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kforeman/20171120 - Extract cases.ipynb

Created November 20, 2017 07:30
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20171120 - Extract cases.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import os, sys"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Make a list of all the codes we're interested in\n",
"_i.e. first two pages of Asher's Excel spreadsheet_"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>addtl</th>\n",
" <th>descr</th>\n",
" <th>description</th>\n",
" <th>odyssey</th>\n",
" <th>statute</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Assault on Person &gt; 60 by Caretaker Causing Bo...</td>\n",
" <td>14588</td>\n",
" <td>11-5-10.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Assault on Person &gt; 60 by Caretaker Causing Bo...</td>\n",
" <td>14589</td>\n",
" <td>11-5-10.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Assault on Person &gt; 60 by Caretaker Causing Se...</td>\n",
" <td>14590</td>\n",
" <td>11-5-10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Assault on Person &gt; 60 by Caretaker Causing Se...</td>\n",
" <td>14591</td>\n",
" <td>11-5-10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Assault on Person &gt; 60 Causing Bodily Injury</td>\n",
" <td>11187</td>\n",
" <td>11-5-10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" addtl descr description odyssey \\\n",
"0 0 NaN NaN Assault on Person > 60 by Caretaker Causing Bo... 14588 \n",
" 1 NaN NaN Assault on Person > 60 by Caretaker Causing Bo... 14589 \n",
" 2 NaN NaN Assault on Person > 60 by Caretaker Causing Se... 14590 \n",
" 3 NaN NaN Assault on Person > 60 by Caretaker Causing Se... 14591 \n",
" 4 NaN NaN Assault on Person > 60 Causing Bodily Injury 11187 \n",
"\n",
" statute \n",
"0 0 11-5-10.3 \n",
" 1 11-5-10.3 \n",
" 2 11-5-10.4 \n",
" 3 11-5-10.4 \n",
" 4 11-5-10 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_codes = pd.concat(pd.read_excel('../data/raw/Elder Offense Codes FINAL.xlsx', [0,1], skiprows=1))\n",
"all_codes.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"statute = all_codes['statute'].dropna().unique().tolist()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"odyssey = all_codes['odyssey'].dropna().unique().tolist()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'11-5-10.3|11-5-10.4|11-5-10|11-5-10.1|11-5-10 and 12-29-2(a)(2)|11-5-10.1 and 12-29-2(a)(2)|11-8-2.3 and 12-29-2(a)(13)|11-68-2|11-68-2(a)(1)|11-68-2(a)(3)|11-68-2(a)(2)|11-41-1|11-41-7|11-39-1|11-5-10.2|11-5-11|11-5-12|23-17.8-1(a)(1)(i)to(iv)|11-5-12(a)|23-17.8-1(a)(1)(i) to (iv)|23-17.8-1A1i-iv|23-17.8-1E|23-17.8-1|40.1-27-1|40.1-27-1/M|11-41-5(b)|14588|14589|14590|14591|11187|14581|14585|11-5-10|11-5-10.3|11050100|11050101J|11-5-10A|14582|14583|14584|14586|14472|14473|11-68-2|14880|14882|14881|11061|14376|11062|14377|11068|14388|11069|14389|11-41-5(b)|11050104|11050101|11-5-10.1A|11-5-10.1|11050102|11050102J|11050110|11050120|11390010|11-5-10.2|11-5-11|11-5-12F|11-5-12M|11585|11605|14587|14592|14594|15403|23-17.8-1A1i-iv|23-17.8-1E|23-17.8-1F|23178011|23178012|23178013|40.1-27-1F|40.1-27-1M|401271A1'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charges = '|'.join([str(c) for c in statute + odyssey if c != ' '])\n",
"charges"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load in the data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_hdf(os.path.join(os.pardir, 'data', 'clean', 'CRIM_CASE.h5'))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IDENTIFIER</th>\n",
" <th>CASE_NO</th>\n",
" <th>CASE_LOCN</th>\n",
" <th>CASE_TYPE</th>\n",
" <th>CASE_FILING</th>\n",
" <th>ARREST_AGENCY</th>\n",
" <th>ARREST_DATE</th>\n",
" <th>CHARGE_NUMBER</th>\n",
" <th>CHARGE_CODE</th>\n",
" <th>CHARGE_MAINT</th>\n",
" <th>CHARGE_FILING</th>\n",
" <th>CHARGE_CITY</th>\n",
" <th>CHARGE_DISP</th>\n",
" <th>CHARGE_DATE</th>\n",
" <th>PLEA_DISP</th>\n",
" <th>PLEA_MAINT</th>\n",
" <th>PLEA_DATE</th>\n",
" <th>PROSECUTOR</th>\n",
" <th>ATTORNEY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4680901</td>\n",
" <td>31-2016-02909</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-03-29T00:00:00.000000000</td>\n",
