Time Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from utils import *\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load and setup dataset\n",
    "crimes_df = pd.read_csv('MontgomeryCountyCrime2013.csv')\n",
    "dataset_conversion(crimes_df)\n",
    "classify_occurrences(crimes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hourly_crimes_df = crimes_df[['Category', 'Dispatch Date / Time']].copy()\n",
    "hourly_crimes_df['Hour'] = hourly_crimes_df['Dispatch Date / Time'].dt.hour\n",
    "hourly_crimes_df['Day Part'] = hourly_crimes_df['Hour'].apply(lambda h: 'Day' if 6 < h < 18 else 'Night')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aggravated assault</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arson</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>78</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>72</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>79</td>\n",
       "      <td>92</td>\n",
       "      <td>88</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto theft</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bad checks</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burglary</th>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>79</td>\n",
       "      <td>66</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disordely conduct</th>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>196</td>\n",
       "      <td>137</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "      <td>103</td>\n",
       "      <td>124</td>\n",
       "      <td>183</td>\n",
       "      <td>165</td>\n",
       "      <td>256</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embezzle</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family offenses</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forgery</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>156</td>\n",
       "      <td>105</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>42</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homicide</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juvenile</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larceny</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>463</td>\n",
       "      <td>566</td>\n",
       "      <td>473</td>\n",
       "      <td>441</td>\n",
       "      <td>...</td>\n",
       "      <td>383</td>\n",
       "      <td>400</td>\n",
       "      <td>397</td>\n",
       "      <td>371</td>\n",
       "      <td>308</td>\n",
       "      <td>241</td>\n",
       "      <td>222</td>\n",
       "      <td>158</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquor laws</th>\n",
       "      <td>37</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>61</td>\n",
       "      <td>53</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous</th>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>62</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>183</td>\n",
       "      <td>220</td>\n",
       "      <td>222</td>\n",
       "      <td>234</td>\n",
       "      <td>...</td>\n",
       "      <td>222</td>\n",
       "      <td>193</td>\n",
       "      <td>197</td>\n",
       "      <td>171</td>\n",
       "      <td>177</td>\n",
       "      <td>172</td>\n",
       "      <td>201</td>\n",
       "      <td>154</td>\n",
       "      <td>115</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous traffic</th>\n",
       "      <td>257</td>\n",
       "      <td>321</td>\n",
       "      <td>298</td>\n",
       "      <td>136</td>\n",
       "      <td>58</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>83</td>\n",
       "      <td>120</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other offenses</th>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>56</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robbery</th>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex offenses</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stolen property</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vandalism</th>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>92</td>\n",
       "      <td>125</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "      <td>65</td>\n",
       "      <td>42</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vice</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon_offenses</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour                   0    1    2    3   4   5    6    7    8    9  ...   \\\n",
       "Category                                                             ...    \n",
       "Aggravated assault     14   10   13    5   2   6    2    0    0    3 ...    \n",
       "Arson                   0    2    0    4   1   0    0    2    1    0 ...    \n",
       "Assault                78   62   59   35  21  17   19   24   32   57 ...    \n",
       "Auto theft              5    7    7    6   7   8   19   29   37   42 ...    \n",
       "Bad checks              0    1    0    1   0   0    1    0    0    1 ...    \n",
       "Burglary               27   23   25   22  26  25   55   67   73   80 ...    \n",
       "Disordely conduct      57   64   29   12   4   2    4    5    1    5 ...    \n",
       "Drugs                 196  137   63   48  19  36   10   19   30   51 ...    \n",
       "Embezzle                1    1    1    0   1   1    0    5    6    7 ...    \n",
       "Family offenses         2    3    1    0   0   1    5    4    9    3 ...    \n",
       "Forgery                 3    4    2    1   0   0  181  156  105   86 ...    \n",
       "Homicide                0    0    0    1   0   0    0    1    0    0 ...    \n",
       "Juvenile               10    7    2    1   3   4    4    3   15    9 ...    \n",
       "Larceny                41   41   36   21  21  34  463  566  473  441 ...    \n",
       "Liquor laws            37   18   14    4   1   2    2    5    4    5 ...    \n",
       "Miscelaneous           86   68   62   38  30  24  183  220  222  234 ...    \n",
       "Miscelaneous traffic  257  321  298  136  58  27   12    7    5    1 ...    \n",
       "Other offenses         25   28   17   16   6   6    8   24   26   44 ...    \n",
       "Rape                    1    2    0    1   0   0    0    0    2    1 ...    \n",
       "Robbery                25   18   17    8  13   9    3    5    5    7 ...    \n",
       "Sex offenses            3    4    3    2   2   1    6    5    5    9 ...    \n",
       "Stolen property         0    0    0    0   0   0    2    0    0    0 ...    \n",
       "Suicide                 4    7    2    2   3   2    5    3    4    3 ...    \n",
       "Vandalism              23   20   26    9   7  15   92  125  111  120 ...    \n",
       "Vice                    0    0    1    0   0   0    0    1    0    4 ...    \n",
       "Weapon_offenses         9    7    6    3   3   2    0    2    8    9 ...    \n",
       "\n",
       "Hour                   14   15   16   17   18   19   20   21   22   23  \n",
       "Category                                                                \n",
       "Aggravated assault     11   10    7    5    7    5   11   12   11   10  \n",
       "Arson                   1    2    1    0    0    1    2    1    3    2  \n",
       "Assault                60   72   87   65   78   78   79   92   88   83  \n",
       "Auto theft             26   20   14   15   18   21   14    5    8   11  \n",
       "Bad checks              2    2    3    1    2    1    1    2    0    0  \n",
       "Burglary               76   79   66   68   65   66   46   48   49   45  \n",
       "Disordely conduct       7   19   22   20   17   31   16   27   41   45  \n",
       "Drugs                  70   74   71   87  103  124  183  165  256  269  \n",
       "Embezzle               14   20   10   14   24    7    5    5    3    3  \n",
       "Family offenses        10    5   16    9    8   12    8    3    5    4  \n",
       "Forgery                73   76   63   62   50   42   18   13    9    8  \n",
       "Homicide                1    0    0    0    0    0    0    0    1    0  \n",
       "Juvenile               10   19   16   17   19   13    5   21   24   23  \n",
       "Larceny               383  400  397  371  308  241  222  158   97   49  \n",
       "Liquor laws            22   28   48   51   61   53   50   64   65   68  \n",
       "Miscelaneous          222  193  197  171  177  172  201  154  115  101  \n",
       "Miscelaneous traffic   12   11   14   28   27   30   51   83  120  208  \n",
       "Other offenses         41   48   61   46   56   34   43   56   45   44  \n",
       "Rape                    4    1    3    3    2    0    3    0    1    1  \n",
       "Robbery                14   12   23   14   20   26   25   25   22   24  \n",
       "Sex offenses           14    8    7    9    8    7    4   10    6    5  \n",
       "Stolen property         0    0    0    1    0    0    1    0    0    0  \n",
       "Suicide                 4    6   13    8   11    9    5    7    7    6  \n",
       "Vandalism              55   69   65   42   49   50   63   54   36   22  \n",
       "Vice                    2    2    0    0    2    1    1    0    0    1  \n",
       "Weapon_offenses         8    3    7    8    7    6    8    7   10    7  \n",
       "\n",
       "[26 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_hour_df = hourly_crimes_df[['Category', 'Hour']]\n",
    "cat_by_hour_pt = pd.pivot_table(cat_hour_df, index='Category', columns='Hour', aggfunc=len, fill_value=0)\n",
    "cat_by_hour_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d11bd47b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAIDhKa8AAAAMT3kFAABgeMorAAAAw1NeAQAAGJ7yCgAAwPCUVwAAAIanvAIAADA85RUA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\nSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d151afef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_by_hour_pt.sum().plot.bar(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Day Part</th>\n",
       "      <th>Day</th>\n",
       "      <th>Night</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aggravated assault</th>\n",
       "      <td>60</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arson</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>591</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto theft</th>\n",
       "      <td>270</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bad checks</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burglary</th>\n",
       "      <td>778</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disordely conduct</th>\n",
       "      <td>119</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>622</td>\n",
       "      <td>1609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embezzle</th>\n",
       "      <td>123</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family offenses</th>\n",
       "      <td>94</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forgery</th>\n",
       "      <td>950</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homicide</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juvenile</th>\n",
       "      <td>138</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larceny</th>\n",
       "      <td>4545</td>\n",
       "      <td>1732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquor laws</th>\n",
