Extract receipt text from pdf file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract text with PDFminer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1596,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADMIN. DE SUPERMERCADOS HIPER\n",
      "LIMITADA\n",
      "76.134.941-4\n",
      "GRANDES ESTABLECIMIENTOS-VENTA DE\n",
      "ALIM.\n",
      "\n",
      "BOLETA ELECTRÓNICA Nº :\n",
      "\n",
      "1680354484\n",
      "\n",
      "LOCAL:\n",
      "\n",
      "0682\n",
      "\n",
      "CAJA:\n",
      "\n",
      "022\n",
      "\n",
      "FECHA EMISION:\n",
      "\n",
      "28-12-2020\n",
      "\n",
      "TRAN. Nº:\n",
      "\n",
      "0145\n",
      "\n",
      "CAJERO:\n",
      "\n",
      "493\n",
      "\n",
      "HORA:\n",
      "\n",
      "18:29\n",
      "\n",
      "CANT.\n",
      "\n",
      "PRECIO UNIT.\n",
      "\n",
      "DESC. ARTICULO\n",
      "\n",
      "VALOR\n",
      "\n",
      "3.900\n",
      "\n",
      "2.090\n",
      "\n",
      "3.590\n",
      "\n",
      "1.490\n",
      "\n",
      "3.490\n",
      "\n",
      "1.390\n",
      "\n",
      "2.190\n",
      "\n",
      "2.990\n",
      "\n",
      "3.390\n",
      "\n",
      "1.150\n",
      "\n",
      "1.090\n",
      "\n",
      "5.650\n",
      "\n",
      "5.990\n",
      "\n",
      "564\n",
      "\n",
      "1.280\n",
      "\n",
      "2.290\n",
      "\n",
      "1.473\n",
      "\n",
      "590\n",
      "\n",
      "650\n",
      "\n",
      "650\n",
      "\n",
      "850\n",
      "\n",
      "950\n",
      "\n",
      "1.390\n",
      "\n",
      "610\n",
      "\n",
      "1.230\n",
      "\n",
      "1.650\n",
      "\n",
      "1.490\n",
      "\n",
      "- 900\n",
      "\n",
      "44.678\n",
      "0\n",
      "8.489\n",
      "53.167\n",
      "\n",
      "CODIGO: 07804144000468\n",
      "2.0X 1.950 HUEVO GRANDE\n",
      "CODIGO: 08410376017694\n",
      "1.0X 2.090 DIET NATURE\n",
      "CODIGO: 07804613390632\n",
      "1.0X 3.590 QUESO CHANCO\n",
      "CODIGO: 07801930014555\n",
      "1.0X 1.490 PECHPAVOPF\n",
      "CODIGO: 07809611715081\n",
      "1.0X 3.490 PECHUGA DESH\n",
      "CODIGO: 07804627330013\n",
      "1.0X 1.390 ACEITUNA REL\n",
      "CODIGO: 07804115001777\n",
      "1.0X 2.190 AZ-MOL PH PA\n",
      "CODIGO: 00614143030901\n",
      "1.0X 2.990 ARANDANO\n",
      "CODIGO: 07804068002043\n",
      "1.0X 3.390 CIRUELAS MED\n",
      "CODIGO: 07801220001203\n",
      "1.0X 1.150 ENSALADA MIX\n",
      "CODIGO: 07801365000284\n",
      "1.0X 1.090 CHAMPINON BA\n",
      "CODIGO: 00722776200629\n",
      "1.0X 5.650 SPLENDA GRAN\n",
      "CODIGO: 07613036150521\n",
      "1.0X 5.990 BUEN DIA 1.1\n",
      "CODIGO: 02030100000004\n",
      "0.355X 1.589 MANZ VERDE\n",
      "CODIGO: 07801420130130\n",
      "1.0X 1.280 ARROZ INTEGR\n",
      "CODIGO: 07804644940301\n",
      "1.0X 2.290 NARANJA 1.5\n",
      "CODIGO: 02006100000009\n",
      "0.74X 1.991 NECTARIN\n",
      "CODIGO: 07804087000051\n",
      "1.0X 590 CILANTRO BOL\n",
      "CODIGO: 07804628680339\n",
      "1.0X 650 PIMIENTO VER\n",
      "CODIGO: 07802341000366\n",
      "1.0X 650 CEBOLLIN\n",
      "CODIGO: 07804613170524\n",
      "1.0X 850 ZANAHORIAS B\n",
      "CODIGO: 07804613170098\n",
      "1.0X 950 HOJAS ESPINA\n",
      "CODIGO: 07804622260063\n",
      "1.0X 1.390 ZAPALLO CAMO\n",
      "CODIGO: 07804602170184\n",
      "1.0X 610 ZAPALLO ITAL\n",
      "CODIGO: 07804930000306\n",
      "1.0X 1.230 LIMP CREMA\n",
      "CODIGO: 07801320220061\n",
      "1.0X 1.650 ACEITE MARA.\n",
      "CODIGO: 07802636002921\n",
      "1.0X 1.490 MRRQTA ATM\n",
      "\n",
      "@@Descuento Global   ($)\n",
      "\n",
      "TOTAL AFECTO\n",
      "TOTAL EXENTO\n",
      "TOTAL IVA (19.0%)\n",
      "TOTAL\n",
      "\n",
      "$\n",
      "$\n",
      "$\n",
      "$\n",
      "\n",
      "TBK CREDITO                 $     53.167\n",
      "VUELTO                      $          0\n",
      "\n",
      "TOTAL ARTICULOS = 28\n",
      "\n",
      "Timbre Electrónico SII\n",
      " Res. 85 del 2011. - Verifique documento: http://www.lider.cl\n",
      "\n",
      "0145 0682/022/493   28.12.20 18:29 AC-00\n",
