaXv7fHxQe3EMSefb3qxcdH1ZuP3E9gEwIx3uBUUiCk11CClN8QrSYOMKUlXII4tGNdpW6BgQAmD7klwkrCqSViJtt9gJeIhU/Vq231g0sP48bPvE0kGEcR0KnC9pb8Yqw65Nmuw6tFhU/bkQWIKUNXC67XsKxzMZF0k6k9SOahHgIgBJLwJiP2Gb2FMYhoakZUj7bw4uHUsv2vd5dClqUN0eEElHA3+x/amO618CXmD7/WUiG22SdrV9Qse1RUgb6Kt8I+/y+3Kb7ZeVjKkfHf3jnqWm/Z6zKARWXaETSc+Q2oXsZvuOfO1O29WlW0v6le3XlI5jqkhqL1ZyBqnNVnvfxesHHtQkKfXA3JeZq48eXGM1co31xt4RmI/0b3R6Te9nkJZvgR2AF5EKNt6fr68FLG77ZyXjGyaxUlgJSW8Frm+lwuRqpNuSqift0Vo5rI1Sb8LtSLO4SwJnl42oL41q9kxK7z1O0h2k6omQKt1eBcSG7HKWlbRytwrEkqqqQNymvQ8ewDzt551ta4bYQ8B9pGrQMHO6Um37PZcZZ9+aSA3ga9N6qL1Y0nmkdiFV5ow1aUCYHdV2/BBwdNu5gdcPNpzJs/1j4Mel45gKuVrn8ZJOJP0OfYM0OKyqeF6eND29dS7p+aSfrXtiQDizWCmshKTrgdfYfjzPRH2dNJBaC9jO9uZFA+xB3nC9DWlvx0qk9IQdbC9dNLA+SbqV9G8xF3AK6XUpf51SWzXVFknLMzbbeXMD9ntUrUkViFtm0RPPtVTtlHQYaa/3L4DTgMsrXr1950T3a01flDQN2JL0e7MRcBKpXUhVhYCapFvBudpJejOwPzOvFB5k+6floupPW3ud1wGXA2fYvqxsVL2T9GPStoQbc8roNaRJ7hVIVZUPKxrgEIlBYSUkXdcqnS3pu8Bttg/K51Wl9Ej6J3Al8Cnyw1Ot6TwAkn7OxGW1a9y78ix5b84+Ub65jI405R8A59v+Vj6v6j1gdkh6oStq+JxTlDYkPUStS6o6ekytWRxNltOutyNNRk7YQD3MOU1735L0XuC/SemjV+XL6wBfAY5zRf1KJf0B+Btphe0ixrIgALB9TZdvG0pq638t6RPAyrbfkRcofuGKei7OaZE+Wg9JWgB4HNiYmdMs5isTUt8+TkpFOBo4TdIZheOZFNsblo5hKuV9Hl9jrCLkUcCRwKtJhUFCGRNVIH5emZCmlqSFSWnxO5NaCSxZNqLZl1cGL86913YEvgjcTmpRUw1J5zLxJFftbQPI1SFbLTeqo9R8e8ZKlO2LS8YzCVWm8U7go8AGHXvuLsqrh5dT18/bH0jvA5vnr3YmrbbX4qm2443J78m2H8l7jkMWg8J6HEbqp/IP4BbbV8GMjbJ/KhlYr/JS/WE5PXFH0sBjSUn7kdJ5qmqQ3LEn6lkq2hfV8m3gGMYqQl4LnAjsYvuJkoGNuD1oSAXidrnNyZakgeBapB6fWwGXloyrF22piTuQ/n3OAtautGLf10oHELqTtBTpZ+sJxlpTbCfpIFJz7vuLBdef5SSN+/loe8LP1iGkbkVYbD9cW+uDhk123yvpw6R9368k7cdvffbU1ud7jor00YrkD4TFgeucGiMjaQngObbvLRrcJOUVkJ2B7W2vWDqeXuSZpmsZa4I6U5GJWvZFtUi61vaabefVpvaG4SbpVNJ+lfMZS1O6w/ZyRQPrkaTHSKuCp+f/zvTBWuHE0AySngOsBtxfY8ugJpF0NnBOlyrE7wC2tb1lkcD6JOl2YNxq1rYvHGA4kybp18D7bF/XcX0N4Nu17fuWtDipdVj7/sijansfyK/jC6Tqo0e19hHnFfe1bcdEWBaDwopJWoE0kNqxlS8dBk/SVqQVzxWBc4DTWqXPa9RWOKc1uP0eY8VzqtpLEIabpGtJBZpOIpU6v6/GSQhJJzB+ymVVE0OSvgkcYfumXJL+CuBpYFFgb9unFQ1whE3UsqXGdi4N3FO4Aenz8njGVnLXAd4JvM325aVi65Wk9YFTgRMYey1rk17LLrZ/USi0KSNpPuCttv+ndCzDIgaFlZG0JClFaWfgFcCBwFm2bygaWA8kPcL4D1D/An4PfLLCWcL2FLLnk17DJWWj6t0sCufYdk17CcKQk7QyaRJiB1JZ+peRqqxWU2SmSTqKMuwJbGh7q5yV8v9cWd/VJpF0u+2Xdrk+F/C7CrNszqltdXNW8u/JB2mr3E1anXqgXFS9k/Qr4AO2f9txfU3gW7ZfXSayyZE0N2mP5E7AZsBltv+rbFTDIwaFlWgrQb8UcGb+Oqe2NKtZyb+wqwHfs71a6Xh6kWN/E2nV8BXAftEDJ4TZJ2lt0vvc9sB9ttcrHNJsyynw+zBzqtXXapqwg2dVuf0J8D+tdMX2e2HwcuuTacCeth/L16YBhwJP2P5IyfhCc0i62fYqvd4bVpLeQFpM+Q9S9fv1geVtP140sCEThWbqcSQpjWfntiIzjRrR58qDu9v+sqQjSsczuyRtRBoIrgtcABze+jeqUZfCOSat4Fxr+5ECIYVxSDrW9vtKxzFVbF8NXC1pX1LLmipI2pJUoOVAxir0rgOcJWlv2+cUC653f1PqhXs/6cFpNwBJ8wDzlwwssA/pZ+xuSXfna8uSCoF9olhUAQBJN9A9y0akLJuaWh9I0iK5Um/7xUVJKf/VkHQfcA+pgN7eueroXTEgfLZYKayEpOeT+irtBCxBWinc1fYyRQPrg6RlgE8z1vLgNNIm4HcAp9reo2B4PcuFZq4nlZw2zy4yUdXsrbo3FF8UWB3YzfZFAw4pjKNpe3LaSbrH9rKl45gdkq4DtrT9h47rLyFldKxRIKy+5H6k3yB9zhzWtkq4ObCZ7Y8VDG+kSXqN7V/lqomtVNHf1/pwK2muVtG8JpD04tYh8BPSqtQMtu9+1jcNqZyd9l5S66NWHYG1gYOA77Z65NYgr7BvBdxI2id5DnBDbXvXByEGhRWStDRp/81OpFSSs21XM0so6WLgEtLK55vy17XAR2vLuweQtCsT9/U6cXDRzDn5A+/MWvcSNJGk82y/qXQcc4Kke2uZ9Grfh9flXnWpVmE4NW0SSNLVpH1rV5aOZao14d8qZwzsy8wp8QfbPrdcVP1R6gmyIem5+T+AhUhZED+1/WjB0IZKDAorl2d1d7T9hdKxzC5J17XPnOel/WWbNGPYVE34oAt1qHCl8K2dfQnzRMq5NaWNSTrT9vb5+CDb+7XdO9/2ZuWiG21Ne/+VtB5wBGmP1/62/144pCnTtH+rJsltdlrFZja3/YLCIQ2N2FNYOadG79UMCFskLcJYy4OHgYXyTA7u0vx1mEn60UT3bf/noGKZkyS9jFQdNoQpMUElYlHX/rXPAhdIOoCZS9HvD+w37ncNp/bqlpsyc/yLDTiWMLPlJ/q8qe2zxvYvJb2K1Avvakk/Bp5pu79XseD6IKl9EDh/x3lV7ZxyXYeJMqCq2hbTzvZTwI+BH+dU7JDFoDCUsBDpwam9yXvrzdJAbXnerwXuJe2N/DUzv67qSDqXZ38YLEpq/Pq2wUcUmsr2gqVjmAq2fyjpLuBjwIfz5ZuA7TsbWVdgovShSC0q68+MFTJqioVI1br/SvqdqTlj6BDS74iAB0jFp9rV1M6pvVje50kTX41j+5+lYxgmkT4awiTlVhSbklIRVidtMD/N9k1FA+tTLt3czqTV3NttP1kgpMBYkYnScYTeSFq2M610mEm6lfReNhdwCqmMu/LXKbZfXjC8kda0liCS3gN8nNRS4+jat5BIWhe41/af8vk7gW2BPwCfqy0LqqVpP3dhfDEorISkCffW1PTQ0Y2kFUgPHzuOV7ChBpLmJT1QHQx83vaRhUOaEpJeADzseMMoRtI1wG9I/S//VjqeMDNJryX1kb3U9oOSVielj76uloI5MKMQ2Lhsv3FQsYSZSTrLdmfLoGpJOp1UYO5PpWOZCvk9ehPbf5H0euB0UubAmsDLa22SHvsjR0cMCivR1v+mPTXRpD0ei9ueu0hgkyBpSVIV1Z1J6SMHAmfV1uwZZgwG30IaEL4E+BGpbPP9JePqh6TXAF8B/gJ8ETgZeAFp5eAdts8rGN7IkjQX8BHgg8AXbZ9cOKSQSToY2IJURXlF4GfAe0jvad+y/UTB8EJDSNqWmVN4q+8h296WIj8TrEtqs1Hjc8CMInqSjgL+bPtz+fxa22uWjK9fTR4USnqf7WNLxzEsYk9hJWy/ov0897/aD9gEOKBASH3L/W92Is2qn0kqC3yO7c8XDaxPkk4CVgN+SlodvLFwSJN1JKkR8kLARcCbc2+slUn7JmNQWEB+cDpM0vnAFZKOZmyiyLanFw1wtL0FWMv2E7mI1r3Aap19C2smaVNgX9ublo5lhG3R5dqiwOqSqushK+ldwCGSHgU+R0olvQ5YQ9K3bHfuyRt2c0uax/a/gY2B97Xdq+p5u6MI2PMk/aN1i2Z93lRdA2KqxUphZSS9FPgk8GrSpuYTcyWlakh6ktSj8GO2r8rX7qy1kWhuXv9Y26XWL1WVb57tM5qSbmnfQxR7C8qStBspJfFw4KhI5x0OnTPpNf+eSNoI+CawJPBDUrPq40nvZ1+2fVbB8EIXtfaQlXQj8AZgQVKRmZfY/rOkacCVtW0lkfRJUg+8h4BlgVfatqQVSc9q6xcNMIRZqGrmYpRJWo00GFwV+Cqwm+2ny0bVtxcB25FmCJcgrRY+p2xI/bM9V+kYplj7Zv/OylwxCClE0i9JBQteZ/uBwuGEmXW2Cliu/byyVgGHkFY4rgDenP+7f1P2RzeR7btz77XaPGX7YeBhSXfY/jOA7cfy5HFVbH9Z0oWkZ5zz2ybt5mKsKnEYMElLkyYcLs/newEL5Nun2r6jWHBDJlYKKyHpaVJK0k+AZw0Ga+0Zk39ZdyClk04Dzrb9ibJR9UfSG0mDdoAbbf+8YDh9yz9rjzHWK+7x1i1gPts1PnxUT9Imti8Y59402491uxfmvC4Ve2di+5JBxTJZXVY9b7P9spIxhYnlHrIn2H5t6Vh6kSvdbkcaNJ0ObM9YpdvTo9JtmAqSTgO+Z/vH+fw24FjgecDKtncpGd8wiUFhJXJp43HZPnFQscwpOTV2J9tfKB1LLyQtBZwFPMFY4+q1SQOqrWssNhOGU/5ZexFwve0nJS0O7AnsanvJstGNrtraTkxE0p3A3m2XDgb2aZ1E+mg5s+gh+3bbvxx8VP2TdNlE922/blCxhOaaKL1f0mXxczYmBoUVkrQAgO1HS8fSD0lvI/3sndxx/e3A07ZPLRNZfySdTSqUc0LH9XcA29reskhgoVEk7UlKIb8DmBc4mrTf6yTgq00p616j9ocOST+wvW3pmPol6fgJbtv2uwcWTJhJ9JANoXeSbra9Stv5oq2ekZ11E0ZdDAorIukDpOpc0/KlR4GDbB9dLqreSfo1sHHnoDZvLr/U9tplIuvPROlVkXoVpoqkm4ENcg+sZYHfAevbvnoW3xrmsI6Z52qLzIQ6SdqAlGWze+lYQhg2+Znz7bZ/13F9ZeAk2+uWiWz4RKGZSkj6FLAesKHtO/O15YHD86zHl4oG2JvndFvlzJvLa9yv1rXQTO4rV13/yDC0nmjNbtq+J084xIBwOHT2jgthjpK0FqnH73bAXaQtDCGEZ/ss8GNJXwauydfWJrXe2qNYVEMoVgorkTfGrtHZBFnS/MB1tlcqE1nvJN0CrNNZGEPSgsBvbK9cJrL+SDqUVMlqz9Zryqueh5Ie5KssAhSGi6QHScUYWnZsP4+fs3JmUZypurY0YThJWolUlG0nUtuDM4C9bb+4aGB9krRU7LkPg5Ar+O/LWDHAm0jbLmrvKz2lYlBYCUm3jjdYmujeMJK0N6mx6/tt352vvQQ4Cvi57YPLRde7vLp5ILArcHe+vCxwIvCJ2OsRpsIoFJsKIYwv98S9jNSS6o58reYevzMVAAkhlBXpo/W4X9LGti9sv5gbDVdVYML21yQ9ClzaKppD2h/5FdvHFAytL7afAvaW9GlgxXz597Yfn+DbQujJRIM+SfFeHuYYScfafl/pOALbkDIELpZ0HilTQGVDmpSaYw+VyHtul7d9Uj7/PqlqL8CXbF9ULLghEyuFlZC0KnAOcDljbQ/WAdYHtrR9U6nYJiOnjGL7kdKx9Ktp1VTDcJJ0ue0N8vHJtt/edi9m3MMcEz9fwyVvT9iSlEa6EakC8dm2zy8aWI9ySvwp4923vdcAwwkNJelC4MO2b87nN5Ayu6aRsrneVDC8oRKzy5WwfVPOid6ZsZzoS4H/7txnWJOaB4NtPkxKh+10FunfKAaFYSpMazteteNezLiHOenB0gGEMXnv+qnAqZIWIRWb2Q+oalAI/JO0tyuEOWl6a0CY3d4q0ibpwEIxDaUYFFYkD/6+WzqO8CxNq6YahtNEaR2R8lGQpGOA/Wz/o3Qsc0LMpA8v238Fjs1ftXnY9ndKBxEab+H2E9vbtJ2+cMCxDLUYFFZC0iN0f/CL6nblzS9p2jjVVJ9bKKbQPAtL2prUAmVhSa0PNgELlQsrAHcCV0v6bKSLhzDbnh7vhqQlbf9xkMGExrpV0lts/6T9oqQtgNsKBfmZoQAAEuFJREFUxTSUYk9hJST9EFiClJJ4RqtqZ1PUXMigadVUw3CSdPxE922/a1CxhGeTtBTwdeAFwDHAM617tqOHXAg9kHSP7WVLxxHqJ2lF4CfAL5m5T+F6wBadTe1HWQwKKyJpIcaqj81H6lF0equhdc1qL2Qg6f3Ax0n9CqHiaqohhP5IegfwZeAixgaFtv3uclH1RtJrbP+qdBxhtEm61/YypeMIzSBpXmAXZu5TeGrNNTnmhBgUVkjSXKSB4TeAA2x/vXBIkybpvCbsW2lCNdUwvCS9Afir7eslbQ+8Hvg9cLTtf5WNbnTl6tDHAH8EPmq7qjZB7SRdA/yGtEfyb6XjCaMpVgrDnJZbVexke/fSsQyLGBRWRNJ6pBLUryO1pjjD9mVlowohDIKko4DVSVkCt5FWpc8jtaWZy/YuBcMbaZJuAfa0/bPSsUxWnnT8CPBB4IudrXZCmCqSjmD8WgnvjFoJYapJWov0HL09cBdwlu0jykY1PGJQWAlJfwD+RmpWexHw7/b7tq/p8m0hhIaQdLPtVSTNB9wPLG77aUkCrrf9isIhjixJ8zZtpVbSKsAVpMJGJoqahSkm6Z0T3bd94qBiCc0laSXSQHAn4CHS1qu9bb+4aGBDKAaFlZD0c8YvO2/bGw0wnBDCgLXvu+3cg1v7ntzaSbqLmd+f1XZu2ysMPqr+SdoN2B84HDjK8aAQQqiUpGeAy4DdbN+Rr91pe/mykQ2faElRCdsblo5hqoxCIYOaq6mGobW4pL1IA47WMfl8sXJhBWCdjvO5SOlJewO/HXw4/ZP0S+APwOtsP1A4nNBgks5l5skUk1ZyLrZ9SpmoQgO1CjReLOk8UsadyoY0nGKlsBJtPcm6qqnk+SgUMoiVmzDVJH12ovu2Pz+oWEJ3eT/e24F9gGtJhcBuLhtVbyRtYvuCce49qx9rCP3KhbM6LQq8Dbjd9v4DDik0mKRpwJakNNKNgJOAs22fXzSwIRKDwkrk5e9r8xfMPMtRW8nzxhcyaEo11RDCrEl6DvBu4KOkImBfaaUp1Sj3XHwRaa/qk5IWB/YEdrW9ZNnoQtNJmhu42vaapWMJzSRpEWA7YAfbG5eOZ1jEoLASkrYiLX+vCJwDnFbzQwdEIYMQepELzOwA/BU4l7Qa1WpJ8UXbDxUMb6RJuo9U/Osw4J7O+5VlcuwJfBK4A5gXOBo4iDSr/tWa222Eeki6NgaFIQxWDAor07b8vQPwfOCTti8pG1XvopBBCL2RdCbwFDANWAS4kTQ43ABY0/YWBcMbaZJOYOJCYDVlctwMbGD7L5KWBX4HrG/76sKhhYaRtGiXy4sA7wBWjDY7IQxWFJqpzxPA34F/AC8m9SyrShQyCKEvq9heTdI8wH22W/txzpN0XcnARp3tXUvHMIWesP0XANv3SLotBoRhDrmasSwh8vHDwM+BDxSKKYSRFYPCSkjaiJQ+ui5wAXC47avKRtW3zzSpkMEoVFMNQ+FJANv/lvTHjntPF4gnZJIOs71nPt7D9uFt906obNC4tKRvtJ2/qP3c9kcKxBQayPZy493L+3RDCAMU6aOVyIVmricVMTAdqUq1fVA3qZDBKFRTDeVJepCxUto75GPy+fa