notebooks/Python Extract Table from PDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Extract Table from PDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example PDFs\n",
    "\n",
    "* McKinsey Global Institute Disruptive technologies\n",
    "\n",
    "https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Disruptive%20technologies/MGI_Disruptive_technologies_Full_report_May2013.ashx\n",
    "\n",
    "* Food Calories List\n",
    "\n",
    "http://www.uncledavesenterprise.com/file/health/Food%20Calories%20List.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With tabula-py\n",
    "\n",
    "#### Installation\n",
    "\n",
    "https://pypi.org/project/tabula-py/\n",
    "\n",
    "`pip install tabula-py`\n",
    "\n",
    "#### tabula-py docs\n",
    "\n",
    "https://www.pydoc.io/pypi/tabula-py-0.9.0/autoapi/wrapper/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabula import read_pdf\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-bc78fb9821b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./tmp/pdf/Food Calories List.pdf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-f85a44f12cbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./tmp/pdf/Food Calories List.pdf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'columns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\")\n",
    "df = df.dropna(axis='columns')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-9a6593114355>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./tmp/pdf/Food Calories List.pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtabulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\", pages=3)\n",
    "print (tabulate(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-9c49bc8c77b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./tmp/pdf/Food Calories List.pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\", pages=3, output_format=\"json\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-942f74bfad35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./tmp/pdf/Food Calories List.pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'all'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiple_tables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\", pages='all', multiple_tables=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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>Fish cake</th>\n",
       "      <th>90 cals per cake</th>\n",
       "      <th>200 cals</th>\n",
       "      <th>Medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fish fingers</td>\n",
       "      <td>50 cals per piece</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gammon</td>\n",
       "      <td>320 cals</td>\n",
       "      <td>280 cals</td>\n",
       "      <td>Med-High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haddock fresh</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>110 cals</td>\n",
       "      <td>Low calorie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Halibut fresh</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>125 cals</td>\n",
       "      <td>Low calorie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ham</td>\n",
       "      <td>6 cals</td>\n",
       "      <td>240 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Herring fresh grilled</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kidney</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>160 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kipper</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>120 cals</td>\n",
       "      <td>Low calorie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Liver</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>150 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Liver pate</td>\n",
       "      <td>150 cals</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lamb (roast)</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>Med-High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lobster boiled</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>100 cals</td>\n",
       "      <td>Low calorie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Luncheon meat</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>400 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mackeral</td>\n",
       "      <td>320 cals</td>\n",
       "      <td>300 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mussels</td>\n",
       "      <td>90 cals</td>\n",
       "      <td>90 cals</td>\n",
       "      <td>Low-Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pheasant roast</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pilchards (tinned)</td>\n",
       "      <td>140 cals</td>\n",
       "      <td>140 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Prawns</td>\n",
       "      <td>180 cals</td>\n",
       "      <td>100 cals</td>\n",
       "      <td>Low- Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pork</td>\n",
       "      <td>320 cals</td>\n",
       "      <td>290 cals</td>\n",
       "      <td>Med-High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pork pie</td>\n",
       "      <td>320 cals</td>\n",
       "      <td>450 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rabbit</td>\n",
       "      <td>200 cals</td>\n",
       "      <td>180 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Salmon fresh</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>180 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sardines tinned in oil</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sardines in tomato sauce</td>\n",
       "      <td>180 cals</td>\n",
       "      <td>180 cals</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sausage pork fried</td>\n",
       "      <td>250 cals</td>\n",
       "      <td>320 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sausage pork grilled</td>\n",
       "      <td>220 cals</td>\n",
       "      <td>280 cals</td>\n",
       "      <td>Med-High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sausage roll</td>\n",
       "      <td>290 cals</td>\n",
       "      <td>480 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Scampi fried in oil</td>\n",
       "      <td>400 cals</td>\n",
       "      <td>340 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Steak &amp; kidney pie</td>\n",
       "      <td>400 cals</td>\n",
       "      <td>350 cals</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Fish cake   90 cals per cake  200 cals       Medium\n",
       "0               Fish fingers  50 cals per piece  220 cals       Medium\n",
       "1                     Gammon           320 cals  280 cals     Med-High\n",
