Q3 Average page count for cases.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "\n",
    "conn = sqlite3.connect(\"./hcap.sqlite\")\n",
    "\n",
    "cases = pd.read_sql_query(\"SELECT * FROM cases\", conn)\n",
    "attscases = pd.read_sql_query(\"SELECT * FROM attorney_cases\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      id             names\n0  34124  william j. tuohy",
      "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>names</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>34124</td>\n      <td>william j. tuohy</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "##\n",
    "## Page count of cases of an attorney?\n",
    "##\n",
    "## We will use the Attorney ID from question no. 2\n",
    "attorney_id = 34124\n",
    "pd.read_sql_query(\"SELECT * FROM attorneys WHERE id = ?\", conn, params=(attorney_id,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\\begin{tabular}{lrrlll}\n\\toprule\n{} &  attorney\\_id &  case\\_id &        party &  party\\_type &                          title \\\\\n\\midrule\n22142 &        34124 &  2509203 &  the people. &  government &                states attorney \\\\\n22169 &        34124 &  2505496 &    appellee. &     private &  state’s attorney on rehearing \\\\\n22298 &        34124 &  2460093 &   appellees. &  government &                states attorney \\\\\n\\bottomrule\n\\end{tabular}\n\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       attorney_id  case_id        party  party_type  \\\n22142        34124  2509203  the people.  government   \n22169        34124  2505496    appellee.     private   \n22298        34124  2460093   appellees.  government   \n\n                               title  \n22142                states attorney  \n22169  state’s attorney on rehearing  \n22298                states attorney  ",
      "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>attorney_id</th>\n      <th>case_id</th>\n      <th>party</th>\n      <th>party_type</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>22142</th>\n      <td>34124</td>\n      <td>2509203</td>\n      <td>the people.</td>\n      <td>government</td>\n      <td>states attorney</td>\n    </tr>\n    <tr>\n      <th>22169</th>\n      <td>34124</td>\n      <td>2505496</td>\n      <td>appellee.</td>\n      <td>private</td>\n      <td>state’s attorney on rehearing</td>\n    </tr>\n    <tr>\n      <th>22298</th>\n      <td>34124</td>\n      <td>2460093</td>\n      <td>appellees.</td>\n      <td>government</td>\n      <td>states attorney</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "attcases = attscases[attscases[\"attorney_id\"] == attorney_id]\n",
    "print(attcases.head(3).to_latex())\n",
    "attcases.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   case_id decision_date  page_count\n0  2509203    1945-05-23          12\n1  2505496    1945-01-17          20\n2  2460093    1947-11-20           9\n3  2617944    1948-11-18          10\n4  2615531    1949-01-19           5",
      "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>case_id</th>\n      <th>decision_date</th>\n      <th>page_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2509203</td>\n      <td>1945-05-23</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2505496</td>\n      <td>1945-01-17</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2460093</td>\n      <td>1947-11-20</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2617944</td>\n      <td>1948-11-18</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2615531</td>\n      <td>1949-01-19</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "##\n",
    "## Merge the case information into the cases for the attorney, \n",
    "##\n",
    "attcaselst = pd.merge(attcases, cases, how=\"left\", left_on=\"case_id\", right_on=\"id\")\n",
    "attcaselst[\"page_count\"] = attcaselst.last_page - attcaselst.first_page \n",
    "casespgcnt = attcaselst[[\"case_id\", \"decision_date\", \"page_count\"]]\n",
    "casespgcnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\\begin{tabular}{lr}\n\\toprule\n{} &   0 \\\\\ndecision\\_date &     \\\\\n\\midrule\n1945-05-23    &  12 \\\\\n1945-01-17    &  20 \\\\\n1947-11-20    &   9 \\\\\n1948-11-18    &  10 \\\\\n1949-01-19    &   5 \\\\\n\\bottomrule\n\\end{tabular}\n\n"
    }
   ],
   "source": [
    "series = pd.Series(casespgcnt[\"page_count\"].values, \n",
    "                   index=pd.DatetimeIndex(casespgcnt[\"decision_date\"]))\n",
    "series = series[series > 0]\n",
    "print(series.head().to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\\begin{tabular}{lr}\n\\toprule\n{} &            0 \\\\\n\\midrule\nmax  &    22.000000 \\\\\nmin  &     1.000000 \\\\\nmean &     6.302083 \\\\\nsum  &  1210.000000 \\\\\n\\bottomrule\n\\end{tabular}\n\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "max       22.000000\nmin        1.000000\nmean       6.302083\nsum     1210.000000\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "##\n",
    "## Some basic stats\n",
    "bstats = series.agg(['max', \"min\", \"mean\", \"sum\"])\n",
    "\n",
    "print(bstats.to_latex())\n",
    "bstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x128929280>"
     },
     "metadata": {},
     "execution_count": 41
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "series.plot.line(x='Page Count', y='Decision Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bit382pyenv05fc67cdd8c348f9a384c10c4419353e",
   "display_name": "Python 3.8.2 64-bit ('3.8.2': pyenv)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}