       "      <td>202</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous</th>\n",
       "      <td>2370</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous traffic</th>\n",
       "      <td>104</td>\n",
       "      <td>1628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other offenses</th>\n",
       "      <td>436</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robbery</th>\n",
       "      <td>123</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex offenses</th>\n",
       "      <td>99</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stolen property</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide</th>\n",
       "      <td>64</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vandalism</th>\n",
       "      <td>872</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vice</th>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon_offenses</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Day Part               Day  Night\n",
       "Category                         \n",
       "Aggravated assault      60    108\n",
       "Arson                   13     16\n",
       "Assault                591    789\n",
       "Auto theft             270    136\n",
       "Bad checks              31      9\n",
       "Burglary               778    522\n",
       "Disordely conduct      119    349\n",
       "Drugs                  622   1609\n",
       "Embezzle               123     52\n",
       "Family offenses         94     52\n",
       "Forgery                950    331\n",
       "Homicide                 2      2\n",
       "Juvenile               138    136\n",
       "Larceny               4545   1732\n",
       "Liquor laws            202    439\n",
       "Miscelaneous          2370   1411\n",
       "Miscelaneous traffic   104   1628\n",
       "Other offenses         436    384\n",
       "Rape                    30     11\n",
       "Robbery                123    235\n",
       "Sex offenses            99     61\n",
       "Stolen property          5      3\n",
       "Suicide                 64     70\n",
       "Vandalism              872    466\n",
       "Vice                    21      6\n",
       "Weapon_offenses         75     75"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_daypart_df = hourly_crimes_df[['Category', 'Day Part']]\n",
    "cat_by_daypart_pt = pd.pivot_table(cat_daypart_df, index='Category', columns='Day Part', aggfunc=len, fill_value=0)\n",
    "cat_by_daypart_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggravated assault\n"
     ]
    },
    {
     "data": {
      "image/png": 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MJUQBAAAYSogCAAAwlBAFAABgKCEKAADAUEIUAACAoYQoAAAAQ+2bewAA9m7ryEUrW9ex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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d156b06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arson\n"
     ]
    },
    {
     "data": {
      "image/png": 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vqhuTvCjJliTp7vOSnJrkWVV1R5LPJTmtu3u/JQYAAGDTWLeUdvdT1ll+TmZfGQMAAAD7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\nAADAMP8fJXnVom0SvrgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d150d98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assault\n"
     ]
    },
    {
     "data": {
      "image/png": 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fQ5N8Tnf/2epHBYD1JEQB4Pb52+5+yOErVfXEJHumqw9P8vXT5ZcmedYS63uz\nCAXgdOPQXAA4+W7N9De3qu6U5M7bbvvrWSYCgBkJUQBYnT9I8oTp8n9I8qbp8sEknz9dfkySM8eO\nBQDrRYgCwOpcmuRJVfX2JN+a5GnT8l9M8oiq+qMsDt+1FxSA01p199wzAAAAcBqxRxQAAIChhCgA\nAABDCVEAAACGEqIAAAAMJUQBAAAYSogCAAAwlBAFAABgKCEKAADAUP8fG5I44lY61cMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15acf240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Auto theft\n"
     ]
    },
    {
     "data": {
      "image/png": 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7b+zuT3X3p5O8OMk5q40FAADAVnVcRbSqzlr38NFJrjnSvAAAALDeUc8RrapXJXlwkrtX\n1UeSPCPJg6vq7CSdZH+Sp25gRgAAALaQoxbR7n78YUa/ZAOyAAAAcAo43osVAQAAwHFRRAEAABhK\nEQUAAGAoRRQAAIChjnqxIgAATh47du9d2bL279m1smVx8vI7xUawRxQAAIChFFEAAACGUkQBAAAY\nShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAAgKEUUQAAAIZSRAEAABhKEQUAAGAo\nRRQAAIChFFEAAACGUkQBAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAAgKEU\nUQAAAIZSRAEAABhKEQUAAGAoRRQAAIChFFEAAACGUkQBAAAYShEFAABgKEUUAACAobbNHQAAAICN\nsWP33pUsZ/+eXStZzkH2iAIAADCUIgoAAMBQiigAAABDKaIAAAAMpYgCAAAw1FGLaFW9tKpuqqpr\n1o27a1VdVlXXTfdnbGxMAAAAtopl9oi+PMm5h4zbneTy7r5vksunxwAAAHBURy2i3f22JJ84ZPQj\nk1w0DV+U5FErzgUAAMAWdbzniJ7Z3TdMwx9NcuaRZqyq86tqX1XtO3DgwHG+HAAAAFvFCV+sqLs7\nSd/K9Au7e2d371xbWzvRlwMAAOAkd7xF9MaqOitJpvubVhcJAACArex4i+ilSc6bhs9Lcslq4gAA\nALDVLfP1La9K8odJvqiqPlJVT0myJ8nDquq6JF83PQYAAICj2na0Gbr78UeY9NAVZwEAAOAUcMIX\nKwIAAIBjoYgCAAAwlCIKAADAUIooAAAAQymiAAAADKWIAgAAMJQiCgAAwFCKKAAAAEMpogAAAAyl\niAIAADCUIgoAAMBQiigAAABDKaIAAAAMpYgCAAAw1La5A8CpaMfuvStb1v49u1a2LAAAjt+q1vFO\nhfU7e0QBAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAAgKEUUQAAAIZSRAEA\nABhq29wBTgY7du9d2bL279m1smWtKtcqM21Gm/XnBwAApyp7RAEAABhKEQUAAGAoRRQAAIChFFEA\nAACGUkQBAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGGrb3AHYWnbs3ruS5ezfs2slywEAADYf\ne0QBAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGOqErppbVfuT/G2STyW5pbt3riIUAAAAW9cq\nvr7lP3X3x1awHAAAAE4BDs0FAABgqBMtop3kTVX17qo6fxWBAAAA2NpO9NDcr+7u66vqc5NcVlUf\n7O63rZ9hKqjnJ8n27duPusAdu/eeYKR/sX/PrpUtCwAA2Bx0hpPfCe0R7e7rp/ubkrwmyTmHmefC\n7t7Z3TvX1tZO5OUAAADYAo67iFbVHarqTgeHk3x9kmtWFQwAAICt6UQOzT0zyWuq6uByfq2737CS\nVAAAAGxZx11Eu/tDSb50hVkAAAA4Bfj6FgAAAIZSRAEAABhKEQUAAGAoRRQAAIChFFEAAACGUkQB\nAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAAgKEUUQAAAIZSRAEAABhKEQUA\nAGAoRRQAAIChFFEAAACGUkQBAAAYShEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAA\ngKEUUQAAAIZSRAEAABhKEQUAAGAoRRQAAIChFFEAAACGUkQBAAAYShEFAABgKEUUAACAoRRRAAAA\nhlJEAQAAGEoRBQAAYChFFAAAgKEUUQAAAIZSRAEAABhKEQUAAGAoRRQAAIChFFEAAACGOqEiWlXn\nVtUfV9WfVtXuVYUCAABg6zruIlpVpyX5hSTfmOT+SR5fVfdfVTAAAAC2phPZI3pOkj/t7g919z8l\neXWSR64mFgAAAFtVdffxPbHqMUnO7e7vnB4/IcmXd/f3HjLf+UnOnx5+UZI/Pv64n+HuST62omWt\nikzLkWl5mzGXTMuRaXmbMZdMy5FpeZsxl0zLkWl5mzGXTMtZZabP6+61o820bUUvdkTdfWGSC1e9\n3Kra1907V73cEyHTcmRa3mbMJdNyZFreZswl03JkWt5mzCXTcmRa3mbMJdNy5sh0IofmXp/k3use\n32saBwAAAEd0IkX0XUnuW1WfX1WfleRxSS5dTSwAAAC2quM+NLe7b6mq703ye0lOS/LS7n7/ypId\n3coP910BmZYj0/I2Yy6ZliPT8jZjLpmWI9PyNmMumZYj0/I2Yy6ZljM803FfrAgAAACOx4kcmgsA\nAADHTBEFAABgKEUUAACAoTb8e0RXoaq+OMkjk9xzGnV9kku7+9r5Um1O03t1zyRXdPfN68af291v\nmCnTOUm6u99VVfdPcm6SD3b36+bIczhV9Svd/cS5cxxUVV+d5Jwk13T3G2fK8OVJru3uT1bV7ZLs\nTvLAJB9I8tPd/Tcz5fq+JK/p7r+Y4/UPZ92Vw/+yu99UVd+e5D8muTbJhd39/2bKdZ8k35zFV219\nKsmfJPm17v7kHHkAAA7a9HtEq+pHk7w6SSV553SrJK+qqt1zZjuSqvqOmV73+5JckuSCJNdU1SPX\nTf7pmTI9I8nPJXlRVf1Mkp9Pcocku6vqx2fKdOkht99N8s0HH8+U6Z3rhr8ri/fpTkmeMePv+UuT\n/P00/Pwkd07yrGncy2bKlCQ/leSKqnp7VX13Va3NmOWglyXZleRpVfWKJN+a5Iok/yHJL88RaPo8\n+MUknz3luG0WhfSPqurBc2SCEarqc+fOcLKoqrvNnYGTU1Xduar2VNUHq+oTVfXxqrp2GneXufMd\nqqpeP+Nrf05V/UxVvWLaUL1+2gtnynSPqnpRVf1CVd2tqp5ZVVdX1cVVddawIN29qW9ZbME//TDj\nPyvJdXPnO0LmP5/pda9OcsdpeEeSfUmeNj1+74yZTkty+ySfTPI50/jbJblqpkzvSfKrSR6c5EHT\n/Q3T8INmyvTedcPvSrI2Dd8hydUzZbp2/Xt2yLQr58h08L3KYiPa1yd5SZIDSd6Q5Lwkd5op01XT\n/bYkNyY5bXpcM/6eX70ux+2TvHUa3j7X58H0+ndOsifJB5N8IsnHs9hzvCfJXebKdSt5Xz/T635O\nkp9J8ook337ItBfOlOkeSV6U5BeS3C3JM6ffs4uTnDVTprsecrtbkv1Jzkhy1xl/b85dN3zn6bPq\nqiS/luTMmTLtSXL3aXhnkg8l+dMkH57xf997kjw9yRfM9bM6TKadSd4yrSfcO8llSf5m+t/8gJky\n3THJTyZ5/5TlQJI/SvKkGd+n30vyo0nusW7cPaZxb5wp0wOPcPuyJDfM+F791vT396gkl06PbztN\ne89Mmd6QxY6r3dNn049Ov+8XJLlkVI6T4dDcTyf5N1l8UK531jRtFlV11ZEmJTlzZJZ1btPT4bjd\nvX/a6/GbVfV5U6453NLdn0ry91X1f3o6JLC7/6Gq5vr57UzytCQ/nuSHu/vKqvqH7v79mfIkyW2q\n6owsCtZp3X0gSbr776rqlpkyXVNV39HdL0vyvqra2d37qup+SWY51HTS3f3pJG9M8saqOj3JNyZ5\nfJLnJJljD+ltpsNz75BF6btzFiXrtklOnyHPQduyOCT3tlmsyKS7/3x6z+ZycZI3J3lwd380WWyZ\nzWJDwsVZbGAYqqoeeKRJSc4emWWdlyW5LosVlidX1bdkUUj/MclXzJTp5Un2ZvF7/pYkr0zy8CxW\nrn4xi1NoRvtY/vX6wT2zKDid5D7DEy38dBYrekny3Cw2dv7nLA6V/6Us3rPRdnX3wSNs/leSx/bi\nlJn7ZVGQd86Q6Ywkd0nylqr6aJJXJfn17v7LGbIc9MIkz5hy/UGSH+juh1XVQ6dpXzlDplcmeU2S\nb0jybVn8Db46ydOr6n7d/WMzZNrR3c9aP2L6TH9WVT15hjzJYmPB7+fw67xz7qX9gu7+lmn4d6Yj\nAt9cVY+YMdOZ3f2CJKmq7173s3xBVT1lVIiToYh+f5LLq+q6JAfPCdue5AuTfO9sqRZl8xuS/NUh\n4yuLD6453FhVZ3f3lUnS3TdX1TdlcYjlv5sp0z9V1e27+++z2CKVZHFIR2bakDCVmOdV1W9M9zdm\n/r+FOyd5dxa/P11VZ3X3DVV1x8y3EeE7kzy/qp6excreH1bVX2Txd/idM2VKDnk/enH+5aVJLq2q\n288TKS/JYg/faVls4PiNqvpQFoXh1TNl+uUk76qqK5J8TRaHVWc6lPkTM2VKrLwsy4rLcn44ycOy\n2Kh49ZTtz7r782fKczg7u/vgBo3nVdV5M+XYVlXbuvuWJLfr7nclSXf/SVXddqZMf9XdP5Tkh6rq\na7LYoPieqro2yau6+8IZMp3e3a9Pkqp6Vnf/ZpJ09+VV9ZwZ8iSLz82XT8M/W1Xv6u6fmk4F+0CS\nOYroh6vqR5Jc1N03JklVnZnkSfmX9fXRrk3y1O6+7tAJ0/rLXG5bVbeZ1j/T3f+zqq5P8rZMG4ln\nsP70zF85ZNppo0LMvfJ9VN39hmlr3Tn5zIsVvWva0zaX12ZxGOyVh06oqreOj5MkeWKSz9h7Nv3D\neWJV/dI8kfK10xb8gwXwoNOz2AMym+7+SJJvrapdWRw2PGeWHUeY9Okkjx4Y5Z/14mJET6qqz0ny\n+Vl8Xnzk4D+cGT32SBOmDR7DdffzqurXp+G/rKpfSfJ1SV7c3e+89WdvWKbnV9WbkvzbJM/t7g9O\n4w8k+do5Mk2svCzHissSuvu509/e86af1TOy2BM6t8+tqv+excaNO1dV9XQ8XOa7PscLk7yuqvYk\neUNVPT/Jbyd5SJJ/tS4zWne/Pcnbq+qCLDYuPDbJHEX0/1bV12exgbir6lHd/TtV9aAsjjCZw99V\n1Vd39zumjVGfSBbrVVU118bqx2ZxWOfvT5/hncWpKZdmsdd2Ds/Mkf++LhiY41C/m8Xf2ZsOjuju\nl09HAbxgpkyXVNUdu/vm7n76wZFV9YVJ/nhUiPqXz0UA2FjTIei7sziM8+BFZQ6uvOzp7kOPMhmR\n6TFZnIv9r/75HlwJnSHTs7M4z+pNh4w/N8kLuvu+M2T6ySTP7nVXZJ/Gf2EWP7vHjM50SI5HZLFn\naEd332PmLM84ZNQLu/vAdBj6s3umq7RPp+z8tyT3y2Lj4l8k+Z0kL502XI/O8+ruftzo1701VfWl\nSZ6dxYbgH8ji/Tovi50g39Xdw496q6p/n8VRLvfN4jzRJ097steSPL67f250pinXFye5V5I/6s3z\nTQ2b7tsjjpLrGw/ugd9EmYa9V4ooAJvCunOSNw2ZlrNZMtXiq6a+oLuv2SyZDrUZc8m0HJk+43W/\nL8n3ZHFEydlZXBzzkmnae7r7SOfen1KZpte+IIvTCTdNrs2SSREFYFOoqj/v7u1z51hPpuXItLzN\nmEum5cj0Ga97dZKvnK5HsiPJbyZ5xXRayHu7+wEybd5cmyXTpj9HFICtozbhFcdlWo5My9uMuWRa\njkxL24zf1LAZM23WXJsikyIKwEib8YrjMi1HpuVtxlwyLUem5WzGb2rYjJk2a65NkUkRBWCkzXjF\ncZmWI9PyNmMumZYj03I24zc1bMZMyebMtSkyOUcUAACAoeb6LisAAABOUYooAAAAQymiAHAMqurm\nQx4/qap+fq48AHAyUkQBYBOoKhcQBOCUoYgCwIpU1Y6qenNVXVVVl1fV9mn8y6vqMevmu3m6f3BV\nvb2qLk3ygZliA8Bwtr4CwLG5XVWt/xqFuya5dBp+QZKLuvuiqnpykp9L8qijLO+BSb6ku/9s9VEB\nYHNSRAHg2PxDd5998EFVPSnJzunhVyb55mn4FUmevcTy3qmEAnCqcWguAGy8WzL9z62q2yT5rHXT\n/m6WRAAwI0UUAFbnD5I8bhr+L0nePg3vT/Jl0/Ajkpw+NhYAbC6KKACszgVJvqOqrkryhCRPm8a/\nOMmDqup9WRy+ay8oAKe06u65MwAAAHAKsUcUAACAoRRRAAAAhlJEAQAAGEoRBQAAYChFFAAAgKEU\nUQAAAIZSRAEAABhKEQUAAGCo/w+lKkXoRO9skwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d155f3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bad checks\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d154d1978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burglary\n"
     ]
    },
    {
     "data": {
      "image/png": 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gfzgzetyRdkwnPIbr7udU1W9Myx+uql9N8ogkv9Ldb7n1e69bpudW1euT/Nsk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15370ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disordely conduct\n"
     ]
    },
    {
     "data": {
      "image/png": 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01lwAAAA4WUufNRcAgE+0qjPBJs4GC9y9GBEFAABgKEUUAACAoRRRAAAAhlJEAQAAGEoR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\nlCIKAADAUIooAAAAQymiAAAADPX/AYCGzhHc+7+2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15137ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drugs\n"
     ]
    },
    {
     "data": {
