case_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Module imports\n",
    "import os\n",
    "import pickle\n",
    "import re\n",
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEARCH_DICTIONARY = {\n",
    "    'search_attorney': r'(\\. .{3,100}? for defendants?)',\n",
    "    'search_appointed': r'under appointment',\n",
    "    'search_outcome': r'(judgm?en?t?\\. .{3,100}?\\.)',\n",
    "    'pitchess_motion': r'pitchess .{0,5}?motion',\n",
    "    'prejudice':'unduly prejudicial',\n",
    "    'no_error':'no error',\n",
    "    'mid_term': r'mid(dle)?[ -]?term',\n",
    "    'disposition': r'disposition .{0:100}?\\.',\n",
    "    'full_disposition':r'disposition.{0:100}?not to be published',\n",
    "    'prior_conviction': r'prior .{3,20}conviction',\n",
    "    'prior_felony_conviction': r'prior felony convictions?',\n",
    "    'ineffective_assistance': r'ineffective assistance',\n",
    "    'habeas_corpus': r'habeas corpus',\n",
    "    'prosecutorial_misconduct': r'(prosecutor|prosecutorial) misconduct',\n",
    "    'polygraph': 'polygraph',\n",
    "    'kamala_harris': r'kamala .{0,10}?harris',\n",
    "    'constitutional_violation': r'constitutional violation',\n",
    "    'three_strikes':r'(?:three|3)(?:\\s|-)strikes?',\n",
    "    'enhancement':r'(?:firearm|strikes|felony) enhancements?',    \n",
    "    'any_enhancement':r'[\\w+]{3,20}\\senhancement\\b\\s?[\\w+]{3,20}',    \n",
    "    'term':r'\\b[\\w+]*\\b(?:\\s|-)?term',\n",
    "    'resentencing':r're-?sentenc'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "MORE_TERMS = {\n",
    "    'felony':r'felon(y|ies)',\n",
    "    'three_strikes':r'(three|3) ?-?strikes'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEARCH_TERMS = [\n",
    "    'felony',\n",
    "    'three strikes',\n",
    "    'prior conviction',\n",
    "    '3 strikes',\n",
    "    'strikes enhancement',\n",
    "    'kamala harris',\n",
    "    'firearm',\n",
    "    'firearm enhancement',\n",
    "    'sentencing',\n",
    "    'high term',\n",
    "    'low term',\n",
    "    'mid term',\n",
    "    'habeas corpus',\n",
    "    'prior convictions',\n",
    "    'reversed',\n",
    "    'remanded',\n",
    "    'affirmed',\n",
    "    'serious felony',\n",
    "    'strike',\n",
    "    'to life',\n",
    "    'years life',\n",
    "    'assault',\n",
    "    'deadly weapon',\n",
    "    'murder',\n",
    "    'felony enhancements',\n",
    "    'felony enhancement',\n",
    "    'guilty plea',\n",
    "    'resentencing',\n",
    "    'under appointment',\n",
    "    'no appearance',\n",
    "    'affirm',\n",
    "    'determinate term',\n",
    "    'indeterminate term',\n",
    "    'raised no',\n",
    "    'no issues',\n",
    "    'felony priors',\n",
    "    'strikes law',\n",
    "    'second strike',\n",
    "    'third strike',\n",
    "    'enhancement',\n",
    "    'ineffective assistance'\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class Case:\n",
    "    # occurrence_dict = {}\n",
    "    # Note to self: These are class elements, so if you want to change them,\n",
    "    # you can change the whole class by setting Case.<element> = <value>\n",
    "    # and change individual cases with case.<element> = value\n",
    "    def __init__(self):\n",
    "        self.occurrence_dict = {}\n",
    "        pass\n",
    "    def set_scanned_text_path(self, scanned_text_path):\n",
    "        self.scanned_text_path = scanned_text_path\n",
    "    def read_scanned_text(self):\n",
    "        assert os.path.isfile(self.scanned_text_path) == True\n",
    "        with open(self.scanned_text_path, \"r\") as file:\n",
    "            self.scanned_text = file.read()\n",
    "            self.appeals_case_number = os.path.splitext(os.path.split(self.scanned_text_path)[1])[0]\n",
    "            self.occurrence_df = pd.DataFrame(index=[self.appeals_case_number])\n",
    "    def set_clean_text_path(self, clean_text_path):\n",
    "        self.clean_text_path = clean_text_path\n",
    "    def read_clean_text(self):\n",
    "        assert os.path.isfile(self.clean_text_path) == True\n",
    "        with open(self.clean_text_path, \"r\") as file:\n",
    "            self.clean_text = file.read()\n",
    "            self.appeals_case_number = os.path.splitext(os.path.split(self.clean_text_path)[1])[0]\n",
    "            self.occurrence_df = pd.DataFrame(index=[self.appeals_case_number])\n",
    "    def make_clean_text(self):\n",
    "        text = self.scanned_text\n",
    "        text = text.lower()\n",
    "        # Strip all newlines:\n",
    "        text = re.sub('\\n', '', text)\n",
    "        # turn all whitespace into single spaces:\n",
    "        self.clean_text = re.sub(r'(\\s+)', ' ', text)\n",
    "    # TODO: either merge the text functions or have them check if casenum\n",
    "    # already exists?\n",
    "    def set_pdf_path(self, pdf_path):\n",
    "        assert os.path.isfile(pdf_path) == True, \\\n",
    "            \"pdf_path must lead to a file.\"\n",
    "        self.pdf_path = pdf_path\n",
    "    def set_save_path(self):\n",
    "        assert (self.appeals_case_number is not None), \\\n",
    "            \"Appeals case number must be set before object can be saved.\"\n",
    "        current_path = os.path.abspath('')\n",
    "        folder = os.path.join(current_path, 'case_objects')\n",
    "        save_path = os.path.join(folder,'%s.bin' % self.appeals_case_number)\n",
    "        self.save_path = save_path\n",
    "    def save(self):\n",
    "        with open(self.save_path, 'wb') as f:\n",
    "            pickle.dump(self, f)\n",
    "        # TODO: test read/write from binary\n",
    "\n",
    "# want a function that takes a dict {key:value}, searches for value,\n",
    "# if found: puts the result in column \"key\" in DataFrame\n",
    "# if not: puts ... None? False? 'Not found?'\n",
    "\n",
    "    def find(self, search_dictionary):\n",
    "    # search_dictionary should be of form {'category':'regex'}\n",
    "        assert (self.clean_text is not None), \\\n",
    "            'Cleaned text must be available to search.'\n",
    "        assert (len(search_dictionary) != 0), \\\n",
    "            'Size of dictionary is zero. Dictionary must have contents.'\n",
    "        text = self.clean_text\n",
    "        for key in search_dictionary:\n",
    "            found = re.findall(search_dictionary[key], text)\n",
    "            if len(found) != 0:\n",
    "                self.occurrence_df[key] = 1\n",
    "                self.occurrence_dict[key] = found\n",
    "            else:\n",
    "                self.occurrence_df[key] = 0\n",
    "                self.occurrence_dict[key] = 'Not found'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I'm writing this, I do have all of the refined corpuses that I want to use in one folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_folder = os.path.join(os.path.abspath(''),'refined_corpuses')\n",
