WithLoveLeena_YT_Channel_Analysis.ipynb

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   "execution_count": 1,
   "id": "f760b76f-9395-49d2-af75-d916dbc53afc",
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    {
     "name": "stdout",
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      "Requirement already satisfied: google-auth-httplib2>=0.1.0 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from google-api-python-client) (0.1.1)\n",
      "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from google-api-python-client) (2.14.0)\n",
      "Requirement already satisfied: uritemplate<5,>=3.0.1 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from google-api-python-client) (4.1.1)\n",
      "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (1.61.0)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2.1.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2023.7.22)\n",
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   "source": [
    "pip install --upgrade google-api-python-client"
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   "cell_type": "code",
   "execution_count": 24,
   "id": "ad3b1bd9-6bb7-4591-b77d-839e107f957b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import googleapiclient.discovery\n",
    "import googleapiclient.errors\n",
    "from IPython.display import JSON\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57f3845d-029b-494b-a432-25938669836a",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key='AIzaSyC7p3scdj-TWZZnt5ENMTweRm2fn8NMXxk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f9abb91-e223-4550-a9b1-592777cc3652",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_id='UCFlVeT7oDLA2JhU57Ed3JKA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a0bb21b-1912-46b4-9945-48244498d770",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_service_name = \"youtube\"\n",
    "api_version = \"v3\"\n",
    "youtube = googleapiclient.discovery.build( api_service_name, api_version, developerKey=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ad13c61-3e79-48ad-9e32-e1b8ea0b98b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_stats(youtube, channel_ids):\n",
    "    all_data=[]\n",
    "    request = youtube.channels().list( part=\"snippet,contentDetails,statistics\", id=channel_id)\n",
    "    response = request.execute()\n",
    "\n",
    "    for item in response['items']:\n",
    "        data={'channelName':item['snippet']['title'],\n",
    "          'subscribers':item['statistics']['subscriberCount'],\n",
    "          'views':item['statistics']['viewCount'],\n",
    "          'totalViews':item['statistics']['videoCount'],\n",
    "          'playlistId':item['contentDetails']['relatedPlaylists']['uploads']\n",
    "        }\n",
    "        all_data.append(data)\n",
    "    return(pd.DataFrame(all_data))\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed2fd881-7505-4c80-bff7-84dc2d4e31e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_stats=get_channel_stats(youtube,channel_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "792fbc89-d3c7-44ef-a067-e7f69a1e7797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelName</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>views</th>\n",
       "      <th>totalViews</th>\n",
       "      <th>playlistId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>996000</td>\n",
       "      <td>112524463</td>\n",
       "      <td>340</td>\n",
       "      <td>UUFlVeT7oDLA2JhU57Ed3JKA</td>\n",
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      "text/plain": [
       "        channelName subscribers      views totalViews  \\\n",
       "0  With Love, Leena      996000  112524463        340   \n",
       "\n",
       "                 playlistId  \n",
       "0  UUFlVeT7oDLA2JhU57Ed3JKA  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4a3490a-0131-4646-a0f7-40aa66384809",
   "metadata": {},
   "outputs": [],
   "source": [
    "playlist_id=\"UUFlVeT7oDLA2JhU57Ed3JKA\"\n",
    "def get_vedio_ids(youtube,playlist_id):\n",
    "    vedio_ids=[]\n",
    "    request = youtube.playlistItems().list(part=\"snippet, contentDetails\",playlistId=playlist_id,maxResults=50)\n",
    "    response = request.execute()\n",
    "    for item in response['items']:\n",
    "        vedio_ids.append(item['contentDetails']['videoId'])\n",
    "    next_page_token=response.get('nextPageToken')\n",
    "    while next_page_token is not None:\n",
    "        \n",
    "         request = youtube.playlistItems().list(part=\"snippet, contentDetails\",playlistId=playlist_id,maxResults=50,pageToken=next_page_token)\n",
    "         response = request.execute()\n",
    "        \n",
    "         for item in response['items']:\n",
    "            vedio_ids.append(item['contentDetails']['videoId'])\n",
    "             \n",
    "         next_page_token=response.get('nextPageToken')\n",
    "                \n",
    "    return vedio_ids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e064ac0-2d91-4e3e-97aa-8de96af3b693",
   "metadata": {},
   "outputs": [],
   "source": [
    "vedio_ids=get_vedio_ids(youtube,playlist_id)"
   ]
  },
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   "execution_count": 13,
   "id": "8c69dc54-78b5-4257-83eb-3cc6615ddc51",
   "metadata": {},
   "outputs": [
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       " '1EX75H3Khdg']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vedio_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b93c2380-869a-4779-9dec-ee90600e7aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vedio_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9e49e7e0-be2c-4e56-b9bb-62ca0deb092b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vedio_details(youtube, vedio_ids):\n",
