YouTube API Queries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbc67255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import json\n",
    "from googleapiclient.discovery import build\n",
    "from pandas import json_normalize\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c216ba3",
   "metadata": {},
   "source": [
    "## Find Channel IDs and Put Them Into a List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4bbc830e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Just a bit of setup for the project\n",
    "api_key = os.environ.get('YOUTUBE_INSIGHTS_API_KEY')\n",
    "\n",
    "channel_ids = ['UCAiLfjNXkNv24uhpzUgPa6A', #MrBeast Philanthropy\n",
    "               'UCUaT_39o1x6qWjz7K2pWcgw', #Beast Reacts\n",
    "               'UCgoFStVyEsm8tBZP5NC-aBQ', #MrBeast\n",
    "               'UC4-79UOlP48-QNGgCko5p2g', #MrBeast 2\n",
    "               'UCIPPMRA040LQr5QPyJEbmXA'  #MrBeast Gaming\n",
    "               ]\n",
    "         \n",
    "\n",
    "channel_id = 'UCgoFStVyEsm8tBZP5NC-aBQ'\n",
    "youtube = build('youtube', 'v3', developerKey=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "855d7aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #MrBeast custom URL was returning values that weren't consistent with statistics on the channel page\n",
    "# #Here I am attempting to extract the channel ID\n",
    "\n",
    "# url = \"https://www.googleapis.com/youtube/v3/channels\"\n",
    "# params = {\n",
    "#     'part': 'snippet',\n",
    "#     'forUsername': 'MrBeast',\n",
    "#     'key': api_key\n",
    "# }\n",
    "\n",
    "# response = requests.get(url, params=params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a987e245",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Check the response status code to see if the request was successful\n",
    "# if response.status_code == 200:\n",
    "#     # If the request was successful, check if the 'items' key is in the response\n",
    "#     if 'items' in response.json():\n",
    "#         channel_id = response.json()['items'][0]['id']\n",
    "#         print(channel_id)\n",
    "#     else:\n",
    "#         # If the 'items' key is not in the response, the channel may not have been found\n",
    "#         print(\"The channel was not found.\")\n",
    "# else:\n",
    "#     # If the request was unsuccessful, you may need to check the API key or URL\n",
    "#     print(\"An error occurred while trying to retrieve the channel ID.\")\n",
    " \n",
    "##Channel ID still yielded values inconsistent with the channel page. I'll just have to work with what I've got for now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbd4dc3",
   "metadata": {},
   "source": [
    "## Get Channel Statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "814e1751",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_stats(youtube, channel_ids):\n",
    "    all_data = []\n",
    "    request = youtube.channels().list(\n",
    "        part = 'snippet, contentDetails, Statistics',\n",
    "        id=','.join(channel_ids))\n",
    "    response=request.execute()\n",
    "\n",
    "    for item in response['items']:\n",
    "        data = dict(Channel_Name = item['snippet']['title'],\n",
    "                Subscribers = item['statistics']['subscriberCount'],\n",
    "                Views = item['statistics']['viewCount'],\n",
    "                Total_videos = item['statistics']['videoCount'],\n",
    "                playlist_id = item['contentDetails']['relatedPlaylists']['uploads'])\n",
    "        \n",
    "        all_data.append(data)\n",
    "    \n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a27735c",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_statistics = get_channel_stats(youtube, channel_ids)\n",
    "# channel_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8dd6213",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data = pd.DataFrame(channel_statistics)\n",
    "# channel_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9b97e3",
   "metadata": {},
   "source": [
    "## Quick Visual Check of Channel Stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa18d329",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data['Channel_Name']= channel_data['Channel_Name'].astype(str)\n",
    "channel_data['Subscribers']= channel_data['Subscribers'].astype('int64')\n",
    "channel_data['Views']= channel_data['Views'].astype('int64')\n",
    "channel_data['Total_videos']= channel_data['Total_videos'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c9340f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel_Name    object\n",
       "Subscribers      int64\n",
       "Views            int64\n",
       "Total_videos     int64\n",
