scrape-pdf-2009-2019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffc3c6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pdfplumber\n",
    "import concurrent.futures\n",
    "from pdfplumber.page import Page, Table\n",
    "from typing import Optional\n",
    "from tqdm.auto import tqdm\n",
    "import math\n",
    "import re\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdb4295e-1eba-4ae7-bf4f-6f79f6dfa140",
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb683c6-444e-4b52-b75f-3a852a62f5bf",
   "metadata": {},
   "source": [
    "## Scrape PDFs from 2009 to 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8abb0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pdfplumber.display.PageImage at 0x11f7e97f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Try one sample to check table finder from PDFPlumber https://github.com/jsvine/pdfplumber\n",
    "pdf_name = \"pdfs-2009-to-2019/2009_12_DAI_High_Security.pdf\"\n",
    "\n",
    "pdf  = pdfplumber.open(pdf_name)\n",
    "page = pdf.pages[23]\n",
    "image = page.to_image(resolution=150)\n",
    "image.reset().debug_tablefinder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e6b5583-bb82-4de9-9a8e-f2cd2c88a1ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to extract background color of a cell. \n",
    "#Thanks to https://stackoverflow.com/a/73759921/838158\n",
    "\n",
    "def to_bbox(rect: dict) -> tuple[float, float, float, float]:\n",
    "    return (rect[\"x0\"], rect[\"top\"], rect[\"x1\"], rect[\"bottom\"])\n",
    "\n",
    "def is_included(cell_box: tuple[float, float, float, float], rect_box: tuple[float, float, float, float]):\n",
    "    c_left, c_top, c_right, c_bottom = tuple([round(j) for j in cell_box])\n",
    "    r_left, r_top, r_right, r_bottom = tuple([round(j) for j in rect_box])\n",
    "    #return c_left >= r_left and c_top >= r_top and c_right <= r_right and c_bottom <= r_bottom\n",
    "    return math.isclose(c_left, r_left, abs_tol=1) and math.isclose(c_top, r_top, abs_tol=1) and math.isclose(c_right, r_right, abs_tol=1) and math.isclose(c_bottom, r_bottom, abs_tol=1)\n",
    "\n",
    "def find_rect_for_cell(cell: tuple[float, float, float, float], rects: list[dict]) -> Optional[dict]:\n",
    "    return next((r for r in rects if is_included(cell, to_bbox(r))), None)\n",
    "\n",
    "\n",
    "def get_cell_color(cell: tuple[float, float, float, float], page: Page) -> tuple[float, float, float]:\n",
    "    rect = find_rect_for_cell(cell, page.rects) if cell else None\n",
    "    return rect[\"non_stroking_color\"] if (rect and \"non_stroking_color\" in rect) else (255,255,255)\n",
    "\n",
    "#print(get_cell_color(tables[0].rows[0].cells[0], page))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dad64349-53a6-4b6e-a9ea-21cb2496c737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d209f5a097034083803a88a378b6503f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/45 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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   "source": [
    "#list of pdf files in the pdfs-2009-2019 folder\n",
    "pdf_files = sorted((f for f in os.listdir(\"pdfs-2009-to-2019\") if not f.startswith(\".\")), key=str.lower)\n",
    "\n",
    "# DataFrame to hold everything\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# Clearn cell data\n",
    "def parse_value(txt):\n",
    "    cid     = re.sub(r'\\(cid\\:\\d+\\)', ' ', txt) #remove (cid:13) type codes\n",
    "    comma   = re.sub(r',', '', cid)             #remove comma separators\n",
    "    newLine = re.sub(r'\\n', '', comma)          #remove new lines\n",
    "    return newLine \n",
    "\n",
    "\n",
    "def extract_pdf(filename):\n",
    "\n",
    "    topcategory         = '' #some rows are top categories, like \"Custody Operations\"\n",
    "    subcategory         = '' #some rows are sub categories, like \"Total Bed Capacity\"\n",
    "    current_institution = '' #some institutions change mid-page, so we identify in loop when this happens\n",
    "\n",
    "    #open PDF\n",
    "    pdf = pdfplumber.open(\"pdfs-2009-to-2019/\" + filename) #open current pdf\n",
    "\n",
    "    #set up progress bars for seeing which page we're processing\n",
    "    pbar2 = tqdm(total=len(pdf.pages), position=1, leave=False)\n",
    "\n",
    "    page_df = pd.DataFrame()\n",
    "    \n",
    "    for ipage,page in enumerate(pdf.pages):\n",
    "    \n",
    "        pbar2.update(1)\n",
    "        pbar2.set_description(\"Processing Page: %s out of %s\" % (ipage+1, len(pdf.pages)))\n",
    "            \n",
    "        table_text = page.extract_table()\n",
    "        table_obj  = page.find_table()\n",
    "        \n",
    "        if table_text is not None:\n",
    "        \n",
    "            #Loop through each row starting on row two, after the year headers [0] and month headers [1]\n",
