Education Channels Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "754101f0",
   "metadata": {},
   "source": [
    "# Use of Google Youtube Analytics API for Analysis of my favourite 5 youtube channels that helped me during my college life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b71aa465",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: google-api-python-client in c:\\users\\kaust\\anaconda3\\lib\\site-packages (2.44.0)\n",
      "Requirement already satisfied: httplib2<1dev,>=0.15.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-python-client) (0.20.4)\n",
      "Requirement already satisfied: google-auth-httplib2>=0.1.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-python-client) (0.1.0)\n",
      "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-python-client) (2.7.2)\n",
      "Requirement already satisfied: uritemplate<5,>=3.0.1 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-python-client) (4.1.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: google-auth<3.0.0dev,>=1.16.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-python-client) (2.3.3)\n",
      "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.52.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (1.56.0)\n",
      "Requirement already satisfied: protobuf>=3.12.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (3.19.3)\n",
      "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (2.26.0)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (4.2.4)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (4.8)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (0.2.8)\n",
      "Requirement already satisfied: six>=1.9.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (1.16.0)\n",
      "Requirement already satisfied: setuptools>=40.3.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (58.0.4)\n",
      "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from httplib2<1dev,>=0.15.0->google-api-python-client) (3.0.4)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.0dev,>=1.16.0->google-api-python-client) (0.4.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (1.26.7)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\kaust\\anaconda3\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client) (3.2)\n"
     ]
    }
   ],
   "source": [
    "pip install google-api-python-client \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b23608",
   "metadata": {},
   "source": [
    "## My favourite 5 youtube channels for college subjects \n",
    "<ol>\n",
    "<li> 5MinEngineering-------->UCyHta2dyCTkf29AB67AYn7A \n",
    "<li> GateSmashers----------->UCJihyK0A38SZ6SdJirEdIOw\n",
    "<li> NesoAcademy------------>UCQYMhOMi_Cdj1CEAU-fv80A\n",
    "<li> Education4U------------>UCKS34cSMNaXaySe2xgXH-3A\n",
    "<li> KnowledgeGate---------->UCA6yfpYhy5sWMjRGOT-OAIQ\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92526540",
   "metadata": {},
   "outputs": [],
   "source": [
    "from googleapiclient.discovery import build\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1aebbc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXX'\n",
    "channel_ids = [\"UCyHta2dyCTkf29AB67AYn7A\",\"UCJihyK0A38SZ6SdJirEdIOw\",\"UCQYMhOMi_Cdj1CEAU-fv80A\",\"UCKS34cSMNaXaySe2xgXH-3A\",\"UCA6yfpYhy5sWMjRGOT-OAIQ\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf76019f",
   "metadata": {},
   "source": [
    "### Getting credentials using api key to setup Youtube service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90bf0415",
   "metadata": {},
   "outputs": [],
   "source": [
    "youtube = build(\"youtube\",'v3', developerKey = api_key)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195c0e2f",
   "metadata": {},
   "source": [
    "### Defining a function to make service calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99d54c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_details(youtube,channel_id):\n",
    "    all_data= []\n",
    "    request = youtube.channels().list(\n",
    "        part=\"snippet,contentDetails,statistics\",\n",
    "        id= channel_ids\n",
    "    )\n",
    "    response = request.execute()\n",
    "    for i in range(len(response['items'])):\n",
    "    \n",
    "        data = dict(Channel_Name=response['items'][i]['snippet']['title'], Views_Count = response['items'][i]['statistics']['viewCount'],Subscriber_Count = response['items'][i]['statistics']['subscriberCount'],videos_posted = response['items'][i]['statistics']['videoCount']\n",
    "                    ,playlist_id= response['items'][i]['contentDetails']['relatedPlaylists']['uploads'])\n",
    "        all_data.append(data)\n",
    "    return all_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c7cabee",
   "metadata": {},
   "outputs": [],
   "source": [
    "Channel_Statistics=get_channel_details(youtube,channel_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4c9d32",
   "metadata": {},
   "source": [
    "### Creating a data frame to perform analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ca91c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data = pd.DataFrame(Channel_Statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76f2ad1f",
   "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>Views_Count</th>\n",
       "      <th>Subscriber_Count</th>\n",
       "      <th>videos_posted</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5 Minutes Engineering</td>\n",
       "      <td>38452090</td>\n",
       "      <td>306000</td>\n",
       "      <td>1399</td>\n",
       "      <td>UUyHta2dyCTkf29AB67AYn7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Education 4u</td>\n",
       "      <td>113774880</td>\n",
       "      <td>631000</td>\n",
       "      <td>2564</td>\n",
       "      <td>UUKS34cSMNaXaySe2xgXH-3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNOWLEDGE GATE</td>\n",
       "      <td>86252607</td>\n",
       "      <td>526000</td>\n",
       "      <td>1210</td>\n",
       "      <td>UUA6yfpYhy5sWMjRGOT-OAIQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gate Smashers</td>\n",
       "      <td>139603930</td>\n",
       "      <td>861000</td>\n",
       "      <td>974</td>\n",
       "      <td>UUJihyK0A38SZ6SdJirEdIOw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neso Academy</td>\n",
       "      <td>315117024</td>\n",
       "      <td>1580000</td>\n",
       "      <td>2211</td>\n",
       "      <td>UUQYMhOMi_Cdj1CEAU-fv80A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Channel_Name Views_Count Subscriber_Count videos_posted  \\\n",
       "0  5 Minutes Engineering    38452090           306000          1399   \n",
       "1           Education 4u   113774880           631000          2564   \n",
       "2         KNOWLEDGE GATE    86252607           526000          1210   \n",
       "3          Gate Smashers   139603930           861000           974   \n",
       "4           Neso Academy   315117024          1580000          2211   \n",
       "\n",
       "                playlist_id  \n",
       "0  UUyHta2dyCTkf29AB67AYn7A  \n",
       "1  UUKS34cSMNaXaySe2xgXH-3A  \n",
       "2  UUA6yfpYhy5sWMjRGOT-OAIQ  \n",
       "3  UUJihyK0A38SZ6SdJirEdIOw  \n",
       "4  UUQYMhOMi_Cdj1CEAU-fv80A  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7fe9dd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel_Name        object\n",
       "Views_Count         object\n",
       "Subscriber_Count    object\n",
       "videos_posted       object\n",
       "playlist_id         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26708b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data['Subscriber_Count'] = pd.to_numeric(channel_data['Subscriber_Count'])\n",
    "channel_data['Views_Count'] = pd.to_numeric(channel_data['Views_Count'])\n",
    "channel_data['videos_posted'] = pd.to_numeric(channel_data['videos_posted'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be8a41e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel_Name        object\n",
       "Views_Count          int64\n",
       "Subscriber_Count     int64\n",
       "videos_posted        int64\n",
       "playlist_id         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f65a737",
   "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>Views_Count</th>\n",
       "      <th>Subscriber_Count</th>\n",
       "      <th>videos_posted</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5 Minutes Engineering</td>\n",
       "      <td>38452090</td>\n",
       "      <td>306000</td>\n",