    "cases = []\n",
    "for f in os.listdir(corpus_folder):\n",
    "    clean_text_path = os.path.join(corpus_folder, f)\n",
    "    case = Case()\n",
    "    case.set_clean_text_path(clean_text_path)\n",
    "    case.read_clean_text()\n",
    "    case.set_save_path()\n",
    "    case.save()\n",
    "    cases.append(case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8474"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking that we got all the cases: should be 8474\n",
    "len(cases)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B120629\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B120629.bin\n",
      "filed 4/10/13 p. v. downing ca2/5 not to be published in the official reports california rules of co\n",
      "B158266\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B158266.bin\n",
      " filed 6/16/15 p. v. pilola ca2/4 not to be published in the official reports california rules of co\n",
      "B197574\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B197574.bin\n",
      "filed 1/26/15 p. v. goodwin ca2/8 not to be published in the official reports california rules of co\n",
      "B211622\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B211622.bin\n",
      "filed 8/30/13 p. v. gutierrez ca2/1 not to be published in the official reports california rules of \n",
      "B213582\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B213582.bin\n",
      "filed 4/2/12 p. v. khrayan ca2/2 not to be published in the official reports california rules of cou\n",
      "B217909\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B217909.bin\n",
      " filed 9/27/12 p. v. alonzo ca2/3 not to be published in the official reports california rules of co\n",
      "B218637\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B218637.bin\n",
      "filed 7/23/12 in re sanchez ca2/7 not to be published in the official reports california rules of co\n",
      "B220072\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B220072.bin\n",
      " filed 6/19/12 p. v. alexander ca2/2 not to be published in the official reports california rules of\n",
      "B220664\n",
      "C:\\Users\\leodb\\Documents\\THESIS\\ldbw\\case_objects\\B220664.bin\n",
      "filed 2/28/12 p. v. romero ca2/1 not to be published in the official reports california rules of cou\n"
     ]
    }
   ],
   "source": [
    "# Checking that properties exist as expected: \n",
    "for c in cases[0:9]:\n",
    "    print(c.appeals_case_number) # should be str of form 'BXXXXXX'\n",
    "    print(c.save_path) # should be absolute path ending in casenum\n",
    "    print(c.clean_text[0:100]) # should be first 101 chars of text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('list_of_felony_cases.bin','wb') as f:\n",
    "    pickle.dump(cases, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "list_of_felony_cases.bin does not include the cases as processed with SEARCH_DICTIONARY."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in cases:\n",
    "    c.find(SEARCH_DICTIONARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('list_of_processed_felony_cases.bin','wb') as f:\n",
    "    pickle.dump(cases, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_dfs = []\n",
    "for c in cases: \n",
    "    list_of_dfs.append(c.occurrence_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_of_dfs)\n",
    "type(list_of_dfs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('list_of_case_dfs.bin','wb') as f:\n",
    "    pickle.dump(list_of_dfs, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>search_attorney</th>\n",
       "      <th>search_appointed</th>\n",
       "      <th>search_outcome</th>\n",
       "      <th>pitchess_motion</th>\n",
       "      <th>prejudice</th>\n",
       "      <th>no_error</th>\n",
       "      <th>mid_term</th>\n",
       "      <th>disposition</th>\n",
       "      <th>full_disposition</th>\n",
       "      <th>prior_conviction</th>\n",
       "      <th>...</th>\n",
       "      <th>habeas_corpus</th>\n",
       "      <th>prosecutorial_misconduct</th>\n",
       "      <th>polygraph</th>\n",
       "      <th>kamala_harris</th>\n",
       "      <th>constitutional_violation</th>\n",
       "      <th>three_strikes</th>\n",
       "      <th>enhancement</th>\n",
       "      <th>any_enhancement</th>\n",
       "      <th>term</th>\n",
       "      <th>resentencing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B120629</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>B158266</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B197574</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B211622</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B213582</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B217909</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B218637</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B220072</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B220664</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B221110</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B221303</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B221381</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B221672</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B221864</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B222428</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B222686</th>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B222714</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B222762</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B222848</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223207</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223531</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223588</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223658</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223722</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223834</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223942</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B223969</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B224130</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B224166</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B224510</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         search_attorney  search_appointed  search_outcome  pitchess_motion  \\\n",
       "B120629                1                 1               1                0   \n",
       "B158266                1                 1               1                0   \n",
       "B197574                1                 1               1                0   \n",
       "B211622                1                 1               1                0   \n",