    "            for r,row in enumerate(table_text[2:], start=2):\n",
    "        \n",
    "                #get the current metric label for this row\n",
    "                metric_title = parse_value(row[1])\n",
    "    \n",
    "                #Change the current institution, so reset sub category and top category\n",
    "                if (row[0] != None) and (row[0] != current_institution):\n",
    "                    current_institution = row[0]\n",
    "                    topcategory = '' #we changed institutions, so blank out topcategory\n",
    "                    subcategory = '' #we changed institutions, so blank out subcategory\n",
    "                    if all(val=='' for val in row[2:]):\n",
    "                        metric_title = '' #we changed institutions, and this row has no values, so blank out metric title\n",
    "        \n",
    "                #this row is all blank and row header background color matches a top-level category, like \"Custody Operations\"\n",
    "                if (all(val=='' for val in row[2:]) and get_cell_color(table_obj.rows[r].cells[1], page) == (0.827, 0.827, 0.827)):\n",
    "                    topcategory = row[1]\n",
    "                    continue\n",
    "                    \n",
    "                #This row is all blank and row header is a colored cell. Create a new sub category\n",
    "                if (all(val=='' for val in row[2:]) and get_cell_color(table_obj.rows[r].cells[1], page) != (255,255,255)):\n",
    "                    subcategory = row[1] #if all values are empty, make a new subcategory\n",
    "                    continue\n",
    "                        \n",
    "                row_builder = { 'Institution':current_institution, 'Year': table_text[0][3], 'Top Category':topcategory, 'Subcategory':subcategory, 'Metric Title': metric_title }\n",
    "                \n",
    "                #Skip Dec column from previous month, for each month Jan-Dec extract value as dict connected to that month\n",
    "                for i, month in enumerate(table_text[1][3:]):\n",
    "                    row_builder[month] = parse_value(row[i + 3])\n",
    "\n",
    "                page_df = pd.concat([page_df, pd.DataFrame([row_builder])], ignore_index=True)\n",
    "                \n",
    "    pbar2.close()\n",
    "    return page_df\n",
    "        \n",
    "\n",
    "def main():\n",
    "    df_document = pd.DataFrame()\n",
    "    pbar1 = tqdm(total=len(pdf_files), position=0)\n",
    "    for pdfs in pdf_files:\n",
    "        pbar1.update(1)\n",
    "        df_document = pd.concat([df_document, extract_pdf(pdfs)], ignore_index=True)\n",
    "\n",
    "    return df_document\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    df = pd.concat([df, main()], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da465565-6f7a-4867-b121-370e903e9c1b",
   "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>Institution</th>\n",
       "      <th>Year</th>\n",
       "      <th>Top Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Metric Title</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCWF</td>\n",
       "      <td>2009</td>\n",
       "      <td>Custody Operations</td>\n",
       "      <td>Total Bed Capacity</td>\n",
       "      <td>Contract Beds</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CCWF</td>\n",
       "      <td>2009</td>\n",
       "      <td>Custody Operations</td>\n",
       "      <td>Total Bed Capacity</td>\n",
       "      <td>Design Beds</td>\n",
       "      <td>1916</td>\n",
       "      <td>1953</td>\n",
       "      <td>1945</td>\n",
       "      <td>1961</td>\n",
       "      <td>1969</td>\n",
       "      <td>1999</td>\n",
       "      <td>2007</td>\n",
       "      <td>1986</td>\n",
       "      <td>1966</td>\n",
       "      <td>2006</td>\n",
       "      <td>1969</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCWF</td>\n",
       "      <td>2009</td>\n",
       "      <td>Custody Operations</td>\n",
       "      <td>Total Bed Capacity</td>\n",
       "      <td>Non-Traditional Beds</td>\n",
       "      <td>265</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCWF</td>\n",
       "      <td>2009</td>\n",
       "      <td>Custody Operations</td>\n",
       "      <td>Total Bed Capacity</td>\n",
       "      <td>Overcrowding Beds</td>\n",
       "      <td>1801</td>\n",
       "      <td>1886</td>\n",
       "      <td>1851</td>\n",
       "      <td>1884</td>\n",
       "      <td>1894</td>\n",
       "      <td>1897</td>\n",
       "      <td>1927</td>\n",
       "      <td>1919</td>\n",
       "      <td>1884</td>\n",
       "      <td>1926</td>\n",
       "      <td>1902</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCWF</td>\n",
       "      <td>2009</td>\n",