      "image/png": 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2rT07t69sW8A8zKACAAAwBAUVAACAISioAAAADEFBBQAAYAgKKgAAAENQUAEAABiCggoA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\nYAgKKgAAAENQUAEAABiCggoAAMAQ/h/mAQ3V1ihWgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11e2dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embezzle\n"
     ]
    },
    {
     "data": {
      "image/png": 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ryWOr6tIkj0ry1Gn5S5Pct6r+LIvDd+0FBeCYVt099wwAAAAcQ+wRBQAAYCgh\nCgAAwFBCFAAAgKGEKAAAAEMJUQAAAIYSogAAAAwlRAEAABhKiAIAADDU/wd3Sjywvg3taAAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1515ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Family offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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I6BOTXL2E9QAAAHAC2FERrap7JNmX5LnLiQMAAMC62+mI6P9K8qQkn15CFgAAAE4Au7b7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\nGEoRBQAAYChFFAAAgKEUUQAAAIb6/2KxpiscOtfMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11ee0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forgery\n"
     ]
    },
    {
     "data": {
      "image/png": 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9uqpul+QeWfwuuXrfH6KZPOJgK6YdIRuuu59RVS+flv++ql6U5LuTPK+733XT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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d156c0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homicide\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZsN0nlv89o6reXFX7krx7aGwA2FL2wgLA5+feVbXxTzN8UZJ9y8svSvIr3f0r\nVfWkJC9M8ujD3N4jkvyz7n7/+kcFgO1HlALA5+dvu/vhB69U1ROS7F1e/aYk37u8/Iokz13h9t4m\nSAG4O3H6LgBsvduz/B1cVfdIcs8N6z45MhEADBGlAHDkvCXJOcvLP5DkzcvLH0jydcvL353k2K0d\nCwC2D1EKAEfO+UmeWFXXJfnBJE9bLv+lJI+sqj/L4hRfR0cBuNuq7p6eAQAAgLspR0oBAAAYI0oB\nAAAYI0oBAAAYI0oBAAAYI0oBAAAYI0oBAAAYI0oBAAAYI0oBAAAY8/8BXheOLNHYxZIAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d152193c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Juvenile\n"
     ]
    },
    {
     "data": {
      "image/png": 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+ZXf/+epHBYD1JEQB4Pb5h+4+//CZqnpCkgPT2S9L8k3T6VckedYS63unCAXg\nTOPQXAA49W7N9D+3qu6Q5DN2XfZ3s0wEADMSogCwOr+X5Num0/8pydun0ztJvng6/Y1Jzh47FgCs\nFyEKAKtzSZLvqKprkzwuyZOm5S9K8tCq+qMsDt+1FxSAM1p199wzAAAAcAaxRxQAAIChhCgAAABD\nCVEAAACGEqIAAAAMJUQBAAAYSogCAAAwlBAFAABgKCEKAADAUP8PWUBGvBfEkV8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15631be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Larceny\n"
     ]
    },
    {
     "data": {
      "image/png": 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9W1X1xCyOSpnD31bVl3T3H08bqj6cLN5bVdVcG7GfkcUhon84vY53Fqe57M1i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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1524b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liquor laws\n"
     ]
    },
    {
     "data": {
      "image/png": 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mK6vqLt39ke5+2pGFVfX5Sd4zKkR96u8iAJxe0yHou7M4jPPISWWOvHnZ091H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\nKAAAAEMpogAAAAyliAIAADDU/wcoBiJQl64IkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15a33b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miscelaneous\n"
     ]
    },
    {
     "data": {
      "image/png": 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5f5T3vzCdIdOLsjiH6x0HjJ+b5GXdfcZmZ5qe/yeSvKjXXSV+Gv+SLP7/njRH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\nAACAISioAAAADOH/ARafCxUa9+JoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d150c8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miscelaneous traffic\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15156a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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xmxMRAACAreSkRbS7fzdJnWD1+auNAwAAwFZ3m86aCwAAAKdKEQUAAGAoRRQAAIChFFEA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\niigAAABD/X+bQtJ3Z6wrAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1526fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rape\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15575b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Robbery\n"
     ]
    },
    {
     "data": {
      "image/png": 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vEqEAnGicmgsAx97Nmf7mVtVJSW6zad0XZpkIAGYkRAFgdf48yWOmy/8pyTum\ny/uSfNt0+WFJThk7FgCsFyEKAKtzQZIfq6qrkzw2yVOm5S9J8sCq+qssTt91FBSAE1p199wzAAAA\ncAJxRBQAAIChhCgAAABDCVEAAACGEqIAAAAMJUQBAAAYSogCAAAwlBAFAABgKCEKAADAUP8Pa4o9\nE000TLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15293400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAEMpogAAAAyliAIAADCUIgoAAMBQiigAAABDKaIAAAAMpYgCAAAwlCIKAADAUIooAAAA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\noRRRAAAAhlJEAQAAGOr/AUVSXyCejyF+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11e895c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stolen property\n"
     ]
    },
    {
     "data": {
      "image/png": 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cmeRRSa5PcnFV7ezuj+zdprtfkuQl0/aPT/Lz3X3j3MU8srs/s9TkAAAAbGqLHGE9Icmu\n7r6mu7+S5PwkJ9/K9qcmOW8Z4QAAADh4LVJYj0xy3dz566exW6iquyY5Kckb54Y7yTur6kNVtePr\nDQoAAMDBZd0lwbfR45P89ZrlwA/v7t1Vdb8k76iqj3b3e9buOJXZHUmybdu2JccCAADgQLPIEdbd\nSY6eO3/UNLYvp2TNcuDu3j19/3SSCzJbYnwL3X12d2/v7u1bt25dIBYAAACb2SKF9eIkx1XVsVV1\nWGaldOfajarqXkkekeQtc2N3q6p77D2d5NFJrlhGcAAAADa3dZcEd/eeqnpOkrcl2ZLknO6+sqqe\nNc2fNW36w0ne3t1fnNv9iCQXVNXe63p9d1+0zB8AAACAzWmh17B294VJLlwzdtaa8+cmOXfN2DVJ\nHny7EgIAAHBQWmRJMAAAAGw4hRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAU\nVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIak\nsAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAk\nhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAh\nKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAM\naaHCWlUnVdXVVbWrqk7bx/yJVfX5qrp0+nrhovsCAADAvhyy3gZVtSXJmUkeleT6JBdX1c7u/sia\nTd/b3T/4de4LAAAAN7PIEdYTkuzq7mu6+ytJzk9y8oKXf3v2BQAA4CC2SGE9Msl1c+evn8bW+r6q\nuqyq3lpV334b9wUAAICbWXdJ8II+nGRbd99UVY9N8uYkx92WC6iqHUl2JMm2bduWFAsAAIAD1SJH\nWHcnOXru/FHT2Nd09xe6+6bp9IVJDq2qwxfZd+4yzu7u7d29fevWrbfhRwAAAGAzWqSwXpzkuKo6\ntqoOS3JKkp3zG1TV/auqptMnTJf72UX2BQAAgH1Zd0lwd++pquckeVuSLUnO6e4rq+pZ0/xZSZ6Y\n5Kerak+SLyc5pbs7yT73vYN+FgAAADaRhV7DOi3zvXDN2Flzp1+Z5JWL7gsAAADrWWRJMAAAAGw4\nhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAh\nKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAM\nSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABg\nSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAA\nQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMaaHCWlUnVdXVVbWrqk7bx/yT\nq+qyqrq8qt5XVQ+em7t2Gr+0qi5ZZngAAAA2r0PW26CqtiQ5M8mjklyf5OKq2tndH5nb7BNJHtHd\nn6uqxyQ5O8n3zM0/srs/s8TcAAAAbHKLHGE9Icmu7r6mu7+S5PwkJ89v0N3v6+7PTWffn+So5cYE\nAADgYLNIYT0yyXVz56+fxvbnJ5O8de58J3lnVX2oqnbsb6eq2lFVl1TVJTfccMMCsQAAANjM1l0S\nfFtU1SMzK6wPnxt+eHfvrqr7JXlHVX20u9+zdt/uPjuzpcTZvn17LzMXAAAAB55FjrDuTnL03Pmj\nprGbqaoHJfm9JCd392f3jnf37un7p5NckNkSYwAAALhVixTWi5McV1XHVtVhSU5JsnN+g6raluRN\nSZ7S3R+bG79bVd1j7+kkj05yxbLCAwAAsHmtuyS4u/dU1XOSvC3JliTndPeVVfWsaf6sJC9Mct8k\nr6qqJNnT3duTHJHkgmnskCSv7+6L7pCfBAAAgE1lodewdveFSS5cM3bW3OlnJnnmPva7JsmD144D\nAADAehZZEgwAAAAbTmEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAh\nKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAM\nSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABg\nSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAA\nQ1JYAQAAGJLCCgAAwJAUVgAAAIaksAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1qosFbV\nSVV1dVXtqqrT9jFfVfXyaf6yqnroovsCAADAvqxbWKtqS5IzkzwmyfFJTq2q49ds9pgkx01fO5K8\n+jbsCwAAALewyBHWE5Ls6u5ruvsrSc5PcvKabU5O8tqeeX+Se1fVAxbcFwAAAG7hkAW2OTLJdXPn\nr0/yPQtsc+SC+yZJqmpHZkdnk+Smqrp6gWzrOTzJZ5ZwOcs0YqZkzFwyLeaAzVQv2oAkN3fA3lYb\n7IDM5Pfpaw7I22rETCsg0+L8Ti3mgMzk3+5rRsy1rEzfuOiGixTWDdHdZyc5e5mXWVWXdPf2ZV7m\n7TVipmTMXDItRqbFjZhLpsXItLgRc8m0GJkWN2IumRYj0+JGzLWKTIsU1t1Jjp47f9Q0tsg2hy6w\nLwAAANzCIq9hvTjJcVV1bFUdluSUJDvXbLMzyVOndwt+WJLPd/cnF9wXAAAAbmHdI6zdvaeqnpPk\nbUm2JDmnu6+sqmdN82cluTDJY5PsSvKlJE+/tX3vkJ9k35a6xHhJRsyUjJlLpsXItLgRc8m0GJkW\nN2IumRYj0+JGzCXTYmRa3Ii5NjxTdfdGXycAAACsa5ElwQAAALDhFFYAAACGpLACAAAwpGE+h3UZ\nqupbk5yc5MhpaHeSnd191epSjWe6nY5M8oHuvmlu/KTuvmiFuU5I0t19cVUdn+SkJB/t7gtXlWle\nVb22u5+66hzzqurhSU5IckV3v31FGb4nyVXd/YWqukuS05I8NMlHkvx6d39+BZl+NskF3X3dRl/3\n/sy9U/o/dPc7q+rHk3xfkquSnN3d/3eF2b4pyY9k9jFkX03ysSSv7+4vrCoTAECyiY6wVtUvJjk/\nSSX54PRVSc6rqtNWmW1fqurpK7ren03yliTPTXJFVZ08N/3rq8iUJFV1epKXJ3l1Vf1GklcmuVuS\n06rql1eQZ+earz9N8iN7z290nrlcH5w7/VOZ3U73SHL6Cn/Pz8ns3cGT5GVJ7pXkRdPYa1aU6deS\nfKCq3ltVP1NVW1eUY95rkjwuyfOq6nVJfizJB5L8qyS/t6pQ033CWUn+xZTlzpkV1/dX1YmrygV3\ntKq636ozHAiq6r6rzsCBq6ruVVVnVNVHq+rGqvpsVV01jd171fnWqqq3ruh671lVv1FVr5ue0J6f\ne9UqMk3Xff+qenVVnVlV962qX6mqy6vqDVX1gA0L0t2b4iuzIwKH7mP8sCQfX3W+feT6+xVd7+VJ\n7j6dPibJJUmeN53/mxXeHpdn9tFHd03yhST3nMbvkuSyFeT5cJL/nuTEJI+Yvn9yOv2IFd5OfzN3\n+uIkW6fTd0ty+YoyXTV/u62Zu3RVt1NmT8g9OsnvJ7khyUVJfiLJPVaU6bLp+yFJPpVky3S+VvE7\nPpfr8rksd03yl9Ppbau6T8jsSY8zknw0yY1JPpvZkegzktx7VbfVreR96wqv+55JfiPJ65L8+Jq5\nV60o0/2TvDrJmUnum+RXpt+zNyR5wIoy3WfN132TXJvkG5LcZ0WZTpo7fa/pvuqyJK9PcsSKMp2R\n5PDp9PYk12T2kYV/t+L/+z6c5AVJvnlVGfaRaXuSd02PFY5O8o4kn5/+b/6uFWW6e5JfTXLllOWG\nJO9P8rQV3k5vS/KLSe4/N3b/aeztK8r00P18fXeST64o0xunv78nJNk5nb/zNPfhVWSarvuizA5y\nnTbdP/3i9Pv+3CRv2agcm2lJ8D8n+ZeZ3anOe8A0t+Gq6rL9TSU5YiOzzLlTT8uAu/va6QjKn1TV\nN065VmVPd381yZeq6n/1tBSxu79cVav499ue5HlJfjnJ87v70qr6cne/ewVZ5t2pqr4hszK2pbtv\nSJLu/mJV7VlRpiuq6und/Zokf1tV27v7kqp6YJJVLXPt7v7nJG9P8vaqOjTJY5KcmuQ3k6ziiOud\npmXBd8usGN4rszJ25ySHriDPvEMyWwp858we8KS7/3663VbhDUn+IsmJ3f2PyexZ3syecHhDZk9E\nbKiqeuj+ppI8ZCOzrPGaJB/P7MHNM6rqRzMrrv8nycNWlOncJH+W2e/6u5L8YWaf1f6EzI7mn7zf\nPe84n8ktHx8cmVkR6iTftOGJZqua9r4M57cye1L08Zktz/+dzG6vjfa47t67WuclSZ7Us5fpPDCz\nIr19BZmS2RML907yrqr6xyTnJfmj7v6HFeVJklclOX3K9b4kP9/dj6qqfzvNfe8KMv1hkguS/Psk\n/yGzv8Hzk7ygqh7Y3b+0gkzHdPeL5gem+/UXVdUzVpAnmT2p8O7s+3Hvqo76fnN3/+h0+s3T6sK/\nqKofWlGevY7o7lckSVX9zNy/5Suq6ic3KsRmKqw/l+TPq+rjSfa+bm1bkm9J8pwVZToiszuNz60Z\nr8zu3FbhU1X1kO6+NEm6+6aq+sHMlnV+54oyJclXququ3f2lzJ7hSjJbSpIVPOEwlZ2XVtUfT98/\nlTH+Xu6V5EOZ/Q51VT2guz9ZVXfP6p5weGaSl1XVCzJ7UPg/q+q6zP4On7miTDe7LXr2+tCdSXZW\n1V1XEym/n9kRwy2ZPRHyx1V1TWal4vwVZUpmy5EvrqoPJPn+zJZzZ1pGfeOKMnmAs7gRH+QM8QBn\njecneVRmT0BePmX7RHcfu6I8a23v7r1PfLy0qn5iRTkOqapDuntPkrt098VJ0t0fq6o7ryhTknyu\nu38hyS9U1fdn9uTjh6vqqiTndffZK8h0aHe/NUmq6kXd/SdJ0t1/XlW/uYI8yey+89zp9G9X1cXd\n/WvTy9A+kmQVhfXvquq/JPmD7v5UklTVEUmelv//eH2jXZXkP3b3x9dOTI9fVuHOVXWn6fFnuvu/\nVdXuJO/J9ETyisy/fPS1a+a2bFSIER6AL0V3XzQ9A3hCbv6mSxdPR+5W4X9ktvz20rUTVfWXGx8n\nSfLUJDc7Ejf9x/TUqvqd1URKkvzAdERgb1nc69DMjqqsRHdfn+THqupxmS1VXqnuPmY/U/+c5Ic3\nMMrX9OxNlZ5WVfdMcmxm9yvX7/2PaUWetL+J6UmRDdfdL62qP5pO/0NVvTbJv0vyu939wVvf+w7N\n9bKqemeSb0vyW9390Wn8hiQ/sKJYHuAsbsQHOUM8wJnX3b81/f29dPr3Oj2zI6urdL+q+k+ZPQly\nr6qqntbgZXXvMfKqJBdW1RlJLqqqlyV5U5J/k+QWj2VWobvfm+S9VfXczJ6EeFKSVRTW/11Vj87s\nieSuqid095ur6hGZrVhZhS9W1cO7+6+mJ61uTGaPq6pqVU9qPymz5aTvnu7HO7OXxezM7CjwKvxK\n9v839twNzDHvTzP7O3vn3oHuPndaUfCKFWVKkrdU1d27+6bufsHewar6liRXb1SI+v/3jQCwetOy\n99MyWzq6941x9j7AOaO7165a2YhMT8zsdeK3+A967wPVjc40XfeLM3sd2DvXjJ+U5BXdfdwKMv1q\nkhf33LvQT+Pfktm/3xM3OtOaHD+U2ZGmY7r7/ivMcfqaoVd19w3T8vcX94relX56qdBPJ3lgZk9A\nXpfkzUnOmZ7gXkWm87v7lFVc9/5U1YOTvDizJ4x/PrPb7CcyO1jyU9294SvpqupBma2aOS6z17E+\nYzo6vjUfai2ZAAACYklEQVTJqd398o3ONOX61iRHJXl/D/LpFDXgJ2bcSqbH7D2aP1iuDbutFFYA\nDhhzr5cexoiZkjFzjZKpZh/B9c3dfcUomebJtLgRc8l0s+v92STPzmyVykMye6PPt0xzH+7u/b0/\nwMGW6bmZvYRxmEwj5VJYAThgVNXfd/e2VeeYN2KmZMxcMi1GpsWNmEumm13v5Um+d3rPlGOS/EmS\n100vR/mb7v4umcbMNFKuTfMaVgA2hxrwHdZHzJSMmUumxci0uBFzybSwET+dQqYDLJfCCsBoRnyH\n9REzJWPmkmkxMi1uxFwyLWbET6eQ6QDLpbACMJoR32F9xEzJmLlkWoxMixsxl0yLGfHTKWRa3BC5\nvIYVAACAIa3qc74AAADgVimsAAAADElhBYA7QFXdtOb806rqlavKAwAHIoUVAA4gVeUNEwE4aCis\nALDBquqYqvqLqrqsqv68qrZN4+dW1RPntrtp+n5iVb23qnYm+ciKYgPAhvMsLQDcMe5SVfMfL3Gf\nJDun069I8gfd/QdV9YwkL0/yhHUu76FJvqO7P7H8qAAwJoUVAO4YX+7uh+w9U1VPS7J9Ovu9SX5k\nOv26JC9e4PI+qKwCcLCxJBgAxrEn0//NVXWnJIfNzX1xJYkAYIUUVgDYeO9Lcsp0+slJ3judvjbJ\nd0+nfyjJoRsbCwDGorACwMZ7bpKnV9VlSZ6S5HnT+O8meURV/W1my4YdVQXgoFbdveoMAAAAcAuO\nsAIAADAkhRUAAIAhKawAAAAMSWEFAABgSAorAAAAQ1JYAQAAGJLCCgAAwJAUVgAAAIb0/wC0FrOD\nnn3JcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d156ea908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suicide\n"