    "    all_vedio_info=[]\n",
    "    for i in range(0, len(vedio_ids), 50):\n",
    "        request = youtube.videos().list(\n",
    "            part=\"snippet,contentDetails,statistics\",\n",
    "            id=','.join(vedio_ids[i:i+50])\n",
    "        )\n",
    "        response = request.execute() \n",
    "\n",
    "        for vedio in response['items']:\n",
    "            stats_to_keep = {'snippet': ['channelTitle', 'title', 'description', 'tags', 'publishedAt'],\n",
    "                             'statistics': ['viewCount', 'likeCount', 'favouriteCount', 'commentCount'],\n",
    "                             'contentDetails': ['duration', 'definition', 'caption']\n",
    "                            }\n",
    "            vedio_info = {}\n",
    "            vedio_info['video_id'] = vedio['id']\n",
    "\n",
    "            for k in stats_to_keep.keys():\n",
    "                for v in stats_to_keep[k]:\n",
    "                    try:\n",
    "                        vedio_info[v] = vedio[k][v]\n",
    "                    except:\n",
    "                        vedio_info[v] = None\n",
    "\n",
    "            all_vedio_info.append(vedio_info)\n",
    "        \n",
    "    return pd.DataFrame(all_vedio_info)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "168220da-4073-4650-870a-96d0c6120619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vedio_df=get_vedio_details(youtube, vedio_ids)\n",
    "len(vedio_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7cc0e35c-789e-402b-8343-965859b524a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "video_id          object\n",
       "channelTitle      object\n",
       "title             object\n",
       "description       object\n",
       "tags              object\n",
       "publishedAt       object\n",
       "viewCount         object\n",
       "likeCount         object\n",
       "favouriteCount    object\n",
       "commentCount      object\n",
       "duration          object\n",
       "definition        object\n",
       "caption           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vedio_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4ce08528-e812-4b93-a4b0-2b207682ce5f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>We Are MOVING! Packing Our Entire House + New ...</td>\n",
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       "      <td>What I Wore and What We Did in Los Cabos! | Vlog</td>\n",
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       "      <td>NYC Vlog! | What I REALLY think about NYFW</td>\n",
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       "      <td>With Love, Leena</td>\n",
       "      <td>Honeymoon Travel Diary</td>\n",
       "      <td>We had the best time on our honeymoon to the M...</td>\n",
       "      <td>None</td>\n",
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       "      <td>With Love, Leena</td>\n",
       "      <td>Q&amp;A With My Sisters!</td>\n",
       "      <td>I hope you enjoy getting to know my sisters an...</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-11-30T01:35:30Z</td>\n",
       "      <td>1271488</td>\n",
       "      <td>18555</td>\n",
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      ],
      "text/plain": [
       "        video_id      channelTitle  \\\n",
       "0    3mSl6cxcGcw  With Love, Leena   \n",
       "1    Z-JVZO8Bvk0  With Love, Leena   \n",
       "2    lM7NA5r_5sA  With Love, Leena   \n",
       "3    -FHMbW7FFQk  With Love, Leena   \n",
       "4    Ssh6HNaE3Nw  With Love, Leena   \n",
       "..           ...               ...   \n",
       "335  NXVBUYbSdvk  With Love, Leena   \n",
       "336  VaGdgIPKF0w  With Love, Leena   \n",
       "337  LhGnXmEoRPw  With Love, Leena   \n",
       "338  b0MsXTAd1b8  With Love, Leena   \n",
       "339  1EX75H3Khdg  With Love, Leena   \n",
       "\n",
       "                                                 title  \\\n",
       "0    Cooking with Baba Asad | Our Thoughts on Pales...   \n",
       "1                              We Stand with Palestine   \n",
       "2    Moving Day, Saying Goodbye & Settling into the...   \n",
       "3    Empty House Tour! Our Dream Home | Transitiona...   \n",
       "4    We Are MOVING! Packing Our Entire House + New ...   \n",
       "..                                                 ...   \n",
       "335                                 Oman Travel Diary!   \n",
       "336   What I Wore and What We Did in Los Cabos! | Vlog   \n",
       "337         NYC Vlog! | What I REALLY think about NYFW   \n",
       "338                             Honeymoon Travel Diary   \n",
       "339                               Q&A With My Sisters!   \n",
       "\n",
       "                                           description  \\\n",
       "0    Palestinian Kufta Recipe//\\nIngredients: \\n2 l...   \n",
       "1    Donate to Palestine: \\nIslamic Relief: https:/...   \n",
       "2    Click my link here to get 10% off your first m...   \n",
       "3    00:00 - Intro\\n1:15 - Entry Hall\\n1:48 - Forma...   \n",
       "4    I am so happy I could partner with Castlery to...   \n",
       "..                                                 ...   \n",
       "335  Come Explore Anantara Al Jabal Al Akhdar with ...   \n",
       "336                                                      \n",
