{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import lzma\n",
    "import json\n",
    "import urllib.request\n",
    "import shutil\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.dropbox.com/s/6psloxyc0ekqzh2/data.jsonl.xz?dl=1'\n",
    "\n",
    "with urllib.request.urlopen(url) as response, open(\"data.jsonl.xz\", 'wb') as out_file:\n",
    "    shutil.copyfileobj(response, out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo is designed to get you up and running with a sample of CAP data available here https://capapi.org/bulk-access/. Feel free to reuse, modify, or distribute it however you'd like. **Most of this code is written to be adaptable to different chunks of CAP data!** You can substitute in another .xz file corresponding to a different jurisdiction or mess around with any number of other parameters.\n",
    "\n",
    "First, let's get the data into a format we can work with by decompressing the Illinois bulk file and pulling 1-year batches of cases at 10 year intervals from 1900 to 2000. This should give us a good sampling over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Cases: 5020\n"
     ]
    }
   ],
   "source": [
    "#a list to hold the cases we're sampling\n",
    "cases = []\n",
    "\n",
    "#decompress the file line by line\n",
    "with lzma.open(\"data.jsonl.xz\") as infile:\n",
    "    for line in infile:\n",
    "        #decode the file into a convenient format\n",
    "        record = json.loads(str(line, 'utf-8'))\n",
    "        #if the decision date on the case matches one we're interested in, add to our list\n",
    "        if int(record['decision_date'][:4]) in range(1900, 2010, 10):\n",
    "            cases.append(record)\n",
    "\n",
    "print(\"Number of Cases: {}\".format(len(cases)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the case format by accessing the first entry in our list of matching cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1152523,\n",
       " 'name': 'Halliday v. Smith',\n",
       " 'name_abbreviation': 'Halliday v. Smith',\n",
       " 'decision_date': '1900-01-06',\n",
       " 'docket_number': '',\n",
       " 'first_page': '310',\n",
       " 'last_page': '313',\n",
       " 'citations': [{'type': 'official', 'cite': '67 Ark. 310'}],\n",
       " 'volume': {'volume_number': '67'},\n",
       " 'reporter': {'full_name': 'Arkansas Reports'},\n",
       " 'court': {'id': 8808,\n",
       "  'name': 'Arkansas Supreme Court',\n",
       "  'name_abbreviation': 'Ark.',\n",
       "  'jurisdiction_url': None,\n",
       "  'slug': 'ark'},\n",
       " 'jurisdiction': {'id': 34,\n",
       "  'slug': 'ark',\n",
       "  'name': 'Ark.',\n",
       "  'name_long': 'Arkansas',\n",
       "  'whitelisted': True},\n",
       " 'casebody': {'data': {'judges': [],\n",
       "   'parties': ['Halliday v. Smith.'],\n",
       "   'opinions': [{'type': 'majority',\n",
       "     'author': 'Bunn, C. J.',\n",
       "     'text': 'Bunn, C. J.\\nThis is a bill to enjoin the defendant, Benjamin H. Smith, from obstructing an alleged public road, and from interfering, to prevent their free passage along said road, with the plaintiff’s employees and tenants.\\nThe complaint stated, among other things, “that there is a public road running from the gate on east bank of Yellow Bayou, near the Yellow Bayou Gin-house, and extending east, by the residence of B. H. Smith, Esq., on the Bellevue plantation, to the Mississippi river, there intersecting the road leading from Lin wood to Luna Landing; that this road has been a public road, and travelled as such for the last thirty years and more, and has been recognized and treated as a public road by the county court and circuit court of the said county of Chicot.”\\nThe answer denied that the road had ever been a public road, and denied also that it had ever been attempted to use and treat it as such until in 1882 or 1883, and denied also that the road at that time was established by the county court, and denied that it has been a public road since that time, and, in fact, put in issue all the material allegations of the complaint.