       "playlist_id     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data.sort_values(by='Subscribers', ascending=False, inplace=True)\n",
    "channel_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6891f77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Channel')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIa8XF0AAOd5eFjk4WFxdRnIJlarTVarzdVlAACAdBCkgBzCw8MiP7+C8vDwdHUpyCZWa6ouXkwiTAEA4IYIUkAOceNslKeOfbBQSedPu7ocZLGCJUrp3jZ95OFhIUgBAOCGCFJADpN0/rSSYn93dRkAAAB5GotNAAAAAIChDAepK1eu2D/funWr3n77bR0/fjwzagIAAAAAt2YcpI4dO6ZHHnlECxculCTNmDFDQ4cO1aRJk/TEE09o7969mV4kAAAAALgT4yA1depUeXp6qlmzZrp27ZreeecdtWrVSjExMWrYsKFmzpyZBWUCAAAAgPswDlLR0dEaOnSoqlWrppiYGP3111/q0KGDChcurI4dO+qnn37KijoBAAAAwG0YB6lr166paNGikqQdO3aoYMGCql27tiQpNTVVXl4sBAgAAAAgdzMOUhUqVNAnn3yis2fPasuWLQoNDZWXl5euXbumlStX6oEHHsiKOgEAAADAbRgHqUGDBmnNmjVq3LixLl26pD59+kiSWrRooW+//Vbh4eGZXiQAAAAAuBPjeXgPPvigNm/erB9//FFBQUG6++67JUndunVTvXr1VKFChUwvEgAAAADcSYYuaCpdurRKly7t0NatW7dMKQgAAAAA3J1xkLJarVqzZo0+//xzJSUlyWq1Omy3WCxatmxZphUIAAAAAO7GOEhNnTpVS5YsUWBgoAICAmSxWBy222y2TCsOAAAAANyRcZDauHGjevTooREjRmRFPQAAAADg9oxX7bty5YoeeuihLCgFAAAAAHIG4yBVu3Ztfffdd1lRCwAAAADkCMZT+3r37q3hw4fr+vXrCgoKUsGCBdP0CQ4OzpTiAAAAAMAdGQepHj16SJLmzJkjSQ6LTdhsNlksFv3yyy+ZVB4AAAAAuB/jIBUVFZUVdQAAAABAjmEcpEJCQrKiDgAAAADIMYyDlCQdO3ZMERER2r17txISEuTn56c6deooPDxc5cqVy+waAQAAAMCtGAep3377TR07dpSXl5eaNGmiO+64Q3Fxcfr888/1xRdf6P333ydMAQAAAMjVjIPU1KlTFRgYqOXLl6tIkSL29r/++kvdunXTjBkzNHv27EwtEgAAAADcifF9pKKjo9WvXz+HECVJRYoU0bPPPqvo6OhMKw4AAAAA3JFxkPLy8pK3t3e627y9vZWSkvKfiwIAAAAAd2YcpKpVq6aVK1fKZrM5tNtsNq1YsUJVq1bNtOIAAAAAwB0ZXyP1/PPPq1OnTmrTpo1atWolf39/xcXFaevWrTpx4oTefvtto9eLj4/X9OnT9cUXX+jy5cuqUKGChg0bpjp16qTb/+LFixo/fry+/PJLSVLLli01cuRI+fj4mL4VAAAAAMiQDJ2RWrRokXx8fDRnzhy9/vrrmjNnjnx8fLRw4UIFBwcbvd7QoUP1ww8/aPr06VqzZo2qVKmiXr166ciRI+n2HzRokE6ePKmlS5dq1qxZ2rVrl8aMGWP6NgAAAAAgwzJ0H6l69erp/fffV1JSkhISEuTr66uCBQsav86JEye0a9cuvfPOO6pVq5YkadSoUfryyy/1wQcf6Pnnn3fov2/fPu3Zs0dbtmyxL7E+duxY9e7dW0OHDlXJkiUz8nYAAAAAwIhTQSo6OlqVK1dWoUKFnFqVz9mzUn5+flqwYIHDdVUWi0U2m02XLl1K0z8mJkb+/v4O96kKCQmRxWLR3r171bp1a6f2mx4vL+OTc5IkT8+MPQ85myu+7oy1vImvOwAA7smpIBUWFqbVq1erevXqCgsLs4cd6f+Dz63//vLLL07t3NfXV40bN3Zo27p1q37//XeFhoam6R8bG6tSpUo5tHl7e6tYsWI6ffq0U/tMj4eHRX5+hTL8fOQ9vr7mZ2CBjGCsAQDgnpwKUlFRUfazQFFRUVlWzN69e/Xyyy+rWbNmatq0aZrtSUlJ6S69nj9/fiUnJ2d4v1arTQkJiRl6rqenB7/o5EEJCUlKTbVm6z4Za3mTK8YaAAB5la9vQadngzgVpEJCQtL9PDNt375dL7zwgoKCgjR9+vR0+xQoUCDd+1QlJyf/51X7rl/nFxU4LzXVyphBtmCsAQDgnpwKUrNnz3b6BS0Wi8LDw42KWLFihd544w01b95cU6dO/ccb/gYEBGj79u0ObSkpKYqPj2ehCQAAAADZxuVBatWqVRo3bpzCwsL08ssvy8Pjn0+lBQcHa+rUqTpx4oTKlCkjSdq9e7ck2Vf9AwAAAICs5lSQOnjwYJbs/NixY5owYYKaN2+uvn376vz58/ZtBQoUkI+Pjy5cuKAiRYqoQIECCgoKUq1atTRkyBCNHj1aiYmJev311/Xkk09yRgoAAABAtnHpuroff/yxrl27pm3btik0NNTh44033tDp06cVGhqqLVu2SLpxtmv27NkKDAxUt27dNHjwYDVq1EijR4925dsAAAAAkMc4dUaqa9euTr+gxWLRsmXLnOrbr18/9evX77Z9Dh065PC4RIkSmjVrltP1AAAAAEBmcypI3bxnVGb3BQAAAICcyKkgtXz58qyuAwAAAAByDJdeIwUAAAAAOZFTZ6QqVaqk9957T9WrV1fFihVlsVj+sa/FYtGBAwcyrUAAAAAAcDdOBanw8HD78uLh4eG3DVIAAAAAkNs5FaQGDBhg/3zgwIFZVgwAAAAA5AROBalTp04Zvehdd92VoWIAAO7Bw8MiDw9mH+QVVqtNViur7gKACaeCVLNmzYxe9JdffslQMQAA1/PwsKiYX0F5eni6uhRkk1RrquIvJhGmAMCA0X2kKleurJYtW8rf3z9LiwIAuI6Hh0WeHp6K3BGlU5diXV0OsthdRUuqb+Ou8vCwEKQAwIBTQWrLli32j7feekshISF69NFH1aJFCxUpUiSrawQAuMCpS7E6cf4PV5cBAIBbcuo+Uvfdd58GDBigLVu2aO3atapWrZrmz5+vBx98UM8995y2bNmipKSkrK4VAAAAANyC8Q15K1asqKFDh2r79u1asWKF7rnnHk2ePFkPPvighg0bps8++ywr6gQAAAAAt2EcpG4VFBSkkSNH6pNPPlH37t318ccfKzw8PLNqAwAAAAC35NQ1UulJTU3V119/ra1bt+rTTz/VpUuXVK1aNbVu3Toz6wMAAAAAt2MUpKxWqz08bd++XZcuXVKlSpXUq1cvtW7dWoGBgVlVJwAAAAC4DaeC1M3wtG3bNl26dEnly5dXt27d1Lp1a5UtWzaLSwQAAAAA9+JUkOrZs6c8PT1Vq1YttWrVSvfff78kKS4uTnFxcWn6BwcHZ26VAAAAAOBGnJ7al5qaqujoaMXExDi037xZr8Vikc1mk8Vi0S+//JK5VQIAAACAG3EqSEVFRWV1HQAAAACQYzgVpEJCQrK6DgAAAADIMf7TfaQAAAAAIC8iSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABhy+j5SN1mtVq1Zs0aff/65kpKSZLVaHbZbLBYtW7Ys0woEAAAAAHdjHKSmTp2qJUuWKDAwUAEBAbJYLA7bb96gFwAAAAByK+MgtXHjRvXo0UMjRozIinoAAAAAwO0ZXyN15coVPfTQQ1lQCgAAAADkDMZBqnbt2vruu++yohYAAAAAyBGMp/b17t1bw4cP1/Xr1xUUFKSCBQum6RMcHJwpxQEAAACAOzIOUj169JAkzZkzR5IcFpuw2WyyWCz65ZdfMqk8AAAAAHA/xkEqKioqK+oAAAAAgBzDOEiFhIRkRR0AAAAAkGMYBylJunDhghYvXqyvv/5acXFxWrRokbZv366KFSvq4YcfzuwaAQAAAMCtGK/ad/LkST3++ONavXq1SpYsqfPnzys1NVXHjh3ToEGD9MUXX2RBmQAAAADgPozPSE2aNEklSpTQ8uXL5ePjo6pVq0qSpk2bpuTkZM2fP5/7TAEAAADI1YzPSH3zzTfq37+/fH19HVbsk6QOHTro8OHDmVYcAAAAALgj4yAlSZ6enum2p6SkpAlXAAAAAJDbGAepOnXqaMGCBUpMTLS3WSwWWa1WvfPOO6pVq1amFggAAAAA7sb4Gqlhw4apU6dOeuSRR1S3bl1ZLBYtXrxYR44c0YkTJ7Rq1aqsqBMAAAAA3IZTZ6SaNWumgwcPSpIeeOABrV27VnXr1tXu3bvl6empr7/+Wvfcc4/effddVapUKUsLBgAAAABXc+qM1J9//qmUlBT747Jly2ratGlZVhQAAAAAuLMMLTYBAAAAAHkZQQoAAAAADDm92ER4eLi8vb3/tZ/FYtH27dv/U1EAAAAA4M6cDlKVK1dW8eLFs7IWAAAAAMgRjM5IVa9ePStrAQAAAIAcgWukAAAAAMAQQQoAAAAADDkVpNq2bSs/P7+srgUAAAAAcgSnrpGaOHFiVtcBAAAAADkGU/sAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMOX1D3lsdO3ZMO3bsUGJioqxWq8M2i8Wi8PDwTCkOAAAAANyRcZDasGGDRo4cKZvNlu52ghQAAACA3M44SM2bN08PPvigxo8fr4CAAFkslqyoCwAAAADclvE1UqdOnVLv3r1VqlQpQhQAAACAPMk4SN177706ffp0VtQCAAAAADmCcZAaNmyY5s6dq927dys5OTkragIAAAAAt2Z8jdQbb7yh8+fPq3v37ulut1gsOnDgwH+tCwAAAADclnGQevzxx7OiDgAAAADIMYyD1IABA7KiDgAAAADIMZwKUtHR0apcubIKFSqk6Ojof+0fHBz8nwsDAAAAAHflVJAKCwvT6tWrVb16dYWFhclisaS5Ie/NNovFol9++SVLigUAAAAAd+BUkIqKilK5cuXsnwMAAABAXuZUkAoJCUn3cwAAAADIi4zvIwUAAAAAeR1BCgAAAAAMEaQAAAAAwBBBCgAAAAAMZUqQ2r9/vz755BMlJCRkxssBAAAAgFszDlJxcXHq2rWr5syZI+nGcugdOnTQoEGD9Mgjj+jw4cOZXiQAAAAAuBPjIDV58mQdPXpU1atXl9Vq1YIFC/Tggw9qw4YNKl++vKZNm5YVdQIAAACA2zAOUjt37tSIESPUsGFDff/99zp37py6du2qihUrqnfv3oqJicmKOgEAAADAbRgHqcTERAUEBEiSduzYIW9vb9WrV0+S5O3tLZvNlrkVAgAAAICbMQ5SZcuWVUxMjFJSUvTRRx8pJCRE+fPnlyRt2rRJZcuWzewaAQAAAMCtGAepvn37avbs2apfv75OnjypHj16SJLat2+vTZs2qVevXpleJAAAAAC4Ey/TJ7Ru3VoBAQGKiYlRSEiIatSoIUmqU6eOBg0apIYNG2Z2jQAAAADgVoyDVL9+/dS1a1c9++yzDu0jRozItKIAAAAAwJ0ZT+2Ljo6Wp6dnVtSiuXPnKiws7LZ91q9frwoVKqT5OHHiRJbUBAAAAAB/Z3xGqkGDBnr//fcVFBSkAgUKZFohS5cu1axZsxQcHHzbfocOHVJISIimT5/u0F68ePFMqwUAAAAAbsc4SOXPn19bt27Vtm3bFBgYqBIlSjhst1gsWrZsmdOvFxsbq1GjRmnv3r269957/7X/r7/+qooVK8rf39+0dAAAAADIFMZB6syZM6pZs6b98d/vG2V6H6mff/5ZRYsW1aZNmzRnzhz9+eeft+1/6NAhtWjRwmgfzvDyMp7lKEny9MzY85CzueLrzljLmxhryC583QHAjHGQWr58eaYW0LRpUzVt2tSpvhcuXNC5c+cUHR2t5cuXKz4+XkFBQXrhhRecOpv1Tzw8LPLzK5Th5yPv8fUt6OoSkEcw1pBdGGsAYMY4SN106dIlxcTE6OzZs2rRooXi4+N17733ymKxZGZ9Dn799VdJkqenpyZNmqTExETNnTtXnTt31ubNm3XHHXdk6HWtVpsSEhIz9FxPTw9++ORBCQlJSk21Zus+GWt5E2MN2cUVYw0A3I2vb0Gnz9BnKEjNmzdPkZGRunr1qiwWi6pXr64ZM2YoPj5eS5Yska+vb0Ze9l/Vq1dPe/bsUdGiRe1tc+bMUZMmTbRu3bo0S7KbuH6dHx5wXmqqlTGDbMFYQ3ZhrAGAGeMJ0StWrFBERIR69Oih1atX26+J6tatm06ePKm33nor04u81a0hSpJ8fHwUGBio2NjYLN0vAAAAANxkHKSWL1+uZ599Vs8//7yqVKlib2/YsKEGDx6szz77LFMLvNWqVatUt25dXb161d52+fJlHT9+XOXLl8+y/QIAAADArYyD1KlTpxQSEpLutvvuu0/nzp37z0XdlJqaqri4OHtwatKkiWw2m1588UUdPnxYP/74owYOHKjixYurbdu2mbZfAAAAALgd4yBVqlQp7du3L91tP/30k0qVKvWfi7rp9OnTCg0N1ZYtW+z7XrZsma5cuaJOnTqpe/fuKlKkiKKiojL15sAAAAAAcDvGi008/fTTioiIUIECBfTQQw9JkhITE/Xxxx8rMjJSPXr0yHAxb775psPjwMBAHTp0yKGtUqVKWrx4cYb3AQAAAAD/lXGQ6tOnj/744w9NnTpVU6dOlSR17dpVkvTYY4+pb9++mVshAAAAALgZ4yBlsVg0duxY9ejRQ7t371Z8fLyKFCmikJAQ3X///VlRIwAAAAC4lQzfkPfee++V1WpVQkKCSpQooXvuuScz6wIAAAAAt5WhIPX+++9r7ty5OnPmjL2tVKlSGjp0qNq0aZNpxQEAAACAOzIOUitWrND48eP18MMPa/DgwSpRooTi4uL04Ycfavjw4fL09FSrVq2yolYAAAAAcAvGQSoqKkrPPPOMXnnlFYf2tm3batSoUZo9ezZBCgAAAECuZnwfqTNnzqhJkybpbmvTpo1Onjz5n4sCAAAAAHdmHKSqVaumb775Jt1tBw4cUIUKFf5zUQAAAADgzpya2hcdHW3//NFHH9XEiRN15coVtWrVSnfccYcSEhL01Vdfafny5RozZkyWFQsAAAAA7sCpIBUWFiaLxWJ/bLPZ9M477+jdd991aJOkoUOHco0UAAAAgFzNqSAVFRWV1XUAAAAAQI7hVJAKCQnJ6joAAAAAIMdwKkjNnj1b7du3V8mSJTV79uzb9rVYLAoPD8+U4gAAAADAHTkdpBo1akSQAgAAAAA5GaQOHjxo//znn3+Wp6dnlhUEAAAAAO7O+D5S7du316effpoVtQAAAABAjmAcpE6ePKnChQtnRS0AAAAAkCMYB6lHH31UkZGROnnyZFbUAwAAAABuz6lrpG51/PhxxcTE6JFHHlGBAgVUvHhxh+0Wi0Xbt2/PtAIBAAAAwN0YB6lSpUrpsccey4paAAAAACBHMA5SEydOTNN2/fp1eXh4yMPDeKYgAAAAAOQ4GUo+8+bNU69eveyP9+7dqwYNGmjp0qWZVRcAAAAAuC3jILVo0SLNnj1bDzzwgL2tTJkyeuKJJzRt2jS99957mVogAAAAALgb46l9q1ev1pAhQ9S7d297W0BAgF566SUVL15cUVFR6tChQ6YWCQAAAADuxPiMVGxsrKpUqZLutmrVqumPP/74z0UBAAAAgDszDlKlS5fW119/ne623bt3KyAg4D8XBQAAAADuzHhqX6dOnTRhwgRdv35dDz/8sEqUKKELFy5o+/btioqK0gsvvJAVdQIAAACA2zAOUl26dNGZM2f09ttv21fps9ls8vLyUrdu3dS9e/dMLhEAAAAA3ItxkJKkYcOG6dlnn9X333+v+Ph4+fr6qnr16vLz88vs+gAAAADA7WT4DrpFihRRw4YN9dhjj8nPz0/R0dFKSEjIzNoAAAAAwC0ZB6m4uDh17dpVc+bMkST7cueDBg3SI488osOHD2d6kQAAAADgToyD1OTJk3X06FFVr15dVqtVCxYs0IMPPqgNGzaofPnymjZtWlbUCQAAAABuwzhI7dy5UyNGjFDDhg31/fff69y5c+ratasqVqyo3r17KyYmJivqBAAAAAC3YRykEhMT7feK2rFjh7y9vVWvXj1Jkre3t2w2W+ZWCAAAAABuxjhIlS1bVjExMUpJSdFHH32kkJAQ5c+fX5K0adMmlS1bNrNrBAAAAAC3Yhyk+vbtq9mzZ6t+/fo6efKkevToIUlq3769Nm3apF69emV6kQAAAADgTozvI9W6dWuVLFlSe/fuVUhIiGrUqCFJqlOnjgYNGqSGDRtmdo0AAAAA4FYydEPe2rVrq3bt2kpKStLZs2dVrFgxjRgxIrNrAwAAAAC3lKEg9fXXXysiIkI//PCDbDabPD09VaNGDQ0ePFh16tTJ7BoBAAAAwK0YXyO1ZcsW9ezZU8nJyRowYIBGjx6tfv36KT4+Xt27d9e3336bFXUCAAAAgNswPiM1b948Pfroo2luvBseHq7+/ftrypQpWrt2baYVCAAAAADuxviM1IkTJ9S2bds07RaLRZ07d9bhw4czpTAAAAAAcFfGQapcuXI6cOBAuttOnz6te+655z8XBQAAAADuzKmpfadOnbJ/3rNnT7322mvy8PBQq1at5O/vr0uXLumrr75SRESE3njjjSwrFgAAAADcgVNBqmnTprJYLPbHNptNU6dOTXOdlM1mU9++ffXLL79kbpUAAAAA4EacClITJkxwCFIAAAAAkJc5FaTatWuX1XUAAAAAQI5hvPx5dHT0v/YJDg7OUDEAAAAAkBMYB6mwsDBZLBbZbDZ729+n/XGNFAAAAIDczDhIRUVFpWlLTEzU3r17tXHjRs2aNStTCgMAAAAAd2UcpEJCQtJtf+ihh+Tj46N58+YpMjLyPxcGAAAAAO7KOEjdTu3atQlRAADAaR4eFnl4sDJwXmG12mS12v69I5ADZGqQ2r59uwoXLpyZLwkAAHIpDw+L/IoVlIenp6tLQTaxpqbqYnwSYQq5gnGQ6tq1a5o2q9Wq06dP69SpU+rTp0+mFAYAAHI3Dw+LPDw99f28SF0+ddrV5SCLFb6rlGo811ceHhaCFHIF4yB162p9N3l4eKhChQrq16+fnnrqqUwpDAAA5A2XT51WwokTri4DAIwYB6nly5dnRR0AAAAAkGN4/NcXuHTpkn788Uf99ddfmVEPAAAAALg9p4PU/v371a9fP23YsMHeFhUVpUaNGul///ufGjZsqMWLF2dFjQAAAADgVpwKUr/88oueeeYZHTx4UD4+PpJuBKuJEyfqnnvuUUREhPr3768ZM2Zo