2X1gqtlHXpB6S0VA8lCJJpKqQOwNbxHtaCIMVK4X1eFfpAKZKZyEDSe2FDNYuGVuf1iFVU71SUiOrqYahsE/bcWeWQK1ZA02hcY6rM9GgL6cuhzClJL2GNBDcitSSYndSj88QwgDFSmGFJC0AYPvR0rH0o6mFDKKaahiU2t8Dmibv6dyQ9Lt/UT5uDQ4vtr1Gmch6J+ly2xvk45Ntv73tXlWrnmG4STqA1BbgHuA04GzgqonSSkMIc85cpQMIs0/SByTdA9wN3C3pbkkfLB1XH2YqZABUX8ggV1M9h7QCOt32dNsLxoAwTKUGvQc0zUKkohlXAdOBa/L51cCCBePqx7S241U77lW9ChqGznuA/wOOAU62/TDjV/ENIcxhkQpSCUmfAtYDNrR9Z762PHC4pEVtf6logL1pVCGDqKYaBqFh7wGNYvslpWOYQhM9lMcDe5hKLwI2BXYCDpN0MTC/pHls/7tsaCGMnkgfrYSk24A1bD/RcX1+4DrbK5WJrHdNK2QgaZMmVVMNw6lJ7wFNI2lzYEHb3++4vi3wD9v/Wyay3km6E/gYKZPoYMb2donUvH6FUrGF5pI0L7AFaW/hBsCFtncuG1UIoyUGhZWQdKvtlXu9V5taZwibVE01DKdReQ+okaRfAFvZ/nPH9RcA59p+bZnIeifp+Inu225M0bMwnCQtCGxt+6TSsYQwSiJ9tB73S9rY9oXtF3P/wj8ViqkvExUyAK4Eqipk0MBqqmE4NeY9oIHm7RwQAth+SNK0bt8wrGLQFwZF0ltJE6l35/PPANuS9kzvUTK2EEZRDArr8RHgHEmXk4oXQGqFsD6wZbGo+tO0QgbvA17WtGqqYeg06T2gaaZ3y3LIDbjnLxRT3yS9Afir7eslbQ+8Hvg9cLTtf5WNLjTIl4HXAEjaAngbaX/hWsA3gc3LhRbC6Inqo5WwfROwGnAp8JL8dSmwWr5Xk6YVMmhcNdUwfBr2HtA0ZwHfbl8VzG1DvpnvVUPSUcCXgO9IOoW0x+tGUgbHd0vGFhrHth/Px9sA37F9te3jgMUKxhXCSIqVwkpIWhF4oe3vdlxfX9IDtn9fKLR+LCxpa9KkxMKStsnXRSrtXptGVVMNwysXmel8D5hL0i62v1corACfIg2k7pZ0d762LPAd4NPFourPG22vImk+4H5gcdtPS/oWcH3h2EKzKE+ePA5sDBzddm++MiGFMLpiUFiPw4CPd7n+j3zvrYMNZ1IuAf6z7bg99ksHH86k7dNxHquEYcpJmg7sDixF6ol5QT7fG7gOiEFhITltdH9JnwdWzJfvsP3PgmH16wlIExCS7rb9dD63pKfKhhYa5jDgWtJzzC22rwKQtBaxTzqEgYvqo5WQ9Bvbrxrn3g22XzHomMKs1VpNNQwfSecAfwWuIM2qL05aXd/D9rUlYwvdSdoU2Nf2pqVjmV2S7gO+TvrZ+mg+Jp/vaXuZUrGF5smVuxcntdV5Jl9bAnhu3o4RQhiQWCmsx8IT3ItCBgU1rZpqGFrLtyZ/JB1HmklftrNvYRi8XAH2m8CSwA9J1YePJw2kvlwwtH58G1iwyzHAcYMPJzSVpBcDf7N9fz5/I7AVqfrokSVjC2EUxaCwHldJeq/tb7dflPQeKktXzIUMVgfmyw25FwDOI1VR/C6wS8Hw+tG0aqphOM1I3ct7vO6LAeHQOIRUhfgK4M35v/vbru7B1vbnS8cQRsaZwNbA3yWtCfwPcCCwBml/4XsKxhbCyIn00UpIeiFwNvAkM5ejfy6pyesDpWLrlaSbxylkIFLPoqpSYSVdY/uVncfdzkPol6Sngcdap6QMgcfzsW1PLxXbqOvye3+b7ZeVjKlf+X15B1Kq8rmkPdOtTI4v2n6oYHihQSRdb3v1fPw14Bnb+0qaC7i2dS+EMBixUlgJ2/8HrJfTK1bLl39i+6KCYfWraYUMmlZNNQwh23OXjiGMq/33HmCe9nPbNbWlOIm0Kj0N+BipHcWRwAbACcAWxSILTdOeSbMRuZie7WfSHHEIYZBipTAMXNMKGUg6fqL7tt81qFhCCIM