json-extractor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JSON Extractor\n",
    "\n",
    "This script extracts data from Facebook's \"takeaway\" files. These are JSON files containing all your data, from search history to friends. They are freely avaiable for download on every Facebook account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prettyPrint(data):\n",
    "    print(json.dumps(data, indent=2, sort_keys=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch local file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n"
     ]
    }
   ],
   "source": [
    "file = \"your_search_history.json\"\n",
    "\n",
    "fline = open(file).readline().strip()\n",
    "print (fline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(file) as json_file:\n",
    "    parsed = json.load(json_file)\n",
    "    prettyPrint(parsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop through data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "type(data)\n",
    "\n",
    "search_terms = []\n",
    "\n",
    "for key in data[\"searches\"]:\n",
    "    elem = key[\"attachments\"][0][\"data\"][0][\"text\"]\n",
    "    search_terms.append(elem)\n",
    "    \n",
    "print(search_terms)"
   ]
  }
 ],
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