+/btWVowAAAAALiaU9dILViwQJUqVdLSpUtVsGBBSf9/rdSUKVNUsWJFSdK5c+e0fPlyPfzww1lULgAAAAC4nlNnpKKjoxUWFmYPUZK0c+dOlS5d2h6iJCk0NFQHDhzI/CoBAAAAwI04FaTi4+MVEBBgf3zkyBFdvHhRdevWdehXsGBBpaSkZG6FAAAAAOBmnApSxYoV07lz5+yPv/32W1ksFtWvX9+h35EjR1S8ePHMrRAAAAAA3IxTQSokJETvvfeerFarrl+/rrVr1yp//vxq2LChvU9KSopWrlypWrVqZVmxAAAAAOAOnFps4rnnnlOHDh3si0icOnVK4eHhKlKkiCRp7dq1WrlypY4dO6bJkydnXbUAAAAA4AacClL333+/Vq9erSVLluj8+fPq06ePOnXqZN8+c+ZMeXl5ac6cOapUqVKWFQsAAAAA7sCpICVJ5cuX14QJE9LdtmbNGvn7+8vDw+n7+wIAAABAjuV0kLqdkiVLZsbLAAAAAECOwCkkAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQ24VpObOnauwsLDb9rl48aKGDRum4OBgBQcH69VXX1ViYmI2VQgAAAAAbhSkli5dqlmzZv1rv0GDBunkyZP2/rt27dKYMWOyoUIAAAAAuMHL1QXExsZq1KhR2rt3r+69997b9t23b5/27NmjLVu2qFy5cpKksWPHqnfv3ho6dKhKliyZHSUDAAAAyONcHqR+/vlnFS1aVJs2bdKcOXP0559//mPfmJgY+fv720OUJIWEhMhisWjv3r1q3bp1huvw8srYyTlPT7c5qYds5IqvO2Mtb2KsIbsw1pBd+Lojt3B5kGratKmaNm3qVN/Y2FiVKlXKoc3b21vFihXT6dOnM1yDh4dFfn6FMvx85D2+vgVdXQLyCMYasgtjDdmFsYbcwuVBykRSUpK8vb3TtOfPn1/JyckZfl2r1aaEhIwtWOHp6cEBIQ9KSEhSaqo1W/fJWMubGGvILow1ZBdXjDXAWb6+BZ0+a5qjglSBAgWUkpKSpj05OVk+Pj7/6bWvX+cbGs5LTbUyZpAtGGvILow1ZBfGGnKLHDVJNSAgQGfPnnVoS0lJUXx8PAtNAAAAAMg2OSpIBQcH68yZMzpx4oS9bffu3ZKkWrVquaosAAAAAHmMWwep1NRUxcXF6erVq5KkoKAg1apVS0OGDNH+/fv17bff6vXXX9eTTz7JGSkAAAAA2catg9Tp06cVGhqqLVu2SJIsFotmz56twMBAdevWTYMHD1ajRo00evRo1xYKAAAAIE9xq8Um3nzzTYfHgYGBOnTokENbiRIlNGvWrOwsCwAAAAAcuPUZKQAAAABwRwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADDk8iBltVo1a9YsNWzYUEFBQerZs6dOnDjxj/3Xr1+vChUqpPm43XMAAAAAIDN5ubqAuXPn6t1339XEiRNVsmRJTZkyRX369NEHH3wgb2/vNP0PHTqkkJAQTZ8+3aG9ePHi2VUyAAAAgDzOpWekUlJStGTJEg0cOFCNGzdWxYoVNWPGDMXGxmrbtm3pPufXX39VxYoV5e/v7/Dh6emZzdUDAAAAyKtcekbq4MGDunLliurVq2dv8/X1VeXKlRUdHa1HH300zXMOHTqkFi1aZHotXl4Zy5Seni6fHQkXcMXXnbGWNzHWkF0Ya8gufN2RW7g0SJ05c0aSVKpUKYf2O++8U6dPn07T/8KFCzp37pyio6O1fPlyxcfHKygoSC+88ILuvffeDNfh4WGRn1+hDD8feY+vb0FXl4A8grGG7MJYQ3ZhrCG3cGmQSkpKkqQ010Llz59fly5dStP/119/lSR5enpq0qRJSkxM1Ny5c9W5c2dt3rxZd9xxR4bqsFptSkhIzNBzPT09OCDkQQkJSUpNtWbrPhlreRNjDdmFsYbs4oqxBjjL17eg02dNXRqkChQoIOnGtVI3P5ek5ORkFSyY9sBar1497dmzR0WLFrW3zZkzR02aNNG6dev07LPPZriW69f5hobzUlOtjBlkC8YasgtjDdmFsYbcwqWTVG9O6Tt79qxD+9mzZxUQEJDuc24NUZLk4+OjwMBAxcbGZk2RAAAAAPA3Lg1SFStWVOHChbV79257W0JCgg4cOKA6deqk6b9q1SrVrVtXV69etbddvnxZx48fV/ny5bOlZgAAAABwaZDy9vbWM888o6lTp+rTTz/VwYMHNWTIEAUEBKh58+ZKTU1VXFycPTg1adJENptNL774og4fPqwff/xRAwcOVPHixdW2bVtXvhUAAAAAeYjL158cNGiQnn76ab3yyivq1KmTPD09tXjxYnl7e+v06dMKDQ3Vli1bJN2YCrhs2TJduXJFnTp1Uvfu3VWkSBFFRUU5XGMFAAAAAFnJpYtNSDdW4Bs+fLiGDx+eZltgYKAOHTrk0FapUiUtXrw4u8oDAAAAgDRcfkYKAAAAAHIaghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGHJ5kLJarZo1a5YaNmyooKAg9ezZUydOnPjH/hcvXtSwYcMUHBys4OBgvfrqq0pMTMzGigEAAADkdS4PUnPnztW7776r8ePH67333pPFYlGfPn2UkpKSbv9Bgwbp5MmTWrp0qWbNmqVdu3ZpzJgx2Vw1AAAAgLzMpUEqJSVFS5Ys0cCBA9W4cWNVrFhRM2bMUGxsrLZt25am/759+7Rnzx5NnDhRVapUUf369TV27Fht3LhRsbGxLngHAAAAAPIiL1fu/ODBg7py5Yrq1atnb/P19VXlypUVHR2tRx991KF/TEyM/P39Va5cOXtbSEiILBaL9u7dq9atW2eoDg8Pi4oXL5Sh51osN/4d0aupUlOtGXoN5Byenjf+9lC0aEHZbNm775tj7f6nB8tmTc3enSPbWTw8Jbl2rA1r3k/XGWu5npcbjLXg4UNlu85Yy+0sXq4ba/YaLK7ZL7JfRseYh4fzg8SlQerMmTOSpFKlSjm033nnnTp9+nSa/rGxsWn6ent7q1ixYun2d5bFYpGn53/7zipauMB/ej5yFg8P153MzVfI12X7RvZz5VjzLVjEZftG9nPlWMvvy3EtL3HlWAMyk0tHclJSkqQbYehW+fPnV3Jycrr9/973dv0BAAAAICu4NEgVKHDjLM7fF5ZITk5WwYIF0+2f3iIUycnJ8vHxyZoiAQAAAOBvXBqkbk7TO3v2rEP72bNnFRAQkKZ/QEBAmr4pKSmKj49XyZIls65QAAAAALiFS4NUxYoVVbhwYe3evdvelpCQoAMHDqhOnTpp+gcHB+vMmTMO95m6+dxatWplfcEAAAAAIBcvNuHt7a1nnnlGU6dOVfHixXX33XdrypQpCggIUPPmzZWamqoLFy6oSJEiKlCggIKCglSrVi0NGTJEo0ePVmJiol5//XU9+eSTnJECAAAAkG0sNpurFqC8ITU1VdOnT9e6det09epVBQcH67XXXlNgYKD++OMPNWvWTBMnTlS7du0kSefPn9eYMWP01VdfKX/+/GrZsqVGjhyp/Pnzu/JtAAAAAMhDXB6kAAAAACCnYSF/AAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwQpAAAAADBEkAIAAAAAQwSpLNK0aVNVqFBBb7/9drrbX3vtNVWoUEERERH/+Bp//PGHKlSo4PBRo0YNPf300/riiy+yqPJ/tnfvXsXExPxrv+3bt6tPnz5q0KCBqlatqsaNG+ull17S0aNHs7zGsLAwvfTSS1m+n5zs5ti8+VG1alW1aNFCixYtyvZaTp06pQ8//PAft69bty7N90ClSpUUEhKiXr166eDBg9lYrXTx4kW9//772brPvCIvHjOvXr2qadOmqWnTpqpZs6batWunTz/9NBsrdK2cdCyKiIhIM7aqV6+uVq1aKTIyUjfvJBMREaGmTZvedl8VKlTQunXrJEkvvfSSwsLCMu+NSDp8+LDDeL91f3AP//V4l9OOdbkZQSoL5cuXTx999FGa9uvXr+uTTz6RxWJx6nUiIiK0c+dOffXVV1q7dq0aNWqk8PBw/fLLL5ld8m117txZv//++237jB07VkOHDlW1atW0cOFCffLJJ5o0aZLOnz+vp59+Wr/99luW1hgREaFRo0Zl6T5yg549e2rnzp3auXOntm7dqoEDB2r27NlauXJlttYxYsQIffXVV//a72atO3fu1Oeff6633npLZ8+eVc+ePZWYmJgNld4wefJkbdq0Kdv2l9fktWPm+PHj9cEHH2jMmDHasGGDWrRooQEDBmj37t3ZWKVr5aRjUUBAgMOxaMuWLXrmmWc0c+ZMLV261Ol97dy5U61bt/6PFf+zvn376scff8yy10fmyIzjXU451uVmBKksVL9+ff3www86ffq0Q/u3334rHx8flSpVyqnXKVq0qPz9/XXnnXeqXLlyGjRokAIDA93uF7qtW7dq5cqVmjFjhgYNGqTKlSvrrrvuUr169RQZGaly5crd9q/JmaFYsWIqUqRIlu4jN/Dx8ZG/v7/8/f1VunRptWnTRo899pjWrl3r6tLSdbNWf39/BQQEqH79+nrttdd0/vx5ffvtt9lWB/cvz1p56ZiZlJSkDRs2aOjQoWrYsKHKlCmjvn37qm7dum77fZgVctKxyNPT0+FYFBgYqC5duqh+/fpGY8vf318FChTIwkqRE2TG8S4nHOtyO4JUFqpevbruuuuuNH9x+PDDD9WqVSuHvzasW7dOTZs21RtvvKE6deqoX79+t33tggULOjz+66+/9Oqrr6pevXqqXbu2unbt6vAXKZvNpkWLFqlVq1aqWrWqateurb59++rkyZP2Pjt27FC7du0UFBSk+vXr66WXXtKlS5ck3ZgaIEkjR478x6lzUVFRqlu3rpo1a5Zmm4eHh2bPnq2JEyfa2w4fPqz+/furbt26qlq1qpo3b65ly5bZt0dERKh79+6KiopSaGioatSooaFDhyouLk4vvviiatasqcaNG2v9+vX259w6te/m/+n69evVvHlzVa1aVU899ZT27dtn75+UlKTXX39ddevWVa1atTRq1CgNGzYsT04P/PuYstlsWrhwoZo1a6agoCA98cQTaQ7On332mTp27KiaNWuqWrVqevrpp/X111/btx8/fly9evVS7dq1VbNmTfXq1UuHDh2SdONrtWfPHq1fv/5fp8KkJ3/+/JJu/HJz0+eff6527dqpevXqat68uWbOnKmUlBT79n8bc5K0a9cudezYUUFBQWrUqJGmTZum1NRUvfTSS1q/fr327Nlj/3643fuDubx0zLRYLJo/f74aNmyYZtvN18irctqxyNPTU97e3g5tCxcuVOPGjVW9enWFhYXp+PHj9m23m2q3d+9e9ejRQ7Vr11bVqlXVpk0bffDBB/btL730koYPH65Jkyapfv36CgoKUv/+/RUXFyfpxpSxP//8U7Nnz3aYMnjs2DH16NFD1atXV2hoqCIjI+3bIiIi1LFjRw0dOlS1atXSmDFjJEn79u1T165dVbt2bdWtW1cvv/yyw9hs2rSp5s+fr759+9qPuTenPl+7dk3169fX7NmzHd7fO++8owcffFDXrl0z+S/OlZw93uX0Y11uR5DKYq1atXL4JklJSdGnn36qRx99NE3fP//8U7GxsVq/fr2GDRuW7utdv35dGzZs0JEjR/Tkk09KuvFN0KdPHx0/flyRkZFavXq1atSooU6dOunAgQOSpGXLlikyMlLDhw/Xxx9/rLlz5+rYsWN68803JUkXLlzQgAED9NRTT2nLli2aPXu2oqOjNXnyZEk3piJI0ssvv5zu1Lnr16/r+++/14MPPviP/xclS5aUj4+PpBsBpkePHvLx8dGqVavsB44JEyY4nJKOiYlRTEyMli1bppkzZ+rjjz9WmzZtVKlSJftp7Ndee00XL15Md59nz57Vu+++qylTpui9996Th4eHRowYYT+zMGLECO3atUszZszQu+++q8uXL992nnxutX//fm3evFkdOnSwt82YMUOrVq3SK6+8os2bN6tr164aPXq0fcrNTz/9pPDwcD3yyCPatGmT3n//fZUoUUIvvPCCPbwMHTpUd955p9auXav3339fHh4eGjBggKQbP7xr1qypVq1aac2aNUb1njx5UlOmTNFdd92l4OBgSdKXX36p559/Xu3bt9cHH3yg119/XVu3btXw4cMlOTfmfvjhB/Xu3Vs1atTQunXrNGHCBL3//vuaNWuWRo0apVatWqlmzZr274fbvT9kTF45ZhYoUEChoaEqVqyYve2HH37Qt99+q9DQUPP/uFwiJx2Lrl69qnXr1mnXrl1q2bKlvf3PP//U3r17FRkZqRUrViguLs6pKeexsbHq2bOnKlasqHXr1mnjxo2qVq2aRo4cqXPnztn7bd26VfHx8VqxYoVmz56tvXv3asaMGZKkNWvWKCAgQD179nSYAbJixQo98cQT+vDDD9W5c2dNnz5d33zzjX37vn37VKJECW3cuFHdunXT/v37FRYWpvLly+u9997TrFmztH//fvXs2VNWq9X+vDlz5qhatWrasGGDunTpotdee01btmxRvnz59Pjjj6cJvBs3btTjjz+ufPnyOf3/nJs5e7zLyce63M7L1QXkdq1atdLixYt1+vRplSpVSrt27ZKfn58qV66cbv/+/furdOnSkm5cTChJffr0sf/V/erVq7JarercubPuv/9+STdOA+/bt0/ffPONihcvLunGD43vvvtOUVFRevPNN3XPPffozTfftP+17e6771arVq3soSE2NlYpKSm66667dPfdd+vuu+/W/PnzlZqaKunGVARJKlKkSLpT5y5cuCCr1Wrf/01jx451OGMk3ThgJyUlqWvXrurcubMKFy4sSRowYIAiIyN16NAhVapUSZJktVo1fvx4+fr6qly5cqpUqZLy5cunHj16SJK6d++u1atX68SJE/Lz80tT17Vr1zR69Gj76/Xt21fh4eGKi4tTcnKyPv74Yy1atMgeACdPnqzvvvsu3a9NbhIZGaklS5ZIuvF/dO3aNQUFBdnn7ScmJmrp0qWaPHmymjRpIkm655579Oeff2rx4sXq0qWLPD099corr6hLly721+3atat69uyp8+fPq1SpUvr999/VoEEDBQYGysvLSxMmTNDRo0dltVpVrFgx5cuXTwUKFEgzbv6uZs2a9s+vXbumfPnyKTQ0VBMnTrSH8/nz5+vpp59Wp06d7PWOGTNG3bp10x9//CEfH59/HXNRUVGqXr26/a9q5cqV07hx43T27FkVKVJEBQoUUL58+ezfD7d7fx4e/J0qI/LKMfPvjh49qvDwcFWtWtUhROR2OelYdOrUKYdjUWJioooUKaJu3bqpW7du9nYvLy9NmTLF/nXv2LGjPejcTkpKigYMGKBevXrZjx99+/bVunXrdPz4cd1xxx2SpMKFC2vs2LHKly+fypUrpyeeeEI7duyQJBUvXlyenp7y8fFxCOmdOnWy/3Ldv39/LVmyRD/99JPq169v7zNo0CB7zYMHD1aFChX02muvSZLKly+vadOm6fHHH9dXX32lxo0bS5IaNGhgD6T33XeffvjhBy1btkytW7fW008/raVLl2rfvn2qWbOmjh8/rn379tnPeMHseJdbjnW5DUEqi1WtWlWlS5fWRx99pB49emjLli1q06bNP/YvW7Zsmrbx48crKChI0o2/qv/444+aNGmSUlNTNXbsWP3888+SlGZKXUpKipKTkyXdOAX/ww8/aNasWTpx4oSOHDmiw4cPq2TJkpKkSpUqqU2bNurXr59KlSqlBx98UA899JDT0xyKFSsmi8Wi+Ph4h/YBAwbYf8B88sknmjp1qqQbB/vOnTtry5YtOnjwoE6cOGE/K3DrX7tKlCghX19f++OCBQs6zBu+Ob3r5vtMT7ly5eyf3/wmv3btmv2vMbf+YMyfP7+qVavm1HvOyTp27Gif9nH9+nUdP35cM2bMUOfOnbV27Vr99ttvSk5O1ogRIzRy5Ej7865fv66UlBRdvXpVlSpVUtGiRbVw4UIdO3ZMx48ft38Nbx5ghwwZogkTJuidd95RvXr11LBhQ7Vq1co4ZGzYsEGSFBcXp1mzZun8+fMaPHiwAgMD7X0OHDig/fv3OwT3m2cejxw5osaNG//rmDt06FCas6rNmzf/x7oy6/3h/+WVY+atvvvuO/Xv31/+/v5asGBBmmliuVlOOhbdeeedWr58uaQbUzMLFCggf3//NIsC3HHHHQ6/UPr6+urq1av/+vqlS5fWU089pRUrVui3335L931IUpkyZRzO6BQpUuRfp8rde++9Do99fX0dfm6WKFHCoeZff/1VDRo0cHhOhQoV5Ovrq0OHDtmDVN26dR361KhRw75q3P33328/W1WzZk2tX79eVatWtU8Fg9nxLjcc63IjglQ2uHnqtnPnzvr0009vu3xyeheglixZUmXKlLE/rlixov0XyhdffFFWq1WFCxdOd871zR/ICxcuVEREhNq1a6eQkBCFhYXp008/dZjGNm3aNIWHh+vLL7/U119/bZ8vHRUV9a/v0dvbW9WqVdOePXv07LPP2tuLFy9u/ytIiRIl7O3nzp3T//73P/n5+alZs2aqX7++qlWrZj8435Te6X/TX1LT+6XEZrPZ/4pza3DLK4oWLeowpsqVK6eiRYuqS5cu+vrrr+1n92bOnKn77rsvzfO9vb0VHR2tnj17qnHjxqpTp44effRRJSUlKTw83N6vS5cuatmypXbs2KFvvvlG06dPV0REhDZs2GD/66ozbtZapkwZRUZGqn379urVq5fWr19vr9Vqtap3795q27Ztmuf7+/s7Nea8vLycXhkuM98fHOWFY+ZN27Zt07Bhw1StWjXNmzfP4Q9HeUFOOhZ5eXk51PpPbr1u08SRI0fUqVMnVa5cWQ0aNFCzZs3k5+en9u3bO/TLSNBOr6ZbF8/5+/eRzWZL91hotVodfi57eTn+Gmmz2Rx+Rj/11FOaMWOGRo0apc2bN6tXr17Gted2zh7vcvqxLrfiz6bZoFWrVvrhhx+0Zs0alS5d2uEMyX9ls9n0wAMP6PLly0pJSVGZMmXsHwsXLrTfk2TevHkaMGCARo8erQ4dOqhGjRo6fvy4/UD6/fffa8KECbrvvvvUvXt3LViwQBMmTNDu3bt1/vx5p2rp3r27fRnO9Ny6Ms3mzZsVHx+vd999V/3791fz5s3tFy5m18poFSpUkMVi0ffff29vu/VMVV5ltVp13333ycvLS6dOnXIYUzt27NDixYvl4eGhxYsXq27dupo9e7a6d++uBg0a2L/GNptN586d09ixY3Xt2jW1a9dOU6ZM0aZNmxQXF6c9e/ZkuL6CBQtq6tSp9te/6f7779fRo0cd6o2NjdXkyZN15coVp8ZcuXLl0iwbvHTpUns4u/UXi6x6f8g7x8zPPvtMgwcP1kMPPaS33347z4Wof+Pux6LM9s4776hEiRJaunSp+vTpo8aNG9uvjcruFUMfeOCBNPcFOnjwoC5fvuzw/fj34+V3333nMC2tTZs2Sk5O1tKlSxUXF3fbs8t5VVYd79zpWJebcUYqG1SqVEllypTR9OnT1bdvX+PnX7p0yb4ij9Vq1ffff69ly5apadOmKlKkiBo2bKhKlSpp8ODBeuWVV3TXXXfp3Xff1dq1a+1zz2/OvW3atKk8PDy0ceNGffLJJw5zrletWqV8+fLpf//7n65evaoPP/xQZcuWtf9F0MfHR0eOHNHFixfTvR7p0Ucf1U8//aTnnntO3bp1U4sWLVSiRAmdOHFCq1ev1tatW1WvXj1JN+7HkZSUpK1bt6pOnTo6evSofUW/W1dZy0qlS5dWq1atNG7cOI0dO1Z33nmnFi5cqNOnTxudlciJEhMT7WPKZrPp999/14QJE3TnnXeqfv36KliwoDp27KiZM2eqUKFCql27tmJiYjRlyhT16dNH0o0xtX37dsXExCggIEC7d+/WW2+9JenG17BUqVL64osv9Pvvv2vYsGEqXLiw1qxZo3z58qlq1aqSpEKFCunPP//UmTNnFBAQ4HT9FStWVO/evTVv3jw99thjatq0qfr06aPBgwcrIiJCbdq00ZkzZ+zfDzeXTf+3Mde7d2899dRTmjlzpp544gn9/vvvioyMtF974ePjo7Nnz+rkyZNOvT9kTF44Zl66dEkjRoxQlSpVNGrUKIfV0PLly+dwfUtultOPRZkpICBAZ86c0Y4dO1S+fHn9/PPPGj9+vP19OKtQoUI6fvy4zp07l+Ez4927d1eXLl00duxYdenSRefPn9fYsWNVuXJlh+uqPvzwQ/tKgNu3b9e2bds0f/58+/YiRYqoefPmmjNnjh5++GEVLVo0Q/XkZv/leJcTjnW5HUEqm7Rq1Urz5s3L0E34Bg4caP/cy8tLJUuWVJs2bTRkyBBJN07ZL1myRFOmTNGQIUOUlJRkv2fTzQPe5MmTNXbsWD311FMqVKiQgoKCNGbMGI0ePVp//PGHypcvr4iICM2ePVurVq2Sh4eH6tWrp4ULF9pP0/fs2VOLFi3S0aNHNW/evHRrHTFihEJDQ/Xuu+8qPDxcFy9eVLFixVSjRg3NmzfPPqe2ZcuW+vnnnzVp0iRdvnxZd999t9q3b69PP/1U+/fvty8YkNXGjRun8ePHa+DAgbLZbGrTpo1q1KiR61cUWrJkif0g6uHhIT8/P9WuXVtTp061L506cuRIFS9eXLNmzdLZs2cVEBCgAQMG2KduDho0SOfOnbMvxVq+fHlNmDBBw4cP1/79+1WuXDktXLhQkyZNUvfu3ZWUlKRKlSppwYIFuueeeyTduD5ixIgRevzxx/XNN98YTYnp37+/Pv74Y40ZM0YhISFq2bKlZsyYocjISEVGRqpo0aJq0qSJfdU+Z8ZcpUqVNHfuXM2aNUuLFi2Sv7+/wsLC7O/xySef1LZt29SmTRtt27btX98fMi63HzO//PJLJSQk6IcfflCjRo0ctoWEhNivxcntcsOxKLN07dpVR48e1YsvvqiUlBSVLVtWQ4cOta+Y9/dx8k/CwsI0adIkHT58OMP3E6pZs6YWLlyot956S08++aQKFy6shx9+WMOGDXP4+fjkk0/qk08+0aRJk1S2bFnNnDkzzRT9du3aafPmzWrXrl2GaskLMnq8ywnHutzOYuMOk8ijkpOT9dVXX6levXr2VdwkqUWLFnr88ccd5tcDAID/17RpU7Vt29bhl/n0bNiwQTNnztRnn33GQjzIdTgjhTzL29tbY8eOVXBwsPr37y9PT0+tWbNGp06dcrgnCAAAMPPzzz/r6NGjmjlzpp555hlCFHIlRjXyLIvFosjISF28eFEdOnRQ27ZttW/fPi1ZsiRTL24HACCv+f777/XKK68oKCjI4T5bQG7C1D4AAAAAMMQZKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACALitH3/8UcOHD9dDDz2k6tWrq1mzZnrllVd08uRJe5+wsDCFhYW5sMqMiYiIUIUKFVxdBgAggwhSAAC3tHLlSnXs2FHnz5/XsGHDtHDhQvXr10/R0dF66qmn9PPPP7u6RABAHubl6gIAAPi7vXv36o033lCXLl00atQoe3vdunXVrFkztWvXTiNHjtSmTZtcWCUAIC/jjBQAwO0sXrxYRYoU0dChQ9NsK168uF566SU98sgjunz5siTJZrNp4cKF9imAHTp00I8//ujwvO3bt6tz586qWbOmqlatqpYtW2rFihX27bt371aFChX0zTffqGfPngoKCtKDDz6oSZMm6fr16/Z+FSpU0MqVKzVq1CiFhISoZs2aGjRokM6dO5dmf+3atVO1atXUoEEDjR8/XomJiZn53wQAcCGCFADArdhsNu3cuVP169dXwYIF0+3TsmVLDRgwQIULF5Z04wzWtm3b9Oqrr2rSpEmKjY1Vv3797AHoiy++UHh4uKpUqaK5c+cqIiJCd999t8aNG6fvvvvO4bVfeOEF1a5dW/Pnz9djjz2mJUuWaM2aNQ59ZsyYIavVqunTp+vFF1/UF198oQkTJti3b968WeHh4brvvvs0Z84cDRgwQJs2bVL//v1ls9ky878LAOAiTO0DALiVixcvKjk5WYGBgU4/x9vbWwsWLFCxYsUkSZcvX9Yrr7yi3377TRUrVtRvv/2mJ5980mGaYM2aNVW3bl1FR0erVq1a9vb27dsrPDxcklS/fn1t375dX3zxhTp27Gjv88ADD2jixIn2x/v379dHH30k6UYQnDp1qho2bKipU6fa+5QtW1bdu3fXjh079NBDDxn9nwAA3A9BCgDgVjw8bkyWSE1Ndfo55cuXt4coSfYQ9tdff0mSevfuLUlKTEzU77//rmPHjtmn/l27ds3htWrWrOnwOCAgIM2UvBo1aqTpk5SUJEk6evSozpw5o759+zpMCQwODlbhwoW1a9cughQA5AIEKQCAWylWrJgKFSqkU6dO/WOfxMREpaSk2MOTj4+Pw/abYcxqtUqSLly4oNdff13bt2+XxWJRmTJlVLt2bUlKM9WuQIECaV7r733+PuXw1j7x8fGSpDFjxmjMmDFpaj979uw/vi8AQM5BkAIAuJ3Q0FDt3r1bycnJyp8/f5rt69at0xtvvKFVq1Y59XovvPCCjhw5orffflu1atWSt7e3kpKS9P7772d26fL19ZUkvfjiiwoJCUmzvWjRopm+TwBA9mOxCQCA2+nZs6fi4+M1Y8aMNNvOnz+vRYsWqUyZMmmm2P2TvXv3qkWLFqpXr568vb0lSV9++aWk/z9rlVnuu+8+lShRQn/88YeqVatm/wgICNC0adN04MCBTN0fAMA1OCMFAHA7NWrU0PPPP6+ZM2fqyJEjatu2rfz8/HT48GEtWbJEV65c0YIFC2SxWJx6verVq2vz5s2qUqWKAgICtG/fPkVGRspisdivbcosnp6eGjJkiF577TV5enqqSZMmSkhI0Ny5cxUbG6sqVapk6v4AAK5BkAIAuKXnnntOlStX1sqVKzVx4kTFx8crICBAjRo1Ur9+/XTXXXc5/Vpvvvmmxo0bp3Hjxkm6sYLemDFjtGnTJsXExGR67e3bt1ehQoW0aNEivffee/Lx8VGtWrU0depUlS5dOtP3BwDIfhYbN7QAAAAAACNcIwUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhv4PNQOQuinkcpkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "channel_data.sort_values(by='Subscribers', ascending=False, inplace=False)\n",
    "ax = sns.barplot(x='Channel_Name', y='Subscribers', data=channel_data)\n",
    "ax.set_ylabel(\"Subscribers in Ten Millions\")\n",
    "ax.set_xlabel(\"Channel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc44e095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Channel')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='Channel_Name', y='Views', data=channel_data)\n",
    "ax.set_ylabel(\"Views in Billions\")\n",
    "ax.set_xlabel(\"Channel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28a05374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Channel')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data = channel_data.sort_values(by='Total_videos', ascending=False)\n",
    "ax = sns.barplot(x='Channel_Name', y='Total_videos', data=sorted_data, order=sorted_data['Channel_Name'])\n",
    "ax.set_ylabel(\"Video Count\")\n",