       "      <td>1399</td>\n",
       "      <td>UUyHta2dyCTkf29AB67AYn7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNOWLEDGE GATE</td>\n",
       "      <td>86252607</td>\n",
       "      <td>526000</td>\n",
       "      <td>1210</td>\n",
       "      <td>UUA6yfpYhy5sWMjRGOT-OAIQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Education 4u</td>\n",
       "      <td>113774880</td>\n",
       "      <td>631000</td>\n",
       "      <td>2564</td>\n",
       "      <td>UUKS34cSMNaXaySe2xgXH-3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gate Smashers</td>\n",
       "      <td>139603930</td>\n",
       "      <td>861000</td>\n",
       "      <td>974</td>\n",
       "      <td>UUJihyK0A38SZ6SdJirEdIOw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neso Academy</td>\n",
       "      <td>315117024</td>\n",
       "      <td>1580000</td>\n",
       "      <td>2211</td>\n",
       "      <td>UUQYMhOMi_Cdj1CEAU-fv80A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Channel_Name  Views_Count  Subscriber_Count  videos_posted  \\\n",
       "0  5 Minutes Engineering     38452090            306000           1399   \n",
       "2         KNOWLEDGE GATE     86252607            526000           1210   \n",
       "1           Education 4u    113774880            631000           2564   \n",
       "3          Gate Smashers    139603930            861000            974   \n",
       "4           Neso Academy    315117024           1580000           2211   \n",
       "\n",
       "                playlist_id  \n",
       "0  UUyHta2dyCTkf29AB67AYn7A  \n",
       "2  UUA6yfpYhy5sWMjRGOT-OAIQ  \n",
       "1  UUKS34cSMNaXaySe2xgXH-3A  \n",
       "3  UUJihyK0A38SZ6SdJirEdIOw  \n",
       "4  UUQYMhOMi_Cdj1CEAU-fv80A  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data.sort_values(\"Views_Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a551d6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Channel_Name', ylabel='Views_Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "sns.barplot(x='Channel_Name',y='Views_Count',data =channel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "906a1e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Channel_Name', ylabel='Subscriber_Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "sns.barplot(x='Channel_Name',y='Subscriber_Count',data =channel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "192c59f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Channel_Name', ylabel='videos_posted'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "sns.barplot(x='Channel_Name',y='videos_posted',data =channel_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0056f7f9",
   "metadata": {},
   "source": [
    "### Deep Diving into \"Neso Academy\" Statistics as it has highest number of subscribers and views count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6c4e717",
   "metadata": {},
   "outputs": [],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_Name']== 'Neso Academy','playlist_id'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c67d4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UUQYMhOMi_Cdj1CEAU-fv80A'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d282e75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_details(youtube,playlist_id):\n",
    "    request = youtube.playlistItems().list(\n",
    "                part = 'contentDetails',\n",
    "                playlistId = playlist_id,\n",
    "                maxResults=50)\n",
    "    response = request.execute()\n",
    "    video_ids = []\n",
    "    \n",
    "    for video in range(len(response['items'])):\n",
    "        video_ids.append(response['items'][video]['contentDetails']['videoId'])\n",
    "    \n",
    "    next_page_token = response.get('nextPageToken')\n",
    "    more_pages = True\n",
    "    \n",
    "    while more_pages:\n",
    "        if next_page_token is None:\n",
    "            more_pages = False\n",
    "        else:\n",
    "            request = youtube.playlistItems().list(\n",
    "                        part = 'contentDetails',\n",
    "                        playlistId = playlist_id,\n",
    "                        pageToken = next_page_token,\n",
    "                        maxResults=50)\n",
    "            response = request.execute()\n",
    "            \n",
    "            for i in range(len(response['items'])):\n",
    "                video_ids.append(response['items'][i]['contentDetails']['videoId'])\n",
    "            next_page_token = response.get('nextPageToken')\n",
    "            \n",
    "    \n",
    "    return (video_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0aa579d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ids = get_video_details(youtube,playlist_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b8fe7b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2211"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(video_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "104a0e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(0, len(video_ids), 50):\n",
    "        request = youtube.videos().list(\n",
    "                    part='snippet,statistics',\n",
    "                    id=','.join(video_ids[i:i+50]))\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",
    "                               )\n",
    "            all_video_stats.append(video_stats)\n",
    "    \n",
    "    return all_video_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60981e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_details = get_video_details(youtube, video_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d5b744bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data = pd.DataFrame(video_details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bfddb990",
   "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>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lexical Analyzer (Solved Problems) - Set 1</td>\n",
       "      <td>2022-04-16T13:30:09Z</td>\n",
       "      <td>1306</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lexical Analyzer – Tokenization</td>\n",
       "      <td>2022-04-14T13:30:12Z</td>\n",
       "      <td>3249</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Introduction to Microsoft Excel</td>\n",
       "      <td>2022-04-10T17:01:32Z</td>\n",
       "      <td>14238</td>\n",
       "      <td>426</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Introduction to Lexical Analyzer</td>\n",
       "      <td>2022-04-08T17:00:01Z</td>\n",
       "      <td>4116</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Symbol Table – Solved PYQs</td>\n",
       "      <td>2022-04-05T16:45:02Z</td>\n",
       "      <td>4649</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>Karnaugh Map (K' Map) - Part 1</td>\n",
       "      <td>2014-09-02T16:18:34Z</td>\n",
       "      <td>1315040</td>\n",
       "      <td>15323</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>Gray Code to Binary &amp; Binary to Gray Code conv...</td>\n",
       "      <td>2014-08-29T14:16:23Z</td>\n",
       "      <td>147275</td>\n",
       "      <td>849</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>Excess 3 Code (XS 3 Code) Old</td>\n",
       "      <td>2014-08-29T14:14:27Z</td>\n",
       "      <td>68073</td>\n",
       "      <td>266</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>Binary Coded Decimal (BCD Code)</td>\n",
       "      <td>2014-08-29T14:12:28Z</td>\n",
       "      <td>243978</td>\n",
       "      <td>1180</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>Neso Academy Introduction Video</td>\n",
       "      <td>2014-07-30T18:31:03Z</td>\n",
       "      <td>52992</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2211 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title        Published_date  \\\n",
       "0            Lexical Analyzer (Solved Problems) - Set 1  2022-04-16T13:30:09Z   \n",
       "1                       Lexical Analyzer – Tokenization  2022-04-14T13:30:12Z   \n",
       "2                       Introduction to Microsoft Excel  2022-04-10T17:01:32Z   \n",
       "3                      Introduction to Lexical Analyzer  2022-04-08T17:00:01Z   \n",
       "4                            Symbol Table – Solved PYQs  2022-04-05T16:45:02Z   \n",
       "...                                                 ...                   ...   \n",
       "2206                     Karnaugh Map (K' Map) - Part 1  2014-09-02T16:18:34Z   \n",