" <td>CRANSTON POLICE DEPARTMENT</td>\n",
" <td>2016-03-09T00:00:00.000000000</td>\n",
" <td>1</td>\n",
" <td>003</td>\n",
" <td>NaN</td>\n",
" <td>2016-03-29T00:00:00.000000000</td>\n",
" <td>CRANSTON</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-03-29T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4551414</td>\n",
" <td>31-2016-02925</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-03-29T00:00:00.000000000</td>\n",
" <td>CRANSTON POLICE DEPARTMENT</td>\n",
" <td>2016-03-09T00:00:00.000000000</td>\n",
" <td>1</td>\n",
" <td>003</td>\n",
" <td>NaN</td>\n",
" <td>2016-03-29T00:00:00.000000000</td>\n",
" <td>CRANSTON</td>\n",
" <td>DMNON</td>\n",
" <td>2016-03-30T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4550775</td>\n",
" <td>31-2016-03096</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-03-30T00:00:00.000000000</td>\n",
" <td>COVENTRY POLICE DEPARTMENT</td>\n",
" <td>2016-03-16T00:00:00.000000000</td>\n",
" <td>1</td>\n",
" <td>003</td>\n",
" <td>NaN</td>\n",
" <td>2016-03-30T00:00:00.000000000</td>\n",
" <td>COVENTRY</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-03-30T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4623177</td>\n",
" <td>31-2016-03286</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-04-13T00:00:00.000000000</td>\n",
" <td>COVENTRY POLICE DEPARTMENT</td>\n",
" <td>NaT</td>\n",
" <td>1</td>\n",
" <td>003</td>\n",
" <td>NaN</td>\n",
" <td>2016-04-13T00:00:00.000000000</td>\n",
" <td>COVENTRY</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-04-27T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4658064</td>\n",
" <td>31-2016-03298</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-04-18T00:00:00.000000000</td>\n",
" <td>COVENTRY POLICE DEPARTMENT</td>\n",
" <td>NaT</td>\n",
" <td>1</td>\n",
" <td>14383</td>\n",
" <td>NaN</td>\n",
" <td>2016-04-18T00:00:00.000000000</td>\n",
" <td>COVENTRY</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-04-18T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDENTIFIER CASE_NO CASE_LOCN CASE_TYPE \\\n",
"0 4680901 31-2016-02909 3D M \n",
"1 4551414 31-2016-02925 3D M \n",
"2 4550775 31-2016-03096 3D M \n",
"3 4623177 31-2016-03286 3D M \n",
"4 4658064 31-2016-03298 3D M \n",
"\n",
" CASE_FILING ARREST_AGENCY \\\n",
"0 2016-03-29T00:00:00.000000000 CRANSTON POLICE DEPARTMENT \n",
"1 2016-03-29T00:00:00.000000000 CRANSTON POLICE DEPARTMENT \n",
"2 2016-03-30T00:00:00.000000000 COVENTRY POLICE DEPARTMENT \n",
"3 2016-04-13T00:00:00.000000000 COVENTRY POLICE DEPARTMENT \n",
"4 2016-04-18T00:00:00.000000000 COVENTRY POLICE DEPARTMENT \n",
"\n",
" ARREST_DATE CHARGE_NUMBER CHARGE_CODE CHARGE_MAINT \\\n",
"0 2016-03-09T00:00:00.000000000 1 003 NaN \n",
"1 2016-03-09T00:00:00.000000000 1 003 NaN \n",
"2 2016-03-16T00:00:00.000000000 1 003 NaN \n",
"3 NaT 1 003 NaN \n",
"4 NaT 1 14383 NaN \n",
"\n",
" CHARGE_FILING CHARGE_CITY CHARGE_DISP \\\n",
"0 2016-03-29T00:00:00.000000000 CRANSTON GPNOL \n",
"1 2016-03-29T00:00:00.000000000 CRANSTON DMNON \n",
"2 2016-03-30T00:00:00.000000000 COVENTRY GPNOL \n",
"3 2016-04-13T00:00:00.000000000 COVENTRY GPNOL \n",
"4 2016-04-18T00:00:00.000000000 COVENTRY GPNOL \n",
"\n",
" CHARGE_DATE PLEA_DISP PLEA_MAINT PLEA_DATE PROSECUTOR \\\n",
"0 2016-03-29T00:00:00.000000000 NaN NaN NaT NaN \n",
"1 2016-03-30T00:00:00.000000000 NaN NaN NaT NaN \n",
"2 2016-03-30T00:00:00.000000000 NaN NaN NaT NaN \n",
"3 2016-04-27T00:00:00.000000000 NaN NaN NaT NaN \n",
"4 2016-04-18T00:00:00.000000000 NaN NaN NaT NaN \n",
"\n",
" ATTORNEY \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Find all of the records that have some version of the desired charge codes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kfor/.conda/envs/forecast-2-fork/lib/python2.7/site-packages/ipykernel_launcher.py:1: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"records = df.loc[df['CHARGE_CODE'].str.contains(charges)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IDENTIFIER</th>\n",
" <th>CASE_NO</th>\n",
" <th>CASE_LOCN</th>\n",
" <th>CASE_TYPE</th>\n",
" <th>CASE_FILING</th>\n",