     ]
    },
    {
     "data": {
      "image/png": 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w2T8sMhEALEiIAsD2/EmSB65Pf0uSl69PX57kC9an75Pk+rNjAcBuEaIAsD3n\nJ3loVb0uybclecR6+S8nuWdV/VlWh+/aCwrAGa26e+kZAAAAOIPYIwoAAMAoIQoAAMAoIQoAAMAo\nIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMCo/w+YdzM1gYY+YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d153b4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vandalism\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAcB464oFbVtye5pbvffLjHdHcn6cM8/4Kquqqqrtq/f/+RxgAAAGCLWGYE9WuTPKKq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\nKm4gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d154e8ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vice\n"
     ]
    },
    {
     "data": {
      "image/png": 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kWVV1R5LPJTmtu3u/JQYAAGDTWLeUdvdT1ll+TmZfGQMAAAD7ZFFX3wUAAIB9ppQCAAAw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\nC0n+4wZG+Sc9u5D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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15626668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weapon_offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwSogAAAIwS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\nFAAAgFFCFAAAgFH/H0F69k4lJ2NDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1516d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot hourly crimes\n",
    "for cat in cat_by_hour_pt.index:\n",
    "    print(cat)\n",
    "    cat_by_hour_pt.loc[cat].plot.bar(figsize=(16, 8))\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vS/LCJHv3l4MXoD4AAADu5LY63LbOD/ub2/eXluTQJGf1y89Kclh//dAk57bWftZa+2aS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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d150cec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_by_daypart_pt['Night'] = - cat_by_daypart_pt['Night']\n",
    "cat_by_daypart_pt.plot.bar(figsize=(16, 8), stacked=True)\n",
    "cat_by_daypart_pt['Night'] = - cat_by_daypart_pt['Night']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1544deb8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pqieM21+S5H8nuX6SP66qJLmku7cmOTDJ68d1eyd5ZXe/Zb0xAQAAsOfbkDG03X1qklO3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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1516cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_df = pd.DataFrame()\n",
    "norm_df['Day'] = cat_by_daypart_pt['Day'] / cat_by_daypart_pt.sum(1).values\n",
    "norm_df['Night'] = - cat_by_daypart_pt['Night'] / cat_by_daypart_pt.sum(1).values\n",
    "norm_df.sort_values('Day').plot.bar(figsize=(16, 8), stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hourly occurrence analysis\n",
    "\n",
    "The analysis made are the how much of each category of crime occurred by each hour of the day e how much crimes occurred in day time and night time. These analysis help us to see how the occurrences are spread along the day and give prediction power about the behavior of the occurrences. Let's see the Drugs category for example, the  Day/Night dsitribution of the crimes clearly points to a high increase of occurrence during the night, more than two times the occurrences during the day. If look for the same category hourly we can see that the increase occurs at 18:00 hours and the decrease at 2:00 hours. Larceny, in the other hand is a crime that occurs more likely by the day having a decrease of almost 3 times. Again, the times are clearly distiguishble at 6:00 and 22:00.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekly_crimes_df = crimes_df[['Category', 'Dispatch Date / Time']].copy()\n",
    "weekly_crimes_df['Weekday'] = weekly_crimes_df['Dispatch Date / Time'].dt.weekday_name\n",
    "weekly_crimes_df['Weekday'] = pd.Categorical(weekly_crimes_df['Weekday'])\n",
    "weekly_crimes_df['Weekpart'] = weekly_crimes_df['Weekday'].apply(lambda wd: 'Weekend' if wd == 'Saturday' or wd == 'Sunday' else 'Weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weekday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aggravated assault</th>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arson</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>169</td>\n",
       "      <td>197</td>\n",
       "      <td>182</td>\n",
       "      <td>198</td>\n",
       "      <td>191</td>\n",
       "      <td>228</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto theft</th>\n",
       "      <td>56</td>\n",
       "      <td>62</td>\n",
       "      <td>49</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bad checks</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burglary</th>\n",
       "      <td>246</td>\n",
       "      <td>232</td>\n",
       "      <td>168</td>\n",
       "      <td>201</td>\n",
       "      <td>198</td>\n",
       "      <td>126</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disordely conduct</th>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>41</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>103</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>236</td>\n",
       "      <td>434</td>\n",
       "      <td>418</td>\n",
       "      <td>367</td>\n",
       "      <td>380</td>\n",
       "      <td>231</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embezzle</th>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family offenses</th>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forgery</th>\n",
       "      <td>276</td>\n",
       "      <td>253</td>\n",
       "      <td>248</td>\n",
       "      <td>212</td>\n",
       "      <td>198</td>\n",
       "      <td>62</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homicide</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juvenile</th>\n",
       "      <td>46</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larceny</th>\n",
       "      <td>1219</td>\n",
       "      <td>1077</td>\n",
       "      <td>1007</td>\n",
       "      <td>929</td>\n",
       "      <td>947</td>\n",
       "      <td>648</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquor laws</th>\n",
       "      <td>56</td>\n",
       "      <td>101</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>133</td>\n",
       "      <td>91</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous</th>\n",
       "      <td>637</td>\n",
       "      <td>660</td>\n",
       "      <td>622</td>\n",
       "      <td>536</td>\n",
       "      <td>551</td>\n",
       "      <td>382</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous traffic</th>\n",
       "      <td>133</td>\n",
       "      <td>148</td>\n",
       "      <td>156</td>\n",
       "      <td>195</td>\n",
       "      <td>308</td>\n",
       "      <td>419</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other offenses</th>\n",
       "      <td>122</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>111</td>\n",
       "      <td>135</td>\n",
       "      <td>94</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robbery</th>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex offenses</th>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stolen property</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide</th>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vandalism</th>\n",
       "      <td>281</td>\n",
       "      <td>203</td>\n",
       "      <td>188</td>\n",
       "      <td>185</td>\n",
       "      <td>158</td>\n",
       "      <td>157</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vice</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon_offenses</th>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weekday               Monday  Tuesday  Wednesday  Thursday  Friday  Saturday  \\\n",
       "Category                                                                       \n",
       "Aggravated assault        27       18         23        21      18        29   \n",
       "Arson                      4        6          3         2       4         3   \n",
       "Assault                  169      197        182       198     191       228   \n",
       "Auto theft                56       62         49        68      66        56   \n",
       "Bad checks                10        7          7         5       6         3   \n",
       "Burglary                 246      232        168       201     198       126   \n",
       "Disordely conduct         41       57         41        76      76       103   \n",
       "Drugs                    236      434        418       367     380       231   \n",
       "Embezzle                  33       35         33        21      38        12   \n",
       "Family offenses           24       29         29        20      24        14   \n",
       "Forgery                  276      253        248       212     198        62   \n",
       "Homicide                   0        0          1         1       0         1   \n",
       "Juvenile                  46       35         50        29      43        36   \n",
       "Larceny                 1219     1077       1007       929     947       648   \n",
       "Liquor laws               56      101         98       100     133        91   \n",
       "Miscelaneous             637      660        622       536     551       382   \n",
       "Miscelaneous traffic     133      148        156       195     308       419   \n",
       "Other offenses           122      136        136       111     135        94   \n",
       "Rape                       6        7          5         7       5         7   \n",
       "Robbery                   46       52         54        37      56        63   \n",
       "Sex offenses              33       31         22        27      18        14   \n",
       "Stolen property            1        2          0         2       0         1   \n",
       "Suicide                   12       18         29        22      10        21   \n",
       "Vandalism                281      203        188       185     158       157   \n",
       "Vice                       8        5          5         3       5         0   \n",
       "Weapon_offenses           12       31         37        29      26         6   \n",
       "\n",
       "Weekday               Sunday  \n",
       "Category                      \n",
       "Aggravated assault        32  \n",
       "Arson                      7  \n",
       "Assault                  215  \n",
       "Auto theft                49  \n",
       "Bad checks                 2  \n",
       "Burglary                 129  \n",
       "Disordely conduct         74  \n",
