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Created April 5, 2013 18:28
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DataPhilly demo of pandas using Philadelphia Police opendata
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dataphilly
{
"metadata": {
"name": "Data Philly"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "# Quick glance of the Philadelphia Police Departments Part 1 Crime incidents\n## Data location: http://opendataphilly.org/opendata/resource/215/philadelphia-police-part-one-crime-incidents/"
},
{
"cell_type": "code",
"collapsed": false,
"input": "import pandas as pd",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "df = pd.read_csv('police_inct/police_inct.csv')\ndf",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 610868 entries, 0 to 610867\nData columns:\nDC_DIST 610868 non-null values\nSECTOR 610868 non-null values\nDISPATCH_DATE_TIME 610868 non-null values\nDISPATCH_DATE 610868 non-null values\nDISPATCH_TIME 610868 non-null values\nHOUR 610868 non-null values\nDC_KEY 610868 non-null values\nLOCATION_BLOCK 610868 non-null values\nUCR_GENERAL 610868 non-null values\nOBJECTID 610868 non-null values\nTEXT_GENERAL_CODE 610868 non-null values\nSHAPE 607127 non-null values\nPOINT_X 605731 non-null values\nPOINT_Y 605731 non-null values\ndtypes: float64(3), int64(4), object(7)"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Columns of our data frame\ndf.columns",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": "Index([DC_DIST, SECTOR, DISPATCH_DATE_TIME, DISPATCH_DATE, DISPATCH_TIME, HOUR, DC_KEY, LOCATION_BLOCK, UCR_GENERAL, OBJECTID, TEXT_GENERAL_CODE, SHAPE, POINT_X, POINT_Y], dtype=object)"
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "# First entry\ndf.ix[0]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": "DC_DIST 14\nSECTOR N\nDISPATCH_DATE_TIME 2010-05-05 11:34:00\nDISPATCH_DATE 2010-05-05\nDISPATCH_TIME 11:34:00\nHOUR 11\nDC_KEY 199814043321\nLOCATION_BLOCK 300 BLOCK E CLIVEDEN ST\nUCR_GENERAL 300\nOBJECTID 602550\nTEXT_GENERAL_CODE Robbery No Firearm\nSHAPE 602550\nPOINT_X -75.177\nPOINT_Y 40.05289\nName: 0"
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": "### Sub select columns we care about"
},
{
"cell_type": "code",
"collapsed": false,
"input": "crime = df[['DC_DIST', 'DISPATCH_DATE_TIME', 'LOCATION_BLOCK', 'UCR_GENERAL', 'OBJECTID', 'TEXT_GENERAL_CODE']]\ncrime",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 610868 entries, 0 to 610867\nData columns:\nDC_DIST 610868 non-null values\nDISPATCH_DATE_TIME 610868 non-null values\nLOCATION_BLOCK 610868 non-null values\nUCR_GENERAL 610868 non-null values\nOBJECTID 610868 non-null values\nTEXT_GENERAL_CODE 610868 non-null values\ndtypes: int64(3), object(3)"
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Count the different types of crime\ncrime.TEXT_GENERAL_CODE.value_counts()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": "Thefts 181307\nTheft from Vehicle 100473\nBurglary Residential 65700\nRecovered Stolen Motor Vehicle 60374\nAggravated Assault No Firearm 46417\nMotor Vehicle Theft 45302\nRobbery No Firearm 35877\nRobbery Firearm 29047\nAggravated Assault Firearm 19478\nBurglary Non-Residential 16652\nRape 7076\nHomicide - Criminal 1932\nHomicide - Gross Negligence 737\nHomicide - Criminal 364\nHomicide - Justifiable 132"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Homicide - Criminal is listed twice because of a trailing space\n# Clean leading and trailing whitespace.\ncrime.TEXT_GENERAL_CODE = crime.TEXT_GENERAL_CODE.map(lambda x: x.strip())",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "crime.TEXT_GENERAL_CODE.value_counts()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 9,
"text": "Thefts 181307\nTheft from Vehicle 100473\nBurglary Residential 65700\nRecovered Stolen Motor Vehicle 60374\nAggravated Assault No Firearm 46417\nMotor Vehicle Theft 45302\nRobbery No Firearm 35877\nRobbery Firearm 29047\nAggravated Assault Firearm 19478\nBurglary Non-Residential 16652\nRape 7076\nHomicide - Criminal 2296\nHomicide - Gross Negligence 737\nHomicide - Justifiable 132"
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Create a new DataFrame for the 22nd district\ndist_22 = crime[crime.DC_DIST == 22]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Look at the number of distinct crimes in District 22\ndist_22['TEXT_GENERAL_CODE'].value_counts()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": "Thefts 8072\nTheft from Vehicle 3851\nBurglary Residential 3674\nAggravated Assault No Firearm 2954\nRecovered Stolen Motor Vehicle 2503\nRobbery No Firearm 1934\nRobbery Firearm 1889\nAggravated Assault Firearm 1646\nMotor Vehicle Theft 1626\nBurglary Non-Residential 705\nRape 468\nHomicide - Criminal 225\nHomicide - Gross Negligence 23\nHomicide - Justifiable 6"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Create a cross tabulation of crime type across all districts\ncrime_counts = pd.crosstab(crime.DC_DIST, crime.TEXT_GENERAL_CODE)\ncrime_counts",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>TEXT_GENERAL_CODE</th>\n <th>Aggravated Assault Firearm</th>\n <th>Aggravated Assault No Firearm</th>\n <th>Burglary Non-Residential</th>\n <th>Burglary Residential</th>\n <th>Homicide - Criminal</th>\n <th>Homicide - Gross Negligence</th>\n <th>Homicide - Justifiable</th>\n <th>Motor Vehicle Theft</th>\n <th>Rape</th>\n <th>Recovered Stolen Motor Vehicle</th>\n <th>Robbery Firearm</th>\n <th>Robbery No Firearm</th>\n <th>Theft from Vehicle</th>\n <th>Thefts</th>\n </tr>\n <tr>\n <th>DC_DIST</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1 </th>\n <td> 538</td>\n <td> 1004</td>\n <td> 302</td>\n <td> 1254</td>\n <td> 51</td>\n <td> 11</td>\n <td> 1</td>\n <td> 887</td>\n <td> 98</td>\n <td> 1002</td>\n <td> 475</td>\n <td> 864</td>\n <td> 1843</td>\n <td> 4471</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 626</td>\n <td> 1902</td>\n <td> 907</td>\n <td> 4289</td>\n <td> 60</td>\n <td> 50</td>\n <td> 7</td>\n <td> 3063</td>\n <td> 282</td>\n <td> 3058</td>\n <td> 1719</td>\n <td> 1930</td>\n <td> 4744</td>\n <td> 10456</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 302</td>\n <td> 1274</td>\n <td> 478</td>\n <td> 1679</td>\n <td> 47</td>\n <td> 20</td>\n <td> 2</td>\n <td> 1677</td>\n <td> 111</td>\n <td> 1359</td>\n <td> 745</td>\n <td> 1434</td>\n <td> 4810</td>\n <td> 7952</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 276</td>\n <td> 871</td>\n <td> 244</td>\n <td> 801</td>\n <td> 34</td>\n <td> 17</td>\n <td> 2</td>\n <td> 655</td>\n <td> 88</td>\n <td> 1217</td>\n <td> 541</td>\n <td> 954</td>\n <td> 2616</td>\n <td> 4449</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 73</td>\n <td> 459</td>\n <td> 301</td>\n <td> 1278</td>\n <td> 4</td>\n <td> 12</td>\n <td> 1</td>\n <td> 622</td>\n <td> 50</td>\n <td> 353</td>\n <td> 194</td>\n <td> 271</td>\n <td> 2413</td>\n <td> 2880</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 282</td>\n <td> 1228</td>\n <td> 850</td>\n <td> 1141</td>\n <td> 39</td>\n <td> 24</td>\n <td> 2</td>\n <td> 1437</td>\n <td> 158</td>\n <td> 1137</td>\n <td> 684</td>\n <td> 1560</td>\n <td> 7514</td>\n <td> 15635</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 129</td>\n <td> 643</td>\n <td> 706</td>\n <td> 2166</td>\n <td> 18</td>\n <td> 43</td>\n <td> 2</td>\n <td> 882</td>\n <td> 91</td>\n <td> 919</td>\n <td> 272</td>\n <td> 543</td>\n <td> 3495</td>\n <td> 4053</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 224</td>\n <td> 1483</td>\n <td> 870</td>\n <td> 2212</td>\n <td> 25</td>\n <td> 51</td>\n <td> 0</td>\n <td> 1496</td>\n <td> 156</td>\n <td> 1535</td>\n <td> 498</td>\n <td> 843</td>\n <td> 5271</td>\n <td> 8911</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 178</td>\n <td> 733</td>\n <td> 714</td>\n <td> 1627</td>\n <td> 18</td>\n <td> 17</td>\n <td> 3</td>\n <td> 936</td>\n <td> 144</td>\n <td> 1072</td>\n <td> 729</td>\n <td> 1352</td>\n <td> 6960</td>\n <td> 14593</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 1660</td>\n <td> 3185</td>\n <td> 752</td>\n <td> 4315</td>\n <td> 200</td>\n <td> 58</td>\n <td> 8</td>\n <td> 3111</td>\n <td> 514</td>\n <td> 4702</td>\n <td> 2010</td>\n <td> 1742</td>\n <td> 4542</td>\n <td> 7001</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 1179</td>\n <td> 2707</td>\n <td> 811</td>\n <td> 5430</td>\n <td> 140</td>\n <td> 24</td>\n <td> 8</td>\n <td> 2352</td>\n <td> 441</td>\n <td> 3823</td>\n <td> 1810</td>\n <td> 1878</td>\n <td> 5331</td>\n <td> 8349</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 1429</td>\n <td> 4749</td>\n <td> 1519</td>\n <td> 5783</td>\n <td> 124</td>\n <td> 77</td>\n <td> 10</td>\n <td> 4458</td>\n <td> 609</td>\n <td> 5808</td>\n <td> 2663</td>\n <td> 3664</td>\n <td> 7805</td>\n <td> 13447</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 738</td>\n <td> 1838</td>\n <td> 398</td>\n <td> 2062</td>\n <td> 114</td>\n <td> 20</td>\n <td> 4</td>\n <td> 1057</td>\n <td> 313</td>\n <td> 1616</td>\n <td> 858</td>\n <td> 1057</td>\n <td> 2471</td>\n <td> 4526</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 915</td>\n <td> 1738</td>\n <td> 284</td>\n <td> 3056</td>\n <td> 107</td>\n <td> 3</td>\n <td> 6</td>\n <td> 1179</td>\n <td> 219</td>\n <td> 1495</td>\n <td> 853</td>\n <td> 1173</td>\n <td> 2963</td>\n <td> 4575</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 995</td>\n <td> 2384</td>\n <td> 688</td>\n <td> 3251</td>\n <td> 107</td>\n <td> 25</td>\n <td> 10</td>\n <td> 1722</td>\n <td> 360</td>\n <td> 2135</td>\n <td> 1834</td>\n <td> 2124</td>\n <td> 5079</td>\n <td> 11113</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 1468</td>\n <td> 3065</td>\n <td> 764</td>\n <td> 4109</td>\n <td> 154</td>\n <td> 26</td>\n <td> 9</td>\n <td> 2160</td>\n <td> 459</td>\n <td> 2755</td>\n <td> 1768</td>\n <td> 1597</td>\n <td> 3763</td>\n <td> 6821</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 1646</td>\n <td> 2954</td>\n <td> 705</td>\n <td> 3674</td>\n <td> 225</td>\n <td> 23</td>\n <td> 6</td>\n <td> 1626</td>\n <td> 468</td>\n <td> 2503</td>\n <td> 1889</td>\n <td> 