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" <th>CHARGE_NUMBER</th>\n",
" <th>CHARGE_CODE</th>\n",
" <th>CHARGE_MAINT</th>\n",
" <th>CHARGE_FILING</th>\n",
" <th>CHARGE_CITY</th>\n",
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" <th>ATTORNEY</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>337</th>\n",
" <td>4791168</td>\n",
" <td>32-1997-06002</td>\n",
" <td>3D</td>\n",
" <td>F</td>\n",
" <td>1997-12-12T00:00:00.000000000</td>\n",
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" <td>WARWICK</td>\n",
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" <tr>\n",
" <th>697</th>\n",
" <td>4791169</td>\n",
" <td>32-1997-06003</td>\n",
" <td>3D</td>\n",
" <td>F</td>\n",
" <td>1997-12-12T00:00:00.000000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1365</th>\n",
" <td>4641670</td>\n",
" <td>32-1999-05699</td>\n",
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" <td>WARWICK POLICE DEPARTMENT</td>\n",
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" <td>2</td>\n",
" <td>11-41-11.1M</td>\n",
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" <td>WARWICK</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1717</th>\n",
" <td>4679963</td>\n",
" <td>31-2016-04011</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>WARWICK POLICE DEPARTMENT</td>\n",
" <td>NaT</td>\n",
" <td>2</td>\n",
" <td>14376</td>\n",
" <td>NaN</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>WARWICK</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1722</th>\n",
" <td>4679963</td>\n",
" <td>31-2016-04011</td>\n",
" <td>3D</td>\n",
" <td>M</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>WARWICK POLICE DEPARTMENT</td>\n",
" <td>NaT</td>\n",
" <td>3</td>\n",
" <td>14376</td>\n",
" <td>NaN</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>WARWICK</td>\n",
" <td>GPNOL</td>\n",
" <td>2016-04-25T00:00:00.000000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDENTIFIER CASE_NO CASE_LOCN CASE_TYPE \\\n",
"337 4791168 32-1997-06002 3D F \n",
"697 4791169 32-1997-06003 3D F \n",
"1365 4641670 32-1999-05699 3D F \n",
"1717 4679963 31-2016-04011 3D M \n",
"1722 4679963 31-2016-04011 3D M \n",
"\n",
" CASE_FILING ARREST_AGENCY \\\n",
"337 1997-12-12T00:00:00.000000000 NaN \n",
"697 1997-12-12T00:00:00.000000000 NaN \n",
"1365 1999-12-04T00:00:00.000000000 WARWICK POLICE DEPARTMENT \n",
"1717 2016-04-25T00:00:00.000000000 WARWICK POLICE DEPARTMENT \n",
"1722 2016-04-25T00:00:00.000000000 WARWICK POLICE DEPARTMENT \n",
"\n",
" ARREST_DATE CHARGE_NUMBER CHARGE_CODE CHARGE_MAINT \\\n",
"337 1997-12-12T00:00:00.000000000 1 11-41-11F NaN \n",
"697 1997-12-12T00:00:00.000000000 1 11-41-11F NaN \n",
"1365 1999-12-04T00:00:00.000000000 2 11-41-11.1M NaN \n",
"1717 NaT 2 14376 NaN \n",
"1722 NaT 3 14376 NaN \n",
"\n",
" CHARGE_FILING CHARGE_CITY CHARGE_DISP \\\n",
"337 1997-12-12T00:00:00.000000000 WARWICK CGINS \n",
"697 1997-12-12T00:00:00.000000000 WARWICK CGINS \n",
"1365 1999-12-04T00:00:00.000000000 WARWICK CGINA \n",
"1717 2016-04-25T00:00:00.000000000 WARWICK GPNOL \n",
"1722 2016-04-25T00:00:00.000000000 WARWICK GPNOL \n",
"\n",
" CHARGE_DATE PLEA_DISP PLEA_MAINT PLEA_DATE PROSECUTOR \\\n",
"337 1998-01-12T00:00:00.000000000 NaN NaN NaT NaN \n",
"697 1998-01-12T00:00:00.000000000 NaN NaN NaT NaN \n",
"1365 1999-12-17T00:00:00.000000000 NaN NaN NaT NaN \n",
"1717 2016-04-25T00:00:00.000000000 NaN NaN NaT NaN \n",
"1722 2016-04-25T00:00:00.000000000 NaN NaN NaT NaN \n",
"\n",
" ATTORNEY \n",
"337 NaN \n",
"697 NaN \n",
"1365 NaN \n",
"1717 NaN \n",
"1722 NaN "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"records.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Count how many records there are"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"12718"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"records.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Count how many there are after removing duplicate case numbers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9479"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"records.drop_duplicates('CASE_NO').shape[0]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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