       "Drugs                    165  \n",
       "Embezzle                   3  \n",
       "Family offenses            6  \n",
       "Forgery                   32  \n",
       "Homicide                   1  \n",
       "Juvenile                  35  \n",
       "Larceny                  450  \n",
       "Liquor laws               62  \n",
       "Miscelaneous             393  \n",
       "Miscelaneous traffic     373  \n",
       "Other offenses            86  \n",
       "Rape                       4  \n",
       "Robbery                   50  \n",
       "Sex offenses              15  \n",
       "Stolen property            2  \n",
       "Suicide                   22  \n",
       "Vandalism                166  \n",
       "Vice                       1  \n",
       "Weapon_offenses            9  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_df = weekly_crimes_df[['Category', 'Weekday']]\n",
    "cat_weekday_pt = pd.pivot_table(weekday_df, index='Category', columns='Weekday', aggfunc=len, fill_value=0)\n",
    "cat_weekday_pt[['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weekpart</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Auto theft</th>\n",
       "      <td>60.2</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bad checks</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burglary</th>\n",
       "      <td>209.0</td>\n",
       "      <td>127.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>367.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embezzle</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family offenses</th>\n",
       "      <td>25.2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forgery</th>\n",
       "      <td>237.4</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juvenile</th>\n",
       "      <td>40.6</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larceny</th>\n",
       "      <td>1035.8</td>\n",
       "      <td>549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquor laws</th>\n",
       "      <td>97.6</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous</th>\n",
       "      <td>601.2</td>\n",
       "      <td>387.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other offenses</th>\n",
       "      <td>128.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex offenses</th>\n",
       "      <td>26.2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vandalism</th>\n",
       "      <td>203.0</td>\n",
       "      <td>161.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vice</th>\n",
       "      <td>5.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon_offenses</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weekpart         Weekday  Weekend\n",
       "Category                         \n",
       "Auto theft          60.2     52.5\n",
       "Bad checks           7.0      2.5\n",
       "Burglary           209.0    127.5\n",
       "Drugs              367.0    198.0\n",
       "Embezzle            32.0      7.5\n",
       "Family offenses     25.2     10.0\n",
       "Forgery            237.4     47.0\n",
       "Juvenile            40.6     35.5\n",
       "Larceny           1035.8    549.0\n",
       "Liquor laws         97.6     76.5\n",
       "Miscelaneous       601.2    387.5\n",
       "Other offenses     128.0     90.0\n",
       "Rape                 6.0      5.5\n",
       "Sex offenses        26.2     14.5\n",
       "Vandalism          203.0    161.5\n",
       "Vice                 5.2      0.5\n",
       "Weapon_offenses     27.0      7.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekpart_df = weekly_crimes_df[['Category', 'Weekpart']]\n",
    "cat_weekpart_pt = pd.pivot_table(weekpart_df, index='Category', columns='Weekpart', aggfunc=len, fill_value=0)\n",
    "cat_weekpart_pt['Weekday'] = cat_weekpart_pt['Weekday'] / 5\n",
    "cat_weekpart_pt['Weekend'] = cat_weekpart_pt['Weekend'] / 2\n",
    "cat_weekpart_pt[cat_weekpart_pt['Weekend'] < cat_weekpart_pt['Weekday']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weekly analysis\n",
    "\n",
    "The same analysis was applied with a week window and also distinguihsing the weekend from the weekday. To make a slight normalization the weekday column was divided by 5 and the weekdend for 2, so both columns have the 'unit' 'occurrences by day'. In these analysis we can see which crimes are more common at weekend or weekday, also is possible to check if exist a seasonality by week. Rapidly when can see that 9 categories are more common at the weekend:\n",
    "\n",
    "- Aggravated assault\n",
    "- Arson\n",
    "- Assault\n",
    "- Disordely conduct\n",
    "- Homicide\n",
    "- Miscelaneous traffic\n",
    "- Robbery\n",
    "- Stolen property\n",
    "- Suicide\n",
    "\n",
    "The ratio between the weekday and weekend data varies from .4 to .87, and the occurrences with greater ratio are:\n",
    "\n",
    "- Robbery\n",
    "- Suicide\n",
    "- Assault\n",
    "\n",
    "There are various category with greate incidence during the weekdays like:\n",
    "\n",
    "- Larceny\n",
    "- Miscelaneous\n",
    "- Drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TYifaEwsAAMD422xCLAAA0F06pdhYQ5vYqaoOq6rrq+qGqjp5WHUAAADQHUMJ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d156b2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot weekly crimes\n",
    "cat_weekpart_pt['Weekend'] = - cat_weekpart_pt['Weekend']\n",
    "cat_weekpart_pt.plot.bar(figsize=(16, 8), stacked=True)\n",
    "cat_weekpart_pt['Weekend'] = - cat_weekpart_pt['Weekend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1544dba8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UZD1gvapa/Gv8N4F/XsFp2wFXVdX9TYxzgJ2B7wGLgO/0Yuhdk/wH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15a56710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_df = pd.DataFrame()\n",
    "norm_df['Weekday'] = cat_weekpart_pt['Weekday'] / cat_weekpart_pt.sum(1).values\n",
    "norm_df['Weekend'] = cat_weekpart_pt['Weekend'] / cat_weekpart_pt.sum(1).values\n",
    "norm_df.sort_values('Weekday').plot.barh(figsize=(16, 8), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggravated assault\n"
     ]
    },
    {
     "data": {
      "image/png": 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JPjNrIgCA/ZAzogCbqKoqw1nRB4yPeya5Osl53f2CObMBAOwvFFGABaiqOyZ5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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15a59390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arson\n"
     ]
    },
    {
     "data": {
      "image/png": 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1ZlUdVRN/XlUfr6p3Jbndsv2eP/3+F1bV8dN9v6+qzl22zwHLtwFgvVI4AVgI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\ng1A4AQAAGITCCQAAwCD+P3R8n2OuMjPAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1562e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assault\n"
     ]
    },
    {
     "data": {
      "image/png": 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6c+xJnPNvK8MJRzKMRz8is6spG5K8MLMfVg9K8rbMhoKxmI5P8q7ezNUh4XTxbSakPCur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\n6bcyW2Jm5XFed39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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15242978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Auto theft\n"
     ]
    },
    {
     "data": {
      "image/png": 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0yR9193s2rLLFpIvy8nLul5vzv7yc++Xm/C8v5365Of9rsM+DFQEAAMC+2J+uuQAAALBu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\nAMCoBFEAAABGJYgCAAAwKkEUAACAUf1/irOHjEPbUjAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1541cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bad checks\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11ce97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burglary\n"
     ]
    },
    {
     "data": {
      "image/png": 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YHyZIg+VVVT+Sa5cZ+drQdpfMfu6fPWpxMFECKsAcDbP5bkpyQHffZVgT743d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\nmIT/DyiFW83SVI+xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15672278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disordely conduct\n"
     ]
    },
    {
     "data": {
      "image/png": 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pkj2Hx1cza1EFAFh6WlAB5qCqTkhypySXJflQkg8m+WB3/9uohQEATIhRfAHm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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d153e5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drugs\n"
     ]
    },
    {
     "data": {
      "image/png": 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WxtoaQukPZtaL/tAkpyZ5dXd/ctTCmKuqOjjJa5PcKclHkxzf3R8YtyrmoarumuSpSX4r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d150da080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embezzle\n"
     ]
    },
    {
     "data": {
      "image/png": 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PTPLXVfUzVXXvuQMxHWVkpb2jql5VVd/kg6jVVFWvrqoTq8ox1oqx71ebY/6N8xdl8T5Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d154489e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Family offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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21kQAAHsgV0QBNlFVVYarovcZP+6W5MokZ3f3c+bMBgCwp1BEARagqm6b5L4Z\nyujDk3xDd99q3lQAAHsGRRRgk1TVT+faK6FfyfDolrWPC7v7qzPGAwDYY5g1F2DzbE3y6iQ/292X\nzJwFAGCP5YooAAAAkzJrLgAAAJNSRAEAAJiUIgrAyquq366qn1m3/Maq+pN1y8+vqmfswvf9wk5s\n85KqesxGvzcA7M0UUQBI/j7DbMepqhslOSjD82DX3CfD7McAwCZQRAFgKJn3Hl/fNclFSa6qqgOq\n6iZJ7pLkvKr6hap6T1VdUFXPW/viqnpCVb27qs6vqj+uqn3Wf/OqOqiqzq6qY2vw+1X1oap6c5Jb\nr9vu2eP3v6iqThq3/ZaqOm/dNoetXwaAvZEiCsDK6+5PJrm6qg7NcPXz7CTvylBOtyW5MMkDkxyW\n5OgkRyb5tqp6QFXdJcnjkty3u49Mck2SH1n73lV1cJIzkjy7u89I8qgkhyc5IsmPjj9vze939726\n+25Jbprk4d39z0k+V1VHjts8KcmLN//fAgBMx3NEAWDwjgyl8D5JXpDkNuPrz2UYuvvQ8eO94/b7\nZyim90jybUneU1XJUCAvG7e5cZK3JHlqd791XPeAJK/o7muSfLKq/nZdhmOq6heTfH2SA5O8P8lf\nJvmTJE8a71N9XIYyDAB7LVdEAWCwdp/o3TMMzX1nhiuia/eHVpJf7+4jx487dvcp4/pT160/vLuf\nO37Pq5Ocm+S7d/TDq2q/JH+Y5DHdffckJyfZb3z7tCTfk+ThSc7t7k9vyj8xAMxEEQWAwTsyFL0r\nu/ua7r4yya0ylNF3JHljkidX1f5JUlW3qapbZ7ji+ZjxdarqwKq6/fg9O8mTk9y5qp45rjsryeOq\nap+qOiTJMeP6tdJ5xfgz/mMm3e7+t/HnvzCG5QKwBAzNBYDBhRlmy335ddbt391XJPmb8X7Qs8ch\nuF9I8oTu/kBV/Y/x/Rsl+UqSpyb5WJJ09zVV9fgkp1fVVRnK5IOSfCDJv2S4HzXd/dmqOjnD1dhL\nk7znOvleluH+0r/Z9H9yAJhYdffcGQCAHaiqn09yy+7+5bmzAMDuckUUAPZwVfXaJN+S4UoqAOz1\nXBEFAABgUiYrAgAAYFKKKAAAAJNSRAEAAJiUIgoAAMCkFFEAAAAm9f8BMsYOKyyDT/AAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d156b2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forgery\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vr62qh1RVjV0LMD9V9e1j1wAAUyegjqSq9hi7BkbzsiRPTPIvVfW7VXWnsQtifoSUpfae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\nAAAmQUAFAABgEgRUAAAAJuH/A1ayYY0cWNhHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1563cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homicide\n"
     ]
    },
    {
     "data": {
      "image/png": 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T3CPJFUk+keT0JKd399eHFgYAsIMx+y7A2tg7yc2SXJLky0kuSvKNoRUBAOyA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15613828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Juvenile\n"
     ]
    },
    {
     "data": {