1934</td>\n <td> 3851</td>\n <td> 8072</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 348</td>\n <td> 813</td>\n <td> 249</td>\n <td> 867</td>\n <td> 58</td>\n <td> 9</td>\n <td> 2</td>\n <td> 441</td>\n <td> 137</td>\n <td> 980</td>\n <td> 502</td>\n <td> 599</td>\n <td> 1566</td>\n <td> 2309</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 1267</td>\n <td> 2907</td>\n <td> 1469</td>\n <td> 4101</td>\n <td> 132</td>\n <td> 49</td>\n <td> 10</td>\n <td> 3601</td>\n <td> 542</td>\n <td> 4806</td>\n <td> 1742</td>\n <td> 2539</td>\n <td> 4655</td>\n <td> 10794</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 1833</td>\n <td> 3286</td>\n <td> 1273</td>\n <td> 3055</td>\n <td> 214</td>\n <td> 55</td>\n <td> 14</td>\n <td> 3906</td>\n <td> 567</td>\n <td> 7249</td>\n <td> 2375</td>\n <td> 2501</td>\n <td> 4025</td>\n <td> 8504</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 640</td>\n <td> 1747</td>\n <td> 835</td>\n <td> 2631</td>\n <td> 95</td>\n <td> 35</td>\n <td> 4</td>\n <td> 2290</td>\n <td> 281</td>\n <td> 3112</td>\n <td> 1013</td>\n <td> 1387</td>\n <td> 5751</td>\n <td> 6268</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 1497</td>\n <td> 3048</td>\n <td> 699</td>\n <td> 3651</td>\n <td> 160</td>\n <td> 34</td>\n <td> 10</td>\n <td> 3428</td>\n <td> 568</td>\n <td> 4641</td>\n <td> 2432</td>\n <td> 2482</td>\n <td> 4058</td>\n <td> 7825</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 1223</td>\n <td> 2365</td>\n <td> 770</td>\n <td> 3259</td>\n <td> 165</td>\n <td> 41</td>\n <td> 11</td>\n <td> 2193</td>\n <td> 403</td>\n <td> 2715</td>\n <td> 1424</td>\n <td> 1401</td>\n <td> 3842</td>\n <td> 5974</td>\n </tr>\n <tr>\n <th>77</th>\n <td> 1</td>\n <td> 16</td>\n <td> 17</td>\n <td> 1</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td> 87</td>\n <td> 5</td>\n <td> 244</td>\n <td> 2</td>\n <td> 6</td>\n <td> 279</td>\n <td> 2221</td>\n </tr>\n <tr>\n <th>92</th>\n <td> 11</td>\n <td> 18</td>\n <td> 47</td>\n <td> 8</td>\n <td> 5</td>\n <td> 11</td>\n <td> 0</td>\n <td> 36</td>\n <td> 12</td>\n <td> 138</td>\n <td> 15</td>\n <td> 42</td>\n <td> 826</td>\n <td> 108</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"prompt_number": 12,
"text": "TEXT_GENERAL_CODE Aggravated Assault Firearm Aggravated Assault No Firearm \\\nDC_DIST \n1 538 1004 \n2 626 1902 \n3 302 1274 \n4 276 871 \n5 73 459 \n6 282 1228 \n7 129 643 \n8 224 1483 \n9 178 733 \n12 1660 3185 \n14 1179 2707 \n15 1429 4749 \n16 738 1838 \n17 915 1738 \n18 995 2384 \n19 1468 3065 \n22 1646 2954 \n23 348 813 \n24 1267 2907 \n25 1833 3286 \n26 640 1747 \n35 1497 3048 \n39 1223 2365 \n77 1 16 \n92 11 18 \n\nTEXT_GENERAL_CODE Burglary Non-Residential Burglary Residential \\\nDC_DIST \n1 302 1254 \n2 907 4289 \n3 478 1679 \n4 244 801 \n5 301 1278 \n6 850 1141 \n7 706 2166 \n8 870 2212 \n9 714 1627 \n12 752 4315 \n14 811 5430 \n15 1519 5783 \n16 398 2062 \n17 284 3056 \n18 688 3251 \n19 764 4109 \n22 705 3674 \n23 249 867 \n24 1469 4101 \n25 1273 3055 \n26 835 2631 \n35 699 3651 \n39 770 3259 \n77 17 1 \n92 47 8 \n\nTEXT_GENERAL_CODE Homicide - Criminal Homicide - Gross Negligence \\\nDC_DIST \n1 51 11 \n2 60 50 \n3 47 20 \n4 34 17 \n5 4 12 \n6 39 24 \n7 18 43 \n8 25 51 \n9 18 17 \n12 200 58 \n14 140 24 \n15 124 77 \n16 114 20 \n17 107 3 \n18 107 25 \n19 154 26 \n22 225 23 \n23 58 9 \n24 132 49 \n25 214 55 \n26 95 35 \n35 160 34 \n39 165 41 \n77 0 2 \n92 5 11 \n\nTEXT_GENERAL_CODE Homicide - Justifiable Motor Vehicle Theft Rape \\\nDC_DIST \n1 1 887 98 \n2 7 3063 282 \n3 2 1677 111 \n4 2 655 88 \n5 1 622 50 \n6 2 1437 158 \n7 2 882 91 \n8 0 1496 156 \n9 3 936 144 \n12 8 3111 514 \n14 8 2352 441 \n15 10 4458 609 \n16 4 1057 313 \n17 6 1179 219 \n18 10 1722 360 \n19 9 2160 459 \n22 6 1626 468 \n23 2 441 137 \n24 10 3601 542 \n25 14 3906 567 \n26 4 2290 281 \n35 10 3428 568 \n39 11 2193 403 \n77 0 87 5 \n92 0 36 12 \n\nTEXT_GENERAL_CODE Recovered Stolen Motor Vehicle Robbery Firearm \\\nDC_DIST \n1 1002 475 \n2 3058 1719 \n3 1359 745 \n4 1217 541 \n5 353 194 \n6 1137 684 \n7 919 272 \n8 1535 498 \n9 1072 729 \n12 4702 2010 \n14 3823 1810 \n15 5808 2663 \n16 1616 858 \n17 1495 853 \n18 2135 1834 \n19 2755 1768 \n22 2503 1889 \n23 980 502 \n24 4806 1742 \n25 7249 2375 \n26 3112 1013 \n35 4641 2432 \n39 2715 1424 \n77 244 2 \n92 138 15 \n\nTEXT_GENERAL_CODE Robbery No Firearm Theft from Vehicle Thefts \nDC_DIST \n1 864 1843 4471 \n2 1930 4744 10456 \n3 1434 4810 7952 \n4 954 2616 4449 \n5 271 2413 2880 \n6 1560 7514 15635 \n7 543 3495 4053 \n8 843 5271 8911 \n9 1352 6960 14593 \n12 1742 4542 7001 \n14 1878 5331 8349 \n15 3664 7805 13447 \n16 1057 2471 4526 \n17 1173 2963 4575 \n18 2124 5079 11113 \n19 1597 3763 6821 \n22 1934 3851 8072 \n23 599 1566 2309 \n24 2539 4655 10794 \n25 2501 4025 8504 \n26 1387 5751 6268 \n35 2482 4058 7825 \n39 1401 3842 5974 \n77 6 279 2221 \n92 42 826 108 "