      "ATENDIDO POR : cajero  exrerno\n",
      "\n",
      "Revisa tu boleta en: WWW.LIDER.CL\n",
      "\n",
      "#############  L I D E R  #############\n",
      "##### PRECIOS BAJOS TODOS LOS DIAS #####\n",
      "\n",
      "\f",
      "\n"
     ]
    }
   ],
   "source": [
    "# !pip install pdfminer.six\n",
    "from io import StringIO\n",
    "\n",
    "from pdfminer.converter import TextConverter\n",
    "from pdfminer.layout import LAParams\n",
    "from pdfminer.pdfdocument import PDFDocument\n",
    "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n",
    "from pdfminer.pdfpage import PDFPage\n",
    "from pdfminer.pdfparser import PDFParser\n",
    "\n",
    "output_string = StringIO()\n",
    "\n",
    "with open(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\Walmart\\2020\\12_Diciembre\\28-12-2020.pdf', 'rb') as in_file:\n",
    "# with open(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\Walmart\\2020\\Receipts_2020.pdf', 'rb') as in_file:\n",
    "    parser = PDFParser(in_file)\n",
    "    doc = PDFDocument(parser)\n",
    "    rsrcmgr = PDFResourceManager()\n",
    "    device = TextConverter(rsrcmgr, output_string, laparams=LAParams())\n",
    "    interpreter = PDFPageInterpreter(rsrcmgr, device)\n",
    "    for page in PDFPage.create_pages(doc):\n",
    "        interpreter.process_page(page)\n",
    "\n",
    "print(output_string.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract fields with Regular Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regex1: CODIGO:\\s(.+?)\\n(.+?)[Xx]\\s(.+?)\\s(.*?)\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regex2: BOLETA ELECTRÓNICA Nº :\\n\\n(.+?)\\n\\nLOCAL:\\n\\n(.+?)\\n\\nCAJA:\\n\\n(.+?)\\n\\nFECHA EMISION:\\n\\n(.+?)\\n\\nTRAN. Nº:\\n\\n(.+?)\\n\\nCAJERO:\\n\\n(.+?)\\n\\nHORA:\\n\\n(.+?)\\n\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regex3: TOTAL ARTICULOS =(.+?)\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regex4: ATENDIDO POR :\\s(.+?)\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1597,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Extract: Code, Quantity, Unit Price, Description\n",
    "raw_data1 = re.findall('CODIGO:\\s(.+?)\\n(.+?)[Xx]\\s(.+?)\\s(.*?)\\n' ,  output_string.getvalue(), re.M)\n",
    "# raw_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1598,
   "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>codigo</th>\n",
       "      <th>cantidad</th>\n",
       "      <th>precio_unitario</th>\n",
       "      <th>descripcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07804144000468</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.950</td>\n",
       "      <td>HUEVO GRANDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08410376017694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.090</td>\n",
       "      <td>DIET NATURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07804613390632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.590</td>\n",
       "      <td>QUESO CHANCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07801930014555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.490</td>\n",
       "      <td>PECHPAVOPF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07809611715081</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.490</td>\n",
       "      <td>PECHUGA DESH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>07804627330013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.390</td>\n",
       "      <td>ACEITUNA REL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07804115001777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.190</td>\n",
       "      <td>AZ-MOL PH PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00614143030901</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.990</td>\n",
       "      <td>ARANDANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>07804068002043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.390</td>\n",
       "      <td>CIRUELAS MED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>07801220001203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.150</td>\n",
       "      <td>ENSALADA MIX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>07801365000284</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.090</td>\n",
       "      <td>CHAMPINON BA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>00722776200629</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.650</td>\n",
       "      <td>SPLENDA GRAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>07613036150521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.990</td>\n",
       "      <td>BUEN DIA 1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>02030100000004</td>\n",
       "      <td>0.355</td>\n",
       "      <td>1.589</td>\n",
       "      <td>MANZ VERDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>07801420130130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.280</td>\n",
       "      <td>ARROZ INTEGR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>07804644940301</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.290</td>\n",
       "      <td>NARANJA 1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>02006100000009</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.991</td>\n",
       "      <td>NECTARIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>07804087000051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>590</td>\n",
       "      <td>CILANTRO BOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>07804628680339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>650</td>\n",
       "      <td>PIMIENTO VER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>07802341000366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>650</td>\n",
       "      <td>CEBOLLIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>07804613170524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>850</td>\n",
       "      <td>ZANAHORIAS B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>07804613170098</td>\n",
       "      <td>1.0</td>\n",
       "      <td>950</td>\n",
       "      <td>HOJAS ESPINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>07804622260063</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.390</td>\n",
       "      <td>ZAPALLO CAMO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>07804602170184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>ZAPALLO ITAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>07804930000306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.230</td>\n",
       "      <td>LIMP CREMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>07801320220061</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.650</td>\n",
       "      <td>ACEITE MARA.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>07802636002921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.490</td>\n",
       "      <td>MRRQTA ATM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            codigo cantidad precio_unitario   descripcion\n",
       "0   07804144000468      2.0           1.950  HUEVO GRANDE\n",
       "1   08410376017694      1.0           2.090   DIET NATURE\n",
       "2   07804613390632      1.0           3.590  QUESO CHANCO\n",
       "3   07801930014555      1.0           1.490    PECHPAVOPF\n",
       "4   07809611715081      1.0           3.490  PECHUGA DESH\n",
       "5   07804627330013      1.0           1.390  ACEITUNA REL\n",
       "6   07804115001777      1.0           2.190  AZ-MOL PH PA\n",
       "7   00614143030901      1.0           2.990      ARANDANO\n",
       "8   07804068002043      1.0           3.390  CIRUELAS MED\n",
       "9   07801220001203      1.0           1.150  ENSALADA MIX\n",
       "10  07801365000284      1.0           1.090  CHAMPINON BA\n",
       "11  00722776200629      1.0           5.650  SPLENDA GRAN\n",
       "12  07613036150521      1.0           5.990  BUEN DIA 1.1\n",
       "13  02030100000004    0.355           1.589    MANZ VERDE\n",
       "14  07801420130130      1.0           1.280  ARROZ INTEGR\n",
       "15  07804644940301      1.0           2.290   NARANJA 1.5\n",
       "16  02006100000009     0.74           1.991      NECTARIN\n",
       "17  07804087000051      1.0             590  CILANTRO BOL\n",
       "18  07804628680339      1.0             650  PIMIENTO VER\n",
       "19  07802341000366      1.0             650      CEBOLLIN\n",