       "2              Haddock fresh           200 cals  110 cals  Low calorie\n",
       "3              Halibut fresh           220 cals  125 cals  Low calorie\n",
       "4                        Ham             6 cals  240 cals       Medium\n",
       "5      Herring fresh grilled           300 cals  200 cals       Medium\n",
       "6                     Kidney           200 cals  160 cals       Medium\n",
       "7                     Kipper           200 cals  120 cals  Low calorie\n",
       "8                      Liver           200 cals  150 cals       Medium\n",
       "9                 Liver pate           150 cals  300 cals       Medium\n",
       "10              Lamb (roast)           300 cals  300 cals     Med-High\n",
       "11            Lobster boiled           200 cals  100 cals  Low calorie\n",
       "12             Luncheon meat           300 cals  400 cals         High\n",
       "13                  Mackeral           320 cals  300 cals       Medium\n",
       "14                   Mussels            90 cals   90 cals      Low-Med\n",
       "15            Pheasant roast           200 cals  200 cals       Medium\n",
       "16        Pilchards (tinned)           140 cals  140 cals       Medium\n",
       "17                    Prawns           180 cals  100 cals     Low- Med\n",
       "18                      Pork           320 cals  290 cals     Med-High\n",
       "19                  Pork pie           320 cals  450 cals         High\n",
       "20                    Rabbit           200 cals  180 cals       Medium\n",
       "21              Salmon fresh           220 cals  180 cals       Medium\n",
       "22    Sardines tinned in oil           220 cals  220 cals       Medium\n",
       "23  Sardines in tomato sauce           180 cals  180 cals       Medium\n",
       "24        Sausage pork fried           250 cals  320 cals         High\n",
       "25      Sausage pork grilled           220 cals  280 cals     Med-High\n",
       "26              Sausage roll           290 cals  480 cals         High\n",
       "27       Scampi fried in oil           400 cals  340 cals         High\n",
       "28        Steak & kidney pie           400 cals  350 cals         High"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_pdf(\"http://www.uncledavesenterprise.com/file/health/Food%20Calories%20List.pdf\", pages=3)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-9aa581060b97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\", encoding = 'ISO-8859-1',\n\u001b[0;32m----> 2\u001b[0;31m          stream=True, area = [269.875, 12.75, 790.5, 961], pages = 4, guess = False,  pandas_options={'header':None})\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/vanx/Software/Tensorflow/environments/venv36/lib/python3.6/site-packages/tabula/wrapper.py\u001b[0m in \u001b[0;36mread_pdf\u001b[0;34m(input_path, output_format, encoding, java_options, pandas_options, multiple_tables, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "df = read_pdf(\"./tmp/pdf/Food Calories List.pdf\", encoding = 'ISO-8859-1',\n",
    "         stream=True, area = [269.875, 12.75, 790.5, 961], pages = 4, guess = False,  pandas_options={'header':None})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_pdf(\"./tmp/pdf/output.pdf\", encoding = 'ISO-8859-1',\n",
    "         stream=True, guess = False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_pdf(\"./tmp/pdf/output.pdf\", encoding = 'ISO-8859-1',\n",
    "         stream=True, area=[269.875, 12.75, 790.5, 961], guess = False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Camelot\n",
    "\n",
    "#### Installation\n",
    "\n",
    "https://pypi.org/project/camelot-py/\n",
    "\n",
    "`pip install camelot-py`\n",
    "\n",
    "#### Camelot readme\n",
    "\n",
    "https://github.com/socialcopsdev/camelot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import camelot\n",
    "tables = camelot.read_pdf(\"./tmp/pdf//Food Calories List.pdf\")\n",
    "tables[0].df[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tables1 = camelot.read_pdf(\"./tmp/pdf/MGI_Disruptive_technologies_Full_report_May2013.pdf\", pages='32', area=[269.875, 120.75, 790.5, 561])\n",
    "print (tabulate(tables1[0].df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(30,35):\n",
    "    print (i)\n",
    "    tables = camelot.read_pdf(\"./tmp/pdf/MGI_Disruptive_technologies_Full_report_May2013.pdf\", pages='%d' %  i)\n",
    "    try:\n",
    "        print (tabulate(tables[0].df))\n",
    "        print (tabulate(tables[1].df))\n",
    "    except IndexError:\n",
    "        print('NOK')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract by PyPDF2\n",
    "\n",
    "#### Installation\n",
    "\n",
    "https://pypi.org/project/PyPDF2/\n",
    "\n",
    "`pip install PyPDF2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-a394e15e2bf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPyPDF2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpdf_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./tmp/pdf/Food Calories List.pdf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mread_pdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPyPDF2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPdfFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdf_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnumber_of_pages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetNumPages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_pdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetPage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './tmp/pdf/Food Calories List.pdf'"
     ]
    }
   ],
   "source": [
    "import PyPDF2\n",
    "pdf_file = open('./tmp/pdf/Food Calories List.pdf', 'rb')\n",
    "read_pdf = PyPDF2.PdfFileReader(pdf_file)\n",
    "number_of_pages = read_pdf.getNumPages()\n",
    "page = read_pdf.getPage(2)\n",
    "page_content = page.extractText()\n",
    "print (page_content.encode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'page_content' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-26f75e28a2c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtable_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpage_content\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'page_content' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "table_list = page_content.split('\\n')\n",
    "l = numpy.array_split(table_list, len(table_list)/4)\n",
    "for i in range(0,5):\n",
    "    print(l[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
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