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Normalize types of crime for each district \ncrime_pct = crime_counts.div(crime_counts.sum(1).astype(float), axis=0)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Sort by thefts column. This creates a view and does not change the original DataFrame\ncrime_pct.sort('Thefts')",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>TEXT_GENERAL_CODE</th>\n <th>Aggravated Assault Firearm</th>\n <th>Aggravated Assault No Firearm</th>\n <th>Burglary Non-Residential</th>\n <th>Burglary Residential</th>\n <th>Homicide - Criminal</th>\n <th>Homicide - Gross Negligence</th>\n <th>Homicide - Justifiable</th>\n <th>Motor Vehicle Theft</th>\n <th>Rape</th>\n <th>Recovered Stolen Motor Vehicle</th>\n <th>Robbery Firearm</th>\n <th>Robbery No Firearm</th>\n <th>Theft from Vehicle</th>\n <th>Thefts</th>\n </tr>\n <tr>\n <th>DC_DIST</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>92</th>\n <td> 0.008614</td>\n <td> 0.014096</td>\n <td> 0.036805</td>\n <td> 0.006265</td>\n <td> 0.003915</td>\n <td> 0.008614</td>\n <td> 0.000000</td>\n <td> 0.028191</td>\n <td> 0.009397</td>\n <td> 0.108066</td>\n <td> 0.011746</td>\n <td> 0.032890</td>\n <td> 0.646829</td>\n <td> 0.084573</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.049112</td>\n <td> 0.094231</td>\n <td> 0.022249</td>\n <td> 0.127663</td>\n <td> 0.005917</td>\n <td> 0.001716</td>\n <td> 0.000237</td>\n <td> 0.092041</td>\n <td> 0.015207</td>\n <td> 0.139112</td>\n <td> 0.059467</td>\n <td> 0.051538</td>\n <td> 0.134379</td>\n <td> 0.207130</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 0.047173</td>\n <td> 0.084566</td>\n <td> 0.032761</td>\n <td> 0.078622</td>\n <td> 0.005507</td>\n <td> 0.001415</td>\n <td> 0.000360</td>\n <td> 0.100522</td>\n <td> 0.014592</td>\n <td> 0.186556</td>\n <td> 0.061122</td>\n <td> 0.064364</td>\n <td> 0.103585</td>\n <td> 0.218854</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 0.043350</td>\n <td> 0.088263</td>\n <td> 0.020242</td>\n <td> 0.105725</td>\n <td> 0.004633</td>\n <td> 0.000985</td>\n <td> 0.000290</td>\n <td> 0.099267</td>\n <td> 0.016448</td>\n <td> 0.134393</td>\n <td> 0.070425</td>\n <td> 0.071873</td>\n <td> 0.117511</td>\n <td> 0.226595</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 0.047429</td>\n <td> 0.091716</td>\n <td> 0.029861</td>\n <td> 0.126386</td>\n <td> 0.006399</td>\n <td> 0.001590</td>\n <td> 0.000427</td>\n <td> 0.085046</td>\n <td> 0.015629</td>\n <td> 0.105290</td>\n <td> 0.055224</td>\n <td> 0.054332</td>\n <td> 0.148996</td>\n <td> 0.231676</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 0.050764</td>\n <td> 0.105989</td>\n <td> 0.026420</td>\n <td> 0.142091</td>\n <td> 0.005325</td>\n <td> 0.000899</td>\n <td> 0.000311</td>\n <td> 0.074694</td>\n <td> 0.015872</td>\n <td> 0.095269</td>\n <td> 0.061138</td>\n <td> 0.055225</td>\n <td> 0.130127</td>\n <td> 0.235874</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 0.024531</td>\n <td> 0.066963</td>\n <td> 0.032006</td>\n <td> 0.100847</td>\n <td> 0.003641</td>\n <td> 0.001342</td>\n <td> 0.000153</td>\n <td> 0.087776</td>\n <td> 0.010771</td>\n <td> 0.119284</td>\n <td> 0.038829</td>\n <td> 0.053164</td>\n <td> 0.220438</td>\n <td> 0.240255</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 0.034390</td>\n <td> 0.078960</td>\n <td> 0.023656</td>\n <td> 0.158388</td>\n <td> 0.004084</td>\n <td> 0.000700</td>\n <td> 0.000233</td>\n <td> 0.068605</td>\n <td> 0.012864</td>\n <td> 0.111513</td>\n <td> 0.052796</td>\n <td> 0.054779</td>\n <td> 0.155500</td>\n <td> 0.243532</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 0.049284</td>\n <td> 0.093612</td>\n <td> 0.015297</td>\n <td> 0.164602</td>\n <td> 0.005763</td>\n <td> 0.000162</td>\n <td> 0.000323</td>\n <td> 0.063503</td>\n <td> 0.011796</td>\n <td> 0.080524</td>\n <td> 0.045944</td>\n <td> 0.063180</td>\n <td> 0.159593</td>\n <td> 0.246418</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 0.027404</td>\n <td> 0.091073</td>\n <td> 0.029130</td>\n <td> 0.110902</td>\n <td> 0.002378</td>\n <td> 0.001477</td>\n <td> 0.000192</td>\n <td> 0.085492</td>\n <td> 0.011679</td>\n <td> 0.111382</td>\n <td> 0.051069</td>\n <td> 0.070266</td>\n <td> 0.149679</td>\n <td> 0.257877</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 0.039189</td>\n <td> 0.091554</td>\n <td> 0.028041</td>\n <td> 0.097635</td>\n <td> 0.006532</td>\n <td> 0.001014</td>\n <td> 0.000225</td>\n <td> 0.049662</td>\n <td> 0.015428</td>\n <td> 0.110360</td>\n <td> 0.056532</td>\n <td> 0.067455</td>\n <td> 0.176351</td>\n <td> 0.260023</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 0.043229</td>\n <td> 0.107662</td>\n <td> 0.023313</td>\n <td> 0.120783</td>\n <td> 0.006678</td>\n <td> 0.001172</td>\n <td> 0.000234</td>\n <td> 0.061914</td>\n <td> 0.018334</td>\n <td> 0.094658</td>\n <td> 0.050258</td>\n <td> 0.061914</td>\n <td> 0.144740</td>\n <td> 0.265112</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 0.055653</td>\n <td> 0.099878</td>\n <td> 0.023837</td>\n <td> 0.124222</td>\n <td> 0.007608</td>\n <td> 0.000778</td>\n <td> 0.000203</td>\n <td> 0.054977</td>\n <td> 0.015824</td>\n <td> 0.084629</td>\n <td> 0.063869</td>\n <td> 0.065391</td>\n <td> 0.130207</td>\n <td> 