    "ax.set_xlabel(\"Channel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49d2a4dd",
   "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>Channel_Name</th>\n",
       "      <th>Subscribers</th>\n",
       "      <th>Views</th>\n",
       "      <th>Total_videos</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MrBeast Gaming</td>\n",
       "      <td>31200000</td>\n",
       "      <td>5483242808</td>\n",
       "      <td>138</td>\n",
       "      <td>UUIPPMRA040LQr5QPyJEbmXA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beast Reacts</td>\n",
       "      <td>21700000</td>\n",
       "      <td>3303128499</td>\n",
       "      <td>232</td>\n",
       "      <td>UUUaT_39o1x6qWjz7K2pWcgw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MrBeast 2</td>\n",
       "      <td>19200000</td>\n",
       "      <td>2881301845</td>\n",
       "      <td>108</td>\n",
       "      <td>UU4-79UOlP48-QNGgCko5p2g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beast Philanthropy</td>\n",
       "      <td>11800000</td>\n",
       "      <td>178640917</td>\n",
       "      <td>20</td>\n",
       "      <td>UUAiLfjNXkNv24uhpzUgPa6A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MrBeast</td>\n",
       "      <td>1630</td>\n",
       "      <td>67378</td>\n",
       "      <td>3</td>\n",
       "      <td>UUgoFStVyEsm8tBZP5NC-aBQ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Channel_Name  Subscribers       Views  Total_videos  \\\n",
       "4      MrBeast Gaming     31200000  5483242808           138   \n",
       "3        Beast Reacts     21700000  3303128499           232   \n",
       "1           MrBeast 2     19200000  2881301845           108   \n",
       "2  Beast Philanthropy     11800000   178640917            20   \n",
       "0             MrBeast         1630       67378             3   \n",
       "\n",
       "                playlist_id  \n",
       "4  UUIPPMRA040LQr5QPyJEbmXA  \n",
       "3  UUUaT_39o1x6qWjz7K2pWcgw  \n",
       "1  UU4-79UOlP48-QNGgCko5p2g  \n",
       "2  UUAiLfjNXkNv24uhpzUgPa6A  \n",
       "0  UUgoFStVyEsm8tBZP5NC-aBQ  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34d94a2",
   "metadata": {},
   "source": [
    "## Finding Video IDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00b1f3d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_Name'] == 'MrBeast Gaming', 'playlist_id'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a89cebe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UUIPPMRA040LQr5QPyJEbmXA'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "24d28c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_ids(youtube, playlist_id):\n",
    "    video_ids = []\n",
    "    next_page_token = None\n",
    "    \n",
    "    while True:\n",
    "        request = youtube.playlistItems().list(\n",
    "            part = 'contentDetails',\n",
    "            playlistId = playlist_id,\n",
    "            maxResults = 50,\n",
    "            pageToken = next_page_token)       \n",
    "        response = request.execute()\n",
    "        \n",
    "        for item in response['items']:\n",
    "            video_ids.append(item['contentDetails']['videoId'])\n",
    "        \n",
    "        if 'nextPageToken' not in response:\n",
    "            break\n",
    "        else:\n",
    "            next_page_token = response['nextPageToken']\n",
    "    \n",
    "    return video_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d7410dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ids_by_channel = {}\n",
    "for channel_name in channel_data['Channel_Name']:\n",
    "    playlist_id = channel_data.loc[channel_data['Channel_Name'] == channel_name, 'playlist_id'].iloc[0]\n",
    "    video_ids = get_video_ids(youtube, playlist_id)\n",
    "    video_ids_by_channel[channel_name] = video_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c2b0858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# video_ids_by_channel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55db3cee",
   "metadata": {},
   "source": [
    "## Get Video Details:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "206b98f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for video in video_ids:\n",
    "        request = youtube.videos().list(\n",
    "                    part='snippet, contentDetails, statistics',\n",
    "                    id=video)\n",
    "        response = request.execute()\n",
    "        \n",
    "        for video in response['items']:\n",
    "            video_stats = dict(Title = video['snippet']['title'],\n",
    "                               Published_date = video['snippet']['publishedAt'],\n",
    "                               Views = video['statistics']['viewCount'],\n",