       "2207  Gray Code to Binary & Binary to Gray Code conv...  2014-08-29T14:16:23Z   \n",
       "2208                      Excess 3 Code (XS 3 Code) Old  2014-08-29T14:14:27Z   \n",
       "2209                    Binary Coded Decimal (BCD Code)  2014-08-29T14:12:28Z   \n",
       "2210                    Neso Academy Introduction Video  2014-07-30T18:31:03Z   \n",
       "\n",
       "        Views  Likes Comments  \n",
       "0        1306     30        0  \n",
       "1        3249     70        5  \n",
       "2       14238    426       30  \n",
       "3        4116     85        9  \n",
       "4        4649     56        7  \n",
       "...       ...    ...      ...  \n",
       "2206  1315040  15323      695  \n",
       "2207   147275    849       86  \n",
       "2208    68073    266       13  \n",
       "2209   243978   1180       62  \n",
       "2210    52992    262       40  \n",
       "\n",
       "[2211 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12a08bdd",
   "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>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lexical Analyzer (Solved Problems) - Set 1</td>\n",
       "      <td>2022-04-16</td>\n",
       "      <td>1306</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lexical Analyzer – Tokenization</td>\n",
       "      <td>2022-04-14</td>\n",
       "      <td>3249</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Introduction to Microsoft Excel</td>\n",
       "      <td>2022-04-10</td>\n",
       "      <td>14238</td>\n",
       "      <td>426</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Introduction to Lexical Analyzer</td>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>4116</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Symbol Table – Solved PYQs</td>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>4649</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>Karnaugh Map (K' Map) - Part 1</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>1315040</td>\n",
       "      <td>15323</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>Gray Code to Binary &amp; Binary to Gray Code conv...</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>147275</td>\n",
       "      <td>849</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>Excess 3 Code (XS 3 Code) Old</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>68073</td>\n",
       "      <td>266</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>Binary Coded Decimal (BCD Code)</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>243978</td>\n",
       "      <td>1180</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>Neso Academy Introduction Video</td>\n",
       "      <td>2014-07-30</td>\n",
       "      <td>52992</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2211 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title Published_date  \\\n",
       "0            Lexical Analyzer (Solved Problems) - Set 1     2022-04-16   \n",
       "1                       Lexical Analyzer – Tokenization     2022-04-14   \n",
       "2                       Introduction to Microsoft Excel     2022-04-10   \n",
       "3                      Introduction to Lexical Analyzer     2022-04-08   \n",
       "4                            Symbol Table – Solved PYQs     2022-04-05   \n",
       "...                                                 ...            ...   \n",
       "2206                     Karnaugh Map (K' Map) - Part 1     2014-09-02   \n",
       "2207  Gray Code to Binary & Binary to Gray Code conv...     2014-08-29   \n",
       "2208                      Excess 3 Code (XS 3 Code) Old     2014-08-29   \n",
       "2209                    Binary Coded Decimal (BCD Code)     2014-08-29   \n",
       "2210                    Neso Academy Introduction Video     2014-07-30   \n",
       "\n",
       "        Views  Likes Comments  \n",
       "0        1306     30        0  \n",
       "1        3249     70        5  \n",
       "2       14238    426       30  \n",
       "3        4116     85        9  \n",
       "4        4649     56        7  \n",
       "...       ...    ...      ...  \n",
       "2206  1315040  15323      695  \n",
       "2207   147275    849       86  \n",
       "2208    68073    266       13  \n",
       "2209   243978   1180       62  \n",
       "2210    52992    262       40  \n",
       "\n",
       "[2211 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data['Published_date'] = pd.to_datetime(video_data['Published_date']).dt.date\n",
    "video_data['Views'] = pd.to_numeric(video_data['Views'])\n",
    "video_data['Likes'] = pd.to_numeric(video_data['Likes'])\n",
    "video_data['Views'] = pd.to_numeric(video_data['Views'])\n",
    "video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "209474c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "top10_videos = video_data.sort_values(by='Views', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee7fe883",
   "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>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>What is Signal?</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>2809989</td>\n",
       "      <td>12180</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>SR Latch | NOR and NAND SR Latch</td>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>2660761</td>\n",
       "      <td>27590</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>Introduction to SR Flip Flop</td>\n",
       "      <td>2015-02-09</td>\n",
       "      <td>2331150</td>\n",
       "      <td>14023</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>Introduction to JK flip flop</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>1888217</td>\n",
       "      <td>12511</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>Introduction to Theory of Computation</td>\n",
       "      <td>2016-12-15</td>\n",
       "      <td>1882337</td>\n",
       "      <td>12218</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>Introduction to Multiplexers | MUX Basic</td>\n",
       "      <td>2014-12-06</td>\n",
       "      <td>1861214</td>\n",
       "      <td>14969</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>Introduction to Sequential Circuits | Important</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>1745505</td>\n",
       "      <td>13529</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>Introduction to Encoders and Decoders</td>\n",
       "      <td>2015-01-23</td>\n",
       "      <td>1639261</td>\n",
       "      <td>11518</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>Introduction to D flip flop</td>\n",
       "      <td>2015-02-09</td>\n",
       "      <td>1552966</td>\n",
       "      <td>8075</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>Full Adder</td>\n",
       "      <td>2014-10-20</td>\n",
       "      <td>1506700</td>\n",
       "      <td>11893</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title Published_date    Views  \\\n",
       "2092                                  What is Signal?     2015-03-29  2809989   \n",
       "2068                 SR Latch | NOR and NAND SR Latch     2015-04-20  2660761   \n",
       "2138                     Introduction to SR Flip Flop     2015-02-09  2331150   \n",
       "2134                     Introduction to JK flip flop     2015-02-18  1888217   \n",
       "1751            Introduction to Theory of Computation     2016-12-15  1882337   \n",
       "2164         Introduction to Multiplexers | MUX Basic     2014-12-06  1861214   \n",
       "2142  Introduction to Sequential Circuits | Important     2015-02-01  1745505   \n",
       "2149            Introduction to Encoders and Decoders     2015-01-23  1639261   \n",
       "2136                      Introduction to D flip flop     2015-02-09  1552966   \n",
       "2184                                       Full Adder     2014-10-20  1506700   \n",
       "\n",
       "      Likes Comments  \n",
       "2092  12180      542  \n",
       "2068  27590     1627  \n",
       "2138  14023      400  \n",
       "2134  12511      390  \n",
       "1751  12218      413  \n",
       "2164  14969      443  \n",
       "2142  13529      311  \n",
       "2149  11518      218  \n",
       "2136   8075      230  \n",