0.272924</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 0.032812</td>\n <td> 0.075284</td>\n <td> 0.038043</td>\n <td> 0.106205</td>\n <td> 0.003418</td>\n <td> 0.001269</td>\n <td> 0.000259</td>\n <td> 0.093256</td>\n <td> 0.014036</td>\n <td> 0.124463</td>\n <td> 0.045113</td>\n <td> 0.065753</td>\n <td> 0.120552</td>\n <td> 0.279536</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.009239</td>\n <td> 0.046054</td>\n <td> 0.050566</td>\n <td> 0.155135</td>\n <td> 0.001289</td>\n <td> 0.003080</td>\n <td> 0.000143</td>\n <td> 0.063171</td>\n <td> 0.006518</td>\n <td> 0.065822</td>\n <td> 0.019481</td>\n <td> 0.038891</td>\n <td> 0.250322</td>\n <td> 0.290288</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.018916</td>\n <td> 0.057474</td>\n <td> 0.027408</td>\n <td> 0.129604</td>\n <td> 0.001813</td>\n <td> 0.001511</td>\n <td> 0.000212</td>\n <td> 0.092557</td>\n <td> 0.008521</td>\n <td> 0.092406</td>\n <td> 0.051945</td>\n <td> 0.058320</td>\n <td> 0.143354</td>\n <td> 0.315958</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.008192</td>\n <td> 0.051509</td>\n <td> 0.033778</td>\n <td> 0.143418</td>\n <td> 0.000449</td>\n <td> 0.001347</td>\n <td> 0.000112</td>\n <td> 0.069801</td>\n <td> 0.005611</td>\n <td> 0.039614</td>\n <td> 0.021771</td>\n <td> 0.030412</td>\n <td> 0.270789</td>\n <td> 0.323196</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.021622</td>\n <td> 0.068233</td>\n <td> 0.019115</td>\n <td> 0.062750</td>\n <td> 0.002664</td>\n <td> 0.001332</td>\n <td> 0.000157</td>\n <td> 0.051312</td>\n <td> 0.006894</td>\n <td> 0.095339</td>\n <td> 0.042382</td>\n <td> 0.074736</td>\n <td> 0.204935</td>\n <td> 0.348531</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 0.031263</td>\n <td> 0.074905</td>\n <td> 0.021617</td>\n <td> 0.102146</td>\n <td> 0.003362</td>\n <td> 0.000785</td>\n <td> 0.000314</td>\n <td> 0.054105</td>\n <td> 0.011311</td>\n <td> 0.067081</td>\n <td> 0.057624</td>\n <td> 0.066736</td>\n <td> 0.159581</td>\n <td> 0.349169</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 0.042028</td>\n <td> 0.078431</td>\n <td> 0.023592</td>\n <td> 0.097961</td>\n <td> 0.003984</td>\n <td> 0.000859</td>\n <td> 0.000078</td>\n <td> 0.069291</td>\n <td> 0.007656</td>\n <td> 0.078275</td>\n <td> 0.037106</td>\n <td> 0.067495</td>\n <td> 0.143973</td>\n <td> 0.349270</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0.013796</td>\n <td> 0.058200</td>\n <td> 0.021836</td>\n <td> 0.076702</td>\n <td> 0.002147</td>\n <td> 0.000914</td>\n <td> 0.000091</td>\n <td> 0.076610</td>\n <td> 0.005071</td>\n <td> 0.062083</td>\n <td> 0.034034</td>\n <td> 0.065509</td>\n <td> 0.219735</td>\n <td> 0.363271</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.009502</td>\n <td> 0.062906</td>\n <td> 0.036903</td>\n <td> 0.093828</td>\n <td> 0.001060</td>\n <td> 0.002163</td>\n <td> 0.000000</td>\n <td> 0.063457</td>\n <td> 0.006617</td>\n <td> 0.065111</td>\n <td> 0.021124</td>\n <td> 0.035758</td>\n <td> 0.223584</td>\n <td> 0.377985</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0.008898</td>\n <td> 0.038749</td>\n <td> 0.026821</td>\n <td> 0.036004</td>\n <td> 0.001231</td>\n <td> 0.000757</td>\n <td> 0.000063</td>\n <td> 0.045344</td>\n <td> 0.004986</td>\n <td> 0.035878</td>\n <td> 0.021583</td>\n <td> 0.049225</td>\n <td> 0.237102</td>\n <td> 0.493358</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.006122</td>\n <td> 0.025210</td>\n <td> 0.024556</td>\n <td> 0.055957</td>\n <td> 0.000619</td>\n <td> 0.000585</td>\n <td> 0.000103</td>\n <td> 0.032191</td>\n <td> 0.004953</td>\n <td> 0.036869</td>\n <td> 0.025072</td>\n <td> 0.046499</td>\n <td> 0.239373</td>\n <td> 0.501892</td>\n </tr>\n <tr>\n <th>77</th>\n <td> 0.000347</td>\n <td> 0.005554</td>\n <td> 0.005901</td>\n <td> 0.000347</td>\n <td> 0.000000</td>\n <td> 0.000694</td>\n <td> 0.000000</td>\n <td> 0.030198</td>\n <td> 0.001736</td>\n <td> 0.084693</td>\n <td> 0.000694</td>\n <td> 0.002083</td>\n <td> 0.096841</td>\n <td> 0.770913</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"prompt_number": 14,
"text": "TEXT_GENERAL_CODE Aggravated Assault Firearm Aggravated Assault No Firearm \\\nDC_DIST \n92 0.008614 0.014096 \n12 0.049112 0.094231 \n25 0.047173 0.084566 \n35 0.043350 0.088263 \n39 0.047429 0.091716 \n19 0.050764 0.105989 \n26 0.024531 0.066963 \n14 0.034390 0.078960 \n17 0.049284 0.093612 \n15 0.027404 0.091073 \n23 0.039189 0.091554 \n16 0.043229 0.107662 \n22 0.055653 0.099878 \n24 0.032812 0.075284 \n7 0.009239 0.046054 \n2 0.018916 0.057474 \n5 0.008192 0.051509 \n4 0.021622 0.068233 \n18 0.031263 0.074905 \n1 0.042028 0.078431 \n3 0.013796 0.058200 \n8 0.009502 0.062906 \n6 0.008898 0.038749 \n9 0.006122 0.025210 \n77 0.000347 0.005554 \n\nTEXT_GENERAL_CODE Burglary Non-Residential Burglary Residential \\\nDC_DIST \n92 0.036805 0.006265 \n12 0.022249 0.127663 \n25 0.032761 0.078622 \n35 0.020242 0.105725 \n39 0.029861 0.126386 \n19 0.026420 0.142091 \n26 0.032006 0.100847 \n14 0.023656 0.158388 \n17 0.015297 0.164602 \n15 0.029130 0.110902 \n23 