3i/cA2373wIKZJEk32l5N0jzAfbaXaLt3ne01CoYXGkTS4cCLSPuj/xNYyfZTkl4EnGt7naIBhjBiYlAYBk7SZye6H3taQghNIemFOdOjCpEOHwYlbxnZgTQwPLOt4MxawGK2zy8ZXwijJgaFIUyBJlVTDSFMjqSFgW2BnYGX216ycEizTdKDwOmkzI0d8jH5fHvbLywVWxgNkl4H7Gh799KxhDBKYlAYBq5phQzaq6kCndVU57JdWzXVEEKPJM0PbEkaCK5FauWwFXBpq/9aDSS9c6L7tk8cVCxhdOTVwZ2B7YC7gB/UWL03hJrFoDAMnKQzGStksAipkMG5pEIGa9quqpBB06qphhB6I+lU4HXA+aSVtYuAO2wvVzSwSZK0AIDtR0vHEppH0krATvnrIeAMYG/bLy4aWAgjKqqPhhJW6Shk8IZ8/TxJ15UMrE9Nq6YaQujNKqTMh1uAW/KkULUzrpI+QKoEOS2fPwocZPvoooGFprkVuAzYwvYdAJI+WjakEEZXDApDCU8C2P63pD923Hu6QDyTtbikvUh7blrH5PPFyoUVQhgE22tKWpm04nGBpIeABWsrMgMg6VPAesCGtu/M15YHDpe0qO0vFQ0wNMk2wI7AxZLOY2wvawihgEgfDQPXtEIGUU01hNBO0tqkAeL2pGyI9QqHNNsk3QasYfuJjuvzA9fZXqlMZKGpJE0j7cfdidSv8CTg7Kg+GsJgxaAwDFwUMgghjAJJcwGfsv2F0rHMLkm32l6513shTAVJi5CKzexge+PS8YQwSmJQGIpqQiGDplVTDSFMHUn32F62dByzS9KFwAG2L+y4vhHwadtvLBNZCCGEOSkGhaGIzkIGQLWFDJpWTTWEMHUk3Wt7mdJxzC5JqwLnAJcDV+fL65Ba7Gxp+6ZSsYUQQphzYlAYBq6tkMGHOgsZAL+urZCBpBs7qqku0XbvOttrFAwvhFBQbSuFMCP7YWdg1XzpZuB7nfsMQwghNEcMCsPANa2QgaRrbL+y87jbeQiheSQ9AnT7MBUwv+3qK33n/ZE72f5e6VhCCCFMveo/qEKV3G3G2fY/JT1TIqBJWlrSN0gPgK1j8vlS5cIKIQyC7QVLxzBVJE0Hdie9d50DXJDP9wauA2JQGEIIDRSDwlDC/ZI2HqeQwZ8KxTQZ+7QdX9Vxr/M8hBCG2cmkollXAO8FPkma4NrK9rUlAwshhDDnRPpoGLgmFzJoQjXVEMLoknSD7Vfk47lJE3XLxn7CEEJotrlKBxBGTx70rQZcCrwkf10KrFbrgFDSByTdA9wN3C3pbkkfLB1XCCH06KnWge2nScWzYkAYQggNFyuFYWjUWsigadVUQwijS9LTwGOtU2B+4PF8bNvTS8UWQghhzolBYRi4WRUysL1lwfB61rRqqiGEEEIIYbREoZlQQtMKGTStmmoIIYQQQhghMSgMJSzfVsjgOOovZNC0aqohhBBCCGGExKAwlDBTIQNJtRcy+AhwjqSu1VSLRRVCCCGEEMJsiD2FYeCaWMhA0nzAzsCq+dLNwPcqH+yGEEIIIYQREIPCEOaQWquphhBCCCGE0RJ9CkOYJEnTJX1c0pGSNlXyIeBOYPvS8YUQQgghhDCRWCkMYZIkncNYNdWNgcVJqbB7VFpNNYQQQgghjJAYFIYwSZJuaKumOjf1V1MNIYQQQggjJNJHQ5i8maqpArVXUw0hhBBCCCMkVgpDmKQmVlMNIYQQQgijIwaFIYQQQgghhDDCIn00hBBCCCGEEEZYDApDCCGEEEIIYYTFoDCEEEIIIYQQRlgMCkMIIYwESUtIOl3S7yVdLemnklYa58/+cor/7s0lXZu/HpV0Wz4+aSr/nhBCCKEfUWgmhBBC40kS8EvgRNvfzNfWAKbbvqztz81j+99zOJafA3vbvmpO/j0hhBDC7IqVwhBCCKPgjcBTrQEhgO3rbF8maUNJl0n6EXAzgKRH8383lHSJpHMk3SnpK5J2kXSlpBskrZD/3GKSfiDpN/lr/dkNTNIvJa3Wdv4rSatK+pKkE/P57ZLe3fZn9s8xXC