       "B213582                1                 0               1                0   \n",
       "B217909                1                 1               1                0   \n",
       "B218637                0                 0               1                0   \n",
       "B220072                1                 0               1                0   \n",
       "B220664                1                 1               1                0   \n",
       "B221110                1                 1               1                0   \n",
       "B221303                1                 1               0                0   \n",
       "B221381                1                 1               1                0   \n",
       "B221672                1                 1               1                0   \n",
       "B221864                1                 1               1                0   \n",
       "B222428                1                 1               1                0   \n",
       "B222686                1                 0               1                0   \n",
       "B222714                1                 1               1                0   \n",
       "B222762                1                 0               1                0   \n",
       "B222848                1                 1               1                0   \n",
       "B223207                1                 1               1                0   \n",
       "B223531                1                 1               1                0   \n",
       "B223588                1                 1               1                0   \n",
       "B223658                1                 1               1                1   \n",
       "B223722                1                 1               1                0   \n",
       "B223834                1                 1               1                0   \n",
       "B223942                1                 0               1                0   \n",
       "B223969                1                 1               1                0   \n",
       "B224130                1                 1               1                1   \n",
       "B224166                1                 1               1                0   \n",
       "B224510                1                 1               1                0   \n",
       "\n",
       "         prejudice  no_error  mid_term  disposition  full_disposition  \\\n",
       "B120629          0         1         0            0                 0   \n",
       "B158266          0         1         0            0                 0   \n",
       "B197574          0         1         0            0                 0   \n",
       "B211622          0         0         0            0                 0   \n",
       "B213582          0         0         1            0                 0   \n",
       "B217909          0         0         0            0                 0   \n",
       "B218637          0         0         0            0                 0   \n",
       "B220072          1         1         0            0                 0   \n",
       "B220664          0         0         0            0                 0   \n",
       "B221110          0         1         0            0                 0   \n",
       "B221303          0         0         0            0                 0   \n",
       "B221381          0         0         0            0                 0   \n",
       "B221672          0         1         0            0                 0   \n",
       "B221864          0         1         0            0                 0   \n",
       "B222428          0         0         1            0                 0   \n",
       "B222686          0         0         0            0                 0   \n",
       "B222714          0         0         0            0                 0   \n",
       "B222762          0         1         0            0                 0   \n",
       "B222848          0         0         0            0                 0   \n",
       "B223207          0         1         0            0                 0   \n",
       "B223531          0         0         0            0                 0   \n",
       "B223588          0         1         1            0                 0   \n",
       "B223658          0         0         0            0                 0   \n",
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       "B223969          0         0         0            0                 0   \n",
       "B224130          0         1         0            0                 0   \n",
       "B224166          0         1         0            0                 0   \n",
       "B224510          0         0         1            0                 0   \n",
       "\n",
       "         prior_conviction  ...  habeas_corpus  prosecutorial_misconduct  \\\n",
       "B120629                 0  ...              1                         0   \n",
       "B158266                 0  ...              0                         0   \n",
       "B197574                 0  ...              0                         1   \n",
       "B211622                 0  ...              0                         0   \n",
       "B213582                 1  ...              0                         0   \n",
       "B217909                 0  ...              1                         0   \n",
       "B218637                 0  ...              1                         0   \n",
       "B220072                 1  ...              0                         1   \n",
       "B220664                 0  ...              0                         0   \n",
       "B221110                 0  ...              0                         0   \n",
       "B221303                 0  ...              1                         1   \n",
       "B221381                 0  ...              0                         0   \n",
       "B221672                 0  ...              0                         0   \n",
       "B221864                 1  ...              0                         0   \n",
       "B222428                 0  ...              0                         0   \n",
       "B222686                 0  ...              0                         0   \n",
       "B222714                 0  ...              0                         0   \n",
       "B222762                 0  ...              1                         1   \n",
       "B222848                 0  ...              0                         1   \n",
       "B223207                 1  ...              0                         0   \n",
       "B223531                 0  ...              0                         0   \n",
       "B223588                 1  ...              0                         0   \n",
       "B223658                 0  ...              0                         0   \n",
