Criminal-Bias-Williams-Statistics-Edelson.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "df_main = pandas.read_csv(\"../source.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def col_search(df, strg = None):\n",
    "    \"\"\"\n",
    "    Searches list of columns and finds those with strg in the name\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    df -- pandas dataframe to be searched\n",
    "    strg -- string that is optionally searched for\n",
    "    \"\"\"\n",
    "    for col in df.columns:\n",
    "        if strg == None:\n",
    "            print(col)\n",
    "        else:\n",
    "            if strg.lower() in col.lower():\n",
    "                print(col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SP_JudgeatSentencing\n"
     ]
    }
   ],
   "source": [
    "# look for column with judge\n",
    "col_search(df_main, \"judge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HAWORTH, LEE', 'HAWORTH, LEE E', 'WILLIAMS, CHARLES',\n",
       "       'WILLIAMS, CHARLES E', 'HAWORTH'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove leading and trailing white space from column and \n",
    "# view unique values\n",
    "df_main[\"SP_JudgeatSentencing\"] = df_main[\"SP_JudgeatSentencing\"].apply(str.strip)\n",
    "df_main.SP_JudgeatSentencing.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WILLIAMS, CHARLES', 'WILLIAMS, CHARLES E'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter on Williams\n",
    "idx_will = df_main.SP_JudgeatSentencing.isin(('WILLIAMS, CHARLES', 'WILLIAMS, CHARLES E'))\n",
    "df_will = df_main[idx_will]\n",
    "df_will.SP_JudgeatSentencing.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'S', 'T', 'P'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter on felonies\n",
    "idx_fel = df_will.CP_DegreeOfCharge.isin((\"F\",\"S\",\"T\", \"P\"))\n",
    "df_will = df_will[idx_fel]\n",
    "df_will.CP_DegreeOfCharge.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filter on if it was a judge trial\n",
    "idx_trial = df_will.CP_TypeOfTrial == \"3\"\n",
    "df_will = df_will[idx_trial]\n",
    "\n",
    "# filter on not juvenile\n",
    "df_will = df_will[ df_will.BP_CourtDesignator != \"J\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e3b470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2L5x7PkNDA60eAzAG70GexXsQYBZLw/WDDz7Q8ePHFRsbK0kqKiqSJH300Uf6xz/+of37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\n4QoAAAAjEK4AAAAwAuEKAAAAI/w/Z4PiiJ5g0UEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bd2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot case\n",
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.countplot(x=\"CP_DegreeOfCharge\", data=df_will, hue=\"BP_Race\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112ea5fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBw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\nYAoEVwAAAJgCwRUAAACm8P8A3YlOXi2xLkMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ebc3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same plot as before, but cases have been filtered on those where guilty\n",
    "# decided by judge\n",
    "df_will_guilt = df_will[df_will.CP_CourtActionTaken == \"G\"]\n",
    "seaborn.countplot(x=\"CP_DegreeOfCharge\", data=df_will_guilt, hue=\"BP_Race\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}