\\nThe orders of the county court of Chicot county, made at its April term, 1882, and January term, 1883, exhibited with the complaint, and the evidence in relation thereto, do not purport to treat said road as a pre-existing public road, and the evidence does not sustain the allegation that it was treated as a public road previously to that time. The said orders of the county court, viewed as an attempt to lay out and establish a new road, do not purport to have been made in compliance with the provisions of the statutes on the subject, either in respect to notice or any other essential particular. They are therefore void, and were without force and effect from the beginning, and are of course subject to collateral, as well as direct, attack, and to be treated as nullities in any case.\\nTbe transcript of the proceedings of the Chicot circuit court, exhibited with the bill, show that, after the defendant had entered his remonstrance in the county court against the attempt to make the road a public road, he had himself indicted in the circuit court for obstructing said road, and was convicted and fined. The reason given by the defendant for this strange proceeding, as given in his testimony, is shown thus:\\n“Question. State, if you remember, why your attorney advised you to submit to an indictment and bring your suit ir the chancery court by injunction?\\n“Answer. He said, if I was indicted, and the cause was brought up, this would settle the whole road case, and I was told by Judge Bradley that the restraining order would be the proper course to pursue, and I told my attorney what Judge Bradley (the then circuit judge) had said, and he made out the papers, and Judge Bradley granted the injunction.”\\nPlaintiff contends that defendant estopped himself by this conduct from denying the existence of the public road. Defendant’s acts may have the appearance of admitting the fact that the road was a public road, but, under the circumstances, it was only for the purpose of laying the foundation of a more formal controversy of that fact in the proper tribunal. Besides, what a defendant may do in a criminal court can hardly be pleaded as an estoppel against him. It is plain that whatever defendant did was done in furtherance of his resistance to the establishment of this road. The evidence of the conduct and acts of the parties to this controversy, subsequent to the said orders of the county court, not only do not show an acquiescence on the part of the defendant, the owner of the plantation directly affected, in the use of the public road, but a constant and continual warfare and struggle against the plaintiff and his friends, who as persistently and continuously sought to have the road treated as a public road. There is, therefore, no ground whatever upon which it can be s.aid that this road was ever made or became a public road, in the meaning of the law then in force, either by prescription, by use, by the establishment by the county court, or by the assent and acquiescence of the owner of the land affected.\\nThere was objection made to the reading of some of the depositions of defendant, one of the grounds being that the same were taken prematurely, that is, before the answer was filed. The complaint was filed September 11, 1890. The answer was filed November 20, 1890. The notice to take depositions was served on plaintiff’s attorney on November 24, 1890, and under that notice the depositions of C. C. Martin, J. F. Ward, James McMurry and Joseph Bryan, for defendant, were taken on November 26, 1890. Notice to take depositions on the 27th day of November, 1890, was served on plaintiff’s attorney November 24, 1890, and the deposition of H. N. Merriman, the county judge presiding when the said orders were made, was taken thereunder. The deposition of J. H. Worner and possibly some others was taken two or three days before the answer was filed. Without stopping to discuss the materiality of the objection on this ground, the depositions taken nQt subject to this particular objection fully sustain the defendant’s contention. The other grounds of objection do not appear material and prejudicial to plaintiff.\\nThe decree was to the effect that the demui’rer to the complaint bé sustained, because the plaintiff, showed no legal capacity to sue, and also to the effect that there is no equity in the bill.\\nThere is no provision in the statutes giving a right of action to an individual against another for obstructing a public road, but, without discussing the demurrer to the bill, we find no error in the decree of the chancellor to the effect that there is no equity in the bill itself. The decree is therefore affirmed.'}],\n",
