02_clean_raw_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "a35cb84f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from PyPDF2 import PdfReader\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import docx\n",
    "from striprtf.striprtf import rtf_to_text\n",
    "import sys\n",
    "import os\n",
    "from bs4 import BeautifulSoup\n",
    "import comtypes.client #throwing error\n",
    "import shutil\n",
    "import win32com.client\n",
    "import pytesseract\n",
    "from PIL import Image\n",
    "from docx.api import Document\n",
    "import time\n",
    "import regex as re\n",
    "max_rec = 0x100000\n",
    "sys.setrecursionlimit(max_rec)\n",
    "pytesseract.pytesseract.tesseract_cmd = \"C:\\\\Users\\\\HaoLuo\\\\AppData\\\\Local\\\\Programs\\\\Tesseract-OCR\\\\tesseract.exe\"\n",
    "st = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "2914a2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_html(soup):\n",
    "  '''retrieve relevant content from b4s'''\n",
    "\n",
    "  #init empty dict\n",
    "  html_dict = dict()\n",
    "\n",
    "  #put all 'b' tags into dictionary to capture meta data\n",
    "  for tag in soup.find_all(name='b'):\n",
    "    html_dict[tag.contents[0].split(':')[0]] = tag.next_sibling.string\n",
    "\n",
    "  #comment is two siblings behind heading\n",
    "    if soup.find(name='h2',string=\"General Comment\") is not None:\n",
    "        html_dict[\"Comment\"] = soup.find(name='h2',string=\"General Comment\").next_sibling.next_sibling.text\n",
    "    elif soup.find(name='h2',string=\"Redacted Comment\") is not None:\n",
    "        html_dict[\"Comment\"] = soup.find(name='h2',string=\"Redacted Comment\").next_sibling.next_sibling.text\n",
    "        \n",
    "  #We only care if attachment exists. Attachment must be grabbed using the comment name, not the attachment name\n",
    "  if soup.find(name='h2',string=\"Attachments\"):\n",
    "    html_dict[\"has_attachments\"] = True\n",
    "  else:\n",
    "    html_dict[\"has_attachments\"] = False\n",
    "\n",
    "  return html_dict\n",
    "\n",
    "def scrape_pdf(file_name):\n",
    "  ''' scrape pdf '''\n",
    "\n",
    "  reader = PdfReader(file_name)\n",
    "  text = \"\"\n",
    "  for page in reader.pages:\n",
    "      text += page.extract_text() + \"\\n\"\n",
    "\n",
    "  return text\n",
    "\n",
    "def scrape_rtf(filename):\n",
    "    with open(filename) as infile:\n",
    "        content = infile.read()\n",
    "        text = rtf_to_text(content)\n",
    "    return text\n",
    "\n",
    "def scrape_txt(filename):\n",
    "    with open(filename) as infile:\n",
    "        text = infile.read()\n",
    "    return text\n",
    "\n",
    "def scrape_docx(file_name):\n",
    "    doc = docx.Document(file_name)\n",
    " \n",
    "    #pull in pararaphs\n",
    "    fullText = []\n",
    "    for para in doc.paragraphs:\n",
    "        fullText.append(para.text)\n",
    " \n",
    "    joinedText = '\\n'.join(fullText)\n",
    " \n",
    "    if len(doc.tables)>0:\n",
    "      #pull in tables afterwards\n",
    "      fullTable = \"\"\n",
    "      for table in doc.tables:\n",
    "          for row in table.rows:\n",
    "              row_data = [cell.text for cell in row.cells]\n",
    "              fullTable += \", \".join(row_data) + \"\\n\"\n",
    "      return (joinedText + '\\n ' + fullTable, True)\n",
    "    else:\n",
    "      return (joinedText, False)\n",
    "\n",
    "def scrape_xlsx(file_name):\n",
    "  #pulls in a dictionary object of all tabs\n",
    "  all_tabs_dict = pd.read_excel(file_name, na_values='', sheet_name=None)\n",
    " \n",
    "  fullText = \"\"\n",
    "  for df in list(all_tabs_dict.values()):\n",
    "    for i in range(df.shape[0]):\n",
    "        row_data = [str(df.iloc[i,j]) for j in range(df.shape[1])]\n",
    "        fullText += \", \".join(row_data) + \"\\n\"\n",
    " \n",
    "  #remove multiple commas resulting from blank cells      \n",
    "  fullText = re.sub(r',{2,}', ',', fullText)\n",
    "  return fullText\n",
    "\n",
    "def scrape_png(file_name):\n",
    "    text = str(pytesseract.image_to_string(Image.open(file_name)))\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "16b351b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe():    \n",
    "    path ='C:/Users/HaoLuo/ACF-2023-0011/ACF-2023-0011' # set your own path\n",
    "    files = os.listdir(path)\n",
    "    os.chdir(path)\n",
    "    set([x.split('.')[-1] for x in files])\n",
    "\n",
    "    files_html = [x for x in files if x.endswith('.html') and x.startswith(\"ACF-\")]\n",
    "    html_dict_list=[]\n",
    "    for file_path in files_html:\n",
    "      #load html\n",
    "      with open(path+\"/\"+file_path, 'r') as file:\n",
    "        html_string=file.read()\n",
    "        soup = BeautifulSoup(html_string, 'html.parser')\n",
    "        print(file_path)\n",
    "      #clean if public submission\n",
    "      if soup.find_all(name='h1')[0].contents == [\"PUBLIC SUBMISSION\"]:\n",
    "        html_dict = scrape_html(soup)\n",
    "\n",
    "        #pull file paths for matching attachments\n",