      "image/png": 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GUkQBWHtV9b+r6gc2Lb+yqp6zafmZVfXU/fi+n9iHzzy/qh611e8NAIcyRRQA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11c3b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Larceny\n"
     ]
    },
    {
     "data": {
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y9/CRecBugPe7To7vVtUHhi5C41dVhwxdg6RhVNV5SZYC+9DdMjTV/r+Gq0qz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\nSZIkNc/wKkmSJElqnuFVkiRJktS8/w+1gCoSBdHb2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11b7a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liquor laws\n"
     ]
    },
    {
     "data": {
      "image/png": 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IQ52Xm/O/vJz75eb8Ly/nfrk5/8vLub8C1mSSJAAAANheazXEFwAAALaLgAoAAMAkCKgA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\nEFABAACYBAEVAACASRBQAQAAmIT/D+mXWSLlCSaPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11b1c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miscelaneous\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11a4fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miscelaneous traffic\n"
     ]
    },
    {
     "data": {
      "image/png": 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N1bOHtcu061VVj+iuNI3IhbTdvAdctIE2jaENdPFehF28N8mA2p2XA6dx//1I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\nknrBgCpJkiRJ6gUDqiRJkiSpF/4/q8DWgQkfq6MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11aeeef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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17ayq965tn8aHwzwn2jZV9ZK+i5DUuR+13/87yWOBO2ke79EYq6qjk5xDc64/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\nJEmDYECVJEmSJA2CAVWSJEmSNAj/Hy6EzW+AdIE0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15728f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rape\n"
     ]
    },
    {
     "data": {
      "image/png": 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qat/u/trYOZi9qvqdJA9Nctckb03ysCTv6e4fHzUYM6F0LK6q2pjk15PcI995sumY0UIx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\nK9Kls6PRTFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15197630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Robbery\n"
     ]
    },
    {
     "data": {
      "image/png": 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O/7lVdeLcBTEZ5361uebfJNO3LFhV3S3JtyZ5Robg/5tJfre7/37Wwli4qjomwy+k70jy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d15128240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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ckuSTSf4lyWdnTQQAsA9yRhRgE1VVZTgreq/xcZckVyQ5u7ufOWc2AIB9hSIK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\nuEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11be6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stolen property\n"
     ]
    },
    {
     "data": {
      "image/png": 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ez/TFtk3j756B99sAhwHeuzwhquonNMP/P9h1LRqtaR4a/1dJLgFO6KIejU5V\nLei6BnWjfZTZrjQXKvbn3uHhDwce1llhGqWp4eDQDAW+HoeDz8jA2gNV9V3AmSEnUFW9c3A5yTuB\nczoqRyOW5NnA24DH0vw9nuphcVjomEsy2JMydZXdf5Ol8fYi4H8AuwGDz928k+YRdxpTSZ4G3FBV\ne7TLv0Jz/+r1wDUdlrZZ8LE2HUqyalPrq+oXRlWL+qG9l/WSqnpc17Vo/iX5OvAmmiuuP+1tn6b3\nTWOmnSlyytRV9v9dVdd2U5GkUUny8qo6s+s6NDpJLgV+vqpuT3IgcAbwG8B+NPc0H9lpgT3n1dxu\nPRO4ATid5rEW2fTmGhdJtqyq9Uku496hIQuAxwDevzo57qiqz3RdhEavqg7uugZJ3aiqM5McBuxN\nczvQVPufdFeV5tmCqrq9ff9KYGV70eLMJJd3WNdmwcDarUcDL6B5jMkvA2cDp/v81YlwEc3kCoNX\n1NYDN1fVf3dTkjrw+STvorl//afHvaou7a4kjUI7I+jLgcUM/FvsCas0/pKsoLln9WCaifaOpDkv\n0PhaMNVZATwfWD6wzjw2A/8Ddaiq7qG5X/Gc9uTlaOBfkry9qt7XbXWaZwGoqn/vuhB16unt18EZ\nYwvwAeLj79PAHTTDwb1IJU2WZ1XVPkmuqKq3J3k34Gib8XY6zaPsbgN+RDPBKkkeR/NvgTbBwNqx\nNqgeRhNWFwPvBc7qsiaNxMIkb97Yyqp6z8bWaXw4LHSi7VZVh3RdhKRO/Kj9+sMkPwPcTnNLkMZU\nVf15kn+iOc6frXsnEdqC5l5WbYKBtUNJTgOeRPPsxbdX1VUdl6TRWQBsh/ctT6RNXawAL1hMiAuS\nPLmqruy6EEkj9/+S7Aj8L5pRFuAzuMdeVV04Tds3uqhlc+MswR1K8hPgB+3i4IHw4eFjLsmlVeUD\nwidUkhPbt3sCTwOmZgx/CXBRVb26k8I075JcBfyE5oLxEuA6miHBU3/39+mwPEnzaODRJje3y68F\nXg18HXjbwKQ8kgYYWKUOJLmsqvbvug51K8n5wGFVdWe7vD1wdlUd2G1lmi9JvkvzGINpVdW3RliO\npBHy0SbS7DgkWOrG87suQL2wC3D3wPLdbZvG1zcNpdLE8tEm0iwYWKUOOOxHrdOAi5JMTbT2UuAj\nHdaj+bezE65JE8tHm0iz4C+HJHWknTXwM8Bz2qZjquqyLmvSvHPCNWly+WgTaRa8h1WSOpTkAGBJ\nVX04yUId2ufgAAAEhUlEQVRgu6r6Ztd1aX444Zo02ZI8g3sfbfKDtu3xNH/7L+20OKmnDKyS1JF2\ntuClwJ5V9fj2eXyfrKpnd1ya5okTrkmS9MBs0XUBkjTBXgb8Au3jrarq28D2nVak+eaEa5IkPQAG\nVknqzt3VDHMpgCTbdlyP5pkTrkmS9MAYWCWpO59I8jfAjkl+DTgP+GDHNUmSJPWG97BK0ogl+W3g\nAuBS4GDghTSzxp5bVZ/rsjZJkqQ+8bE2kjR6uwF/BTwBuBL4Mk2AvaTLoiRJkvrGHlZJ6kiSrWlm\nCX4W8Mz29b2q2qvTwiRJknrCHlZJ6s5DgYcDO7Svb9P0uEqSJAl7WCVp5JKsBPYG7gS+ClwIXFhV\n3+20MEmSpJ5xlmBJGr1FwEOAm4EbgXXA9zqtSJIkqYfsYZWkDiQJTS/rs9rXk4Dbga9U1Yld1iZJ\nktQXBlZJ6lCS3YBn04TWw4FHVdWO3VYlSZLUDwZWSRqxJL/JvT2rP6Z5pM3U68qq+kmH5UmSJPWG\nswRL0ugtBj4JvKmqbuq4FkmSpN6yh1WSJEmS1EvOEixJkiRJ6iUDqyRJkiSplwyskiQNIcn/SfLb\nA8vnJvnQwPK7k7x5Fp971xDbnJrkyAf62ZIkbe4MrJIkDefLNDM7k2QLYCeaZ+lOeRbNTM+SJGmO\nGFglSRrOBcAz2/d7A1cBdyZ5RJKHAE8ELk3yliQXJ7kiydundk7y6iQXJbk8yd8kWTD44Ul2SvKV\nJIel8b4k1yY5D9h5YLsT2s+/KsnKdtufS3LpwDZLBpclSdpcGVglSRpCVX0bWJ9kEU1v6leAr9KE\n2KXAlcBBwBJgGbAf8NQkByZ5IvBK4NlVtR9wD/Cqqc9OsgtwNnBCVZ0NvAzYE9gLeG37/aa8r6qe\nVlVPAh4KHF5V/w7ckWS/dptjgA/P/X8FSZJGy+ewSpI0vAtowuOzgPcAu7bv76AZMvzC9nVZu/12\nNAF2H+CpwMVJoAmat7TbbAX8E3BsVX2hbTsQOL2q7gG+neSfB2o4OMnvAQ8DHglcDfwj8CHgmPY+\n2lfShGZJkjZr9rBKkjS8qftYn0wzJPhCmh7WqftXA/xlVe3Xvh5XVSe37R8ZaN+zqt7WfuZ64BLg\nRTN98yTbAO8HjqyqJwMfBLZpV58JHAocDlxSVd+Zk59YkqQOGVglSRreBTSB8Paquqeqbgd2pAmt\nFwDnAq9Lsh1Akl2T7EzTg3pk+54kj0zy2PYzC3gd8IQkv9+2nQ+8MsmCJI8BDm7bp8Lpbe33+OnM\nwVX1X+33/wAOB5YkjQmHBEuSNLwraWYH/tgGbdtV1W3AZ9v7Vb/SDv29C3h1VV2T5I/b9VsAPwaO\nBb4FUFX3JDkaWJXkTprQ+TzgGuA/aO6Xpaq+l+SDNL27NwMXb1DfR2nuf/3snP/kkiR1IFXVdQ2S\nJGkOJPldYIeqemvXtUiSNBfsYZUkaQwkOQv4OZqeWUmSxoI9rJIkSZKkXnLSJUmSJElSLxlYJUmS\nJEm9ZGCVJEmSJPWSgVWSJEmS1EsGVkmSJElSL/1/ry4irCvvrm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d154a9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suicide\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11c24630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vandalism\n"
     ]
    },
    {
     "data": {
      "image/png": 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TUEdSVbuNXQOjeXmSJyT5ZFX9QVXdYeyCmB8hZal9sKpeX1U/7QLV8qmqN1TVkVXl3GvJ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\nzKscceYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11cc47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vice\n"
     ]
    },
    {
     "data": {
      "image/png": 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47iMzOduxJcmLMvnG88Ak/5DJ0Cvm0/Yk7+4dnLlRNuffDkrHM7KsdCRROubXqUn+Jslj\nlp9oSnJmVb1spEzMUFWdmOTATP4e/Fh3f3n60Rur6szxkjE0+35xOeZfHVc410BVXZjk5CTHdfcp\n1/jsz7r7meMkYxaq6s5J7ppkz6V13f368RIxK1X1yUwOOF55jdKRqvqN7v7DcZIxtKqqHZ1oYnFU\n1eHdffLYOZg9+35xOeZfHYVzDVTV3t39jbFzMHtV9T+SPCzJnZO8I8nDk3ygu39y1GDMhNKxuKpq\nU5JfT3K3fO/JpiNGC8XMVdVB+a8nHF8zXiJmxb5fTI75V8eQ2rVxZVU9Lf/1wOOp40ViRh6fyfDJ\ns7v7SVV12ySvGjcSM3TLqlI6FtPrkrwxyaOS/LckP5vkklETMVNV9fwkD8qkdPxDkkcm+UASpWPO\n2fcLzTH/Kngsytr4myS3yeTq1nuT7J/k8lETMSvf6u6rMvkGtE8mj0a5/ciZmJ3XJfl4JvdqHpvk\noiQfGjMQM3OL7j4uyXe6+73Tgw0nGhbLUUkenOTi7n5Kknsm2XfcSMyIfb+4HPOvgsK5Nu7Y3b+T\n5Jvd/epMbib+oZEzMRvnVNV+mUyLfWaSM6YvFoPSsbiWHnvw5ekD4A9JcvMxAzFz3+ru72ZywvGm\nSb6S5PtHzsRs2PeLyzH/KhhSuzaWDjy+Oh3Tf3GSW42Yhxnp7l+avn1JVb0jyU27++wxMzFT31M6\nMpkaX+lYDL87fQbfryT58yQ3TfKccSMxY2dOTzi+PMlZSb6RyezFzD/7fnE55l8Fkwatgar6hSQn\nJLlHklcm2TvJ87rb1PhzqqrucV2fd/dHZpWF8VTVo5K8P5Mz20ul49juftuowYCZqqotmZxw9L1/\nwdj3i8Ux/+oonLAKVfX+6dsbJTkkyQVJKpObyM/u7vuOlQ0YTlX9eZJr/cFpSvz5V1X3uq7PjXKZ\nX/Y9rI4htddDVT33uj7v7hfPKguz1d0PTJKqekuSbd197nT5nkl+e8xsDE/pWGhLD3U/NJMZKt84\nXf6pJB8dJRGz9qLpr3sm2Zrkw5mccLxHJn8/7jdSLoZn3y8ox/zXj8J5/ewz/fXAJPdJsjSM7sdi\n4phFcZelspkk3f3hqrrbmIGYCaVjQU0niUhV/XKSB3T3ldPll2UyvJo5192HJ0lVvTXJvbr7vOny\nQUleMGI0BmbfLzTH/NeDIbVroKrel+TI7r58urxPkpO6+7BxkzG0qnpTksuSvHa66mcymbn0ceOl\nYlaq6rR8b+m4QZL3d/cPj5uMoVXVJ5Lcr7svmy7fLMlp3X3guMmYlaq6oLvvtrN1zB/7fnE55l8d\nVzjXxq2TXLFs+YrpOubfzyZ5epLfmC6/L8l1Drtgrtwsk4mCLpsu7z1dx/x7YSaPRTo5kyF1h8UV\njkXzkap6Rb73hKOJYxbDefb9wnLMvwqucK6BqvrtJI9LcuJ01WOSvKm7f3+8VMzadIr07+tuQyoX\nRFU9JZOS8T2lY2nYJfOtqm6Tq5+/dnp3XzxmHmarqvZM8suZ/LtPJiccX9rd/zFeKmbBvl9cjvlX\nR+FcI1V17yQPmC6+r7vPGTMPs1FV/5TkJ5JsSHJ2Jle6/rm7f23UYMyM0rGYqurQJOd29zer6ugk\n90ryp9392ZGjAQOqqg1JXtPdPzN2FsbhmH/XKZxrZPoN6NZZNky5uz83XiJmoarO6e5Dqurnk2zp\n7t+pqo9093U+p5P5oHQsrqr6SJJ75upnsR2X5HHd/SOjBmNwVfWm7n5cVZ2XHcxW7fv//KuqDyQ5\noruv2OnGzB3H/LvOPZxroKqekeT5Sf41yVWZDK3rTA5EmG8bq2pTJrOTPm/sMMzcS5Pcc/o4nOdm\nUjpek0TpmH9XdndX1Y8neUl3Hzc98cT8e9b010eNmoIxXZjkg1X1tiTfXFrp0RjzzzH/6iica+NZ\nSQ7s7n8bOwgz93tJ3pvkA919RlX9QJLPjJyJ2VE6FtflVfWbSY5OclhV7ZHkBiNnYga6+8vTKxyv\nWnpMBgvn09PXHrn6cRksBsf8q2BI7RqYzlL40KVHIwCLoarem+TtSZ6a5IFJvpLkw91991GDMbjp\nvbs/neRD3f3+qtqc5EHd/ZqRozEj03v4f7K7vzZ2FmA2HPOvjsK5BqrquEweBHtSkm8vrTe0Yv5V\n1R2TvCTJbbr7nlV1j0yez/QHI0djBpSOxTS9uvVuV7cWW1X9bZJDkrwr3zus8pmjhWImpqVjR/fv\nHjFCHGbIMf/qGFK7Nj43fd1w+mJxvCLJb2VSOpPkvCTHJ1E4F0B3X1xVJyQ5YLrq0lw9VTpzqruv\nqqrvVtW+rm4ttLdOXyyeX132fs8kj03iitdicMy/Cq5wrqGq2jtJuvsbY2dhNqrqQ919n6XZaqfr\nzu3ug8fOxvCq6heTbEty8+7+wao6IMnLuvvBI0djYK5uLa6q2mxGSq6pqs7o7vuOnQN2R65wroGq\nOijJ3yS5+XT50iRP7u4LRg3GLPxbVd0h06E1VfWYJJ7DuDieluS+SU5Pku7+l6q61biRmBFXtxbX\n/83kEUipqhO6+7Ej52HGqurmyxb3SHLvJPuOFIcZMpx6dRTOtbE9yXO7++QkqaoHJXl5kvuPGYqZ\neHomj8K4c1V9NsmXkzxx3EjM0Le7+4qqSpJU1cbs4AcR86e7Xz12BkZTy97/wGgpGNNZmXyvr0yG\n0n4miRnKF4Ph1KugcK6NvZbKZpJ093uqaq8xAzEb3f2pJEdU1b6ZDFH/6tiZmKn3VtVvJblxVT00\nyX9P8ncjZ2IGquoz2fFZbgVk/vW1vGdx3KW7/2P5iqq60VhhmJ3uPusaqz5YVWeMEmYdUTjXxoVV\n9TuZDKtNJs9lu3DEPMzItGwsX06SdPfvjxKIWTsmk7Pa5yX5pST/kMlEUsy/rcve75nkpzK9rYK5\nd8+q+nomV7duPH2f6XJ3903Hi8aMnJLpsOplTt3BOubMDoZTb43h1DulcK6NpyY5Nlffz/P+6Trm\n31XL3u+Z5Mgk7t1dEN393UyGz7987CzM1g4e+v0nVXVWkueNkYfZ6e4NY2dgHNNHYd0ukxMNh+Tq\n4dU3TXKT0YIxS0vDqZPJUNqLYjj1Timca6C7/z2JmQkXUHf/4fLlqvrDJG8fKQ4zVlWHJnlBkttn\n8v106QqHYZVzrqqWX8lYOsvtZyrMt4cn+bkk+ydZ/tzFyzN5RBpzqqruk+Tz3X2H6fLPZnL/5kVJ\nPjpitHXBY1Guh6p623V93t2PnlUWdg/TeznP6u47jp2F4VXVx5M8J5Mznv95tXsHV7+YM9OZCpcs\nneX+4+7+xDiJgFmpqsd29wlj52B2qursJA/p7suq6rAkb0jyjCQHZ3JP71GjBtzNORt7/dwvyeeT\nHJ/JYxHqujdnXlTVxu6+sqrOydVDKzYkuW0S928ujq919z+OHYLZ6+7Dx84AjKO7T6iqI5PcLZPb\naZbW/8/xUjGwDd192fT945Nsn550OKGqzh0x17qgcF4/t0ny0Eweg/HTSU5Kcrznby6EMzKZHGD5\nGa0rk1zc3d8eJxIjOLmq/iiT+7f/c79399njRWIWpjNSPjbJliz7WeqAE+ZfVb0sk3s2D89korij\nMjkuYH5tWLrYkOTBSbYt+0yf2gl/QNdDd1+Vyf16b58efDwxyXuq6tju/otx0zGwSpLu/vTYQRjV\nD01/XT5jaSfxAOj597dJvpbJcGonmWCx3L+771FVH+nuY6vqRUmMdplvx2fyKLRLk3wrkwlCU1V3\nzORnAddB4byepkXzyEzK5pYkf5bkxDEzMRObquq51/Zhd7/42j5jfhhWudD27+5HjB0CGMW3pr/+\nv6r6viSXZXJLDXOqu3+vqv4pk/38zr56Epw9MrmXk+ugcF4PVfWaJAdl8uy9Y7v7/JEjMTsbkuwd\n9+0upOs62ZA44bAgTqmqu3f3eWMHAWbu76tqvyT/O5NRDolnMM+97j5tB+s+OUaW9cYstddDVX03\nyTeni8v/ID38ec5V1dnd7QHPC6qqnj99e2CS+yRZmrH6x5Kc0d1HjxKMwVXV+Um+m8kJ2wOSXJjJ\nkNql7/v3GDEeMKBlj8a4eLr85CRHJ/l4khcsm1QGWEbhhFWoqnO6+5CxczCuqnpfkiO7+/Lp8j5J\nTuruw8ZNxlCq6t8zmQZ/h7r7szOMA8yQR2PA6hhSC6vz4LEDsFu4dZIrli1fMV3H/PqMUgkLy6Mx\nYBUUTlgFw2aYek2SM6pqaaKwxyR59Yh5GN6tTBgGC8ujMWAV/OMAWKXprHX/mOSB01VP6e5zxszE\n4EwYBovLozFgFdzDCXA9VNUDkhzQ3a+sqk1J9u7uz4ydi2GYMAwWW1X9cK5+NMY3p+vulMn3/rNH\nDQe7KYUTYJWms9VuTXJgd99p+jy2N3f3oSNHYyAmDAOAXbPH2AEA1rGfSPLoTB+P1N1fSrLPqIkY\nmgnDAGAXKJwAq3dFT4aJdJJU1V4j52FgJgwDgF2jcAKs3puq6q+S7FdVv5jk3UlePnImAIDdhns4\nAXZRVT07ySlJzk5yeJKHZTJr6Tu6+11jZgMA2J14LArArts/yZ8kuXOS85J8MJMCetaYoQAAdjeu\ncAKsUlXdMJNZau+f5H7T11e7+66jBgMA2E24wgmwejdOctMk+05fX8rkiicAAHGFE2CXVdX2JHdL\ncnmS05OcluS07v73UYMBAOxmzFILsOs2J7lRkouTfDHJF5J8ddREAAC7IVc4AVahqiqTq5z3n74O\nSnJZklO7+/ljZgMA2F0onADXQ1Xtn+TQTErno5Lcorv3GzcVAMDuQeEE2EVV9cxcfWXzO5k8EmXp\ndV53f3fEeAAAuw2z1ALsui1J3pzkOd395ZGzAADstlzhBAAAYBBmqQUAAGAQCicAAACDUDgBWAhV\n9X+q6tnLlt9RVa9YtvyiqnruKr7uN1awzauq6qhd/doAsN4pnAAsig9mMrNwqmqPJLfM5FmqS+6f\nyUzDAMAaUTgBWBSnJLnf9P3dkpyf5PKqullV3SjJXZKcXVW/VlUfqqqPVNWxS/9xVR1dVWdU1blV\n9VdVtWH5F6+qW1bVqVV1ZE38RVV9oqreneRWy7Z73vTrn19V26fb/mBVnb1smwOWLwPAeqVwArAQ\nuvtLSa6sqs2ZXM08NcnpmZTQrUnOS/KgJAckuW+Sg5Pcu6oOq6q7JHl8kkO7++AkVyX5maWvXVW3\nTnJSkud190lJfiLJgUnumuTJ099vyV909326+6AkN07yqO7+dJKvVdXB022ekuSVa/+nAACz5Tmc\nACySUzIpf/dP8uIkt5u+/1omQ24fNn2dM91+70wK6D2S3DvJh6oqmRTFr0y3uUGSf0rytO5+73Td\nYUmO7+6rknypqv55WYbDq+rXk9wkyc2TXJDk75K8IslTpveRPj6T0gsA65ornAAskqX7OO+eyZDa\n0zK5wrl0/2Yl+YPuPnj6umN3Hzdd/+pl6w/s7hdMv+aVSc5K8vCd/eZVtWeSv0xyVHffPcnLk+w5\n/fiEJI9M8qgkZ3X3v63J/zEAjEjhBGCRnJJJobusu6/q7suS7JdJ6TwlyTuSPLWq9k6SqrpdVd0q\nkyuYR03fp6puXlW3n37NTvLUJHeuqt+YrntfksdX1Yaqum2Sw6frl8rlpdPf4z9nru3u/5j+/i+N\n4bQAzAlDagFYJOdlMjvt66+xbu/uvjTJO6f3a546HTr7jSRHd/dHq+p/TD/fI8l3kjwtyWeTpLuv\nqqonJnlbVV2eSWk8IslHk3wuk/tF091fraqXZ3J19eIkH7pGvtdlcv/nO9f8/xwARlDdPXYGACBJ\nVf1qkn27+3fGzgIAa8EVTgDYDVTViUl+MJMrowAwF1zhBAAAYBAmDQIAAGAQCicAAACDUDgBAAAY\nhMIJAADAIBROAAAABvH/AZBg2sary9R3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d1523b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weapon_offenses\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d11cb1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cat in cat_weekday_pt.index:\n",