       "20  07804613170524      1.0             850  ZANAHORIAS B\n",
       "21  07804613170098      1.0             950  HOJAS ESPINA\n",
       "22  07804622260063      1.0           1.390  ZAPALLO CAMO\n",
       "23  07804602170184      1.0             610  ZAPALLO ITAL\n",
       "24  07804930000306      1.0           1.230    LIMP CREMA\n",
       "25  07801320220061      1.0           1.650  ACEITE MARA.\n",
       "26  07802636002921      1.0           1.490    MRRQTA ATM"
      ]
     },
     "execution_count": 1598,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.DataFrame(raw_data1, columns =['codigo', 'cantidad', 'precio_unitario', 'descripcion'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1599,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data2 = re.findall('BOLETA ELECTRÓNICA Nº :\\n\\n(.+?)\\n\\nLOCAL:\\n\\n(.+?)\\n\\nCAJA:\\n\\n(.+?)\\n\\nFECHA EMISION:\\n\\n(.+?)\\n\\nTRAN. Nº:\\n\\n(.+?)\\n\\nCAJERO:\\n\\n(.+?)\\n\\nHORA:\\n\\n(.+?)\\n\\n' ,  output_string.getvalue(), re.M)\n",
    "# raw_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1600,
   "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>boleta_electronica</th>\n",
       "      <th>local</th>\n",
       "      <th>caja</th>\n",
       "      <th>fecha_emision</th>\n",
       "      <th>trans_n</th>\n",
       "      <th>cajero</th>\n",
       "      <th>hora</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1680354484</td>\n",
       "      <td>0682</td>\n",
       "      <td>022</td>\n",
       "      <td>28-12-2020</td>\n",
       "      <td>0145</td>\n",
       "      <td>493</td>\n",
       "      <td>18:29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  boleta_electronica local caja fecha_emision trans_n cajero   hora\n",
       "0         1680354484  0682  022    28-12-2020    0145    493  18:29"
      ]
     },
     "execution_count": 1600,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(raw_data2, columns =['boleta_electronica', 'local', 'caja', 'fecha_emision', 'trans_n', 'cajero', 'hora']\n",
    ")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1601,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data3 = re.findall('TOTAL ARTICULOS =(.+?)\\n' ,  output_string.getvalue(), re.M)\n",
    "# raw_data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1602,
   "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>total_articulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_articulos\n",
       "0              28"
      ]
     },
     "execution_count": 1602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame(raw_data3, columns =['total_articulos'])\n",
    "df3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1603,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data4 = re.findall('ATENDIDO POR :\\s(.+?)\\n' ,  output_string.getvalue(), re.M)\n",
    "# raw_data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1604,
   "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>atendido_por</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cajero  exrerno</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      atendido_por\n",
       "0  cajero  exrerno"
      ]
     },
     "execution_count": 1604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame(raw_data4, columns =['atendido_por'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the data to a csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1605,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    " df1.to_csv(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\reading_receipts\\Data from individual receips\\data1.csv', index = False)\n",
    "# df2.to_csv(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\reading_receipts\\Data from individual receips\\data2.csv', index = False)\n",
    "# df3.to_csv(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\reading_receipts\\Data from individual receips\\data3.csv', index = False)\n",
    "# df4.to_csv(r'C:\\Users\\rc_sh\\OneDrive\\Escritorio\\AI\\Machine_Learning\\reading_receipts\\Data from individual receips\\data4.csv', index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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