/pM5P/vyeEEMIom6d0ACGEEMIArAZcPcH9VwKr2b6ry701gJcDfwHuBI6zva6kPYAPA3sChwOH2r5c0rLAz/L3zI7vALsCe0tahZTFc1Na3OQVwHrAdOAaST8B1gaWBV5N6of6U0nr2Z7SlNcQQgijIwaFIYQQAlw5zoAQ4De2/wQg6ffA+fn6DaQVSIBNgFXyQA5guqQFbD86G3/3GcBvJe0PvBs4vu3eD20/ATwh6VLgVfnvejPw2/xnFgBWIqXHhhBCCD2LQWEIIYRRcBPwXxPcf2yCe/9qO36m7fwZxj5H5wJekwdwPbH9aN5n+J/AtsCa7bc7/zhpdfBLtr/T698VQgghdBN7CkMIIYyCi4B5Jb2vdUHS6pJeN0X/++eTUklb/9trTvBnuzkOOBL4pe2/t13fStK8khYDXgdcRUpN3U3StPx3LS3pBZOKPoQQwkiLQWEIIYTGcyq1vTWwSW5JcRNwIPDAFP0VHwHWyYVfbgbe32N8vwYeZ+bUUYAbgUtIqaGftf1/tn8KfB/4laQbgDNJKaQhhBBCX6IlRQghhFCYpGWA/wVengewSPoS8JDtw4oGF0IIofFipTCEEEIoSNK7SCuBn3DM1IYQQiggVgpDCCGEEEIIYYTFSmEIIYQQQgghjLAYFIYQQgghhBDCCItBYQghhBBCCCGMsBgUhhBCCCGEEMIIi0FhCCGEEEIIIYyw/w8NhZaGCLEmSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.bar(crime_freq.keys(), crime_freq.values())\n",
    "plt.xlabel('Crime Type')\n",
    "plt.ylabel('Crime Frequency')\n",
    "plt.title('Frequency of Crimes v/s Crime Type')\n",
    "for x, y in zip(crime_freq.keys(), crime_freq.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As seen from plot above, `Larceny` is the most frequent crime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'00:00-00:59': 50,\n",
       " '01:00-01:59': 67,\n",
       " '02:00-02:59': 26,\n",
       " '03:00-03:59': 21,\n",
       " '04:00-04:59': 25,\n",
       " '05:00-05:59': 24,\n",
       " '06:00-06:59': 24,\n",
       " '07:00-07:59': 50,\n",
       " '08:00-08:59': 73,\n",
       " '09:00-09:59': 92,\n",
       " '10:00-10:59': 116,\n",
       " '11:00-11:59': 137,\n",
       " '12:00-12:59': 134,\n",
       " '13:00-13:59': 119,\n",
       " '14:00-14:59': 127,\n",
       " '15:00-15:59': 145,\n",
       " '16:00-16:59': 160,\n",
       " '17:00-17:59': 159,\n",
       " '18:00-18:59': 140,\n",
       " '19:00-19:59': 114,\n",
       " '20:00-20:59': 106,\n",
       " '21:00-21:59': 109,\n",
       " '22:00-22:59': 75,\n",
       " '23:00-23:59': 60}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_freq_crime_data = baltimore[baltimore['Description'] == 'LARCENY']\n",