    "                               Likes = video['statistics']['likeCount'],\n",
    "                               Comments = video['statistics']['commentCount'],\n",
    "                               CategoryId = video['snippet']['categoryId'],\n",
    "                               VideoId = video['id'],\n",
    "                               Channel = video['snippet']['channelTitle']\n",
    "                               )\n",
    "            all_video_stats.append(video_stats)\n",
    "    \n",
    "    return all_video_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d2db241",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_video_stats_by_channel = {}\n",
    "for channel_name in video_ids_by_channel.keys():\n",
    "    video_ids = video_ids_by_channel[channel_name]\n",
    "    all_video_stats = get_video_details(youtube, video_ids)\n",
    "    all_video_stats_by_channel[channel_name] = all_video_stats\n",
    "\n",
    "json_all_video_stats_by_channel = json.dumps(all_video_stats_by_channel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6258abd9",
   "metadata": {},
   "source": [
    "## Prepare Data For Export:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a17fc610",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_video_stats_by_channel\n",
    "#json_all_video_stats_by_channel\n",
    "# channel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ae3baf88",
   "metadata": {},
   "outputs": [],
   "source": [
    "beastdata_stats = []\n",
    "for channel_name in video_ids_by_channel.keys():\n",
    "    video_ids = video_ids_by_channel[channel_name]\n",
    "    all_video_stats = get_video_details(youtube, video_ids)\n",
    "    beastdata_stats.append(all_video_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b20f3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "beastdata = pd.DataFrame(beastdata_stats)\n",
    "beastdata = beastdata.transpose()\n",
    "channel_names = ['MrBeast Gaming','Beast Reacts', 'MrBeast 2', 'Beast Philanthropy', 'MrBeast']\n",
    "beastdata.columns = channel_names\n",
    "# beastdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14db814",
   "metadata": {},
   "source": [
    "## Export Data As CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "391380d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Data to CSV\n",
    "# beastdata.to_csv('Data/BeastData.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5408dead",
   "metadata": {},
   "source": [
    "## Export Data As JSON "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1cf9f12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(\"Data/JSON/BeastData.json\", \"w\") as file:\n",
    "#     json.dump(json_all_video_stats_by_channel, file)\n",
    "\n",
    "# MrBeast = pd.json_normalize(json.loads(json_all_video_stats_by_channel), 'MrBeast')\n",
    "# MrBeast = MrBeast[['Title', 'Published_date', 'Views', 'Likes', 'Comments', 'CategoryId', 'VideoId', 'Channel']]\n",
    "# with open(\"Data/JSON/MrBeast.json\", \"w\") as file:\n",
    "#     json.dump(MrBeast.to_json(orient='records'), file)\n",
    "\n",
    "# MrBeast_2 = pd.json_normalize(json.loads(json_all_video_stats_by_channel), 'MrBeast 2')\n",
    "# MrBeast_2 = MrBeast_2[['Title', 'Published_date', 'Views', 'Likes', 'Comments', 'CategoryId', 'VideoId', 'Channel']]\n",
    "# with open(\"Data/JSON/MrBeast_2.json\", \"w\") as file:\n",
    "#     json.dump(MrBeast_2.to_json(orient='records'), file)\n",
    "\n",
    "# MrBeast_Gaming = pd.json_normalize(json.loads(json_all_video_stats_by_channel), 'MrBeast Gaming')\n",
    "# MrBeast_Gaming = MrBeast_Gaming[['Title', 'Published_date', 'Views', 'Likes', 'Comments', 'CategoryId', 'VideoId', 'Channel']]\n",
    "# with open(\"Data/JSON/MrBeastGaming.json\", \"w\") as file:\n",
    "#     json.dump(MrBeast_Gaming.to_json(orient='records'), file)\n",
    "\n",
    "# Beast_Philanthropy = pd.json_normalize(json.loads(json_all_video_stats_by_channel), 'Beast Philanthropy')\n",
    "# Beast_Philanthropy = Beast_Philanthropy[['Title', 'Published_date', 'Views', 'Likes', 'Comments', 'CategoryId', 'VideoId', 'Channel']]\n",
    "# with open(\"Data/JSON/BeastPhilanthropy.json\", \"w\") as file:\n",
    "#     json.dump(Beast_Philanthropy.to_json(orient='records'), file)\n",
    "\n",
    "# BeastReacts = pd.json_normalize(json.loads(json_all_video_stats_by_channel), 'Beast Reacts')\n",
    "# BeastReacts = BeastReacts[['Title', 'Published_date', 'Views', 'Likes', 'Comments', 'CategoryId', 'VideoId', 'Channel']]\n",
    "# with open(\"Data/JSON/BeastReacts.json\", \"w\") as file:\n",
    "#     json.dump(BeastReacts.to_json(orient='records'), file)"
   ]
  }
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