       "   'head_matter': 'Halliday v. Smith.\\nOpinion delivered January 6, 1900.\\nJno. M. Rose, Special Judge.\\nJD. R. Reynolds and Jno. 0. Gonnerly, for appellant.\\nRose, Remingway & Rose, for appellee.\\nAppeal from Chicot Circuit Court in Chancery.\\nAs to the right to an injunction, see 35 Ark. 497; 40 Ark. 83. A judgment establishing a road cannot be collaterally attacked. 47 Ark. 431.\\nThe order of the <o mty court was obtained through fraud, and is void. 42 Ark. 348; 2 Fr. Judg. § 489; Big. Fraud, 87. The statutory notice was requisite to the validity of the order. 51 Ark. 34; 65 Ark. 94; id. 142, 143; 13 Ark. 491; 52 id. 312; 55 id. 30; 54 id. 642; 59 id. 487; Sand. & H. Dig., § 4190. It was a taking of property without due process of law. 43 Ark. 545; 5 id. 409; id. 217; 3 id. 536; Ell. Streets and Roads, 233. There never having been any petition to the county court, it had no jurisdiction. Sand. & H. Dig., § 2817; 2 Rap. & Law. Diet. 958; 93 ü. S. 283; 55 Ark. 566. Nor did appellee’s appearance a year later, to file his protest, validate the order. 58 Ark. 186; 47 Pac. 330; 64 Ark. 108. Nor can the conviction in the criminal ease amount to an estoppel here. 1 Greenl. Ev. § 537; 13 Ark. 217; 15 id. 319. At most, it was an admission, and appellee can show it to have been made under a mistake of law. 15 Ark. 62; 1 Greenl. Ev. §§ 204, 205 , 2062, 209 ; 22 Ark. 496; 23 Ark. 134; 11 id. 263; 49 id. 300; 17 id. 221; 32 id. 266.\\nEstoppel—Indictment.—Where, for the purpose of laying a foundation for a formal contest as to the existence of a public road, defendant had himself indicted, and was convicted and fined, for obstructing the same, such proceedings do not estop him from subsequently denying that the road was a public highway, in an action brought to restrain him from obstructing such road. (Page 312.)',\n",
       "   'attorneys': ['Jno. M. Rose, Special Judge.',\n",
       "    'JD. R. Reynolds and Jno. 0. Gonnerly, for appellant.',\n",
       "    'Rose, Remingway & Rose, for appellee.']},\n",
       "  'status': 'ok'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of info here, but it's quite messy. Let's pull out a few case metadata attributes we're interested in, leaving the actual case text aside for now – the decision date (year only), case name, case citation, court, and opinion count. We'll put them into a Pandas Dataframe for easier manipulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>citation</th>\n",
       "      <th>court</th>\n",
       "      <th>name</th>\n",
       "      <th>opinion_count</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67 Ark. 310</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>Halliday v. Smith</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67 Ark. 314</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>Leach v. State</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67 Ark. 325</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>Doster v. Manistee National Bank</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67 Ark. 318</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>Cash v. Kirkham</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67 Ark. 320</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>Rowland v. McGuire</td>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      citation                   court                              name  \\\n",
       "0  67 Ark. 310  Arkansas Supreme Court                 Halliday v. Smith   \n",
       "1  67 Ark. 314  Arkansas Supreme Court                    Leach v. State   \n",
       "2  67 Ark. 325  Arkansas Supreme Court  Doster v. Manistee National Bank   \n",
       "3  67 Ark. 318  Arkansas Supreme Court                   Cash v. Kirkham   \n",
       "4  67 Ark. 320  Arkansas Supreme Court                Rowland v. McGuire   \n",
       "\n",
       "   opinion_count  year  \n",
       "0              1  1900  \n",
       "1              1  1900  \n",
       "2              1  1900  \n",
       "3              1  1900  \n",
       "4              1  1900  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use a list comprehension to pull out the metadata attributes specified above\n",