       "2184  11893      320  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "100f71d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzAAAAHiCAYAAAA3YSkWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABvnElEQVR4nO3deXxN1/7/8ddJYg6aGqtoVQnX2FGUFlFzklMSlEq1VWMNvYgGqamhIYYSaihVV80yiaFoo4MiWuWidZWakiKIIELGc35/+DlfaRKihtOdvJ+Px308ZA9rfdY6u9/veWevvWOyWq1WREREREREDMDB3gWIiIiIiIjklQKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhpO9CxCRhycxMRmLRW9Of9jKlHEmIeGqvcsokDT39qF5tx/NvX1o3u8vBwcTLi4lct2vACNSgFgsVgUYO9G824/m3j407/ajubcPzfvDoyVkIiIiIiJiGAowIiIiIiJiGCar1ar7XSIiIiIickcpqekkXUl5oH04OJgoU8Y51/16BkakABn8cQQXEpPtXYaIiIgY1PIpb5DEgw0wd6IlZCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMPKP8/bbb/P111/bfp48eTLPPPMMaWlptm1NmzYlLi4Od3d34uLi8tz2tm3bWLx4cbbtK1asYMWKFXlqIy0tjfHjx+Ph4YGnpydvvPEG+/fvB+DAgQOMHj06z/XkRVhYGP7+/gD8+uuvdOnSBS8vL7p168aRI0fua18iIiIi/3RO9i5A5K/c3NzYs2cPr776KgA7duygYcOG7Nmzh8aNG3Py5EmKFy9O5cqV77rtgwcP5ri9W7dueW7jiy++wGKxEBUVhclkYs+ePQwYMIBt27ZRr1496tWrd9d15dXQoUMZN24cjRs3ZvPmzfj5+REREfHA+hMRERH5p9EdGPnHady4MXv37gUgPj6ewoUL06ZNG7Zv3w7Azz//TJMmTWzHz5kzh9dee402bdrw3//+F4Ddu3fTrVs3OnbsSMuWLfn66685evQoK1euZOXKlYSGhmbpMyQkhJCQENLT0/Hz8+O1117jtddeY/Xq1dnqu3DhAunp6aSnpwPw3HPPMWnSJCwWCzExMfj6+gLw+++/06lTJ8xmMx999BGtWrUCwN/fn8DAQLp164a7u7utlvj4eHr16kWXLl1o3rw5M2fOzNJveno677zzDo0bNwbgxRdf5OzZs/c22SIiIiIGowAj/zh16tTh1KlTpKamsn37dpo0aUKTJk1yDTBPP/00ERER+Pr6smjRIgC+/PJLAgMDCQ8PJzAwkJkzZ/L000/z+uuv8/rrr+Pt7Z1j33v37uXy5ctEREQwf/58fv7552zHvPnmm/z3v/+lcePG9O/fn//85z8888wzFClSJMtx/v7+DBkyhMjISKpUqUJmZqZt39mzZ1m+fDlz585lypQpAKxfvx4PDw9Wr15NVFQUS5Ys4eLFi7ZzChUqRNeuXYEbYWbs2LH4+Pj8nSkWERERMSwFGPnHcXR0pEGDBhw4cIDt27fTtGlTqlSpQkpKCpcvX2bv3r24ubnZjr+51Ozpp58mMTERgODgYI4cOcKcOXNYvHgxycnJeeq7Ro0aHD9+nF69evHVV18xYsSIbMdUrlyZ9evXs3jxYho0aEBERARms5krV67Yjrl06RJ//vknzZo1A8gWmJo0aYLJZKJmzZpcunQJgF69evHYY4+xaNEiJk6cSHp6OtevX8+xzo8++ghnZ2eGDRuWp3GJiIiI5BcKMPKP5Obmxi+//ML+/ftp2LAhcGNp2TfffIOLiwvOzs62Yx0dHQEwmUy2bd27d2f//v3UrVuXfv365blfFxcXNmzYQI8ePTh+/DgdO3bMEkwApk+fzrlz56hfvz79+vUjLCyM8uXL8+OPP2apyWq15trPzbs1t9YcFBTE0qVLqVSpEv3798fFxSXHNjIzM9m8eTPjxo3Lcr6IiIhIQaAAI/9IjRs3JjIykpo1a+LkdONdE02aNGHx4sVZlo/l5NKlS5w4cYIhQ4bwyiuv8M0339iWbzk6OpKRkZHrud988w1+fn40b96cgIAAihcvzpkzZ7IcEx8fz5w5c2xvRTt//jwXL16kZs2atmNKlixJlSpV+O677wCIioq645h//PFHevXqRbt27Th+/Djx8fFYLJZsx1ksFqZOnUrhwoXv2KaIiIhIfqMAI/9IN5dWNW3a1LbNzc2NY8eO8dJLL9323EceeQQfHx86dOhAu3btSE5OJiUlhWvXrvHCCy8QFRXF0qVLczz3lVdeoWjRonTo0IHOnTvj5eWFq6trlmM+/PBDrFYrbdu2pUOHDvTu3Zvhw4dTvXr1LMdNmTKFTz/9lI4dO7J//36KFi1627r79u3LiBEj8PDw4Msvv6Ru3bo5viL64sWLTJ069bZtiYiIiORXJuvt1rmIyN82e/ZsunTpQvny5dmyZQtRUVGEhITYtabBH0dwITFvzwOJiIiI/NXyKW9w/nzSA+3DwcFEmTLOue7X34EReUAqVarEO++8g5OTE6VKlWLixIn2LklERETE8BRgRB6QTp060alTJ3uXISIiIpKv6BkYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDJPVarXauwgREREREfnnS0lNJ+lKygPtw8HBRJkyzrnud3qgvYvIP0pCwlUsFv3O4mErV64k588n2buMAklzbx+ad/vR3NuH5v3h0hIyERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDP0dGBERERERg8pISyXxcpq9y7iv9HdgRMTmwLwPSLuSYO8yRERE5D55bsRCIH8FmDvREjIRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETGMAhdgvvrqKzp16oSXlxeenp4sXLjQts/X15dWrVphNpsxm820bNmSt956iwsXLmRrx93dnbi4uDz3u3r1atavX5/r/ri4ONzd3e/Yjq+vb479urq6smbNmmzHxsTEAJCens6MGTNo3bo1np6e+Pj4sHHjRtuxISEhNGnSxDb2du3a4enpyZ49e/I6xDy7ta6b4uLicHV15ccff8yy/a/z/Pvvv+Pq6srmzZuztTl8+PAs20JCQggJCbHtv/nZenh48Prrr7Njx44c6zt9+jT9+vXD09MTDw8PhgwZQkJCAgBhYWG8+OKLtnny8PCgdevWfP3119naubX/vIiNjWXUqFG3PSanuRMREREpSJzsXcDDFB8fz+TJkwkLC8PFxYXk5GR8fX2pVq0aLVu2BCAwMJBGjRoBYLFYGDx4MIsXL8bPz++e+v7ll1948cUX73kMtzNt2jSaNm3KY489lm3fhx9+SGpqKmFhYTg7OxMbG0vv3r1JS0vjtddeA+D1119n0KBBtnO++OILgoKCsgWjB6VQoUJ8+OGHrFu3Dmdn5xyPCQ0NpW3btqxatYo2bdpk2ffVV1/Rtm1bXn311RzPvfWzPXDgAO+++y7Lli3j6aefznLcmDFjeO211/Dw8ABg/vz5jB07ltmzZwM3QlVQUJDt+K+//poxY8bk2m9enT59mtjY2HtqQ0RERCS/K1B3YBITE0lPTyclJQWAEiVKEBQUlO0L7E3Xrl0jMTGR0qVL57mPTZs20aVLF7y8vGjbti2//PILO3bsIDo6mlmzZvHDDz/w559/8uabb+Lh4YGPjw//+9//AEhJSeHf//43Hh4edO/encTExLsaX8+ePQkICMi2PTY2ls2bNzNx4kRbMKhSpQojR460fSn/K4vFwtmzZ3Mc+++//46vry/e3t60aNGCFStWADfuOAQEBODr64u7uztz584FIC0tDT8/P9q1a8e7776b67jKly/PSy+9xOTJk3Pcn56eTlRUFO+//z6//vorp06dyrK/f//+jB8/nkuXLuU8QbeoV68e7dq1yzGcXbhwgevXr9t+fuONN3jjjTdybevPP/+8q2skPj6eXr160aVLF5o3b87MmTOBGwHr4MGDjB8/HqvVSnBwMG3atKF9+/YsWbLEdv7atWvp2LEjLVu2JDo6Os/9ioiIiOQHBSrA1KpVi5YtW/Lqq6/i4+NDcHAwFouFJ554wnZMQEAAXl5eNG3alK5du/LSSy/x1ltv5al9i8XCypUrmTdvHuvWrePdd99lwYIFvPTSS7i7uzN48GBefvllxo8fT5s2bVi/fj2DBg2yfdG/ePEib7/9NuvXr6ds2bJZlnjlRe/evUlMTMz2pfzgwYNUr16d4sWLZ9n+/PPPExsba/vCv3LlSsxmMy1atKBFixZcv36dSZMmZetnzZo1DBgwgNDQUP7zn/8wZcoU277Dhw+zaNEi1qxZw4IFC7hy5QpLly4FboS7gICAbMHjVv7+/mzfvj3bUjKA7777jkqVKlGtWjVeffVVVq1alW08bdu2JTAw8PYT9f/VqFGDY8eOZds+dOhQpk6dyiuvvMIHH3zAd999l+XuWXR0tG2JYZMmTfj111/59NNP89QnwPr16/Hw8GD16tVERUWxZMkSLl68SEBAAHXr1mXs2LF89dVX/PLLL0RFRbFmzRrCwsI4f/48ACVLliQ8PJyAgADmzJmT535FRERE8oMCFWAAxo8fT3R0NN26deP06dN06dKFLVu22PYHBgaybt06Zs2axeXLl2nVqhWFCxfOU9sODg7MmTOH7du3M3PmTMLDw0lOTs523E8//YTZbAagWbNmtt/Aly9fnvr16wPw9NNP3/UdGCcnJ4KCgpg2bRpnzpyxbTeZTGRmZmY7PiMjw7Yfbiwhi4yMZPXq1ZQoUYKGDRtSvnz5bOf5+/uTmprK/Pnz+eSTT7h27ZptX6NGjShcuDBlypThkUceISkpid27d9OuXTsAnnzySZ555plcx+Ds7MxHH33Ehx9+yNWrV7PsCw0NtS3rat++PWFhYaSlpWU5ZujQoezfvz/HZ1L+ymQyUbRo0WzbX3nlFb7//nsCAwN59NFHCQ4OzrK0zt3dncjISCIjI3niiSd48sknqVat2h37u6lXr1489thjLFq0iIkTJ5Kenp7ljg/cuEbatWtH4cKFKVGiBJGRkZQrVw7AtlTt71wjIiIiIkZXoALMt99+y8aNG6lQoQLe3t7MmDGDgIAA1q5dm+3YZ599Fl9fX4YNG2b7on8nycnJ+Pj4EBcXxwsvvICvr2+Oxzk5/d+jR1arlaNHj2bbbjKZsFqtdzM8AGrWrJltKVn9+vU5ceIEly9fznLs3r17qVKlSrblT+XKlSMwMJAJEybk+EzG+++/z9atW6levTrvv/9+ln1FihTJNoa/juXWceakadOm2ZaSJSQk8MMPP/D555/j7u5OQEAAV65cYevWrVnOLVasGJMmTWL8+PHZxvtXhw8fpnr16lm2Xbp0iUmTJlGkSBHbHZioqCh+/PFHLl68mOVYZ2dnJk+ezIIFC9i7d+9t+7pVUFAQS5cupVKlSvTv3x8XF5dsn7WTk5MtWMKNlxzcDIqOjo4AWfaLiIiIFBQFKsAULVqUadOm2d5qZbVaOXToELVr187x+Lfffpvk5ORsS5Vyc+LECUwmE/369aNRo0Zs3brVdufD0dHR9u/nn3+eDRs2ALBjxw4+/PDDex1aFjeXkt38Ul2pUiU8PT0ZPXq07Y7QqVOn+Pjjjxk4cGCObTz77LM0b96c4ODgbPt+/PFHBg8ezKuvvsr3338PkOMdnpsaN25MVFQUFouFP//8k19++eWOY7i5lOzcuXMAREZG4ubmxvfff090dDTbtm2jX79+rFy5Mtu5N5eS5bTvpv3797N582Z8fHyybC9ZsiTR0dFERETYth09epQyZcrk+JxLlSpV6NGjBxMnTsxz4Pzxxx/p1asX7dq14/jx48THx2OxWHB0dLSF5RdeeIEtW7bY7s68++67xMfH56l9ERERkfysQL2FzM3NjYEDB9KvXz/S09MBePnll3nvvfdyPL5w4cK8//77TJo0CS8vL0qWLJllv4eHR5bfgv/888/Url2bdu3aYTKZaNq0qe01xC+99BLTp0+nZMmSjBkzhoCAAJYvX06xYsXy/MxGXt1cStapUyfbtrFjxzJ//nx8fHxwdHSkcOHCDBkyhPbt2+faztChQ2nfvj0///wzzz//vG37oEGD6N69O0WKFKFWrVo8/vjjt32ldPfu3Tly5Ajt2rXj8ccfp2bNmnccw82lZL169QIgPDycf//731mOeeONN1i4cCF//PFHjrV/9913WbYFBARQvHhx29KxGTNmULly5SzHODo6smDBAoKCgpg5cyZFixalfPnyzJs3z3bn46/69u3L2rVriYqKwsvLK8u++fPn8/nnn9t+Hj9+PH379mXEiBEULVqUihUrUrduXeLi4qhduzZJSUn4+fkRHBzMwYMH6dSpExaLhTfffPOulqmJiIiI5Fcm699ZpyR24+vry8cff5zti7dIXhyY9wFpVxLsXYaIiIjcJ8+NWMj580n2LuO+cnAwUaZMzn9SAwrYEjIRERERETG2ArWELD+4+UpiEREREZGCSHdgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMExWq9Vq7yJEREREROTuZaSlkng5zd5l3FcODibKlHHOdb/TQ6xFROwsIeEqFot+Z/GwlStXkvPnk+xdRoGkubcPzbv9aO7tQ/P+cGkJmYiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIb+kKWIiIiIGFpqWhpXLqfarX/9HZj7S3/IUkRshq8Zz4WrF+1dhoiIyH31xdszAfsFGHm4tIRMREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMQwFGREREREQMw8neBcjdc3V15fDhw7c9xtfXl6VLl/7tPkJCQgAYNGjQXZ0XGxvL3LlzmTRpEgcOHGDlypVMnDjxb9cBsH//fjZv3oyfn1+ez/nqq69YsGABGRkZWK1WzGYz7777LnBjbs6ePUvx4sUBuHr1KlWqVGHq1KmULVs2Szt/PRagS5cuPP3008yePZulS5cyevRoXn/9derVq5en2lxdXalVq1aWbRMmTOD7778H7n7ORURERAoSBZh8avfu3Xbp9/Tp08TGxgJQr169PH+pv52jR4+SkJCQ5+Pj4+OZPHkyYWFhuLi4kJycjK+vL9WqVaNly5YABAYG0qhRIwAsFguDBw9m8eLFOYakW4+9KSYmxvbvvxPQIiMjs227GWBEREREJHcKMAYWExPD/PnzKVq0KH/88Qeurq5MnTqVKVOmANC5c2fWrFmDm5sbdevW5fz586xdu5ZFixaxbt06HB0dadKkCX5+fjg6OrJw4UJWr16Ni4sLpUqVon79+kDWOz5hYWHs3r2boKAgduzYQVBQEFarlUqVKjFt2jQCAwOJi4tj/PjxtG3b1naX4vjx44wZM4ZLly5RvHhxRo8eTf369fH398fZ2Zlff/2V+Ph43nvvPby9vW1jvHLlCrNmzeLatWvMnTuXvn37MmnSJHbu3InJZMLLy4s+ffpkmZfExETS09NJSUkBoESJEgQFBVGkSJEc5/HatWskJibaxnu3fH19GThwIACffvopTk5OxMXFUb9+fSZOnEjhwoXvus1t27bxySefYLFYqFKlChMmTKBs2bK4u7vTtm1bduzYAcCkSZP417/+9bfqFhERETEiBRiD27t3L5s2baJ8+fJ06dKF7du3ExAQwNKlS1mzZg1w4wt97969adSoEd999x3R0dGEhoZSqFAhBg0axMqVK6lfvz6hoaGEh4djMpno2rXrbb/Qp6WlMXz4cBYtWkTt2rWZNm0a4eHhBAQEMHv2bMaOHZvlLoWfnx99+vShdevW7Nu3jyFDhrB582YAzp49y/Lly/n999958803swSYUqVKMXjwYHbv3k3//v1ZtmwZZ86cYd26daSlpeHr60vNmjVp3ry57ZxatWrRsmVLXn31VWrXrk2jRo3w9PTkiSeesB0TEBBAsWLFuHjxIqVLl6Z9+/a89dZbOY41ICDAtoSsRIkSLF++/LafR0REBNWqVWPIkCEsW7aMt99+O9txZrPZ9u9GjRoxatQo288JCQmMGTOGFStWULlyZRYuXMiECROYNWsWAMWLFyciIoLo6Gg++OADoqKicq1HREREJL9RgDG4GjVqULFiRQCqV6/O5cuXczyuQYMGAOzatYsOHTpQrFgxALy9vYmIiCAlJYVmzZpRokQJANq2bYvFYsm138OHD1OhQgVq164NwLBhw4CsS6tuSk5O5tSpU7Ru3RqAhg0bUrp0aY4dOwZAkyZNMJlM1KxZk0uXLt12vDExMXTs2BFHR0eKFSuGp6cnO3fuzBJgAMaPH8+AAQPYvn0727dvp0uXLkydOtVWw81lYb/88guDBw+mVatWud4pyWkJWW5eeOEFnnrqKeBGSFm9enWOASanJWQ37d+/n/r161O5cmUAunbtyoIFC2z7u3TpAoC7uzv+/v5cvHiRRx99NE/1iYiIiBid3kJmcLcuizKZTFit1hyPK1q0KECOoSQjIyPbuU5OWbPtzX0ZGRkAFCpUCJPJZNuflJTE2bNnc+w7p5qsViuZmZlZxnBre7n5a/23tnPTt99+y8aNG6lQoQLe3t7MmDGDgIAA1q5dm629Z599Fl9fX4YNG2Yb271wdHTMUtutP+dVTmO8tbZbPxuLxfK3+hARERExKgWYfMrR0THHL+Rubm5s2LCBlJQUMjIyCA0Nxc3NjcaNG7Nt2zaSkpJITU1l69attnNcXFw4cuQIVquV6OhoAKpVq0ZCQgJHjx4FYOHChaxYsSLHfp2dnalcuTJbtmwBYN++fVy4cIEaNWrc9Vjc3NyIiIggMzOT69evExUVle3uSNGiRZk2bRpxcXHAjQBw6NAh292iv3r77bdJTk5m1apVearndvbs2UN8fDwWi4WIiAheeeWVu26jQYMG/Pe//7XVv2rVqixj3LBhAwBbt26levXqlC5d+p7rFhERETEKLSHLp1q2bInZbCYsLCzL9hYtWnDo0CG8vb3JyMigadOm9OjRAycnJ3r27ImPjw+lSpWiUqVKtnOGDRtGv379KFu2LM899xyJiYkUKVKE4OBgRowYQXp6OlWrVmXKlCmkpaWRlJSEn58fPj4+tjaCg4MZN24cISEhFCpUiJCQkDw/3F6/fn1mz57N1KlTGTJkCCdOnMBsNpOeno6npyetWrXKcrybmxsDBw6kX79+pKenA/Dyyy/z3nvv5dh+4cKFef/995k0aRJeXl6ULFkyT3XlpHz58owYMYL4+HiaNGlC586d77qNsmXLMmHCBAYOHEh6ejqVKlXK8qazX375hbVr11KsWDGCgoL+dq0iIiIiRmSy5rbmSETuSkxMjO2taw+Ku7s7//nPf2zPx9yt4WvGc+HqxftclYiIiH198fZMzp9Pslv/5cqVtGv/+Y2Dg4kyZZxz3/8QaxEREREREbknWkImcp80atQoz28r+7tuPoMkIiIiUlDpDoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGyWq1Wu1dhIiIiIjI35WalsaVy6l2679cuZKcP59kt/7zGwcHE2XKOOe63+kh1iIidpaQcBWLRb+zeNj0/9jsR3NvH5p3+9HcS0GgJWQiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYeo2ySAFyu3eqy4NVrlxJe5dQYGnu7UPzfmfpKalcSkqzdxkihqMAI1KAbBs6nOsXEuxdhoiIAO3/sxgUYETumpaQiYiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjA5GOurq53PMbX1/ee+ggJCSEkJOSuz4uNjWXUqFEAHDhwgNGjR99THQD79+8nODj4b50bFhaGv78/cGNOYmJibPvWr19P8+bNOXbsWJZzrl69SqdOnfDw8GDVqlW283v37k18fHye+x45ciQtW7Zk/vz5uLu7/636RURERAoKJ3sXIPa1e/duu/R7+vRpYmNjAahXrx716tW75zaPHj1KQkLCPbdzq02bNjF9+nS++OILnnzyySz7Dh06ROHChQkLCyMsLMy2/bPPPrurPsLDw9m/fz/nzp1j1apV96NsERERkXxLd2AKgJiYGN555x0GDBhAmzZtGDx4MGlpaQQGBgLQuXNnANzc3Hj33Xcxm82kp6czb9482rdvj6enJ0FBQWRmZgKwcOFCWrduTdeuXdm/f7+tn1vv+Nx6R2PHjh14eXnh6elJ3759uXr1KoGBgRw8eJDx48cTExNjuxN0/PhxfH198fT0zNK+v78/gYGBdOvWDXd3d0JDQ7OM8cqVK8yaNYvo6Gjmzp2LxWIhMDCQDh064OHhwYIFC+563rZs2cL06dNZsmRJtvCSkJDAqFGjOHz4MP369cuyz93dnbi4OMLCwhg8eDBvvPEGrVu35uOPP8ZqtWY5tl+/flitVjp37szFixdt2y9cuEDfvn3x9PSkY8eOfP/998CNO14jR46kS5cutGrVioULF971uERERESMTAGmgNi7dy9jxoxh06ZNnD59mu3btxMQEADAmjVrAEhMTKR3795ERkayY8cOoqOjCQ0NJTw8nJMnT7Jy5UoOHDhg27Z48WLOnj17237T0tIYPnw4kydPJioqipo1axIeHk5AQAB169Zl7NixWY738/PD19eXqKgoRo4cyZAhQ0hLSwPg7NmzLF++nLlz5zJlypQs55UqVYrBgwfj7u5O//79WbFiBWfOnGHdunWsWbOGLVu28O233+Z5vr755huGDh2Kp6cnVapUyba/TJkyBAYGUrduXebNm5drO3v27GHmzJmsX7+e//73v2zdujXL/pvnRkZG8uijj9q2f/TRR7i5uREVFcWsWbMYNWoUFy5cAODgwYMsXryYsLAwVq1axa+//prncYmIiIgYnQJMAVGjRg0qVqyIg4MD1atX5/Llyzke16BBAwB27dpFhw4dKFasGE5OTnh7e7Nz5052795Ns2bNKFGiBMWLF6dt27a37ffw4cNUqFCB2rVrAzBs2LBcn7tJTk7m1KlTtG7dGoCGDRtSunRp27MnTZo0wWQyUbNmTS5dunTbfmNiYujYsSOOjo4UK1YMT09Pdu7cedtzbhUdHc3ChQv58ssvOXDgQJ7P+6uWLVtStmxZChcuTPv27dm1a1eeztu1axc+Pj4AVKlShQYNGvDf//4XAA8PD0qUKEHJkiVxd3fPc5siIiIi+YECTAFRpEgR279NJlO2pUw3FS1aFACLxZJtX0ZGRrZznZyyPkZ1c19GRgYAhQoVwmQy2fYnJSXletcmp5qsVqtt6drNMdzaXm7+Wv+t7dz0888/2x62t1qtODo62vaNGzcONzc3hg8fzrBhw0hOTr5jnzm5tU2LxZLl59v561zcWv/fbVNEREQkP1CAKeAcHR1tYeNWbm5ubNiwgZSUFDIyMggNDcXNzY3GjRuzbds2kpKSSE1NzbIkysXFhSNHjmC1WomOjgagWrVqJCQkcPToUeDG8zMrVqzIsV9nZ2cqV67Mli1bANi3bx8XLlygRo0adz0WNzc3IiIiyMzM5Pr160RFRdGoUaMsx4eGhvL1118DN+4U3bpUrFChQgB06dKFp556igkTJuSphr/64YcfbHO1YcMGXnnllTyd5+bmxtq1a4Ebb2z75ZdfaNiwIQBff/01aWlpXL58mW3bttG0adO/VZuIiIiIEektZAVcy5YtMZvNWd6iBdCiRQsOHTqEt7c3GRkZNG3alB49euDk5ETPnj3x8fGhVKlSVKpUyXbOsGHD6NevH2XLluW5554jMTGRIkWKEBwczIgRI0hPT6dq1apMmTKFtLQ0kpKS8PPzsy2VAggODmbcuHGEhIRQqFAhQkJCKFy4cJ7GUr9+fWbPns3UqVMZMmQIJ06csL2QwNPTk1atWmU5vk+fPowYMYIvv/ySihUr8sknn+TY7sSJE/Hw8CAqKgpPT888zuwNjz76KL179yYxMREvLy9efvnlPJ03evRoxowZY/tcAgMDKV++PHDjTlT37t25evUqffv25emnn76rmkRERESMzGTNbS2RiNyTsLAwdu/eTVBQ0H1r8+bf3Bk0aNDfOn/b0OFcv3B/XzUtIiJ/T/v/LOb8+aT72ma5ciXve5tyZ5r3+8vBwUSZMs6573+ItYiIiIiIiNwTLSETeUA6depEp06d7mubf/fOi4iIiEh+oTswIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGAowIiIiIiJiGE72LkBEHp4W06fauwQREfn/0lNS7V2CiCEpwIgUIAkJV7FYrPYuo8ApV64k588n2buMAklzbx+adxF5kLSETEREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEN/B0akAClTxtneJRRY5cqVtHcJBZbm3j4077lLS83g8pXr9i5DxLAUYEQKkDnBG7l86Zq9yxARKdBGTfSxdwkihqYlZCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgFJsC4urre8RhfX9976iMkJISQkJC7Pi82NpZRo0YBcODAAUaPHn1PdQDs37+f4ODgPB/fuXNnzGYzzZs358UXX8RsNmM2mzl8+DC+vr7ExMTcc0330+rVq3n55ZeZPHlytn0RERF4e3tjNpvx9PTkP//5jx0qhKSkJN577707Hjdy5Ej+/PNPAHr37k18fPyDLk1ERETEsJzsXcA/ye7du+3S7+nTp4mNjQWgXr161KtX757bPHr0KAkJCXk+fs2aNQCEhYWxe/dugoKC7rmGB2n9+vV8/PHHNG3aNMv2VatWsXLlSubPn0/58uW5cuUK77zzDsWKFaNz584PtcbLly9z6NChOx4XExNjCzqfffbZgy5LRERExNAKzB2Ym2JiYnjnnXcYMGAAbdq0YfDgwaSlpREYGAhg+5Lr5ubGu+++i9lsJj09nXnz5tG+fXs8PT0JCgoiMzMTgIULF9K6dWu6du3K/v37bf3cescnLCwMf39/AHbs2IGXlxeenp707duXq1evEhgYyMGDBxk/fjwxMTG2O0HHjx/H19cXT0/PLO37+/sTGBhIt27dcHd3JzQ0NMsYr1y5wqxZs4iOjmbu3LlYLBYCAwPp0KEDHh4eLFiw4K7nbe3atXTs2JGWLVsSHR0NwIULFxgwYACdOnXC29ubHTt2AHD9+nWGDRuGh4cHnp6eRERE2Obh5ng+/vhjGjVqxNWrVwGIi4ujffv22foNDQ21tePv709ycjKzZ8/mwIEDjB8/nu+++y7L8XPnzsXPz4/y5csDUKpUKSZPnkzNmjUB2LdvH507d8bLy4uePXty8uRJ4Mbdt48//hgfHx88PT357rvv6NWrF82bN+eLL74AbtxhGzlyJF26dKFVq1YsXLgw2+d7s62YmBgCAwM5d+6cLZzMmDGDLl260KZNG3x9fblw4QILFizg3Llz9OnTh8TERNzd3YmLi8v1M8vt+hUREREpKArkHZi9e/eyadMmypcvT5cuXdi+fTsBAQEsXbrUdiciMTGR3r1706hRI7777juio6MJDQ2lUKFCDBo0iJUrV1K/fn1CQ0MJDw/HZDLRtWtX6tevn2u/aWlpDB8+nEWLFlG7dm2mTZtGeHg4AQEBzJ49m7Fjx2ZZquXn50efPn1o3bo1+/btY8iQIWzevBmAs2fPsnz5cn7//XfefPNNvL29beeVKlWKwYMHs3v3bvr378+yZcs4c+YM69atIy0tDV9fX2rWrEnz5s3zPGclS5YkPDycbdu2MXv2bNzd3Zk4cSLe3t60bNmSc+fO0b17dyIiIvj0009xcXFh/fr1XLx4kc6dO1OrVi0A4uPj2bhxI05OTly6dImvvvoKHx8fIiIieO2117L0efjwYebNm8fq1atxcXFh/PjxzJ49mw8++ICYmBgGDhxIo0aNbMdfvHiRM2fO8K9//StLO9WrV7fN/9ChQ/nkk0+oX78+mzZtYujQobYAaLVaWbt2LbNnzyYwMJB169Zx8eJFXnvtNd566y0ADh48yMqVK7FYLHTq1InGjRvnOmcBAQG8+eabzJkzh5MnT3Ls2DFWrlyJg4MDI0aMYN26dfTp04eVK1eyYMECXFxcbOeuWLEix8+sWLFiOV6/7u7uef4sRURERIyswN2BAahRowYVK1bEwcGB6tWrc/ny5RyPa9CgAQC7du2iQ4cOFCtWDCcnJ7y9vdm5cye7d++mWbNmlChRguLFi9O2bdvb9nv48GEqVKhA7dq1ARg2bFiuz90kJydz6tQpWrduDUDDhg0pXbo0x44dA6BJkyaYTCZq1qzJpUuXbttvTEwMHTt2xNHRkWLFiuHp6cnOnTtve85fvfrqqwA8/fTTJCYmAjfuJs2aNQuz2Uzv3r3JyMggNjaWXbt24ePjA8Cjjz5Ky5Ytbcvz/vWvf+HkdCM3e3t7ExkZCdxYEmY2m7P0+dNPP9GiRQvbF/uuXbuya9euXGt0cLhxORcpUiTH/SdOnKBUqVK2kNmuXTtOnTpFUlISAK+88goAlSpVokGDBhQrVozHH3+cK1eu2Nrw8PCgRIkSlCxZEnd399vWc6snnniCDz74gDVr1hAUFMS+ffu4du1arsff7jPL6/UrIiIikh8VyDswt37BNZlMWK3WHI8rWrQoABaLJdu+jIyMbOc6OTllWc5jtVoxmUxkZGQAUKhQIUwmk21/UlISycnJOfadU01Wq9W2dO3mGG5tLzd/rf/WdvLK0dExW38Wi4UlS5bwyCOPAHDu3DnKlCmTrfZb+7s5pwAvvPAC586dY8uWLVSuXJkKFSrcse6bc5mTRx55hCpVqnDw4EFeeOEF2/bdu3fz/fff4+Hhke2cW2srVKiQbfvNkPVXN+fhZn2Ojo7ZroP09PRs5x08eJBhw4bx1ltv0aZNGxwcHHK97m62nVudeb1+RURERPKjAnkHJjeOjo45fkF2c3Njw4YNpKSkkJGRQWhoKG5ubjRu3Jht27aRlJREamoqW7dutZ3j4uLCkSNHsFqttmdGqlWrRkJCAkePHgVuPD+zYsWKHPt1dnamcuXKbNmyBbjx7MaFCxeoUaPGXY/Fzc2NiIgIMjMzuX79OlFRUVmWXv1dbm5uLF++HLjx0gBPT0+uX7+Om5sba9euBW4s6/rmm2948cUXs51vMpl47bXXCAwMpFOnTtn2v/jii0RHR9vuMK1evfqOdffq1YugoCDOnz9v6z8oKIgnnniCp556ikuXLtmeJdq4cSOVKlWyBbC8+Prrr0lLS+Py5cts27aNpk2b4uLiwh9//IHVaiU2NpbDhw8DN0LQzc/gp59+4sUXX6Rbt248+eSTfPvtt7ZA4ujomC1QPqjPTERERMToCuQdmNy0bNkSs9lMWFhYlu0tWrTg0KFDeHt7k5GRQdOmTenRowdOTk707NkTHx8fSpUqRaVKlWznDBs2jH79+lG2bFmee+45EhMTKVKkCMHBwYwYMYL09HSqVq3KlClTSEtLIykpCT8/P9vSK4Dg4GDGjRtHSEgIhQoVIiQkhMKFC+dpLPXr12f27NlMnTqVIUOGcOLECdsLCTw9PWnVqtU9z1dAQABjxozB09MTgClTpuDs7Mx7773HuHHj8PT0JDMzk379+lGnTh3bF/tbdejQgc8//9y2RO1WtWrVom/fvvj6+pKenk6dOnUYP378bWvq1q0bGRkZvPPOO7a7E127drW9nGHGjBl89NFHXL9+ndKlSzNjxoy7GnORIkXo3r07V69epW/fvjz99NNUrVqV0NBQ2rZtS7Vq1XjuuecAKFOmDJUqVcLX15epU6cycOBA21zVrVuXuLg4AJo3b06fPn1sLwWAG8vlcvrM/mmvsxYRERF52ExWrT8RO7FYLKxYsYLjx48TEBBg73Lu6Obf+Bk0aJCdK/n75gRv5PKl3J+9ERGRB2/URB/On096IG2XK1fygbUtudO8318ODibKlHHOdb/uwIjdDBw4kDNnzrBo0SJ7lyIiIiIiBqEAI3bz6aef2ruEu2LkOy8iIiIi+YUe4hcREREREcNQgBEREREREcNQgBEREREREcNQgBEREREREcNQgBEREREREcNQgBEREREREcPIU4D56quvmDFjBtevX2f9+vUPuiYREREREZEc3THALFiwgBUrVvDVV1+RkpLC7NmzmTNnzsOoTUREREREJIs7BpgNGzbw2WefUaxYMVxcXFi9erXuwoiIiIiIiF3cMcA4OTlRuHBh28+lSpXCycnpgRYlIiIiIiKSkzsmkccee4xvv/0Wk8lEWloaixYt4vHHH38YtYnIffaeX3t7lyAiUuClpWbYuwQRQzNZrVbr7Q6Ij49nxIgR/PTTTwA0aNCAqVOnKsSIGFBCwlUsltv+Jy8PQLlyJTl/PsneZRRImnv70Lzbj+bePjTv95eDg4kyZZxz3X/HOzAVKlRgyZIlXL9+nczMTJydc29MRERERETkQco1wAQGBt72xICAgPtejIiIiIiIyO3kGmAeeeSRh1iGiIiIiIjIneUaYAYOHAjA8uXL6d69e5Z9CxYseLBViYiIiIiI5CDXALNixQpSUlL44osvSE1NtW1PT09n5cqV9OnT56EUKCIiIiIiclOuAcbJyYnff/+dlJQUfv/9d9t2R0dH/P39H0pxIiIiIiIit7rja5S//vprXn311YdVj4iIiEi+kpaayuUraQ+lL73O1z407/fX336N8oIFC+jTpw87d+5k165d2fbrLWQixrNw8iiuXEqwdxkiIgXK0I/nAw8nwIgUBA657ZgzZw4ALi4uPPLII9n+JyIiIiIi8rDlegemWrVqwP+9jUxERERERMTecg0wqamp/Pbbb+T2iEydOnUeWFEiIiIiIiI5yTXAxMbGMmjQoBwDjMlk4ptvvnmghYmIiIiIiPxVrgHm6aefJiIi4iGWIiIiIiIicnu5PsQvIiIiIiLyT5NrgHn++ecfZh0iIiIiIiJ3lGuA