0.028041 0.097635 \n16 0.023313 0.120783 \n22 0.023837 0.124222 \n24 0.038043 0.106205 \n7 0.050566 0.155135 \n2 0.027408 0.129604 \n5 0.033778 0.143418 \n4 0.019115 0.062750 \n18 0.021617 0.102146 \n1 0.023592 0.097961 \n3 0.021836 0.076702 \n8 0.036903 0.093828 \n6 0.026821 0.036004 \n9 0.024556 0.055957 \n77 0.005901 0.000347 \n\nTEXT_GENERAL_CODE Homicide - Criminal Homicide - Gross Negligence \\\nDC_DIST \n92 0.003915 0.008614 \n12 0.005917 0.001716 \n25 0.005507 0.001415 \n35 0.004633 0.000985 \n39 0.006399 0.001590 \n19 0.005325 0.000899 \n26 0.003641 0.001342 \n14 0.004084 0.000700 \n17 0.005763 0.000162 \n15 0.002378 0.001477 \n23 0.006532 0.001014 \n16 0.006678 0.001172 \n22 0.007608 0.000778 \n24 0.003418 0.001269 \n7 0.001289 0.003080 \n2 0.001813 0.001511 \n5 0.000449 0.001347 \n4 0.002664 0.001332 \n18 0.003362 0.000785 \n1 0.003984 0.000859 \n3 0.002147 0.000914 \n8 0.001060 0.002163 \n6 0.001231 0.000757 \n9 0.000619 0.000585 \n77 0.000000 0.000694 \n\nTEXT_GENERAL_CODE Homicide - Justifiable Motor Vehicle Theft Rape \\\nDC_DIST \n92 0.000000 0.028191 0.009397 \n12 0.000237 0.092041 0.015207 \n25 0.000360 0.100522 0.014592 \n35 0.000290 0.099267 0.016448 \n39 0.000427 0.085046 0.015629 \n19 0.000311 0.074694 0.015872 \n26 0.000153 0.087776 0.010771 \n14 0.000233 0.068605 0.012864 \n17 0.000323 0.063503 0.011796 \n15 0.000192 0.085492 0.011679 \n23 0.000225 0.049662 0.015428 \n16 0.000234 0.061914 0.018334 \n22 0.000203 0.054977 0.015824 \n24 0.000259 0.093256 0.014036 \n7 0.000143 0.063171 0.006518 \n2 0.000212 0.092557 0.008521 \n5 0.000112 0.069801 0.005611 \n4 0.000157 0.051312 0.006894 \n18 0.000314 0.054105 0.011311 \n1 0.000078 0.069291 0.007656 \n3 0.000091 0.076610 0.005071 \n8 0.000000 0.063457 0.006617 \n6 0.000063 0.045344 0.004986 \n9 0.000103 0.032191 0.004953 \n77 0.000000 0.030198 0.001736 \n\nTEXT_GENERAL_CODE Recovered Stolen Motor Vehicle Robbery Firearm \\\nDC_DIST \n92 0.108066 0.011746 \n12 0.139112 0.059467 \n25 0.186556 0.061122 \n35 0.134393 0.070425 \n39 0.105290 0.055224 \n19 0.095269 0.061138 \n26 0.119284 0.038829 \n14 0.111513 0.052796 \n17 0.080524 0.045944 \n15 0.111382 0.051069 \n23 0.110360 0.056532 \n16 0.094658 0.050258 \n22 0.084629 0.063869 \n24 0.124463 0.045113 \n7 0.065822 0.019481 \n2 0.092406 0.051945 \n5 0.039614 0.021771 \n4 0.095339 0.042382 \n18 0.067081 0.057624 \n1 0.078275 0.037106 \n3 0.062083 0.034034 \n8 0.065111 0.021124 \n6 0.035878 0.021583 \n9 0.036869 0.025072 \n77 0.084693 0.000694 \n\nTEXT_GENERAL_CODE Robbery No Firearm Theft from Vehicle Thefts \nDC_DIST \n92 0.032890 0.646829 0.084573 \n12 0.051538 0.134379 0.207130 \n25 0.064364 0.103585 0.218854 \n35 0.071873 0.117511 0.226595 \n39 0.054332 0.148996 0.231676 \n19 0.055225 0.130127 0.235874 \n26 0.053164 0.220438 0.240255 \n14 0.054779 0.155500 0.243532 \n17 0.063180 0.159593 0.246418 \n15 0.070266 0.149679 0.257877 \n23 0.067455 0.176351 0.260023 \n16 0.061914 0.144740 0.265112 \n22 0.065391 0.130207 0.272924 \n24 0.065753 0.120552 0.279536 \n7 0.038891 0.250322 0.290288 \n2 0.058320 0.143354 0.315958 \n5 0.030412 0.270789 0.323196 \n4 0.074736 0.204935 0.348531 \n18 0.066736 0.159581 0.349169 \n1 0.067495 0.143973 0.349270 \n3 0.065509 0.219735 0.363271 \n8 0.035758 0.223584 0.377985 \n6 0.049225 0.237102 0.493358 \n9 0.046499 0.239373 0.501892 \n77 0.002083 0.096841 0.770913 "
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Plot normalize crime vs district\ncolors=['r', 'g', 'b', 'c', 'y', 'w', 'm', 'k', 'burlywood','navy', 'teal', 'LightSteelBlue', 'Honeydew', 'Goldenrod']\np = crime_pct.plot(kind='bar', stacked=True, color=colors)\np.legend(loc=0, bbox_to_anchor=(1,1))",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 15,
"text": "<matplotlib.legend.Legend at 0x10fcb4910>"
},
{
"output_type": "display_data",
"png": 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g1+vp169fuRtd16SHvYXL/QgMDMTW1tZkPP/6179o0aJFrbQXGhqKjY2NMpcv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\n"
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "def code_rename(code):\n \"\"\"\" Lazy consolidattion of crime codes\"\"\"\n lower_code = code.lower()\n new_codes = ['Assault', 'Burglary', 'Homicide', 'Vehicle', 'Robbery', 'Theft']\n for new_code in new_codes:\n if new_code.lower() in lower_code:\n return new_code\n return code",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Consolidate crimes\ncrime.TEXT_GENERAL_CODE = crime.TEXT_GENERAL_CODE.map(code_rename)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "crime.TEXT_GENERAL_CODE.unique()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 18,
"text": "array(['Robbery', 'Theft', 'Vehicle', 'Assault', 'Burglary', 'Homicide',\n 'Rape'], dtype=object)"