    "case_metadata = [{'year': int(case['decision_date'][:4]),\n",
    "                'name': case['name'],\n",
    "                'citation': case['citations'][0]['cite'],\n",
    "                'court': case['court']['name'],\n",
    "                'opinion_count': len(case['casebody']['data']['opinions'])} \n",
    "                 for case in cases]\n",
    "\n",
    "# lists of dictionaries like `case_metadata` convert easily into Dataframes\n",
    "metadata_df = pd.DataFrame(case_metadata)\n",
    "metadata_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay, we've got our first usable data! As minimal as this metadata is, we should still be able to get some useful insights out of it. First, let's check how many cases we have from each year in our sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1900    164\n",
       "1910    493\n",
       "1920    565\n",
       "1930    506\n",
       "1940    394\n",
       "1950    326\n",
       "1960    258\n",
       "1970    352\n",
       "1980    767\n",
       "1990    594\n",
       "2000    601\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df['year'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's clearly a lot of variation in publication volume year to year, with pronounced upticks in 1980 and 2000. Let's break it down by court."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arkansas Court of Appeals     776\n",
       "Arkansas Supreme Court       4244\n",
       "Name: court, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df['court'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the cases in our sample are from either the Arkansas Court of Appeals or the Arkansas Supreme Court, with a large majority belonging to the latter. Let's look at the number of opinions per case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4279\n",
       "2     633\n",
       "3      89\n",
       "4      16\n",
       "5       3\n",
       "Name: opinion_count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df['opinion_count'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of our cases have a single opinion. Let's try to identify some trends in opinion volume over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get frequency of opinion counts for each year\n",
    "n_opinions = [[year, \n",
    "               metadata_df[(metadata_df['year'] == year) & (metadata_df[\"opinion_count\"] == 1)].shape[0],\n",
    "               metadata_df[(metadata_df['year'] == year) & (metadata_df[\"opinion_count\"] == 2)].shape[0],\n",
    "               metadata_df[(metadata_df['year'] == year) & (metadata_df[\"opinion_count\"] == 3)].shape[0],\n",
    "               metadata_df[(metadata_df['year'] == year) & (metadata_df[\"opinion_count\"] >= 4)].shape[0]]\n",
    "              for year in metadata_df['year'].unique()]\n",
    "\n",
    "# reformat for graph\n",
    "n_opinions = list(zip(*n_opinions))\n",
    "n_opinions = [list(item) for item in n_opinions]\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "ind = n_opinions[0]\n",
    "handles = []\n",
    "for i, count in enumerate(n_opinions[1:]):\n",
    "    bot = n_opinions[1:i+1]\n",
    "    bot = [sum(x) for x in zip(*n_opinions[1:i+1])]\n",
    "    bot = [0]*len(ind) if not bot else bot\n",
    "    h = plt.bar(ind, count, 5, bottom=bot, label=i+1)\n",
    "    handles.append(h)\n",
    "    \n",
    "plt.legend(handles=handles[::-1], title=\"Opinion Count\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Cases\")\n",
    "plt.title(\"Opinion counts in cases by year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting – it seems that cases from 1980, 1990, and 2000 tend to have more opinions than those from earlier years in the sample.\n",
    "\n",
    "Now let's get to the rich part of the dataset – the opinions themselves! These are a bit messier to wrangle than the metadata was. There are a couple ways that we might structure our dataframe, but to keep it simple we'll just do one opinion per row. If a case has multiple opinions, each will be a separate row (linked by the case id)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>decision_date</th>\n",
       "      <th>court</th>\n",
       "      <th>citations</th>\n",
       "      <th>author</th>\n",
       "      <th>type</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1152523</td>\n",