       "B223722                 0  ...              0                         1   \n",
       "B223834                 0  ...              0                         0   \n",
       "B223942                 0  ...              0                         1   \n",
       "B223969                 0  ...              0                         0   \n",
       "B224130                 0  ...              0                         0   \n",
       "B224166                 1  ...              0                         0   \n",
       "B224510                 0  ...              0                         0   \n",
       "\n",
       "         polygraph  kamala_harris  constitutional_violation  three_strikes  \\\n",
       "B120629          0              1                         0              1   \n",
       "B158266          0              1                         0              0   \n",
       "B197574          1              1                         1              0   \n",
       "B211622          0              0                         0              0   \n",
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       "B221672          0              1                         0              0   \n",
       "B221864          0              1                         0              0   \n",
       "B222428          0              1                         0              0   \n",
       "B222686          0              1                         0              0   \n",
       "B222714          0              1                         0              0   \n",
       "B222762          0              1                         0              0   \n",
       "B222848          0              1                         0              0   \n",
       "B223207          0              1                         0              0   \n",
       "B223531          0              1                         0              0   \n",
       "B223588          0              1                         0              1   \n",
       "B223658          0              1                         0              1   \n",
       "B223722          0              1                         1              0   \n",
       "B223834          0              1                         0              0   \n",
       "B223942          0              1                         0              0   \n",
       "B223969          0              1                         0              0   \n",
       "B224130          0              1                         0              0   \n",
       "B224166          0              1                         0              0   \n",
       "B224510          1              1                         0              0   \n",
       "\n",
       "         enhancement  any_enhancement  term  resentencing  \n",
       "B120629            0                0     1             0  \n",
       "B158266            0                0     0             0  \n",
       "B197574            0                0     1             0  \n",
       "B211622            0                1     1             0  \n",
       "B213582            0                0     1             0  \n",
       "B217909            1                1     1             1  \n",
       "B218637            0                0     1             0  \n",
       "B220072            0                0     1             0  \n",
       "B220664            0                0     1             0  \n",
       "B221110            0                0     1             1  \n",
       "B221303            1                1     1             1  \n",
       "B221381            0                0     1             0  \n",
       "B221672            1                1     1             1  \n",
       "B221864            0                1     1             0  \n",
       "B222428            0                1     1             0  \n",
       "B222686            0                0     0             0  \n",
       "B222714            0                0     0             0  \n",
       "B222762            0                0     1             0  \n",
       "B222848            0                0     1             0  \n",
       "B223207            0                1     1             1  \n",
       "B223531            0                0     1             0  \n",
       "B223588            0                1     1             1  \n",
       "B223658            0                0     1             1  \n",
       "B223722            0                0     1             0  \n",
       "B223834            1                1     1             1  \n",
       "B223942            1                0     1             0  \n",
       "B223969            0                0     1             0  \n",
       "B224130            0                0     1             0  \n",
       "B224166            1                1     1             0  \n",
       "B224510            0                1     1             1  \n",
       "\n",
       "[30 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_cases = pd.concat(list_of_dfs, join='outer')\n",
    "display(all_cases.head(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>search_attorney</th>\n",
       "      <th>search_appointed</th>\n",
       "      <th>search_outcome</th>\n",
       "      <th>pitchess_motion</th>\n",
       "      <th>prejudice</th>\n",
       "      <th>no_error</th>\n",
       "      <th>mid_term</th>\n",
       "      <th>disposition</th>\n",
       "      <th>full_disposition</th>\n",
       "      <th>prior_conviction</th>\n",
       "      <th>...</th>\n",
       "      <th>habeas_corpus</th>\n",
       "      <th>prosecutorial_misconduct</th>\n",
       "      <th>polygraph</th>\n",
       "      <th>kamala_harris</th>\n",
       "      <th>constitutional_violation</th>\n",
       "      <th>three_strikes</th>\n",
       "      <th>enhancement</th>\n",
       "      <th>any_enhancement</th>\n",
       "      <th>term</th>\n",
       "      <th>resentencing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8474.000000</td>\n",
       "      <td>8474.000000</td>\n",
       "      <td>8474.000000</td>\n",
       "      <td>8474.000000</td>\n",
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       "      <td>8474.000000</td>\n",
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       "      <td>8474.000000</td>\n",
       "      <td>8474.000000</td>\n",
       "      <td>8474.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.949375</td>\n",
       "      <td>0.889781</td>\n",
       "      <td>0.946542</td>\n",
       "      <td>0.040477</td>\n",
       "      <td>0.053340</td>\n",
       "      <td>0.153646</td>\n",
       "      <td>0.202620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.291244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086618</td>\n",
       "      <td>0.072693</td>\n",