       "      <td>Custody Operations</td>\n",
       "      <td>Total Bed Capacity</td>\n",
       "      <td>Temporary Beds (FamilyVisiting/Tank Beds)</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201282</th>\n",
       "      <td>SVSP</td>\n",
       "      <td>2019</td>\n",
       "      <td>Surplus/Deficit</td>\n",
       "      <td>Adverse Actions</td>\n",
       "      <td>Per 100 Staff</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201283</th>\n",
       "      <td>SVSP</td>\n",
       "      <td>2019</td>\n",
       "      <td>Surplus/Deficit</td>\n",
       "      <td>Adverse Actions</td>\n",
       "      <td>Dismissals (Non-Medical)</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>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>201284</th>\n",
       "      <td>SVSP</td>\n",
       "      <td>2019</td>\n",
       "      <td>Surplus/Deficit</td>\n",
       "      <td>Adverse Actions</td>\n",
       "      <td>Adverse Actions Total (Medical)</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201285</th>\n",
       "      <td>SVSP</td>\n",
       "      <td>2019</td>\n",
       "      <td>Surplus/Deficit</td>\n",
       "      <td>Adverse Actions</td>\n",
       "      <td>Per 100 Staff</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201286</th>\n",
       "      <td>SVSP</td>\n",
       "      <td>2019</td>\n",
       "      <td>Surplus/Deficit</td>\n",
       "      <td>Adverse Actions</td>\n",
       "      <td>Dismissals (Medical)</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201287 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Institution  Year        Top Category         Subcategory  \\\n",
       "0             CCWF  2009  Custody Operations  Total Bed Capacity   \n",
       "1             CCWF  2009  Custody Operations  Total Bed Capacity   \n",
       "2             CCWF  2009  Custody Operations  Total Bed Capacity   \n",
       "3             CCWF  2009  Custody Operations  Total Bed Capacity   \n",
       "4             CCWF  2009  Custody Operations  Total Bed Capacity   \n",
       "...            ...   ...                 ...                 ...   \n",
       "201282        SVSP  2019     Surplus/Deficit     Adverse Actions   \n",
       "201283        SVSP  2019     Surplus/Deficit     Adverse Actions   \n",
       "201284        SVSP  2019     Surplus/Deficit     Adverse Actions   \n",
       "201285        SVSP  2019     Surplus/Deficit     Adverse Actions   \n",
       "201286        SVSP  2019     Surplus/Deficit     Adverse Actions   \n",
       "\n",
       "                                     Metric Title   Jan   Feb   Mar   Apr  \\\n",
       "0                                   Contract Beds     0     0     0     0   \n",
       "1                                     Design Beds  1916  1953  1945  1961   \n",
       "2                            Non-Traditional Beds   265    62    72    72   \n",
       "3                               Overcrowding Beds  1801  1886  1851  1884   \n",
       "4       Temporary Beds (FamilyVisiting/Tank Beds)     0     0     0     0   \n",
       "...                                           ...   ...   ...   ...   ...   \n",
       "201282                              Per 100 Staff  0.44  0.09  0.51  0.00   \n",
       "201283                   Dismissals (Non-Medical)     0     1     1     0   \n",
       "201284            Adverse Actions Total (Medical)     2     1     1     1   \n",
       "201285                              Per 100 Staff  0.17  0.09  0.09  0.08   \n",
       "201286                       Dismissals (Medical)     0     0     0     0   \n",
       "\n",
       "         May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  \n",
       "0          0     0     0     0     0     0     0     0  \n",
       "1       1969  1999  2007  1986  1966  2006  1969  1993  \n",
       "2         36     0     0    72    67     0     0     0  \n",
       "3       1894  1897  1927  1919  1884  1926  1902  1920  \n",
       "4          0     0     0     0     0     0     0     0  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "201282  0.00  0.26  0.69  0.09  0.35  0.34  0.60  0.51  \n",
       "201283     0     0     0     0     0     0     0     0  \n",
       "201284     0     0     0     0     0     1     0     0  \n",
       "201285  0.00  0.00  0.00  0.00  0.00  0.09  0.00  0.00  \n",
       "201286     0     0     0     0     0     0     0     0  \n",
       "\n",
       "[201287 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fe033f4-bc30-4a34-951f-729c4f5a6733",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"finished-csvs/finished-2009-2019-everything.csv\", encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c96c301-67b7-4655-9fc3-6a3e828160f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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