       "      <td>Halliday v. Smith</td>\n",
       "      <td>1900</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>67 Ark. 310</td>\n",
       "      <td>Bunn, C. J.</td>\n",
       "      <td>majority</td>\n",
       "      <td>Bunn, C. J.\\nThis is a bill to enjoin the defe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1152531</td>\n",
       "      <td>Leach v. State</td>\n",
       "      <td>1900</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>67 Ark. 314</td>\n",
       "      <td>Bunn, C. J.</td>\n",
       "      <td>majority</td>\n",
       "      <td>Bunn, C. J.\\nThis is an indictment against Rob...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1152554</td>\n",
       "      <td>Doster v. Manistee National Bank</td>\n",
       "      <td>1900</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>67 Ark. 325</td>\n",
       "      <td>Wood, J.</td>\n",
       "      <td>majority</td>\n",
       "      <td>Wood, J.\\nThis suit is between judgment credit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1152577</td>\n",
       "      <td>Cash v. Kirkham</td>\n",
       "      <td>1900</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>67 Ark. 318</td>\n",
       "      <td>Battle, J.</td>\n",
       "      <td>majority</td>\n",
       "      <td>Battle, J.\\nZ. L. Kirkham presented two accoun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1152578</td>\n",
       "      <td>Rowland v. McGuire</td>\n",
       "      <td>1900</td>\n",
       "      <td>Arkansas Supreme Court</td>\n",
       "      <td>67 Ark. 320</td>\n",
       "      <td>Battle, J.</td>\n",
       "      <td>majority</td>\n",
       "      <td>Battle, J.\\nAlice J. Rowland, a married woman,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                              name  decision_date  \\\n",
       "0  1152523                 Halliday v. Smith           1900   \n",
       "1  1152531                    Leach v. State           1900   \n",
       "2  1152554  Doster v. Manistee National Bank           1900   \n",
       "3  1152577                   Cash v. Kirkham           1900   \n",
       "4  1152578                Rowland v. McGuire           1900   \n",
       "\n",
       "                    court    citations       author      type  \\\n",
       "0  Arkansas Supreme Court  67 Ark. 310  Bunn, C. J.  majority   \n",
       "1  Arkansas Supreme Court  67 Ark. 314  Bunn, C. J.  majority   \n",
       "2  Arkansas Supreme Court  67 Ark. 325     Wood, J.  majority   \n",
       "3  Arkansas Supreme Court  67 Ark. 318   Battle, J.  majority   \n",
       "4  Arkansas Supreme Court  67 Ark. 320   Battle, J.  majority   \n",
       "\n",
       "                                                text  \n",
       "0  Bunn, C. J.\\nThis is a bill to enjoin the defe...  \n",
       "1  Bunn, C. J.\\nThis is an indictment against Rob...  \n",
       "2  Wood, J.\\nThis suit is between judgment credit...  \n",
       "3  Battle, J.\\nZ. L. Kirkham presented two accoun...  \n",
       "4  Battle, J.\\nAlice J. Rowland, a married woman,...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loop through cases and build rows with case metadata AND opinion metadata/text.\n",
    "#We load in all of the keys initially, then modify the ones we want to.\n",
    "\n",
    "opinion_data = []\n",
    "for case in cases:\n",
    "    for opinion in case[\"casebody\"][\"data\"][\"opinions\"]:\n",
    "        temp = {}\n",
    "        keys = list(case.keys())\n",
    "        keys.remove('casebody')\n",
    "        for key in keys:         \n",
    "            temp[key] = case[key]\n",
    "        keys = list(opinion.keys())\n",
    "        for key in keys:         \n",
    "            temp[key] = opinion[key]\n",
    "        opinion_data.append(temp)\n",
    "\n",
    "opinions_df = pd.DataFrame(opinion_data)\n",
    "opinions_df[\"citations\"] = opinions_df[\"citations\"].apply(lambda x:x[0]['cite'])\n",
    "opinions_df[\"court\"] = opinions_df[\"court\"].apply(lambda x:x['name'])\n",
    "opinions_df[\"decision_date\"] = opinions_df[\"decision_date\"].apply(lambda x:int(x[:4]))\n",
    "opinions_df = opinions_df.drop([\"docket_number\", \"first_page\", \n",
    "                                \"last_page\", \"name_abbreviation\",\n",