       "      <td>0.004248</td>\n",
       "      <td>0.521359</td>\n",
       "      <td>0.023720</td>\n",
       "      <td>0.241444</td>\n",
       "      <td>0.160609</td>\n",
       "      <td>0.289002</td>\n",
       "      <td>0.807175</td>\n",
       "      <td>0.369601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.219245</td>\n",
       "      <td>0.313182</td>\n",
       "      <td>0.224958</td>\n",
       "      <td>0.197086</td>\n",
       "      <td>0.224723</td>\n",
       "      <td>0.360631</td>\n",
       "      <td>0.401975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281291</td>\n",
       "      <td>0.259647</td>\n",
       "      <td>0.065044</td>\n",
       "      <td>0.499573</td>\n",
       "      <td>0.152183</td>\n",
       "      <td>0.427984</td>\n",
       "      <td>0.367192</td>\n",
       "      <td>0.453325</td>\n",
       "      <td>0.394540</td>\n",
       "      <td>0.482725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       search_attorney  search_appointed  search_outcome  pitchess_motion  \\\n",
       "count      8474.000000       8474.000000     8474.000000      8474.000000   \n",
       "mean          0.949375          0.889781        0.946542         0.040477   \n",
       "std           0.219245          0.313182        0.224958         0.197086   \n",
       "min           0.000000          0.000000        0.000000         0.000000   \n",
       "25%           1.000000          1.000000        1.000000         0.000000   \n",
       "50%           1.000000          1.000000        1.000000         0.000000   \n",
       "75%           1.000000          1.000000        1.000000         0.000000   \n",
       "max           1.000000          1.000000        1.000000         1.000000   \n",
       "\n",
       "         prejudice     no_error     mid_term  disposition  full_disposition  \\\n",
       "count  8474.000000  8474.000000  8474.000000       8474.0            8474.0   \n",
       "mean      0.053340     0.153646     0.202620          0.0               0.0   \n",
       "std       0.224723     0.360631     0.401975          0.0               0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "25%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "50%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "75%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0               0.0   \n",
       "\n",
       "       prior_conviction  ...  habeas_corpus  prosecutorial_misconduct  \\\n",
       "count       8474.000000  ...    8474.000000               8474.000000   \n",
       "mean           0.291244  ...       0.086618                  0.072693   \n",
       "std            0.454362  ...       0.281291                  0.259647   \n",
       "min            0.000000  ...       0.000000                  0.000000   \n",
       "25%            0.000000  ...       0.000000                  0.000000   \n",
       "50%            0.000000  ...       0.000000                  0.000000   \n",
       "75%            1.000000  ...       0.000000                  0.000000   \n",
       "max            1.000000  ...       1.000000                  1.000000   \n",
       "\n",
       "         polygraph  kamala_harris  constitutional_violation  three_strikes  \\\n",
       "count  8474.000000    8474.000000               8474.000000    8474.000000   \n",
       "mean      0.004248       0.521359                  0.023720       0.241444   \n",
       "std       0.065044       0.499573                  0.152183       0.427984   \n",
       "min       0.000000       0.000000                  0.000000       0.000000   \n",
       "25%       0.000000       0.000000                  0.000000       0.000000   \n",
       "50%       0.000000       1.000000                  0.000000       0.000000   \n",
       "75%       0.000000       1.000000                  0.000000       0.000000   \n",
       "max       1.000000       1.000000                  1.000000       1.000000   \n",
       "\n",
       "       enhancement  any_enhancement         term  resentencing  \n",
       "count  8474.000000      8474.000000  8474.000000   8474.000000  \n",
       "mean      0.160609         0.289002     0.807175      0.369601  \n",
       "std       0.367192         0.453325     0.394540      0.482725  \n",
       "min       0.000000         0.000000     0.000000      0.000000  \n",
       "25%       0.000000         0.000000     1.000000      0.000000  \n",
       "50%       0.000000         0.000000     1.000000      0.000000  \n",
       "75%       0.000000         1.000000     1.000000      1.000000  \n",
       "max       1.000000         1.000000     1.000000      1.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cases.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('processed_cases_df.bin','wb') as f:\n",
    "    pickle.dump(all_cases, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>prior_conviction</th>\n",
       "      <th>...</th>\n",
       "      <th>habeas_corpus</th>\n",
       "      <th>prosecutorial_misconduct</th>\n",
       "      <th>polygraph</th>\n",
       "      <th>kamala_harris</th>\n",
       "      <th>constitutional_violation</th>\n",
       "      <th>three_strikes</th>\n",
       "      <th>enhancement</th>\n",
       "      <th>any_enhancement</th>\n",
       "      <th>term</th>\n",
       "      <th>resentencing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8045.0</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.0</td>\n",
       "      <td>8045.0</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "      <td>8045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.928278</td>\n",
       "      <td>0.975761</td>\n",
       "      <td>0.041641</td>\n",
       "      <td>0.055935</td>\n",
       "      <td>0.157862</td>\n",
       "      <td>0.203356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081666</td>\n",
       "      <td>0.075699</td>\n",
       "      <td>0.004475</td>\n",
       "      <td>0.542946</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.242387</td>\n",
       "      <td>0.163083</td>\n",
       "      <td>0.294966</td>\n",
       "      <td>0.812803</td>\n",
       "      <td>0.369671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.258042</td>\n",
       "      <td>0.153799</td>\n",
       "      <td>0.199779</td>\n",
       "      <td>0.229811</td>\n",
       "      <td>0.364634</td>\n",
       "      <td>0.402520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.455776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.273872</td>\n",
       "      <td>0.264533</td>\n",
       "      <td>0.066748</td>\n",
       "      <td>0.498183</td>\n",
       "      <td>0.154948</td>\n",
       "      <td>0.428554</td>\n",
       "      <td>0.369464</td>\n",
       "      <td>0.456056</td>\n",
       "      <td>0.390094</td>\n",
