{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "pd.options.display.max_columns = 2000\n",
    "pd.options.display.max_rows = 2000\n",
    "\n",
    "def flatten_json(nested_json, exclude=['']):\n",
    "    out = {}\n",
    "\n",
    "    def flatten(x, name='', exclude=exclude):\n",
    "        if type(x) is dict:\n",
    "            for a in x:\n",
    "                if a not in exclude: flatten(x[a], name + a + '_')\n",
    "        elif type(x) is list:\n",
    "            i=0\n",
    "            for a in x:\n",
    "                flatten(a, name + str(i) + '_')\n",
    "                i += 1\n",
    "        else:\n",
    "            out[name[:-1]] = x\n",
    "\n",
    "    flatten(nested_json)\n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('result.json') as json_file:\n",
    "    data = json.load(json_file)\n",
    "\n",
    "data = pd.DataFrame([flatten_json(x) for x in data['messages']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = data[['from', 'text','text_entities_45_text','text_entities_46_text','text_entities_47_text','text_entities_48_text']]\n",
    "words_freq = messages.replace(to_replace=[',','.','(',')'],value='')\n",
    "words_freq = words_freq.text.str.lower().str.split().explode().value_counts()\n",
    "words_freq = pd.DataFrame(words_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text    220\n",
       "Name: 1337, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_freq.loc['1337']"
   ]
  }
 ],
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