0d95ERERERGRfxotIRMREREREcNQgBEREREREcNQgBEREREREcNQgBEREREREcMwfIBxdXW94zG+vr731EdISAghISF3fV5sbCyjRo0C4MCBA4wePfqe6gDYv38/wcHBeT4+Li4OV1dXxowZk2X7oUOHcHV1JSws7Lbnu7u7ExcXd9dj8ff3v2PbD5q7u3u2bTExMbi6ujJ//vws27/++mtcXV2JiYkBcr6ubs5FTEwMTZs2JSEhwbZv4cKFDBo0KMdz2rdvj9lspkOHDgwcOJDLly//rfGYzea/dZ6IiIhIfmL4AJMXu3fvtku/p0+fJjY2FoB69eoxceLEe27z6NGjWb4458UjjzzCDz/8QGZmpm3bxo0befTRR/PcxoMYi71UqFCBzZs3Z9l2N/PRqFEjPD09bW/q27t3L6tXr851ThYsWEBkZCQbNmzgscceyxae8ioyMvJvnSciIiKSn+SbABMTE8M777zDgAEDaNOmDYMHDyYtLY3AwEAAOnfuDICbmxvvvvsuZrOZ9PR05s2bR/v27fH09CQoKMj2JX/hwoW0bt2arl27sn//fls/t/5mPiwsDH9/fwB27NiBl5cXnp6e9O3bl6tXrxIYGMjBgwcZP348MTExtjtBx48fx9fXF09Pzyzt+/v7ExgYSLdu3XB3dyc0NDTLGK9cucKsWbOIjo5m7ty5WCwWAgMD6dChAx4eHixYsCDHuSlRogS1a9fmp59+sm378ccfeemll+44rptyG4uvry8TJ06kY8eOtG/fnu3bt2frPyIigo4dO2I2mxk1ahSpqan8+uuvNGnShIsXL3Lp0iVatGjBoUOHSE5O5oMPPqBTp06YzWbWr19vq+nmnE2fPp2oqCjMZjOdOnVi8ODBpKam5jj2nDzxxBNYLBZbIEtJSeHkyZM8/fTTeW7j3//+N6dPn+Y///kPH3zwAZMnT6ZUqVK3PcdisZCcnEzZsmUBiI+Pp1evXnTp0oXmzZszc+ZMAP73v//RpUsXOnXqRLdu3Thx4gTwf5/RpUuXeO+992jXrh1ms5mdO3fmuW4RERERo8s3AQZu/CZ8zJgxbNq0idOnT7N9+3bbb8nXrFkDQGJiIr179yYyMpIdO3YQHR1NaGgo4eHhnDx5kpUrV3LgwAHbtsWLF3P27Nnb9puWlsbw4cOZPHkyUVFR1KxZk/DwcAICAqhbty5jx47Ncryfnx++vr5ERUUxcuRIhgwZQlpaGgBnz55l+fLlzJ07lylTpmQ5r1SpUgwePBh3d3f69+/PihUrOHPmDOvWrWPNmjVs2bKFb7/9Nsca27VrZ7vrsH//flxdXSlUqFCe5za3sQBcvXqV8PBwpk2bhr+/v20sAEeOHGH16tWsXLmSyMhIypQpw6JFi6hTpw5du3ZlypQpfPTRR3Tr1o3atWszd+5c6tSpQ1hYGMuWLWPevHm2oBEfH094eDhDhw7lk08+4fPPPycsLIzHH3+cY8eO5XksAG3btrXNx7Zt22jRosVdnV+4cGGmTp1KUFAQ7du355lnnsn12D59+mA2m3nllVf48ccfadu2LQDr16/Hw8OD1atXExUVxZIlS7h48SJLlizh7bffJiwsjC5durBv374s7c2cOZOqVauyadMmpkyZwieffHJXtYuIiIgYWb4KMDVq1KBixYo4ODhQvXr1XJ81aNCgAQC7du2iQ4cOFCtWDCcnJ7y9vdm5cye7d++mWbNmlChRguLFi9u+cObm8OHDVKhQgdq1awMwbNiwXJ+7SU5O5tSpU7Ru3RqAhg0bUrp0adsX8CZNmmAymahZsyaXLl26bb8xMTF07NgRR0dHihUrhqenZ66/jXd3d+f777/HYrGwadMm2rVrd9u270aXLl0AqF27NuXKlePw4cNZajx58iRdunTBbDbzzTff2Mbav39/fvvtN2JjY3n33XeBG3eyVq5cidls5o033uDatWscOXIEgH/96184OTkB0KJFC7p168aUKVNo06aNbe7zql27dmzZsgUgx/kwmUzZzrFarTg4/N9/Mr/88gsuLi7s3LmTjIyMXPu6uYRs+/bt9OzZk169emG1WunVqxePPfYYixYtYuLEiaSnp3P9+nWaNWvGRx99xKhRoyhZsiSenp5Z2vvpp59sz8O4urqyatWquxq7iIiIiJHlqwBTpEgR279NJhNWqzXH44oWLQrcWNLzVxkZGdnOvfml+aab+25+aS1UqFCWL7xJSUm53rXJqSar1WpbunZzDDl9gf6rv9Z/azt/VaJECWrVqsWePXvYtWtXluVjf63tdl/Gc+Lo6JilplvnKzMzk3bt2hEZGUlkZCRr1qyxvVAgKSmJ5ORkEhISbGHNYrEQHBxsO3716tW8/PLLwP99bnDjjtCsWbMoXbo0fn5+d/18yBNPPEF6ejpHjx7l7NmzVK9ePcv+0qVLc+XKlSzbEhMTbcvEjh49SkhICCtXrqRw4cLMnTs3T/127tyZY8eOkZiYSFBQEEuXLqVSpUr0798fFxcXrFYrbdu2JTw8nPr16/PFF19ku+vl5OSU5fr4448/cryWRURERPKjfBVgcuPo6Jjjl3I3Nzc2bNhASkoKGRkZhIaG4ubmRuPGjdm2bRtJSUmkpqaydetW2zkuLi4cOXIEq9VKdHQ0ANWqVSMhIYGjR48CN56fWbFiRY79Ojs7U7lyZdtv//ft28eFCxeoUaPGXY/Fzc2NiIgIMjMzuX79OlFRUTRq1CjXc9u1a8e0adOoW7dutlCW07hy6/evNm7cCNx4O9mVK1eoWbOmbV+jRo3YunUrCQkJWK1Wxo0bx5IlSwAYP348PXr0oHv37owfP942phUrVgBw7tw5vLy8OHPmTJb+MjIyaN26NS4uLvTt2xez2cyhQ4duO285adu2LQEBATm+rczNzY21a9fafo6IiKBGjRo4OzuTmprKv//9b/z8/KhSpQpBQUF8+eWX2ZZ65WTnzp089thjPProo/z444/06tWLdu3acfz4ceLj47FYLLz//vscOHCA119/nSFDhvDbb79laeP5559nw4YNwI3w0rt37zwFXhEREZH8wOnOhxhfy5YtMZvN2V7re/PBcW9vbzIyMmjatCk9evTAycmJnj174uPjQ6lSpahUqZLtnGHDhtGvXz/Kli3Lc889R2JiIkWKFCE4OJgRI0aQnp5O1apVmTJlCmlpaSQlJeHn54ePj4+tjeDgYMaNG0dISAiFChUiJCSEwoUL52ks9evXZ/bs2UydOpUhQ4Zw4sQJ2wsJPD09adWqVa7ntmjRgtGjRzNkyJBs+3Ia162qV6+e41jgxuuiO3bsCMCMGTOy3JGpVasWAwcOpGfPnlgsFmrXrk2fPn3YuHEjsbGxTJ8+HavVire3Nxs3bmTgwIGMGzcODw8PMjMz8fPzo2rVqvz888+2Np2cnBg8eDDvvPMORYoUoUyZMgQFBeVp/m7Vrl07pk+fnu1ZI7hxh2fcuHGEhYVhtVp57LHHmD59OgCTJk2iRo0atmVcjz/+OCNHjsTPz4+IiAhKlCiRpa0+ffpQqFAhHBwccHR0tLXTt29fRowYQdGiRalYsSJ169YlLi6Ofv36MXr0aObMmUOhQoUYN25clvYGDx5MQEAAXl5eODk5MWXKFAUYERERKTBM1tzWWYnkga+vLwMHDrztnR97cXd3z/FuUkG2cPIorly6u9dwi4jIvRn68XzOn096KH2VK1fyofUl/0fzfn85OJgoU8Y59/0PsRYREREREZF7UiCWkMmDs3TpUnuXkCvdfRERERHJf3QHRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDEMBRkREREREDMNktVqt9i5CREREJL9KS03l8pW0h9JXuXIlOX8+6aH0Jf9H835/OTiYKFPGOdf9Tg+xFhGxs4SEq1gs+p3Fw6b/x2Y/mnv70LyLyIOkJWQiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIYCjAiIiIiImIY+jswIgXI7f4olDxY5cqVtHcJBZbm3j6MOu8ZaRkkXr5u7zJE5DYUYEQKkN+W7iI9KcXeZYiI/GM1GNDc3iWIyB1oCZmIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBjGAwswrq6udzzG19f3nvoICQkhJCTkrs+LjY1l1KhRABw4cIDRo0ffUx0A+/fvJzg4+K7O+eqrr+jUqRNeXl54enqycOHCe67jXiUlJfHee+8BEB8fT+/evW97fG6fgdVqZfHixZjNZsxmMx07dmTDhg22/Waz+Z5rvbW+bdu2sXjx4jyf6+7unuP2vFy399PIkSP5888//9a5dztmERERkfzAyZ6d79692y79nj59mtjYWADq1atHvXr17rnNo0ePkpCQkOfj4+PjmTx5MmFhYbi4uJCcnIyvry/VqlWjZcuW91zP33X58mUOHToEQIUKFfjss8/+VjszZszgt99+48svv6RkyZKcPXuWHj164OLiwksvvURkZOQ913prfQcPHrzn9uwhJibGFhjvllHHLCIiInIvHniAiYmJYf78+RQtWpQ//vgDV1dXpk6dypQpUwDo3Lkza9aswc3Njbp163L+/HnWrl3LokWLWLduHY6OjjRp0gQ/Pz8cHR1ZuHAhq1evxsXFhVKlSlG/fn3gxm/ODx8+DEBYWBi7d+8mKCiIHTt2EBQUhNVqpVKlSkybNo3AwEDi4uIYP348bdu2Zfbs2SxdupTjx48zZswYLl26RPHixRk9ejT169fH398fZ2dnfv31V+Lj43nvvffw9va2jfHKlSvMmjWLa9euMXfuXPr27cukSZPYuXMnJpMJLy8v+vTpk2VeEhMTSU9PJyUlBYASJUoQFBREkSJFgBt3dD7++GNSUlJwcXFh/PjxVKlShYMHD/Lhhx8C8PLLL7N+/Xqio6Px9/fnxRdfpFOnTlnmIzk5mQkTJnDkyBEyMzPp3bs3Hh4ehIWF8cMPP3D58mViY2Np0qQJ48aNIzAwkHPnzvHee+8xcuRI3nzzTaKjo/n999/56KOPuHbtGhcvXqRPnz5069Ytx888OTmZJUuWsG7dOkqWLAlAxYoVmT59OsWKFctSX0hICPv27ePMmTP06NGDZ555hjFjxpCSkkLp0qWZOnUqJ0+etH1GgG2sL774Im+++SYLFixg5cqVAFSqVIlKlSrZ7oaVLl2aadOm8eijj971dTtv3jwKFSpEXFwc7u7uFC9enK+//hqABQsWULZsWRo3bkyrVq3Yu3cvJUqUYOrUqVSuXJl9+/YxceJEUlNTcXFxYcKECTzxxBP4+vpSunRpjhw5gre3N+fOnaNPnz4sW7aMXbt2sXjxYlJSUkhLS2PSpEk8++yz+Pr6Uq9ePfbs2cPFixcJCAjg8ccfzzLmW69HERERkfzsoTwDs3fvXsaMGcOmTZs4ffo027dvJyAgAIA1a9YAN77Q9+7dm8jISHbs2EF0dDShoaGEh4dz8uRJVq5cyYEDB2zbFi9ezNmzZ2/bb1paGsOHD2fy5MlERUVRs2ZNwsPDCQgIoG7duowdOzbL8X5+fvj6+hIVFcXIkSMZMmQIaWlpAJw9e5bly5czd+5cW/i6qVSpUgwePBh3d3f69+/PihUrOHPmDOvWrWPNmjVs2bKFb7/9Nss5tWrVomXLlrz66qv4+PgQHByMxWLhiSeeIC0tjYCAAKZNm0Z4eDhvv/22LbSMGDGCoUOHEh4eTuHChe8493PnzqVOnTqEhYWxbNky5s2bZ7v7tHfvXmbNmsW6devYtm0bhw8fJiAggPLlyzNnzpws7axZs4YBAwYQGhrKf/7zn2xzcKtjx47h5OTEE088kWV7/fr1qVGjRrbj09LS2LhxI927d2f48OEMGDCAqKgo2rdvz5IlS+44xqeffprXX3+d119/HW9vbz799FPGjRtHWFgYL730Er/99tsd28jJf//7X8aPH09oaCjLli3j0UcfJSwsDFdXV9tyuIsXL/LMM88QFRVFhw4dCAwMJC0tjaFDh/Lhhx+ybt06Xn/9dYYOHWpr19XVlc2bN9OnTx/Kly/PggULKF26NCtXrmTevHmsW7eOd999lwULFtjOSU9PZ9WqVYwcOZKZM2dmG7OIiIhIQfFQlpDVqFGDihUrAlC9enUuX76c43ENGjQAYNeuXXTo0MH223pvb28iIiJISUmhWbNmlChRAoC2bdtisVhy7ffw4cNUqFCB2rVrAzBs2DDgxm/X/yo5OZlTp07RunVrABo2bEjp0qU5duwYAE2aNMFkMlGzZk0uXbp02/HGxMTQsWNHHB0dKVasGJ6enuzcuZPmzZtnOW78+PEMGDCA7du3s337drp06cLUqVN58skniY2NpX///rZjr169ysWLF7lw4QIvv/wycOPuVVhY2G1r2bFjBykpKYSGhgJw7do1jhw5AsAzzzyDs7MzAFWqVOHy5cu2uf0rf39/fvjhB+bPn8/vv//OtWvXcu3TwcEhT+Hqppt30S5evMj58+dp0aIFAN27dwdy/rxup2XLlgwcOJBXX32Vli1b0qRJk7s6/6aaNWvy2GOPAeDi4kLjxo2BG3c8rly5AkCRIkV47bXXAOjYsSPTp0/nxIkTWe4OtmvXjjFjxpCUlJRlvLdycHBgzpw5REdHc/z4cXbv3o2Dw//9fuHmZ16jRo07Xn8iIiIi+dlDCTA3l0UBmEwmrFZrjscVLVoUIMdQkpGRke1cJycn2x0SuPHguMlkIiMjA4BChQphMpls+5OSkkhOTs6x75xqslqtZGZmZhnDre3l5q/139rOTd9++y3Xrl2jffv2eHt74+3tzerVq1m7di1Dhw6lcuXKtudEMjMzuXDhQpZ5vDm+m26dm/T09Cy1BAcHU6dOHQAuXLhA6dKliYqKyvPnAvD+++9TqlQpWrRoQfv27Vm/fn2ux1avXp2UlBROnz5NpUqVbNs3bNjAhQsX6NmzZ5bjb37uf/28UlNTOXfuXLbabh1fTt566y1atGjBtm3bCA4OZv/+/VnCYF7dOr8Ajo6O2Y5xcHCw1WyxWHB0dMzx+r31Grg53lslJyfj4+ODl5cXL7zwAq6urixbtsy2/26uPxEREZH8zK6vUXZ0dLSFjVu5ubmxYcMGUlJSyMjIIDQ0FDc3Nxo3bsy2bdtISkoiNTWVrVu32s5xcXHhyJEjWK1WoqOjAahWrRoJCQkcPXoUgIULF7JixYoc+3V2dqZy5cps2bIFgH379nHhwoUclzzdaSxubm5ERESQmZnJ9evXiYqKolGjRlmOL1q0KNOmTSMuLg648QX30KFD1K5dm6eeeorLly/z888/AxAaGsrw4cMpUaIETz31FN988w0AUVFRtvYeeeQR2zhvPqdxs5YVK1YAcO7cOby8vDhz5kyu43BycsrxM/nxxx8ZPHgwr776Kt9//z1AtlB269jeeOMNxo0bx9WrVwGIi4tj+vTpVK9ePde+S5YsSYUKFdi+fTsAkZGRzJw5ExcXF2JjY0lNTeXSpUvs2bMn27m3zn/nzp1JTk7mrbfe4q233vrbS8jy4vr167brLSwsjFdeeYWnnnqKS5cusX//fgA2btxIpUqVeOSRR3KsOzMzkxMnTmAymejXrx+NGjVi69atuc7vrefm9FmJiIiI5Gd2fQtZy5YtMZvN2ZZBtWjRgkOHDuHt7U1GRgZNmzalR48eODk50bNnT3x8fChVqlSW3+4PGzaMfv36UbZsWZ577jkSExMpUqQIwcHBjBgxgvT0dKpWrcqUKVNIS0sjKSkJPz8/fHx8bG0EBwczbtw4QkJCKFSoECEhIXleClW/fn1mz57N1KlTGTJkCCdOnMBsNpOeno6npyetWrXKcrybmxsDBw6kX79+tjsKL7/8Mu+99x6FCxdm5syZtofAnZ2dmTx5MgCTJ08mICCAkJCQLGGgW7duvP/++3h6euLm5ka5cuUAGDhwIOPGjcPDw4PMzEz8/PyoWrWqLRz9VZkyZahUqRK+vr58/PHHtu2DBg2ie/fuFClShFq1avH444/bwldO/v3vfzNnzhy6dOmCk5MTjo6ODBs2jKZNm952Hm9+BsHBwbi4uDBlyhTKly9Ps2bN6NChA48//jjPPfdctvNeeOEFPvjgA8qWLcvQoUPx9/fHycmJ4sWLExgYeNs+79VXX33FjBkzKF++PJMnT6Zw4cLMmDGDjz76iOvXr1O6dGlmzJiR47nNmzenT58+fPbZZ9SuXZt27dphMplo2rRpjkHtVreO+V5fSS4iIiJiFCbr7dYNyT9aXFyc7S1hcnfc3d3vy7zd+vY7I/ht6S7Sk1LsXYaIyD9WgwHNOX8+yd5l/G3lypU0dP1GpXm/vxwcTJQp45z7/odYi4iIiIiIyD1RgDGwypUr6+7L33S/5s1Id19ERERE8gMFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwFGBERERERMQwnexcgIg/Pv3zd7F2CiMg/WkZahr1LEJE7UIARKUASEq5isVjtXUaBU65cSc6fT7J3GQWS5t4+NO8i8iBpCZmIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiG/g6MSAFSpoyzvUsosMqVK2nvEgoszb19PIx5T0tL5/LllAfej4j8syjAiBQgn332GVeuXLF3GSIi98WwYcMABRiRgkZLyERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDDyXYBxdXW94zG+vr731EdISAghISF3fV5sbCyjRo0C4MCBA4wePfqe6gDYv38/wcHBeT4+Li6OunXrYjabs/xv2bJl91xLbvz9/QkLC3tg7f8dOV0nYWFhvPjii5jNZry8vGjbti2zZ88mMzPzgdURFxeHu7v7A2tfREREJL9xsncB9rB792679Hv69GliY2MBqFevHvXq1bvnNo8ePUpCQsJdnVO+fHkiIyPvue/8yN3dnaCgIACuXbvGgAEDCAkJ4f3337dvYSIiIiIC5OMAExMTw/z58ylatCh//PEHrq6uTJ06lSlTpgDQuXNn1qxZg5ubG3Xr1uX8+fOsXbuWRYsWsW7dOhwdHWnSpAl+fn44OjqycOFCVq9ejYuLC6VKlaJ+/frAjd/kHz58GLjxG/zdu3cTFBTEjh07CAoKwmq1UqlSJaZNm0ZgYCBxcXGMHz/e9tv9pUuXcvz4ccaMGcOlS5coXrw4o0ePpn79+vj7++Ps7Myvv/5KfHw87733Ht7e3rYxXrlyhVmzZnHt2jXmzp1L3759mTRpEjt37sRkMuHl5UWfPn3uat6aNm1KmzZt2LNnD46OjnzyySdUqVIlx/EUL148x/6sVitBQUF8++23lC9fnszMTF588UUAIiIiWLJkCRaLhTp16jB27FiKFCmS5XOYN28efn5+XLt2DQcHBwICAmjYsGGWOjdt2sTixYtJSUkhLS2NSZMm8eyzz+Lr60u9evXYs2cPFy9eJCAggGbNmhEXF2drs0GDBnmai+LFizN06FB69+7NkCFDuHbtGhMmTODIkSNkZmbSu3dvPDw8SE1NZfz48ezZs4dChQoxYMAA2rdvz759+5g4cSKpqam4uLgwYcIEnnjiCX777Tfb3bdatWrZ+rtw4QJjxozh7NmzmEwmhg0bxksvvURISAj79u3jzJkz9OjRg9TUVMLDw3FwcKB+/fpMmDDhrj5jERERESPLd0vIbrV3717GjBnDpk2bOH36NNu3bycgIACANWvWAJCYmEjv3r2JjIxkx44dREdHExoaSnh4OCdPnmTlypUcOHDAtm3x4sWcPXv2tv2mpaUxfPhwJk+eTFRUFDVr1iQ8PJyAgADq1q3L2LFjsxzv5+eHr68vUVFRjBw5kiFDhpCWlgbA2bNnWb58OXPnzrWFr5tKlSrF4MGDcXd3p3///qxYsYIzZ86wbt061qxZw5YtW/j222+z1Xfu3LlsS8huhrDz58/TuHFjIiIieOGFF1i2bFmu48mtv82bN/Pbb7+xfv16Zs6cyalTpwA4cuQIq1evZuXKlURGRlKmTBkWLVqU7XMIDQ2lefPmhIWFMXjwYPbs2ZOlfovFwsqVK5k3bx7r1q3j3XffZcGCBbb96enprFq1ipEjRzJz5kwAPvroIzp16kRkZCTPPvvsbT+/W9WoUYNLly5x8eJF5s6dS506dQgLC2PZsmXMmzeP2NhYli5dyrVr12yhas6cOaSlpTF06FA+/PBD1q1bx+uvv87QoUMB+OCDDxg+fDjh4eFUrlzZ1tfEiRPx9vYmLCyMuXPnMmbMGK5evWq7pjZu3EjXrl2ZP38+oaGhhIWFkZ6eTnx8fJ7HIyIiImJ0+fYODNz48lmxYkUAqlevzuXLl3M87uZv5Hft2kWHDh0oVqwYAN7e3kRERJCSkkKzZs0oUaIEAG3btsViseTa7+HDh6lQoQK1a9cGYNiwYcCNu0J/lZyczKlTp2jdujUADRs2pHTp0hw7dgyAJk2aYDKZqFmzJpcuXbrteGNiYujYsSOOjo4UK1YMT09Pdu7cSfPmzbMcd6clZC+//DJwY/5+/vnnXMczePDgHPtLT0+ndevWFCpUiEcffZRXXnnFVt/Jkyfp0qULcCNo/Otf/7L1e/NzaNy4MYMGDeLQoUM0a9aMHj16ZKnPwcGBOXPmEB0dzfHjx9m9ezcODv+XxW+t/+ac7d69m2nTpgHg5eVlC7J3YjKZAChSpAg7duwgJSWF0NBQ4MYSsyNHjvDTTz/RpUsXHBwcKFeuHBs2bOD333/PcqeuXbt2jBkzhj///JNz587RpEkTADp16mRrb8eOHRw7doxZs2YBkJGRYVtyeLMdR0dHnnnmGXx8fGjZsiVvv/02FSpUyNNYRERERPKDfB1gihQpYvu3yWTCarXmeFzRokUBcgwlGRkZ2c51cnKy3SEBsFqtmEwmMjIyAChUqJDtiy9AUlISycnJOfadU01Wq9X24PjNMdzaXm7+Wv+t7dyNW/u0Wq25jie3/nKaL4DMzEzatWtnCw/JyclZ6rv5OTz33HNs2LCBb7/9lo0bN9rufN2UnJyMj48PXl5evPDCC7i6umZ5CUFuc3azJpPJlCXw3M7hw4epWLEizs7OWCwWgoODqVOnDnBjyVfp0qUJDQ3N0tfJkydzvJasVivFixfPMjeOjo62f1ssFpYsWcIjjzwC3LhTVqZMGb7++mvb3AB8+umn7Nu3j++//553332XqVOn2pboiYiIiOR3+XoJWW4cHR1tYeNWbm5ubNiwgZSUFDIyMggNDcXNzY3GjRuzbds2kpKSSE1NZevWrbZzXFxcOHLkCFarlejoaACqVatGQkICR48eBWDhwoWsWLEix36dnZ2pXLkyW7ZsAWDfvn1cuHCBGjVq3PVY3NzciIiIIDMzk+vXrxMVFUWjRo3ufoL+Irfx5NZf48aN2bRpE2lpaVy+fJkffvgBgEaNGrF161YSEhKwWq2MGzeOJUuWZOtvypQprFu3jo4dOzJmzBh+++23LPtPnDiByWSiX79+tjbvFNReeukl1q1bB8CWLVtITU2947iTkpKYOXMmb7zxBnBjflesWAHcCBdeXl6cOXOGF154gY0bN2K1WklISKBHjx48/vjjXLp0if379wOwceNGKlWqhIuLC5UqVbIt7Vu/fr2tPzc3N5YvXw7ceDmDp6cn169fz1LTxYsXad++PTVr1mTIkCE0adLEtvxPREREpCDI13dgctOyZUvMZnO2V/u2aNGCQ4cO4e3tTUZGBk2bNqVHjx44OTnRs2dPfHx8KFWqFJUqVbKdM2zYMPr160fZsmV57rnnSExMpEiRIgQHBzNixAjS09OpWrUqU6ZMIS0tjaSkJPz8/PDx8bG1ERwczLhx4wgJCaFQoUKEhIRQuHDhPI2lfv36zJ49m6lTpzJkyBBOnDiB2WwmPT0dT09PWrVqle2cm8/A3OqFF17IdVlVbuMpXLhwrv0dOHAADw8PypYtS/Xq1YEbD6wPHDiQnj17YrFYqF27do4vGfD19WXYsGGEhYXh6OjI5MmTs+yvVasWtWvXpl27dphMJpo2bZrtOZm/GjNmDH5+fqxatYq6devalgP+VXR0NGazGZPJRGZmJq1bt6Z3794ADBw4kHHjxuHh4UFmZiZ+fn5UrVqV7t27ExgYiJeXFwAffvghJUuWZMaMGXz00Udcv36d0qVLM2PGDODG5z1y5Eg++eSTLC8nCAgIYMyYMXh6egI3gpyzs3OW+h599FG6du2Kj48PxYoVo1q1alle7CAiIiKS35msua2rEpF857PPPuPKlSv2LkNE5L4YNmwY588n2buMf5Ry5UpqTuxA835/OTiYKFPGOff9D7EWERERERGRe6IAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihqEAIyIiIiIihuFk7wJE5OHp3bu3vUsQEblv0tLS7V2CiNiBAoxIAZKQcBWLxWrvMgqccuVKcv58kr3LKJA09/aheReRB0lLyERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDAUYERERERExDD0d2BECpAyZZztXUKBVa5cSXuXUGBp7u0jL/OekZ5G4qXUh1CNiOQnCjAiBUjMN5+Qev2SvcsQEQHgFY9xgAKMiNwdLSETERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIARERERERHDUIApYFxdXe94jK+v7z31ERISQkhIyF2fFxsby6hRowA4cOAAo0ePvqc6APbv309wcHCej4+Li6Nu3bqYzWbMZjNt2rRh5MiRXLhwIcfjR44cScuWLZk/fz7u7u4AzJw5k2+++SbPfa5evZqXX36ZyZMn4+7uTlxcXJ7PFRERESlonOxdgPzz7N692y79nj59mtjYWADq1atHvXr17rnNo0ePkpCQcFfnlC9fnsjISACsVivTp09n8ODBLF++PNux4eHh7N+/n3PnzrFq1SoAhgwZclf9rV+/no8//pimTZuyefPmuzpXREREpKDRHZgCKiYmhnfeeYcBAwbQpk0bBg8eTFpaGoGBgQB07twZADc3N959913MZjPp6enMmzeP9u3b4+npSVBQEJmZmQAsXLiQ1q1b07VrV/bv32/r59Y7PmFhYfj7+wOwY8cOvLy88PT0pG/fvly9epXAwEAOHjzI+PHjiYmJsd0JOn78OL6+vnh6emZp39/fn8DAQLp164a7uzuhoaFZxnjlyhVmzZpFdHQ0c+fOxWKxEBgYSIcOHfDw8GDBggV3nCeTycSgQYM4cuQI//vf/7Ls69evH1arlc6dO3Px4kXbdn9/f8LCwoiLi8PT05MhQ4bQoUMHevfuzaVLl7K0MXv2bA4cOMD48eP57rvvbNtzqzUmJoaePXvSq1cv2rRpg5+fH2lpaXcch4iIiEh+oQBTgO3du5cxY8awadMmTp8+zfbt2wkICABgzZo1ACQmJtK7d28iIyPZsWMH0dHRhIaGEh4ezsmTJ1m5ciUHDhywbVu8eDFnz569bb9paWkMHz6cyZMnExUVRc2aNQkPDycgIIC6desyduzYLMf7+fnh6+tLVFQUI0eOZMiQIbYv7WfPnmX58uXMnTuXKVOmZDmvVKlSDB48GHd3d/r378+KFSs4c+YM69atY82aNWzZsoVvv/32jvNUuHBhnnjiCY4dO5Zl+7x58wCIjIzk0UcfzfHc33//ne7du7NhwwaqV6/O7Nmzs+wfOHAgdevWJTAwkGbNmtm2367WvXv3Mnr0aL766itSU1NZtmzZHccgIiIikl8owBRgNWrUoGLFijg4OFC9enUuX76c43ENGjQAYNeuXXTo0IFixYrh5OSEt7c3O3fuZPfu3TRr1owSJUpQvHhx2rZte9t+Dx8+TIUKFahduzYAw4YNy/W5m+TkZE6dOkXr1q0BaNiwIaVLl7aFiSZNmmAymahZs2a2uxt/FRMTQ8eOHXF0dKRYsWJ4enqyc+fO255zk8lkomjRonk69lZPPvkkjRo1AuC1115j165deTrvdrW+8MILPPXUU5hMJsxmc57bFBEREckP9AxMAVakSBHbv00mE1arNcfjbn5xt1gs2fZlZGRkO9fJySnLsiar1YrJZCIjIwOAQoUKYTKZbPuTkpJITk7Ose+carJarbalazfHcGt7uflr/be2cztpaWkcP36cp59++o7H/pWT0//9J2a1WnF0dMzTeber9dY27qZNERERkfxAd2AkG0dHR1vYuJWbmxsbNmwgJSWFjIwMQkNDcXNzo3Hjxmzbto2kpCRSU1PZunWr7RwXFxeOHDmC1WolOjoagGrVqpGQkMDRo0eBG8/PrFixIsd+nZ2dqVy5Mlu2bAFg3759XLhwgRo1atz1WNzc3IiIiCAzM5Pr168TFRVluzuSG4vFQkhICA0aNKBq1ap56vNWx48f59ChQwCEhobyyiuv5Om829W6Z88e4uPjsVgsRERE5LlNERERkfxAd2Akm5YtW2I2mwkLC8uyvUWLFhw6dAhvb28yMjJo2rQpPXr0wMnJiZ49e+Lj40OpUqWoVKmS7Zxhw4bRr18/ypYty3PPPUdiYiJFihQhODiYESNGkJ6eTtWqVZkyZQppaWkkJSXh5+eHj4+PrY3g4GDGjRtHSEgIhQoVIiQkhMKFC+dpLPXr12f27NlMnTqVIUOGcOLECdsLCTw9PWnVqlW2c86dO4fZbAZuBJjatWszffr0vzOVlC5dmlmzZnHq1ClcXV1tL0m4k65du+ZYa0xMDOXLl2fEiBHEx8fTpEkT2wsXRERERAoCkzW3dUMick/i4uJ48803bXee7oeYmBhmz57N0qVL/97533xC6vVL960eEZF78YrHOM6fT7J3GflKuXIlNad2oHm/vxwcTJQp45z7/odYi4iIiIiIyD3REjKRB6Ry5cr39e4LQKNGje743I6IiIhIfqY7MCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhhO9i5ARB6eRi3ft3cJIiI2Gelp9i5BRAxIAUakAElIuIrFYrV3GQVOuXIlOX8+yd5lFEiae/vQvIvIg6QlZCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhgKMCIiIiIiYhj6OzAiBUiZMs72LqHAKleupL1LKLA09/ZR6pFiXLl03d5liEg+pAAjUoB8tHkXiddS7V2GiBQA0zs2s3cJIpJPaQmZiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMiIiIiIgYhgKMFGhxcXHUrVsXs9mc5X9nzpzJ9ZyQkBBCQkIAcHV1zfGYxMRE6tWrx+eff37bvt3d3XPcl1u7IiIiIgWdk70LELG38uXLExkZeV/bjIqKwt3dnVWrVvH2229jMpnua/siIiIiBZXuwIjkwt/fn7CwMNvPd3NXJCwsjO7du1O4cGF27dpl2/7bb7/RsWNHOnbsyJw5c2zb4+Li6NatG2azmTFjxti2Jycn88EHH9CpUyfMZjPr16+3te/r64unpyfTp0+/l2GKiIiIGIoCjBR4586dy7J8bOHChffU3v/+9z8uXLjA888/T7t27Vi1apVt3wcffMDw4cMJDw+ncuXKtu0fffQRnTp1IjIykmeffda2fe7cudSpU4ewsDCWLVvGvHnziI2NBSA+Pp7w8HCGDh16T/WKiIiIGImWkEmBd7+XkK1du5a2bdvi6OhI+/bt+fTTT7lw4QIODg6cO3eOJk2aANCpUydCQ0MB2L17N9OmTQPAy8uLgIAAAHbs2EFKSortuGvXrnHkyBEA/vWvf+HkpP+ERUREpGDRtx+RXJhMJqxWKwDp6el5OictLY3169fj5OREdHS0bXtYWBidO3e2tQfg6OiY5dyb+0wmEw4ON26OWiwWgoODqVOnDgAXLlygdOnSREVFUbRo0b8/OBERERGD0hIykVw88sgjHD16FICvv/46T+ds27YNFxcXtm/fTnR0NNHR0UyYMIFVq1bxyCOPUKlSJb799lsA2/MsAC+99BLr1q0DYMuWLaSmpgLg5ubGihUrgBtL3by8vG77hjQRERGR/E4BRiQX3bp1IyYmBk9PT3755RfKlSt3x3NuPrx/Kw8PD1JTU/nhhx8IDg5m9uzZvPbaa5w6dcp2zJgxY9i8eTNeXl589913lChRAoCBAweSkpKCh4cHPXv2xM/Pj6pVq97fgYqIiIgYiMl665oWEcnXPtq8i8RrqfYuQ0QKgOkdm3H+fJK9yyhwypUrqXm3A837/eXgYKJMGefc9z/EWkRERERERO6JAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGAoyIiIiIiBiGk70LEJGH58M2bvYuQUQKiNT0DHuXICL5lAKMSAGSkHAVi8Vq7zIKnHLlSnL+fJK9yyiQNPf2Ua5cSa5cum7vMkQkn9ISMhERERERMQzdgREpQBwcTPYuocDS3NuP5t4+NO/2o7m3D837/XOnuTRZrVatJxEREREREUPQEjIRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRgRERERETEMBRiRfCYqKor27dvTunVrli1blm3/oUOH6NSpE23atGH06NFkZGTYocr86U5zP3v2bFq0aIHZbMZsNud4jPw9V69excPDg7i4uGz7dM0/WLebe13zD8bs2bPp0KEDHTp0YMqUKdn265p/cO4097rmHxKriOQbZ8+etbZo0cKamJhoTU5Otnp6elqPHDmS5ZgOHTpY9+7da7VardaRI0daly1bZodK85+8zH3fvn2tv/zyi50qzL/27dtn9fDwsNapU8caGxubbb+u+QfnTnOva/7++/HHH61du3a1pqamWtPS0qxvvvmmdcuWLVmO