       "      <td>0.482745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       search_attorney  search_appointed  search_outcome  pitchess_motion  \\\n",
       "count           8045.0       8045.000000     8045.000000      8045.000000   \n",
       "mean               1.0          0.928278        0.975761         0.041641   \n",
       "std                0.0          0.258042        0.153799         0.199779   \n",
       "min                1.0          0.000000        0.000000         0.000000   \n",
       "25%                1.0          1.000000        1.000000         0.000000   \n",
       "50%                1.0          1.000000        1.000000         0.000000   \n",
       "75%                1.0          1.000000        1.000000         0.000000   \n",
       "max                1.0          1.000000        1.000000         1.000000   \n",
       "\n",
       "         prejudice     no_error     mid_term  disposition  full_disposition  \\\n",
       "count  8045.000000  8045.000000  8045.000000       8045.0            8045.0   \n",
       "mean      0.055935     0.157862     0.203356          0.0               0.0   \n",
       "std       0.229811     0.364634     0.402520          0.0               0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "25%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "50%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "75%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0               0.0   \n",
       "\n",
       "       prior_conviction  ...  habeas_corpus  prosecutorial_misconduct  \\\n",
       "count       8045.000000  ...    8045.000000               8045.000000   \n",
       "mean           0.294344  ...       0.081666                  0.075699   \n",
       "std            0.455776  ...       0.273872                  0.264533   \n",
       "min            0.000000  ...       0.000000                  0.000000   \n",
       "25%            0.000000  ...       0.000000                  0.000000   \n",
       "50%            0.000000  ...       0.000000                  0.000000   \n",
       "75%            1.000000  ...       0.000000                  0.000000   \n",
       "max            1.000000  ...       1.000000                  1.000000   \n",
       "\n",
       "         polygraph  kamala_harris  constitutional_violation  three_strikes  \\\n",
       "count  8045.000000    8045.000000               8045.000000    8045.000000   \n",
       "mean      0.004475       0.542946                  0.024612       0.242387   \n",
       "std       0.066748       0.498183                  0.154948       0.428554   \n",
       "min       0.000000       0.000000                  0.000000       0.000000   \n",
       "25%       0.000000       0.000000                  0.000000       0.000000   \n",
       "50%       0.000000       1.000000                  0.000000       0.000000   \n",
       "75%       0.000000       1.000000                  0.000000       0.000000   \n",
       "max       1.000000       1.000000                  1.000000       1.000000   \n",
       "\n",
       "       enhancement  any_enhancement         term  resentencing  \n",
       "count  8045.000000      8045.000000  8045.000000   8045.000000  \n",
       "mean      0.163083         0.294966     0.812803      0.369671  \n",
       "std       0.369464         0.456056     0.390094      0.482745  \n",
       "min       0.000000         0.000000     0.000000      0.000000  \n",
       "25%       0.000000         0.000000     1.000000      0.000000  \n",
       "50%       0.000000         0.000000     1.000000      0.000000  \n",
       "75%       0.000000         1.000000     1.000000      1.000000  \n",
       "max       1.000000         1.000000     1.000000      1.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found_cases = all_cases[all_cases['search_attorney']==1]\n",
    "found_cases.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('found_cases_df.bin','wb') as f: \n",
    "    pickle.dump(found_cases, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = ' three strikes and you\\'re out, 3-strike policy, three-strikes left right' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>search_attorney</th>\n",
       "      <th>search_appointed</th>\n",
       "      <th>search_outcome</th>\n",
       "      <th>pitchess_motion</th>\n",
       "      <th>prejudice</th>\n",
       "      <th>no_error</th>\n",
       "      <th>mid_term</th>\n",
       "      <th>disposition</th>\n",
       "      <th>full_disposition</th>\n",
       "      <th>prior_conviction</th>\n",
       "      <th>...</th>\n",
       "      <th>habeas_corpus</th>\n",
       "      <th>prosecutorial_misconduct</th>\n",
       "      <th>polygraph</th>\n",
       "      <th>kamala_harris</th>\n",
       "      <th>constitutional_violation</th>\n",
       "      <th>three_strikes</th>\n",
       "      <th>enhancement</th>\n",
       "      <th>any_enhancement</th>\n",
       "      <th>term</th>\n",
       "      <th>resentencing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1411.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.967399</td>\n",
       "      <td>0.050319</td>\n",
       "      <td>0.102764</td>\n",
       "      <td>0.231751</td>\n",
       "      <td>0.228916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.336641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227498</td>\n",
       "      <td>0.214033</td>\n",
       "      <td>0.009922</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.043940</td>\n",
       "      <td>0.264352</td>\n",
       "      <td>0.243090</td>\n",
       "      <td>0.386960</td>\n",
       "      <td>0.869596</td>\n",
       "      <td>0.341602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.177653</td>\n",
       "      <td>0.218680</td>\n",
       "      <td>0.303758</td>\n",
       "      <td>0.422100</td>\n",
       "      <td>0.420284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.472728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.419366</td>\n",
       "      <td>0.410295</td>\n",
       "      <td>0.099149</td>\n",
       "      <td>0.492327</td>\n",
       "      <td>0.205035</td>\n",
       "      <td>0.441144</td>\n",
       "      <td>0.429101</td>\n",
       "      <td>0.487227</td>\n",
       "      <td>0.336867</td>\n",
       "      <td>0.474415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       search_attorney  search_appointed  search_outcome  pitchess_motion  \\\n",
       "count           1411.0            1411.0     1411.000000      1411.000000   \n",
       "mean               1.0               1.0        0.967399         0.050319   \n",
       "std                0.0               0.0        0.177653         0.218680   \n",
       "min                1.0               1.0        0.000000         0.000000   \n",
       "25%                1.0               1.0        1.000000         0.000000   \n",