    "    print(cat)\n",
    "    cat_weekday_pt.loc[cat][['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']].plot.bar(figsize=(16, 4))\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryuga/anaconda3/envs/Topicos_F/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Interval Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Larceny</th>\n",
       "      <td>6276</td>\n",
       "      <td>0 days 00:42:11.967654</td>\n",
       "      <td>0 days 01:21:34.320357</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:07:07</td>\n",
       "      <td>0 days 00:17:57.500000</td>\n",
       "      <td>0 days 00:41:42.250000</td>\n",
       "      <td>0 days 15:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous</th>\n",
       "      <td>3780</td>\n",
       "      <td>0 days 01:09:58.218253</td>\n",
       "      <td>0 days 01:29:38.004431</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:14:56</td>\n",
       "      <td>0 days 00:39:35</td>\n",
       "      <td>0 days 01:26:01.250000</td>\n",
       "      <td>0 days 11:46:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>2230</td>\n",
       "      <td>0 days 01:58:43.822869</td>\n",
       "      <td>0 days 03:06:28.173612</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:19:20</td>\n",
       "      <td>0 days 00:52:30.500000</td>\n",
       "      <td>0 days 02:09:51.750000</td>\n",
       "      <td>1 days 16:25:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscelaneous traffic</th>\n",
       "      <td>1731</td>\n",
       "      <td>0 days 02:33:02.288272</td>\n",
       "      <td>0 days 04:53:36.491527</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:12:16</td>\n",
       "      <td>0 days 00:34:00</td>\n",
       "      <td>0 days 01:44:31</td>\n",
       "      <td>1 days 07:06:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>1379</td>\n",
       "      <td>0 days 03:11:55.451776</td>\n",
       "      <td>0 days 03:49:52.979589</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:46:19</td>\n",
       "      <td>0 days 01:53:04</td>\n",
       "      <td>0 days 04:03:49</td>\n",
       "      <td>1 days 16:48:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vandalism</th>\n",
       "      <td>1337</td>\n",
       "      <td>0 days 03:17:43.124906</td>\n",
       "      <td>0 days 03:54:14.821860</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:39:17</td>\n",
       "      <td>0 days 01:43:07</td>\n",
       "      <td>0 days 04:32:37</td>\n",
       "      <td>1 days 06:17:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burglary</th>\n",
       "      <td>1299</td>\n",
       "      <td>0 days 03:23:33.076982</td>\n",
       "      <td>0 days 04:02:03.494336</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:45:31</td>\n",
       "      <td>0 days 01:54:47</td>\n",
       "      <td>0 days 04:32:51.500000</td>\n",
       "      <td>1 days 03:30:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forgery</th>\n",
       "      <td>1280</td>\n",
       "      <td>0 days 03:26:42.166406</td>\n",
       "      <td>0 days 05:53:24.545809</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:18:46.500000</td>\n",
       "      <td>0 days 01:05:01</td>\n",
       "      <td>0 days 03:12:47.250000</td>\n",
       "      <td>1 days 22:27:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other offenses</th>\n",
       "      <td>819</td>\n",
       "      <td>0 days 05:22:55.964590</td>\n",
       "      <td>0 days 05:49:36.056457</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 01:16:05</td>\n",
       "      <td>0 days 03:15:02</td>\n",
       "      <td>0 days 07:38:53.500000</td>\n",
       "      <td>1 days 11:19:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquor laws</th>\n",
       "      <td>640</td>\n",
       "      <td>0 days 06:50:16.095312</td>\n",
       "      <td>0 days 09:55:47.879855</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 00:52:37.500000</td>\n",
       "      <td>0 days 02:25:23</td>\n",
       "      <td>0 days 10:32:04.750000</td>\n",
       "      <td>3 days 14:57:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disordely conduct</th>\n",
       "      <td>467</td>\n",
       "      <td>0 days 09:20:46.824411</td>\n",
       "      <td>0 days 11:59:41.871914</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 01:11:01.500000</td>\n",
       "      <td>0 days 04:14:40</td>\n",
       "      <td>0 days 14:51:33</td>\n",
       "      <td>3 days 23:19:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto theft</th>\n",
       "      <td>405</td>\n",
       "      <td>0 days 10:50:30.866666</td>\n",
       "      <td>0 days 12:29:46.926863</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>0 days 02:09:59</td>\n",
       "      <td>0 days 06:30:14</td>\n",
       "      <td>0 days 15:39:32</td>\n",
       "      <td>4 days 00:10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robbery</th>\n",
       "      <td>357</td>\n",
       "      <td>0 days 12:19:01.350140</td>\n",
       "      <td>0 days 13:05:09.713536</td>\n",
       "      <td>0 days 00:00:01</td>\n",
       "      <td>0 days 02:52:40</td>\n",
       "      <td>0 days 08:10:31</td>\n",
       "      <td>0 days 17:43:59</td>\n",
       "      <td>3 days 16:46:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juvenile</th>\n",
       "      <td>273</td>\n",
       "      <td>0 days 16:05:31.333333</td>\n",
       "      <td>0 days 17:40:14.679139</td>\n",
       "      <td>0 days 00:00:45</td>\n",
       "      <td>0 days 03:13:07</td>\n",
       "      <td>0 days 10:44:50</td>\n",
       "      <td>0 days 22:34:25</td>\n",
       "      <td>4 days 16:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embezzle</th>\n",
       "      <td>174</td>\n",
       "      <td>1 days 01:13:14.097701</td>\n",
       "      <td>1 days 07:34:59.792209</td>\n",
       "      <td>0 days 00:01:21</td>\n",
       "      <td>0 days 03:30:40.250000</td>\n",
       "      <td>0 days 14:47:53</td>\n",
       "      <td>1 days 08:14:51</td>\n",
       "      <td>6 days 22:01:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aggravated assault</th>\n",
       "      <td>167</td>\n",
       "      <td>1 days 01:34:31.604790</td>\n",
       "      <td>1 days 03:26:53.829812</td>\n",
       "      <td>0 days 00:02:55</td>\n",
       "      <td>0 days 06:51:01.500000</td>\n",
       "      <td>0 days 18:45:20</td>\n",
       "      <td>1 days 08:22:27.500000</td>\n",
       "      <td>5 days 18:51:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex offenses</th>\n",
       "      <td>159</td>\n",
       "      <td>1 days 03:36:12.528301</td>\n",
       "      <td>1 days 06:45:01.370944</td>\n",
       "      <td>0 days 00:00:48</td>\n",
       "      <td>0 days 07:01:56</td>\n",
       "      <td>0 days 19:32:28</td>\n",
       "      <td>1 days 10:18:58</td>\n",
       "      <td>7 days 23:24:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon_offenses</th>\n",
       "      <td>149</td>\n",
       "      <td>1 days 05:03:36.906040</td>\n",
       "      <td>1 days 08:44:29.930459</td>\n",
       "      <td>0 days 00:13:24</td>\n",
       "      <td>0 days 06:35:51</td>\n",
       "      <td>0 days 19:32:37</td>\n",
       "      <td>1 days 10:40:54</td>\n",
       "      <td>6 days 08:29:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family offenses</th>\n",
       "      <td>145</td>\n",
       "      <td>1 days 05:59:11.420689</td>\n",
       "      <td>1 days 08:13:19.711287</td>\n",
       "      <td>0 days 00:00:28</td>\n",
       "      <td>0 days 05:50:31</td>\n",
       "      <td>0 days 20:00:08</td>\n",
       "      <td>1 days 16:36:29</td>\n",
       "      <td>6 days 15:20:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide</th>\n",
       "      <td>133</td>\n",
       "      <td>1 days 09:09:22.819548</td>\n",
       "      <td>1 days 11:59:19.871769</td>\n",
       "      <td>0 days 00:02:08</td>\n",
       "      <td>0 days 08:25:30</td>\n",
       "      <td>0 days 18:52:48</td>\n",
       "      <td>1 days 22:13:42</td>\n",
       "      <td>8 days 05:20:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>40</td>\n",
       "      <td>4 days 05:17:33.275000</td>\n",
       "      <td>4 days 04:36:40.037677</td>\n",
       "      <td>0 days 00:22:27</td>\n",
       "      <td>1 days 12:16:24.750000</td>\n",
       "      <td>2 days 23:27:45.500000</td>\n",
       "      <td>5 days 21:39:16</td>\n",
       "      <td>18 days 04:16:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bad checks</th>\n",
       "      <td>39</td>\n",
       "      <td>4 days 10:21:24.333333</td>\n",
       "      <td>3 days 22:34:45.507525</td>\n",
       "      <td>0 days 01:44:34</td>\n",
       "      <td>1 days 10:58:42</td>\n",
       "      <td>3 days 23:58:36</td>\n",
       "      <td>6 days 00:26:10</td>\n",
       "      <td>16 days 20:21:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arson</th>\n",
       "      <td>28</td>\n",
       "      <td>6 days 03:36:01.250000</td>\n",
       "      <td>5 days 01:57:17.640670</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>1 days 16:09:30.500000</td>\n",
       "      <td>4 days 14:06:33.500000</td>\n",
       "      <td>11 days 07:33:36.750000</td>\n",
       "      <td>14 days 15:18:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vice</th>\n",
       "      <td>26</td>\n",
       "      <td>5 days 16:26:01.115384</td>\n",
       "      <td>5 days 20:53:05.222136</td>\n",
       "      <td>0 days 00:01:20</td>\n",
       "      <td>2 days 01:48:39.750000</td>\n",
       "      <td>4 days 05:51:40.500000</td>\n",
       "      <td>7 days 13:25:03</td>\n",
       "      <td>27 days 01:02:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stolen property</th>\n",