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": "simple_crime_count = pd.crosstab(crime.DC_DIST, crime.TEXT_GENERAL_CODE)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Normalize crime types\ncrime_pct = simple_crime_count.div(simple_crime_count.sum(1).astype(float), axis=0)\ncrime_pct",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>TEXT_GENERAL_CODE</th>\n <th>Assault</th>\n <th>Burglary</th>\n <th>Homicide</th>\n <th>Rape</th>\n <th>Robbery</th>\n <th>Theft</th>\n <th>Vehicle</th>\n </tr>\n <tr>\n <th>DC_DIST</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1 </th>\n <td> 0.120459</td>\n <td> 0.121553</td>\n <td> 0.004921</td>\n <td> 0.007656</td>\n <td> 0.104601</td>\n <td> 0.349270</td>\n <td> 0.291540</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.076391</td>\n <td> 0.157012</td>\n <td> 0.003535</td>\n <td> 0.008521</td>\n <td> 0.110265</td>\n <td> 0.315958</td>\n <td> 0.328317</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0.071996</td>\n <td> 0.098538</td>\n <td> 0.003152</td>\n <td> 0.005071</td>\n <td> 0.099543</td>\n <td> 0.363271</td>\n <td> 0.358429</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.089855</td>\n <td> 0.081864</td>\n <td> 0.004152</td>\n <td> 0.006894</td>\n <td> 0.117117</td>\n <td> 0.348531</td>\n <td> 0.351586</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.059701</td>\n <td> 0.177197</td>\n <td> 0.001908</td>\n <td> 0.005611</td>\n <td> 0.052183</td>\n <td> 0.323196</td>\n <td> 0.380204</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0.047648</td>\n <td> 0.062825</td>\n <td> 0.002051</td>\n <td> 0.004986</td>\n <td> 0.070809</td>\n <td> 0.493358</td>\n <td> 0.318324</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.055293</td>\n <td> 0.205701</td>\n <td> 0.004512</td>\n <td> 0.006518</td>\n <td> 0.058373</td>\n <td> 0.290288</td>\n <td> 0.379315</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.072407</td>\n <td> 0.130732</td>\n <td> 0.003224</td>\n <td> 0.006617</td>\n <td> 0.056882</td>\n <td> 0.377985</td>\n <td> 0.352153</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.031332</td>\n <td> 0.080513</td>\n <td> 0.001307</td>\n <td> 0.004953</td>\n <td> 0.071571</td>\n <td> 0.501892</td>\n <td> 0.308433</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.143343</td>\n <td> 0.149911</td>\n <td> 0.007870</td>\n <td> 0.015207</td>\n <td> 0.111006</td>\n <td> 0.207130</td>\n <td> 0.365533</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 0.113351</td>\n <td> 0.182044</td>\n <td> 0.005017</td>\n <td> 0.012864</td>\n <td> 0.107575</td>\n <td> 0.243532</td>\n <td> 0.335618</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 0.118477</td>\n <td> 0.140033</td>\n <td> 0.004046</td>\n <td> 0.011679</td>\n <td> 0.121335</td>\n <td> 0.257877</td>\n <td> 0.346553</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 0.150890</td>\n <td> 0.144096</td>\n <td> 0.008083</td>\n <td> 0.018334</td>\n <td> 0.112172</td>\n <td> 0.265112</td>\n <td> 0.301312</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 0.142896</td>\n <td> 0.179899</td>\n <td> 0.006248</td>\n <td> 0.011796</td>\n <td> 0.109124</td>\n <td> 0.246418</td>\n <td> 0.303620</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 0.106168</td>\n <td> 0.123763</td>\n <td> 0.004462</td>\n <td> 0.011311</td>\n <td> 0.124360</td>\n <td> 0.349169</td>\n <td> 0.280768</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 0.156754</td>\n <td> 0.168511</td>\n <td> 0.006536</td>\n <td> 0.015872</td>\n <td> 0.116364</td>\n <td> 0.235874</td>\n <td> 0.300090</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 0.155532</td>\n <td> 0.148059</td>\n <td> 0.008588</td>\n <td> 0.015824</td>\n <td> 0.129260</td>\n <td> 0.272924</td>\n <td> 0.269813</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 0.130743</td>\n <td> 0.125676</td>\n <td> 0.007770</td>\n <td> 0.015428</td>\n <td> 0.123986</td>\n <td> 0.260023</td>\n <td> 0.336374</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 0.108096</td>\n <td> 0.144248</td>\n <td> 0.004946</td>\n <td> 0.014036</td>\n <td> 0.110867</td>\n <td> 0.279536</td>\n <td> 0.338271</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 0.131739</td>\n <td> 0.111383</td>\n <td> 0.007283</td>\n <td> 0.014592</td>\n <td> 0.125486</td>\n <td> 0.218854</td>\n <td> 0.390663</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 0.091494</td>\n <td> 0.132853</td>\n <td> 0.005136</td>\n <td> 0.010771</td>\n <td> 0.091993</td>\n <td> 0.240255</td>\n <td> 0.427498</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 0.131613</td>\n <td> 0.125966</td>\n <td> 0.005907</td>\n <td> 0.016448</td>\n <td> 0.142299</td>\n <td> 0.226595</td>\n <td> 0.351171</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 0.139145</td>\n <td> 0.156248</td>\n <td> 0.008415</td>\n <td> 0.015629</td>\n <td> 0.109556</td>\n <td> 0.231676</td>\n <td> 0.339331</td>\n </tr>\n <tr>\n <th>77</th>\n <td> 0.005901</td>\n <td> 0.006248</td>\n <td> 0.000694</td>\n <td> 0.001736</td>\n <td> 0.002777</td>\n <td> 0.770913</td>\n <td> 0.211732</td>\n </tr>\n <tr>\n <th>92</th>\n <td> 0.022709</td>\n <td> 0.043070</td>\n <td> 0.012529</td>\n <td> 0.009397</td>\n <td> 0.044636</td>\n <td> 0.084573</td>\n <td> 0.783085</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"prompt_number": 20,