       "50%                1.0               1.0        1.000000         0.000000   \n",
       "75%                1.0               1.0        1.000000         0.000000   \n",
       "max                1.0               1.0        1.000000         1.000000   \n",
       "\n",
       "         prejudice     no_error     mid_term  disposition  full_disposition  \\\n",
       "count  1411.000000  1411.000000  1411.000000       1411.0            1411.0   \n",
       "mean      0.102764     0.231751     0.228916          0.0               0.0   \n",
       "std       0.303758     0.422100     0.420284          0.0               0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "25%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "50%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "75%       0.000000     0.000000     0.000000          0.0               0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0               0.0   \n",
       "\n",
       "       prior_conviction  ...  habeas_corpus  prosecutorial_misconduct  \\\n",
       "count       1411.000000  ...    1411.000000               1411.000000   \n",
       "mean           0.336641  ...       0.227498                  0.214033   \n",
       "std            0.472728  ...       0.419366                  0.410295   \n",
       "min            0.000000  ...       0.000000                  0.000000   \n",
       "25%            0.000000  ...       0.000000                  0.000000   \n",
       "50%            0.000000  ...       0.000000                  0.000000   \n",
       "75%            1.000000  ...       0.000000                  0.000000   \n",
       "max            1.000000  ...       1.000000                  1.000000   \n",
       "\n",
       "         polygraph  kamala_harris  constitutional_violation  three_strikes  \\\n",
       "count  1411.000000    1411.000000               1411.000000    1411.000000   \n",
       "mean      0.009922       0.588235                  0.043940       0.264352   \n",
       "std       0.099149       0.492327                  0.205035       0.441144   \n",
       "min       0.000000       0.000000                  0.000000       0.000000   \n",
       "25%       0.000000       0.000000                  0.000000       0.000000   \n",
       "50%       0.000000       1.000000                  0.000000       0.000000   \n",
       "75%       0.000000       1.000000                  0.000000       1.000000   \n",
       "max       1.000000       1.000000                  1.000000       1.000000   \n",
       "\n",
       "       enhancement  any_enhancement         term  resentencing  \n",
       "count  1411.000000      1411.000000  1411.000000   1411.000000  \n",
       "mean      0.243090         0.386960     0.869596      0.341602  \n",
       "std       0.429101         0.487227     0.336867      0.474415  \n",
       "min       0.000000         0.000000     0.000000      0.000000  \n",
       "25%       0.000000         0.000000     1.000000      0.000000  \n",
       "50%       0.000000         0.000000     1.000000      0.000000  \n",
       "75%       0.000000         1.000000     1.000000      1.000000  \n",
       "max       1.000000         1.000000     1.000000      1.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = found_cases['ineffective_assistance']*found_cases['search_appointed']\n",
    "found_cases[f==1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = r'(?:three|3)(?:\\s|-)strikes?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['three strikes', '3-strike', 'three-strikes']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall(reg, test_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# r'(?:this|or this)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "disposition_test = '. affirmed as modified. sarah a. stockwell, under appointment by the court of appeal, for defendant'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['high term', 'mid-term', 'middle-term']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_test = 'defendant was sentenced to the high term for count one, mid-term for count two, and the middle-term for count three.'\n",
    "term_reg = r'\\b[\\w+]*\\b(?:\\s|-)?term'\n",
    "re.findall(term_reg, term_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-16ac6ca2acdd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df.groupby([col1, col2]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B120629\n",
      "{'search_attorney': ['. ronald s. coen, judge. affirmed. siri shetty, under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': ['no error'], 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': ['ineffective assistance'], 'habeas_corpus': ['habeas corpus', 'habeas corpus'], 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': ['three strikes', 'three strikes', 'three strikes', 'three strikes'], 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['a term', 'prison term'], 'resentencing': 'Not found'}\n",
      "B158266\n",
      "{'search_attorney': ['. torribio, judge. affirmed. charles r. khoury, jr., under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': ['no error'], 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': ['ineffective assistance'], 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': 'Not found', 'resentencing': 'Not found'}\n",
      "B197574\n",
      "{'search_attorney': ['. teri schwartz, judge. affirmed. gail harper, under appointment by the court of appeal, for defendant', '. thompson over business matters in the three years preceding the murders, lawsuits that ended badly for defendant', '. but after 1987, gregory smith, the 2 charles stewart linkletter, who worked for defendant', '. kathy weese kathy weese, who was then known as kathy johnson, worked for defendant', '. benice, paid mr. jones, whose job was as a runner, a messenger, and a researcher for defendant', '. avila (2009) 46 cal.4th 680, 710 (avila) [police searched unsuccessfully for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.', 'judgment. facts 1.', 'judgment. purged, so defendant could not impeach mr.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': ['no error', 'no error', 'no error', 'no error', 'no error', 'no error', 'no error', 'no error', 'no error', 'no error'], 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': 'Not found', 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': ['prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial'], 'polygraph': ['polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph', 'polygraph'], 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': ['constitutional violation'], 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['consecutive term', 'in term', 'jail term', 'in term', 'settlement term', 'employment term', 'in term', 'plain term', 'settlement term'], 'resentencing': 'Not found'}\n",