    "                                \"reporter\", \"volume\", \"jurisdiction\"], axis=1)\n",
    "opinions_df = opinions_df[[\"id\", \"name\", \"decision_date\", \"court\", \"citations\", \"author\", \"type\", \"text\"]]\n",
    "\n",
    "opinions_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping some extraneous information, we're left with a number of useful attributes for each opinion:\n",
    "\n",
    "- id (assigned by CAP database): A unique case identifier that we can use to link opinions belonging to the same case\n",
    "- name: The case's name\n",
    "- court: The court in which the case was heard and decided\n",
    "- citations: The official citation to the case\n",
    "- author: The author of the opinion\n",
    "- type: The type of the opinion (ex. 'majority,''dissent,''concurrence')\n",
    "- text: The full text of the opinion\n",
    "\n",
    "Let's try to a more complicated question using this corpus. Comparing cases from 1900, 1910, 1920, and 1930 against cases from 1970, 1980, 1990, and 2000, what words can we say are distinctive to each time period? Are there words from opinions dating to the beginning of the 20th century that don't occur in opinions dating to the end of the 20th century?\n",
    "\n",
    "We'll start by implementing a basic n-gram search function and graphing our results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def search_ngram(ngram):\n",
    "    pairs = []\n",
    "    for year in opinions_df[\"decision_date\"].unique():\n",
    "        temp = opinions_df[opinions_df[\"decision_date\"] == year][\"text\"].tolist()\n",
    "        temp = \" \".join(temp).lower()\n",
    "        n = len(temp.split(\" \"))\n",
    "        ngram_count = temp.count(ngram.lower())\n",
    "        pairs.append((year, ngram_count/n))\n",
    "    return pairs\n",
    "\n",
    "def graph_ngram(pairs, ax, title):\n",
    "    x,y = [list(x) for x in zip(*pairs)]\n",
    "    ax.plot(x,y)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Year\")\n",
    "    ax.set_ylabel(\"fraction of words\")\n",
    "    return ax\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15,8))\n",
    "graph_ngram(search_ngram(\"plow\"), axes[0,0], \"Plow\")\n",
    "graph_ngram(search_ngram(\"computer\"), axes[0,1], \"Computer\")\n",
    "graph_ngram(search_ngram(\"horse\"), axes[1,0], \"Horse\")\n",
    "graph_ngram(search_ngram(\"truck\"), axes[1,1], \"Truck\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fewer horses and plows, more trucks and computers! Let's get a little bit more sophisticated (still keeping it simple) and find a list of words which occur fewer than 10 times in cases from 1900-1940 but frequently in cases from 1970-2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_cases(cases):\n",
    "    cases = \" \".join(cases).lower()\n",
    "    cases = cases.replace(\"\\n\", \" \").replace(\",\", \"\").replace(\";\", \"\").replace(\"'\", \"\").replace(\"’\", \"\")\n",
    "    cases = cases.replace(\"(\", \"\").replace(\")\", \"\").replace(\".\", \"\").replace(\"?\", \"\").replace(\"!\", \"\").split(\" \")\n",
    "    return cases\n",
    "\n",
    "early_cases = tokenize_cases(opinions_df[opinions_df[\"decision_date\"] <= 1930][\"text\"].tolist())\n",
    "late_cases = tokenize_cases(opinions_df[opinions_df[\"decision_date\"] >= 1970][\"text\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41725"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_cases_dict = {}\n",
    "\n",
    "for word in early_cases:\n",
    "    if word in early_cases_dict:\n",
    "        early_cases_dict[word] += 1\n",
    "    else:\n",
    "        early_cases_dict[word] = 1\n",
    "\n",
    "len(early_cases_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_words_dict = {}\n",
    "\n",
    "for word in late_cases:\n",
    "    if word in early_cases_dict:\n",
    "        if early_cases_dict[word] < 10:\n",
    "            if word in new_words_dict:\n",
    "                new_words_dict[word] += 1\n",
    "            else:\n",
    "                new_words_dict[word] = 1\n",
    "    else:\n",
    "        if word in new_words_dict:\n",
    "            new_words_dict[word] += 1\n",
    "        else:\n",
    "            new_words_dict[word] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words from 1970-2000 that occured <10 times from 1900-1930\n",
      "\n",
