data_set_analysis.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_dir = os.path.abspath(os.curdir) + '/files/'\n",
    "\n",
    "\n",
    "word_counts = []\n",
    "document_types = {}\n",
    "tmp = []\n",
    "\n",
    "# Loop through all relevant\n",
    "for filename in os.listdir(files_dir):\n",
    "    \n",
    "    # Skip directories\n",
    "    if os.path.isdir(os.path.join(files_dir, filename)):\n",
    "        continue\n",
    "    \n",
    "    \n",
    "    # Open every file\n",
    "    with open(files_dir + filename, 'r') as file:\n",
    "        \n",
    "        file_data = json.loads(file.read())\n",
    "        \n",
    "        word_counts.append(file_data['word_counts'].values())\n",
    "        \n",
    "        tmp = list(file_data['word_counts'].keys())\n",
    "        current_type = file_data['document_type']\n",
    "        \n",
    "        if current_type:\n",
    "            document_types[current_type] = document_types.get(current_type, 0) + 1\n",
    "        else:\n",
    "            document_types['Unknown'] = document_types.get('Unknown', 0) + 1\n",
    "\n",
    "averages = [sum(col) / float(len(col)) for col in zip(*word_counts)]\n",
    "relevant = dict(zip(tmp,averages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(json.dumps(document_types, indent=2))\n",
    "\n",
    "# print(json.dumps(relevant, indent=2))\n",
    "\n",
    "print(json.dumps(document_types, indent=2))\n"
   ]
  }
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