    "        attachment_paths = [x for x in files if html_dict['Document'] in x and not x.endswith(\".html\")]\n",
    "        attachment_found = False\n",
    "        html_dict[\"attachment_count\"]=len(attachment_paths) #new line\n",
    "\n",
    "        for attachment_path in attachment_paths:\n",
    "            html_dict[\"attachment_name\"] = attachment_path\n",
    "            if attachment_path.endswith('.pdf'):\n",
    "                html_dict[\"attachment_text\"] = scrape_pdf(path+\"/\"+attachment_path)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "            elif attachment_path.endswith('.docx'):\n",
    "                html_dict[\"attachment_text\"], html_dict[\"attachment_has_tables\"] = scrape_docx(path+\"/\"+attachment_path) # new line\n",
    "                html_dict[\"attachment_error\"] = (len(\"attachment_text\")==0)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "            elif attachment_path.endswith('.rtf'):\n",
    "                html_dict[\"attachment_text\"] = scrape_rtf(path+\"/\"+attachment_path)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "            elif attachment_path.endswith('.txt'):\n",
    "                html_dict[\"attachment_text\"] = scrape_txt(path+\"/\"+attachment_path)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "            elif attachment_path.endswith('.png'):\n",
    "                html_dict[\"attachment_text\"] = scrape_png(path+\"/\"+attachment_path)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "            elif attachment_path.endswith('.xlsx'):\n",
    "                html_dict[\"attachment_text\"] = scrape_xlsx(path+\"/\"+attachment_path)\n",
    "                html_dict_list.append(html_dict.copy())\n",
    "                attachment_found = True\n",
    "\n",
    "        if attachment_found == False:\n",
    "          #add row without attachment text\n",
    "          html_dict_list.append(html_dict)\n",
    "\n",
    "    df=pd.DataFrame(html_dict_list)\n",
    "    \n",
    "    for col_name in [\"Comment\",\"attachment_text\"]:\n",
    "      col_name_new = col_name+\"_clean\"\n",
    "\n",
    "      #text cleaning\n",
    "      df[col_name_new] = df[col_name].replace(to_replace=float('nan'),value='')\n",
    "\n",
    "      df[col_name_new] = df[col_name_new].replace(to_replace='\\xa0',value='')\n",
    "      df[col_name_new] = df[col_name_new].str.replace(pat='§§',repl='bill number ')\n",
    "      df[col_name_new] = df[col_name_new].str.replace(pat='§',repl='bill number ')\n",
    "      df[col_name_new] = df[col_name_new].str.replace(pat='\\u2009',repl='')\n",
    "\n",
    "      df[col_name_new] = df[col_name_new].apply(lambda x: x.strip())\n",
    "      df[col_name_new] = df[col_name_new].apply(lambda x: re.sub(r' +', ' ', x))\n",
    "      df[col_name_new] = df[col_name_new].apply(lambda x: re.sub(r'\\n +', '\\n', x))\n",
    "      df[col_name_new] = df[col_name_new].apply(lambda x: re.sub(r'\\n+', '\\n', x))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "1a697618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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       "      <td>January 20, 2024</td>\n",
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       "      <td>November 23, 2023</td>\n",
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       "      <td>January 19, 2024</td>\n",
       "      <td>API</td>\n",
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       "      <td>ACF-2023-0011-0001</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Head Start teachers should be salaried. We wor...</td>\n",
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       "      <th>3</th>\n",
       "      <td>January 20, 2024</td>\n",
       "      <td>We want to get PAID!!!! We work hard for the l...</td>\n",
       "      <td>November 21, 2023</td>\n",
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       "      <td>We want to get PAID!!!! We work hard for the l...</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>January 20, 2024</td>\n",
       "      <td>Are you going to address increased funding to ...</td>\n",
       "      <td>November 22, 2023</td>\n",
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       "              As of                                            Comment  \\\n",
       "0  January 20, 2024  While I am in support of increased wages and b...   \n",
       "1  January 20, 2024  -First while I commend the need for HS staff t...   \n",
       "2  January 20, 2024  Head Start teachers should be salaried. We wor...   \n",
       "3  January 20, 2024  We want to get PAID!!!! We work hard for the l...   \n",
       "4  January 20, 2024  Are you going to address increased funding to ...   \n",
       "\n",
       "            Received  Status             Posted  Tracking No.   \\\n",
       "0  November 21, 2023  Posted  November 28, 2023  lp8-i19p-nqbk   \n",
       "1  November 22, 2023  Posted  November 28, 2023  lpa-9kok-o7f7   \n",
       "2  November 23, 2023  Posted  November 28, 2023  lpb-j5uy-g8uv   \n",
       "3  November 21, 2023  Posted  November 28, 2023  lp8-s4af-cz42   \n",
       "4  November 22, 2023  Posted  November 28, 2023  lp9-wsu0-lx3k   \n",