       "      <td>7</td>\n",
       "      <td>12 days 17:37:45.142857</td>\n",
       "      <td>13 days 03:54:28.077315</td>\n",
       "      <td>1 days 00:51:40</td>\n",
       "      <td>5 days 00:15:31</td>\n",
       "      <td>6 days 12:51:54</td>\n",
       "      <td>16 days 03:40:30.500000</td>\n",
       "      <td>39 days 05:48:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homicide</th>\n",
       "      <td>3</td>\n",
       "      <td>13 days 10:13:50.666666</td>\n",
       "      <td>12 days 00:53:14.431489</td>\n",
       "      <td>0 days 19:45:34</td>\n",
       "      <td>7 days 17:42:17</td>\n",
       "      <td>14 days 15:39:00</td>\n",
       "      <td>19 days 17:27:59</td>\n",
       "      <td>24 days 19:16:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Interval Time                           \\\n",
       "                             count                     mean   \n",
       "Category                                                      \n",
       "Larceny                       6276   0 days 00:42:11.967654   \n",
       "Miscelaneous                  3780   0 days 01:09:58.218253   \n",
       "Drugs                         2230   0 days 01:58:43.822869   \n",
       "Miscelaneous traffic          1731   0 days 02:33:02.288272   \n",
       "Assault                       1379   0 days 03:11:55.451776   \n",
       "Vandalism                     1337   0 days 03:17:43.124906   \n",
       "Burglary                      1299   0 days 03:23:33.076982   \n",
       "Forgery                       1280   0 days 03:26:42.166406   \n",
       "Other offenses                 819   0 days 05:22:55.964590   \n",
       "Liquor laws                    640   0 days 06:50:16.095312   \n",
       "Disordely conduct              467   0 days 09:20:46.824411   \n",
       "Auto theft                     405   0 days 10:50:30.866666   \n",
       "Robbery                        357   0 days 12:19:01.350140   \n",
       "Juvenile                       273   0 days 16:05:31.333333   \n",
       "Embezzle                       174   1 days 01:13:14.097701   \n",
       "Aggravated assault             167   1 days 01:34:31.604790   \n",
       "Sex offenses                   159   1 days 03:36:12.528301   \n",
       "Weapon_offenses                149   1 days 05:03:36.906040   \n",
       "Family offenses                145   1 days 05:59:11.420689   \n",
       "Suicide                        133   1 days 09:09:22.819548   \n",
       "Rape                            40   4 days 05:17:33.275000   \n",
       "Bad checks                      39   4 days 10:21:24.333333   \n",
       "Arson                           28   6 days 03:36:01.250000   \n",
       "Vice                            26   5 days 16:26:01.115384   \n",
       "Stolen property                  7  12 days 17:37:45.142857   \n",
       "Homicide                         3  13 days 10:13:50.666666   \n",
       "\n",
       "                                                                \\\n",
       "                                          std              min   \n",
       "Category                                                         \n",
       "Larceny                0 days 01:21:34.320357  0 days 00:00:00   \n",
       "Miscelaneous           0 days 01:29:38.004431  0 days 00:00:00   \n",
       "Drugs                  0 days 03:06:28.173612  0 days 00:00:00   \n",
       "Miscelaneous traffic   0 days 04:53:36.491527  0 days 00:00:00   \n",
       "Assault                0 days 03:49:52.979589  0 days 00:00:00   \n",
       "Vandalism              0 days 03:54:14.821860  0 days 00:00:00   \n",
       "Burglary               0 days 04:02:03.494336  0 days 00:00:00   \n",
       "Forgery                0 days 05:53:24.545809  0 days 00:00:00   \n",
       "Other offenses         0 days 05:49:36.056457  0 days 00:00:00   \n",
       "Liquor laws            0 days 09:55:47.879855  0 days 00:00:00   \n",
       "Disordely conduct      0 days 11:59:41.871914  0 days 00:00:00   \n",
       "Auto theft             0 days 12:29:46.926863  0 days 00:00:00   \n",
       "Robbery                0 days 13:05:09.713536  0 days 00:00:01   \n",
       "Juvenile               0 days 17:40:14.679139  0 days 00:00:45   \n",
       "Embezzle               1 days 07:34:59.792209  0 days 00:01:21   \n",
       "Aggravated assault     1 days 03:26:53.829812  0 days 00:02:55   \n",
       "Sex offenses           1 days 06:45:01.370944  0 days 00:00:48   \n",
       "Weapon_offenses        1 days 08:44:29.930459  0 days 00:13:24   \n",
       "Family offenses        1 days 08:13:19.711287  0 days 00:00:28   \n",
       "Suicide                1 days 11:59:19.871769  0 days 00:02:08   \n",
       "Rape                   4 days 04:36:40.037677  0 days 00:22:27   \n",
       "Bad checks             3 days 22:34:45.507525  0 days 01:44:34   \n",
       "Arson                  5 days 01:57:17.640670  0 days 00:00:00   \n",
       "Vice                   5 days 20:53:05.222136  0 days 00:01:20   \n",
       "Stolen property       13 days 03:54:28.077315  1 days 00:51:40   \n",
       "Homicide              12 days 00:53:14.431489  0 days 19:45:34   \n",
       "\n",
       "                                                                      \\\n",
       "                                         25%                     50%   \n",
       "Category                                                               \n",
       "Larceny                      0 days 00:07:07  0 days 00:17:57.500000   \n",
       "Miscelaneous                 0 days 00:14:56         0 days 00:39:35   \n",
       "Drugs                        0 days 00:19:20  0 days 00:52:30.500000   \n",
       "Miscelaneous traffic         0 days 00:12:16         0 days 00:34:00   \n",
       "Assault                      0 days 00:46:19         0 days 01:53:04   \n",
       "Vandalism                    0 days 00:39:17         0 days 01:43:07   \n",
       "Burglary                     0 days 00:45:31         0 days 01:54:47   \n",
       "Forgery               0 days 00:18:46.500000         0 days 01:05:01   \n",
       "Other offenses               0 days 01:16:05         0 days 03:15:02   \n",
       "Liquor laws           0 days 00:52:37.500000         0 days 02:25:23   \n",
       "Disordely conduct     0 days 01:11:01.500000         0 days 04:14:40   \n",
       "Auto theft                   0 days 02:09:59         0 days 06:30:14   \n",
       "Robbery                      0 days 02:52:40         0 days 08:10:31   \n",
       "Juvenile                     0 days 03:13:07         0 days 10:44:50   \n",
       "Embezzle              0 days 03:30:40.250000         0 days 14:47:53   \n",
       "Aggravated assault    0 days 06:51:01.500000         0 days 18:45:20   \n",
       "Sex offenses                 0 days 07:01:56         0 days 19:32:28   \n",
       "Weapon_offenses              0 days 06:35:51         0 days 19:32:37   \n",
       "Family offenses              0 days 05:50:31         0 days 20:00:08   \n",
       "Suicide                      0 days 08:25:30         0 days 18:52:48   \n",
       "Rape                  1 days 12:16:24.750000  2 days 23:27:45.500000   \n",
       "Bad checks                   1 days 10:58:42         3 days 23:58:36   \n",
       "Arson                 1 days 16:09:30.500000  4 days 14:06:33.500000   \n",
       "Vice                  2 days 01:48:39.750000  4 days 05:51:40.500000   \n",
       "Stolen property              5 days 00:15:31         6 days 12:51:54   \n",
       "Homicide                     7 days 17:42:17        14 days 15:39:00   \n",
       "\n",
       "                                                                 \n",
       "                                          75%               max  \n",
       "Category                                                         \n",
       "Larceny                0 days 00:41:42.250000   0 days 15:02:07  \n",
       "Miscelaneous           0 days 01:26:01.250000   0 days 11:46:07  \n",
       "Drugs                  0 days 02:09:51.750000   1 days 16:25:30  \n",
       "Miscelaneous traffic          0 days 01:44:31   1 days 07:06:02  \n",
       "Assault                       0 days 04:03:49   1 days 16:48:20  \n",
       "Vandalism                     0 days 04:32:37   1 days 06:17:53  \n",
       "Burglary               0 days 04:32:51.500000   1 days 03:30:51  \n",
       "Forgery                0 days 03:12:47.250000   1 days 22:27:53  \n",
       "Other offenses         0 days 07:38:53.500000   1 days 11:19:25  \n",
       "Liquor laws            0 days 10:32:04.750000   3 days 14:57:08  \n",
       "Disordely conduct             0 days 14:51:33   3 days 23:19:31  \n",
       "Auto theft                    0 days 15:39:32   4 days 00:10:07  \n",
       "Robbery                       0 days 17:43:59   3 days 16:46:57  \n",
       "Juvenile                      0 days 22:34:25   4 days 16:09:21  \n",
       "Embezzle                      1 days 08:14:51   6 days 22:01:04  \n",
       "Aggravated assault     1 days 08:22:27.500000   5 days 18:51:35  \n",
       "Sex offenses                  1 days 10:18:58   7 days 23:24:32  \n",
       "Weapon_offenses               1 days 10:40:54   6 days 08:29:42  \n",
       "Family offenses               1 days 16:36:29   6 days 15:20:08  \n",
       "Suicide                       1 days 22:13:42   8 days 05:20:41  \n",
       "Rape                          5 days 21:39:16  18 days 04:16:30  \n",
       "Bad checks                    6 days 00:26:10  16 days 20:21:02  \n",
       "Arson                 11 days 07:33:36.750000  14 days 15:18:18  \n",
       "Vice                          7 days 13:25:03  27 days 01:02:11  \n",
       "Stolen property       16 days 03:40:30.500000  39 days 05:48:39  \n",
       "Homicide                     19 days 17:27:59  24 days 19:16:58  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean between time of occurrences in the same catgory\n",
    "interval_time_df = crimes_df[['Category', 'Dispatch Date / Time']].copy()\n",
    "interval_time_df.sort_values(['Category', 'Dispatch Date / Time'], inplace=True)\n",
    "interval_time_df['Interval Time'] = interval_time_df['Dispatch Date / Time'].diff()\n",
    "interval_time_df['Interval Time'][interval_time_df['Category'] != interval_time_df['Category'].shift()] = pd.NaT\n",
    "interval_time_df.dropna(inplace=True)\n",
    "interval_time_df.groupby('Category').describe().sort_values(('Interval Time', 'count'), ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interval between occurrences\n",
    "\n",
    "It's important to analysis the interval between crimes so it's possible to manage resource and adapt to respond quickly enough and be prepared to next occurrence. Considering that the crimes with less than 100 occurrences are not trustable to extract same info it's possible to say that all crimes will probaly repeat within 2 days, speaking in terms of mean.\n",
    "\n",
    "Naturally the crimes with more incidence have the least minimum. The standard deviation to this data is not much greater than the mean. This shows same reliabilty on the information that can be extracted."
   ]
  }
 ],
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