      "Word          |Occurances\n",
      "-------------------------\n",
      "sw2d          | 8878\n",
      "repl          | 1700\n",
      "coverage      |  774\n",
      "victim        |  678\n",
      "emphasis      |  606\n",
      "sw3d          |  598\n",
      "timely        |  549\n",
      "marijuana     |  536\n",
      "percent       |  507\n",
      "disagree      |  492\n",
      "factors       |  486\n",
      "suppress      |  482\n",
      "cir           |  479\n",
      "compensable   |  440\n",
      "problem       |  422\n",
      "mistrial      |  401\n",
      "probation     |  400\n",
      "victims       |  399\n",
      "surgery       |  393\n",
      "sentencing    |  389\n",
      "convictions   |  385\n",
      "problems      |  374\n",
      "aggravated    |  368\n",
      "procedures    |  353\n",
      "visitation    |  343\n",
      "cocaine       |  332\n",
      "subsection    |  319\n",
      "program       |  317\n",
      "factual       |  300\n",
      "wal-mart      |  297\n",
      "activity      |  294\n",
      "ineffective   |  292\n",
      "counsels      |  290\n",
      "f2d           |  284\n",
      "pm            |  273\n",
      "activities    |  268\n",
      "someone       |  260\n",
      "parole        |  259\n",
      "evidentiary   |  257\n",
      "voter         |  257\n",
      "prosecutors   |  252\n",
      "underlying    |  251\n",
      "despite       |  251\n",
      "eg            |  248\n",
      "sentences     |  246\n",
      "im            |  241\n",
      "document      |  239\n",
      "unemployment  |  239\n",
      "pretrial      |  237\n",
      "informant     |  236\n",
      "arguing       |  235\n",
      "plus          |  232\n",
      "allegedly     |  232\n",
      "rehabilitation|  232\n",
      "scheduled     |  231\n",
      "additionally  |  231\n",
      "questioning   |  231\n",
      "paternity     |  231\n",
      "states:       |  228\n",
      "prison        |  224\n",
      "thats         |  222\n",
      "ie            |  219\n",
      "basic         |  216\n",
      "jeopardy      |  216\n",
      "contraband    |  215\n",
      "certification |  215\n",
      "disclosure    |  213\n",
      "ultimately    |  212\n",
      "part:         |  211\n",
      "initially     |  207\n",
      "potential     |  205\n",
      "trailer       |  204\n",
      "newbern       |  201\n",
      "parental      |  197\n",
      "group         |  196\n",
      "battery       |  195\n",
      "popular       |  195\n",
      "reveals       |  194\n",
      "miranda       |  194\n",
      "sera          |  192\n",
      "indication    |  189\n",
      "workmens      |  186\n",
      "sellers       |  186\n",
      "10%           |  184\n",
      "fogleman      |  183\n",
      "similarly     |  182\n",
      "cert          |  182\n",
      "proffered     |  181\n",
      "procedural    |  181\n",
      "hearings      |  179\n",
      "wage          |  179\n",
      "habitual      |  178\n",
      "firearm       |  174\n",
      "commitment    |  172\n",
      "smiths        |  172\n",
      "womack        |  170\n",
      "model         |  170\n",
      "motorist      |  169\n",
      "bureau        |  168\n",
      "parking       |  168\n"
     ]
    }
   ],
   "source": [
    "def isInt(s):\n",
    "    try: \n",
    "        int(s)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False\n",
    "\n",
    "sorted_new_words = sorted(new_words_dict.items(), key=lambda x:-x[1])\n",
    "sorted_new_words = [item for item in sorted_new_words if not isInt(item[0])]\n",
    "sorted_new_words = sorted_new_words[:100]\n",
    "\n",
    "print(\"Most common words from 1970-2000 that occured <10 times from 1900-1930\\n\")\n",
    "print (\"{0:14}|{1:5}\".format(\"Word\", \"Occurances\"))\n",
    "print (\"-------------------------\")\n",
    "for word in sorted_new_words:\n",
    "    print (\"{0:14}|{1:5}\".format(word[0], word[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15,8))\n",
    "graph_ngram(search_ngram(\"coverage\"), axes[0,0], \"Coverage\")\n",
    "graph_ngram(search_ngram(\"victim\"), axes[0,1], \"Victim\")\n",
    "graph_ngram(search_ngram(\"marijuana\"), axes[1,0], \"Marijuana\")\n",
    "graph_ngram(search_ngram(\"wal-mart\"), axes[1,1], \"Wal-Mart\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}