"text": "TEXT_GENERAL_CODE Assault Burglary Homicide Rape Robbery Theft \\\nDC_DIST \n1 0.120459 0.121553 0.004921 0.007656 0.104601 0.349270 \n2 0.076391 0.157012 0.003535 0.008521 0.110265 0.315958 \n3 0.071996 0.098538 0.003152 0.005071 0.099543 0.363271 \n4 0.089855 0.081864 0.004152 0.006894 0.117117 0.348531 \n5 0.059701 0.177197 0.001908 0.005611 0.052183 0.323196 \n6 0.047648 0.062825 0.002051 0.004986 0.070809 0.493358 \n7 0.055293 0.205701 0.004512 0.006518 0.058373 0.290288 \n8 0.072407 0.130732 0.003224 0.006617 0.056882 0.377985 \n9 0.031332 0.080513 0.001307 0.004953 0.071571 0.501892 \n12 0.143343 0.149911 0.007870 0.015207 0.111006 0.207130 \n14 0.113351 0.182044 0.005017 0.012864 0.107575 0.243532 \n15 0.118477 0.140033 0.004046 0.011679 0.121335 0.257877 \n16 0.150890 0.144096 0.008083 0.018334 0.112172 0.265112 \n17 0.142896 0.179899 0.006248 0.011796 0.109124 0.246418 \n18 0.106168 0.123763 0.004462 0.011311 0.124360 0.349169 \n19 0.156754 0.168511 0.006536 0.015872 0.116364 0.235874 \n22 0.155532 0.148059 0.008588 0.015824 0.129260 0.272924 \n23 0.130743 0.125676 0.007770 0.015428 0.123986 0.260023 \n24 0.108096 0.144248 0.004946 0.014036 0.110867 0.279536 \n25 0.131739 0.111383 0.007283 0.014592 0.125486 0.218854 \n26 0.091494 0.132853 0.005136 0.010771 0.091993 0.240255 \n35 0.131613 0.125966 0.005907 0.016448 0.142299 0.226595 \n39 0.139145 0.156248 0.008415 0.015629 0.109556 0.231676 \n77 0.005901 0.006248 0.000694 0.001736 0.002777 0.770913 \n92 0.022709 0.043070 0.012529 0.009397 0.044636 0.084573 \n\nTEXT_GENERAL_CODE Vehicle \nDC_DIST \n1 0.291540 \n2 0.328317 \n3 0.358429 \n4 0.351586 \n5 0.380204 \n6 0.318324 \n7 0.379315 \n8 0.352153 \n9 0.308433 \n12 0.365533 \n14 0.335618 \n15 0.346553 \n16 0.301312 \n17 0.303620 \n18 0.280768 \n19 0.300090 \n22 0.269813 \n23 0.336374 \n24 0.338271 \n25 0.390663 \n26 0.427498 \n35 0.351171 \n39 0.339331 \n77 0.211732 \n92 0.783085 "
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": "plot = crime_pct.plot(kind='bar', stacked=True, color=['r', 'g', 'b', 'c', 'y', 'k', 'm', 'w'])\nplot.legend(loc=0, bbox_to_anchor=(1,1))",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 21,
"text": "<matplotlib.legend.Legend at 0x10fdc8490>"
},
{
"output_type": "display_data",
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j2rVr2LZtG55++ukKY+pXBYMrERHJRgiBIUOGQKlUStMzzzwDhUKBgwcP4tatW5g9ezbs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\nGFyJiIhkxuBKREQkMwZXIiIimf0f5P210kaS6K4AAAAASUVORK5CYII=\n"
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": "df.TEXT_GENERAL_CODE = df.TEXT_GENERAL_CODE.map(code_rename)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Time Series"
},
{
"cell_type": "code",
"collapsed": false,
"input": "ct_date = pd.crosstab(df['DISPATCH_DATE'],df['TEXT_GENERAL_CODE'])\nct_date",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 23,
"text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 2628 entries, 2006-01-01 to 2013-03-12\nData columns:\nAssault 2628 non-null values\nBurglary 2628 non-null values\nHomicide 2628 non-null values\nRape 2628 non-null values\nRobbery 2628 non-null values\nTheft 2628 non-null values\nVehicle 2628 non-null values\ndtypes: int64(7)"
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Set index as a DateTime format\nct_date.index = pd.to_datetime(ct_date.index)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Date filtering\nct_date.ix['2012-01'].sum()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 25,
"text": "TEXT_GENERAL_CODE\nAssault 639\nBurglary 1066\nHomicide 41\nRape 57\nRobbery 652\nTheft 2012\nVehicle 1905"
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": "year_2012 = ct_date.ix['2012']",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Resample data to monthly periods\nyear_2012.resample('M', how='mean', kind='period').plot()\nlegend(loc=0, bbox_to_anchor=(1,1))",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 27,
"text": "<matplotlib.legend.Legend at 0x10fcf2b90>"
},
{
"output_type": "display_data",
"png": 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7FhYPPzmzVD1SU1O1Nc/Dhw9z4sQJvLy86NKlC4GBgXz88cf4+PhU25JRDZ2h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\n/AC73Q6j0Yivv/5aluP2lvJ9/cc8z3uUu2/fPjAMg2HDhuHcuXPSfbZ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}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Plot of all years available\nct_date.resample('M', how='mean', kind='period').plot()\nlegend(loc=0, bbox_to_anchor=(1,1))",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 28,
"text": "<matplotlib.legend.Legend at 0x10fcfd4d0>"
},
{
"output_type": "display_data",
"png": 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3wtKy1qOgqzsYJSXBClbVNrC1tYWmpiZYLBZMTU0xY8YMlJaWKlqWQpDa0Pr5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\n/s8y6s9R3OW9q0rXXgi+wLZb2jK3sPITeUnRNmxeiKhjWZ4MPXb0wI6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++dKXzw0gWXZr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\nqEaroqKioqIyivw/yQZoKxBafusAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
}
],
"metadata": {}
}
]
}
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