    "freq_crime_time_dict = {}\n",
    "larceny_lats, larceny_longs = [], []\n",
    "for index, row in most_freq_crime_data.iterrows():\n",
    "    hour = row['CrimeTime'].split(':')[0]\n",
    "    lat, long = row['Location 1'].strip('()').split(',')\n",
    "    larceny_lats.append(float(lat))\n",
    "    larceny_longs.append(float(long.strip()))\n",
    "    if len(hour) > 2:\n",
    "        hour = hour[:2]\n",
    "    hour_key = hour + ':00-' + hour + ':59'\n",
    "    if hour_key not in freq_crime_time_dict.keys():\n",
    "        freq_crime_time_dict[hour_key] = 1\n",
    "    else:\n",
    "        freq_crime_time_dict[hour_key] += 1\n",
    "\n",
    "freq_crime_time_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.bar(freq_crime_time_dict.keys(), freq_crime_time_dict.values())\n",
    "plt.xlabel('Time (24Hr Format)')\n",
    "plt.ylabel('Crime Frequency')\n",
    "plt.title('Frequency of LARCENY v/s Time')\n",
    "for x, y in zip(freq_crime_time_dict.keys(), freq_crime_time_dict.values()):\n",
    "    plt.text(x, y, str(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It can be seen that the most common crime, i.e. `Larceny`, happens most at `16:00 - 17:59`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"index1.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
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       "<IPython.lib.display.IFrame at 0x7f8de98c6470>"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "gmap = gmplot.GoogleMapPlotter(39.299236, -76.609383, 13)\n",
    "gmap.heatmap(larceny_lats, larceny_longs)\n",
    "gmap.draw('./index1.html')\n",
    "\n",
    "IFrame(src = \"index1.html\", width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the heatmap we can see that `Larceny` crime is more concentrated in the centre of Baltimore. \n",
    "### There is a bit of redness near `Wendy's` and `Five Below`.\n",
    "### A bit of yellowness can be found on the `East Side` of Baltimore."
   ]
  }
 ],
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