      "B211622\n",
      "{'search_attorney': ['. wapner, judge. affirmed. ______ vanessa place, under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.', 'judgment. discussion i.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': 'Not found', 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': 'Not found', 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': 'Not found', 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': ['sentencing enhancement under'], 'term': ['aggregate term', 'life term'], 'resentencing': 'Not found'}\n",
      "B213582\n",
      "{'search_attorney': ['. kathleen kennedy, judge. affirmed. geragos & geragos and mark j. geragos for defendant'], 'search_appointed': 'Not found', 'search_outcome': ['judge. affirmed.', 'judgment. (united states v.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': 'Not found', 'mid_term': ['', ''], 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': ['prior serious felony conviction', 'prior serious felony conviction', 'prior strike conviction'], 'prior_felony_conviction': 'Not found', 'ineffective_assistance': 'Not found', 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': ['constitutional violation'], 'three_strikes': ['three strikes'], 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['this term', 'high term', 'consecutive term', 'year term', 'levon term', 'grigor term', ' term', 'grigor term', 'levon term', 'grigor term', '14 term', 'levon term', 'levon term', 'prison term'], 'resentencing': 'Not found'}\n",
      "B217909\n",
      "{'search_attorney': ['. writ denied. alan siraco, under appointment by the court of appeal, for defendant', '. carla castillo, under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment', 'under appointment'], 'search_outcome': ['judgment. [citation.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': 'Not found', 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': ['ineffective assistance', 'ineffective assistance', 'ineffective assistance', 'ineffective assistance', 'ineffective assistance'], 'habeas_corpus': ['habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus'], 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': ['firearm enhancements', 'firearm enhancement'], 'any_enhancement': ['gang enhancement findings', 'use enhancement against', 'that enhancement depended', 'gang enhancement statute', 'gang enhancement statute'], 'term': ['prison term', 'on term', 'the term', 'term', 'consecutive term', 'minimum term', 'prison term', 'a term', 'a term', 'minimum term', 'the term', 'a term', 'the term', 'concurrent term'], 'resentencing': ['resentenc', 'resentenc']}\n",
      "B218637\n",
      "{'search_attorney': 'Not found', 'search_appointed': 'Not found', 'search_outcome': ['judge. petition denied.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': 'Not found', 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': ['ineffective assistance', 'ineffective assistance', 'ineffective assistance', 'ineffective assistance', 'ineffective assistance'], 'habeas_corpus': ['habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus', 'habeas corpus'], 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['prison term'], 'resentencing': 'Not found'}\n",
      "B220072\n",
      "{'search_attorney': ['. david s. wesley, judge. affirmed. angelyn gates for defendant'], 'search_appointed': 'Not found', 'search_outcome': ['judge. affirmed.', 'judgment. (united states v.'], 'pitchess_motion': 'Not found', 'prejudice': ['unduly prejudicial'], 'no_error': ['no error'], 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': ['prior postconviction'], 'prior_felony_conviction': 'Not found', 'ineffective_assistance': ['ineffective assistance', 'ineffective assistance', 'ineffective assistance'], 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': ['prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial', 'prosecutorial'], 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': ['constitutional violation'], 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['consecutive term', 'term', 'life term'], 'resentencing': 'Not found'}\n",
      "B220664\n",
      "{'search_attorney': ['. tomson t. ong, judge. affirmed. ______ jean matulis, under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': 'Not found', 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': 'Not found', 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['his term'], 'resentencing': 'Not found'}\n",
      "B221110\n",
      "{'search_attorney': ['. kathryne a. stoltz, judge. affirmed. kevin s. lacey, under appointment by the court of appeal, for defendant'], 'search_appointed': ['under appointment'], 'search_outcome': ['judge. affirmed.'], 'pitchess_motion': 'Not found', 'prejudice': 'Not found', 'no_error': ['no error'], 'mid_term': 'Not found', 'disposition': 'Not found', 'full_disposition': 'Not found', 'prior_conviction': 'Not found', 'prior_felony_conviction': 'Not found', 'ineffective_assistance': 'Not found', 'habeas_corpus': 'Not found', 'prosecutorial_misconduct': 'Not found', 'polygraph': 'Not found', 'kamala_harris': ['kamala d. harris'], 'constitutional_violation': 'Not found', 'three_strikes': 'Not found', 'enhancement': 'Not found', 'any_enhancement': 'Not found', 'term': ['prison term', 'prison term'], 'resentencing': ['resentenc']}\n"
     ]
    }
   ],
   "source": [
    "for case in cases[0:10]:\n",
    "    print(case.appeals_case_number)\n",
    "    print(case.occurrence_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases[1].find(SEARCH_DICTIONARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'search_attorney': ['. torribio, judge. affirmed. charles r. khoury, jr., under appointment by the court of appeal, for defendant'],\n",
       " 'search_appointed': ['under appointment'],\n",
       " 'search_outcome': ['judge. affirmed.'],\n",
       " 'pitchess_motion': 'Not found',\n",
       " 'prejudice': 'Not found',\n",
       " 'no_error': ['no error'],\n",
       " 'mid_term': 'Not found',\n",
       " 'disposition': 'Not found',\n",
       " 'full_disposition': 'Not found',\n",
       " 'prior_conviction': 'Not found',\n",
       " 'prior_felony_conviction': 'Not found',\n",
       " 'ineffective_assistance': ['ineffective assistance'],\n",
       " 'habeas_corpus': 'Not found',\n",
       " 'prosecutorial_misconduct': 'Not found',\n",
       " 'polygraph': 'Not found',\n",
       " 'kamala_harris': ['kamala d. harris'],\n",
       " 'constitutional_violation': 'Not found',\n",
       " 'three_strikes': 'Not found',\n",
       " 'enhancement': 'Not found',\n",
       " 'any_enhancement': 'Not found',\n",
       " 'term': 'Not found',\n",
       " 'resentencing': 'Not found'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases[1].occurrence_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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