0TX/YORl7nXNPxy6AyOSj+zYsQM3NzceeeQRihcvTps2bfjqq69s+//8809SUlJo2LAhAJ06dcqyX/6+O809wMGDB5k/fz6enp5MmDCB1NRUO1Wbv6xevZqxY8dSvnz5bPt0zT9Yt5t70DX/IJQrVw5/f38KFy5MoUKFqF69OqdPn7bt1zX/4Nxp7kHX/MOiACOSj5w7d45y5crZfi5fvjzx8fG57i9XrlyW/fL33Wnuk5OTqV27Nn5+foSHh3PlyhU+/fRTe5Sa70ycOJHnn38+x3265h+s2829rvkHo0aNGrZwcuLECTZt2kSzZs1s+3XNPzh3mntd8w+PAoxIPmKxWDCZTLafrVZrlp/vtF/+vjvNbYkSJfjss8+oXr06Tk5OvPPOO3z33Xf2KLVA0TVvP7rmH6wjR47wzjvvMGLECJ588knbdl3zD15uc69r/uFRgBHJRypWrMj58+dtP58/fz7L0o6/7r9w4UKuSz/k7txp7k+fPs3atWttP1utVpycnB5qjQWRrnn70TX/4OzZs4e33nqLYcOG0bFjxyz7dM0/WLebe13zD48CjEg+8tJLL7Fz504uXrzI9evX2bJlC6+88opt/+OPP06RIkXYs2cPAJGRkVn2y993p7kvWrQowcHBxMbGYrVaWbZsGa1atbJjxQWDrnn70TX/YJw5c4b33nuPqVOn0qFDh2z7dc0/OHeae13zD49ioUg+UqFCBf7973/z5ptvkp6ejo+PD/Xr16d3794MHjyYevXqMXXqVAICArh69Sp16tThzTfftHfZ+UJe5n7ChAn079+f9PR0nn32Wd5++217l51v6Zq3H13zD9aiRYtITU0lKCjItu31118nOjpa1/wDlpe51zX/cJisVqvV3kWIiIiIiIjkhZaQiYiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIiIYSjAiIiIiIjIfXX16lU8PDyIi4u77XHHjh3D19cXLy8vevXqxeXLl+/YtgKMiIhIAefr68uCBQuybf/888+pW7dujvtERHLz3//+l27dunHixInbHme1Wunfvz+9e/dm3bp11K5dO0//90YBRkREpIDr3r07oaGh2bavXr2aefPm0adPHztUJSJGtXr1asaOHUv58uVt2yIiIujYsSNms5lRo0aRmprKr7/+SvHixW1/bLVfv3688cYbd2xfAUZERKSAa9WqFdeuXePnn3+2bdu9ezdWq5VffvmFCRMmABAfH897771Hp06d8PT0ZN68eQAMGDCANWvWALB3715cXV2JjY0F4NNPPyU4OJg//viD119/nU6dOtGxY0eWLVv2kEcpIg/LxIkTef75520/HzlyhNWrV7Ny5UoiIyMpU6YMixYt4tSpU5QtW5ZRo0bRsWNHxo4dS/Hixe/YvgKMiIhIAefk5ESXLl1Yu3atbduqVavo3r07JpPJts3Pzw9vb2/CwsJYu3YtO3bsYOPGjbRu3ZoffvgBgB9++IFy5cqxY8cOAKKjo2nTpg2LFi3C3d2dsLAwFixYwM8//4zFYnm4AxURu4iJieHkyZN06dIFs9nMN998w7Fjx8jIyGD37t1069aN8PBwqlSpQlBQ0B3bc3oINYuIiMg/XJcuXejQoQNXr14lIyOD7du3M27cOL744gsArl27xk8//cTly5eZOXOmbdv//vc/evXqxccff2w7r3///vz44480b96cixcvUq9ePRISEvjggw/Yv38/jRs3JiAgAAcH/R5VpCDIzMykXbt2BAQEAJCcnExmZia//vorTzzxBPXq1QPAw8ODwYMH37E9BRgRERGhQoUKvPTSS2zcuJFr167Rpk0bSpYsadtvsViwWq2sXLmSYsWKAXDx4kWKFClCiRIlqF27Ntu2bePq1auYzWY+/fRTvv76a1599VVMJhMtWrRg8+bN7Nixg507dzJnzhzCwsKoWLGivYYsIg9Jo0aN+Pzzz+nfvz+PPvoo48aNo2rVqvTu3ZuLFy/yv//9j1q1ahEdHU2dOnXu2J5+9SEiIiIAvPHGG0RFRREREZHtQVpnZ2caNmzI4sWLAbhy5QrdunXjm2++AW48RzN9+nQaN26Ms7MzTz75JJ999hmtW7cGYNiwYWzcuJEOHTowduxYnJ2dOXXq1MMdoIjYRa1atRg4cCA9e/akQ4cOWCwW+vTpQ9GiRZkzZw4BAQF06NCBmJgY/P3979ieyWq1Wh9C3SIiImIAnp6elC5dmi+//BKAkJAQEhMTGTNmDHFxcXz00UecPn2atLQ0PDw8GDRoEHDjAf9mzZoxZ84cWrZsydy5c1m2bBnff/89Dg4O/PHHH4wePZpr167h6OhI48aN8fPzy/KMjYhIXijAiIiIiIiIYWgJmYiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGIYCjIiIiIiIGMb/A9iIXVgJCqZgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.barplot(x='Views', y='Title', data=top10_videos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68e0857e",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data['Month'] = pd.to_datetime(video_data['Published_date']).dt.strftime('%b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a24d9e3c",
   "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>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lexical Analyzer (Solved Problems) - Set 1</td>\n",
       "      <td>2022-04-16</td>\n",
       "      <td>1306</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lexical Analyzer – Tokenization</td>\n",
       "      <td>2022-04-14</td>\n",
       "      <td>3249</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Introduction to Microsoft Excel</td>\n",
       "      <td>2022-04-10</td>\n",
       "      <td>14238</td>\n",
       "      <td>426</td>\n",
       "      <td>30</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Introduction to Lexical Analyzer</td>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>4116</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Symbol Table – Solved PYQs</td>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>4649</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>Karnaugh Map (K' Map) - Part 1</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>1315040</td>\n",
       "      <td>15323</td>\n",
       "      <td>695</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>Gray Code to Binary &amp; Binary to Gray Code conv...</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>147275</td>\n",
       "      <td>849</td>\n",
       "      <td>86</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>Excess 3 Code (XS 3 Code) Old</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>68073</td>\n",
       "      <td>266</td>\n",
       "      <td>13</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>Binary Coded Decimal (BCD Code)</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>243978</td>\n",
       "      <td>1180</td>\n",
       "      <td>62</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>Neso Academy Introduction Video</td>\n",
       "      <td>2014-07-30</td>\n",
       "      <td>52992</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2211 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title Published_date  \\\n",
       "0            Lexical Analyzer (Solved Problems) - Set 1     2022-04-16   \n",
       "1                       Lexical Analyzer – Tokenization     2022-04-14   \n",
       "2                       Introduction to Microsoft Excel     2022-04-10   \n",
       "3                      Introduction to Lexical Analyzer     2022-04-08   \n",
       "4                            Symbol Table – Solved PYQs     2022-04-05   \n",
       "...                                                 ...            ...   \n",
       "2206                     Karnaugh Map (K' Map) - Part 1     2014-09-02   \n",
       "2207  Gray Code to Binary & Binary to Gray Code conv...     2014-08-29   \n",
       "2208                      Excess 3 Code (XS 3 Code) Old     2014-08-29   \n",
       "2209                    Binary Coded Decimal (BCD Code)     2014-08-29   \n",
       "2210                    Neso Academy Introduction Video     2014-07-30   \n",
       "\n",
       "        Views  Likes Comments Month  \n",
       "0        1306     30        0   Apr  \n",
       "1        3249     70        5   Apr  \n",
       "2       14238    426       30   Apr  \n",
       "3        4116     85        9   Apr  \n",
       "4        4649     56        7   Apr  \n",
       "...       ...    ...      ...   ...  \n",
       "2206  1315040  15323      695   Sep  \n",
       "2207   147275    849       86   Aug  \n",
       "2208    68073    266       13   Aug  \n",
       "2209   243978   1180       62   Aug  \n",
       "2210    52992    262       40   Jul  \n",
       "\n",
       "[2211 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55422b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "videos_per_month = video_data.groupby('Month', as_index=False).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5bf811ea",
   "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>Month</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dec</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jul</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mar</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nov</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Oct</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sep</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  size\n",
       "0    Apr   217\n",
       "1    Aug   193\n",
       "2    Dec   149\n",
       "3    Feb   136\n",
       "4    Jan   178\n",
       "5    Jul   208\n",
       "6    Jun   185\n",
       "7    Mar   224\n",
       "8    May   207\n",
       "9    Nov   157\n",
       "10   Oct   193\n",
       "11   Sep   164"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f9070be",
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n",
    "             'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eab624a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "videos_per_month.index = pd.CategoricalIndex(videos_per_month['Month'], categories=sort_order, ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cd573754",
   "metadata": {},
   "outputs": [],
   "source": [
    "videos_per_month = videos_per_month.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d76e42b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = sns.barplot(x='Month', y='size', data=videos_per_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "643f3ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data.to_csv('Video_Details(Neso Academy).csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd66cf5",
   "metadata": {},
   "source": [
    "# Author : Kaustubh Dwivedi (VIT Vellore : 2019-2023)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}