       "\n",
       "       Comments Due Submission Type         Docket          Comment On  ...  \\\n",
       "0  January 19, 2024             API  ACF-2023-0011  ACF-2023-0011-0001  ...   \n",
       "1  January 19, 2024             Web  ACF-2023-0011  ACF-2023-0011-0001  ...   \n",
       "2  January 19, 2024             API  ACF-2023-0011  ACF-2023-0011-0001  ...   \n",
       "3  January 19, 2024             Web  ACF-2023-0011  ACF-2023-0011-0001  ...   \n",
       "4  January 19, 2024             API  ACF-2023-0011  ACF-2023-0011-0001  ...   \n",
       "\n",
       "         Phone Organization attachment_name  attachment_text  \\\n",
       "0          NaN          NaN             NaN              NaN   \n",
       "1          NaN          NaN             NaN              NaN   \n",
       "2          NaN          NaN             NaN              NaN   \n",
       "3   8475325635          NaN             NaN              NaN   \n",
       "4          NaN          NaN             NaN              NaN   \n",
       "\n",
       "   attachment_has_tables attachment_error  \\\n",
       "0                    NaN              NaN   \n",
       "1                    NaN              NaN   \n",
       "2                    NaN              NaN   \n",
       "3                    NaN              NaN   \n",
       "4                    NaN              NaN   \n",
       "\n",
       "                                       Comment_clean attachment_text_clean  \\\n",
       "0  While I am in support of increased wages and b...                         \n",
       "1  -First while I commend the need for HS staff t...                         \n",
       "2  Head Start teachers should be salaried. We wor...                         \n",
       "3  We want to get PAID!!!! We work hard for the l...                         \n",
       "4  Are you going to address increased funding to ...                         \n",
       "\n",
       "  head_start_commenter congress_commenter  \n",
       "0                  NaN              False  \n",
       "1                  NaN              False  \n",
       "2                  NaN              False  \n",
       "3                  NaN              False  \n",
       "4                  NaN              False  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff = get_dataframe()\n",
    "congress = pd.read_excel(\"2024_jan_congress_members.xlsx\")\n",
    "congress_names = congress.Name.to_list()\n",
    " \n",
    "dff[\"head_start_commenter\"] = dff.Organization.str.contains(pat=r\"Head Start.*?Association\",case=False)\n",
    "dff[\"congress_commenter\"] = dff[\"Name\"].apply(lambda x: x in congress_names)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "e5cd332c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff.loc[~dff['Government Agency'].isna(), 'Government Agency'] = dff.loc[~dff['Government Agency'].isna(), 'Government Agency'].apply(lambda x: x[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "eb18ffb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff.loc[dff['Government Agency'].isin([\"Senate HELP Committee, Chair Sanders\",'U.S. House Committee on Education and the Workforce','Office of Congressman Adam B. Schiff']),'congress_commenter'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "ac741820",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff[(dff['head_start_commenter']==1) | (dff['congress_commenter']==1)].to_excel('important_commenter.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2cbd5ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# single out importanta commenter and put html and attachment into a folder\n",
    "import pandas as pd\n",
    "import shutil\n",
    "import os\n",
    "import glob\n",
    "dff1 = dff[(dff['head_start_commenter']==1) | (dff['congress_commenter']==1)]\n",
    "file_source = 'C:\\\\Users\\\\HaoLuo\\\\ACF-2023-0011\\\\ACF-2023-0011'\n",
    "file_destination = 'C:\\\\Users\\\\HaoLuo\\\\ACF-2023-0011\\\\important_commenter\\\\'\n",
    "shutil.copy2('important_commenter.xlsx', file_destination)\n",
    "\n",
    "os.chdir(file_source)\n",
    "directory = glob.glob('*')\n",
    "htm = [x+'.html' for x in dff1['Document']]\n",
    "\n",
    "for file in directory:\n",
    "    if file in dff1['attachment_name'].tolist():\n",
    "        shutil.copy2(file_source+'\\\\'+file, file_destination)\n",
    "    if file in htm:\n",
    "        shutil.copy2(file_source+'\\\\'+file, file_destination)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "14d1ee68",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff['Document']=np.where((dff['attachment_name'].apply(lambda x: str(x).split('.')[0])!='nan'),\n",
    "dff['attachment_name'].apply(lambda x: str(x).split('.')[0]),\n",
    "dff['Document'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "4570e1d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time consumed:  78.34600853919983\n"
     ]
    }
   ],
   "source": [
    "dff.to_pickle('2023_scrape.pkl')\n",
    "print('Total Time consumed: ', time.time()-st)"
   ]
  }
 ],
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    "name": "ipython",
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