       "337                                                      \n",
       "338  We had the best time on our honeymoon to the M...   \n",
       "339  I hope you enjoy getting to know my sisters an...   \n",
       "\n",
       "                                                  tags           publishedAt  \\\n",
       "0                                      [withloveleena]  2023-11-08T21:05:46Z   \n",
       "1                                      [withloveleena]  2023-10-13T19:33:46Z   \n",
       "2                                      [withloveleena]  2023-09-30T15:11:00Z   \n",
       "3                                      [withloveleena]  2023-09-28T15:28:04Z   \n",
       "4                                      [withloveleena]  2023-09-17T14:57:49Z   \n",
       "..                                                 ...                   ...   \n",
       "335  [oman travel, oman travel review, anantara al ...  2017-12-23T14:55:52Z   \n",
       "336  [hijab tutorial, withloveleena, los cabos, cab...  2017-11-11T20:29:24Z   \n",
       "337  [hijab fashion, hijab, nyfw, new york fashion ...  2017-10-08T22:25:28Z   \n",
       "338                                               None  2017-03-09T19:47:36Z   \n",
       "339                                               None  2016-11-30T01:35:30Z   \n",
       "\n",
       "     viewCount  likeCount  favouriteCount  commentCount  duration definition  \\\n",
       "0       152795       9552             NaN          1212  PT54M45S         hd   \n",
       "1       146838      13569             NaN          2143    PT8M4S         hd   \n",
       "2       105564       3078             NaN           196  PT12M50S         hd   \n",
       "3       367101      11336             NaN          1190   PT35M2S         hd   \n",
       "4       107381       3013             NaN           198  PT13M36S         hd   \n",
       "..         ...        ...             ...           ...       ...        ...   \n",
       "335     167615       3608             NaN           156   PT1M20S         hd   \n",
       "336     152726       3647             NaN           158  PT12M16S         hd   \n",
       "337     129288       2730             NaN           139   PT9M49S         hd   \n",
       "338     224832       5161             NaN           190    PT3M7S         hd   \n",
       "339    1271488      18555             NaN          1310  PT13M11S         hd   \n",
       "\n",
       "    caption  \n",
       "0      true  \n",
       "1     false  \n",
       "2      true  \n",
       "3      true  \n",
       "4      true  \n",
       "..      ...  \n",
       "335   false  \n",
       "336   false  \n",
       "337   false  \n",
       "338   false  \n",
       "339   false  \n",
       "\n",
       "[340 rows x 13 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericCols=['viewCount','likeCount','favouriteCount','commentCount']\n",
    "vedio_df[numericCols]=vedio_df[numericCols].apply(pd.to_numeric)\n",
    "vedio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6b6aefe-8248-4cae-9cea-979e79fdd197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python-dateutil in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from python-dateutil) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install python-dateutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "63005117-cf77-49d7-8218-14913325952d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil import parser\n",
    "vedio_df['publishedAt'] = vedio_df['publishedAt'].apply(lambda x: parser.parse(x))\n",
    "vedio_df['pushblishDayName'] = vedio_df['publishedAt'].apply(lambda x: x.strftime(\"%A\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23cc8350-e2e7-4e68-a519-580cc6ac2d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting isodate\n",
      "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
      "     ---------------------------------------- 0.0/41.7 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/41.7 kB ? eta -:--:--\n",
      "     -------------------------------------- 41.7/41.7 kB 983.8 kB/s eta 0:00:00\n",
      "Requirement already satisfied: six in c:\\users\\rimsha fatima\\pythonlab\\lib\\site-packages (from isodate) (1.16.0)\n",
      "Installing collected packages: isodate\n",
      "Successfully installed isodate-0.6.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install isodate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "87467fad-4290-4350-80e6-ff8daababd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import isodate\n",
    "vedio_df['durationSecs'] =vedio_df['duration'].apply(lambda x: isodate.parse_duration(x))\n",
    "vedio_df['durationSecs'] = vedio_df['durationSecs'].astype('timedelta64[s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bd6c84c6-5691-40ff-a7de-5d865b23097e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video_id</th>\n",
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       "      <th>pushblishDayName</th>\n",
       "      <th>durationSecs</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3mSl6cxcGcw</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Cooking with Baba Asad | Our Thoughts on Pales...</td>\n",
       "      <td>Palestinian Kufta Recipe//\\nIngredients: \\n2 l...</td>\n",
       "      <td>[withloveleena]</td>\n",
       "      <td>2023-11-08 21:05:46+00:00</td>\n",
       "      <td>152795</td>\n",
       "      <td>9552</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Z-JVZO8Bvk0</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>We Stand with Palestine</td>\n",
       "      <td>Donate to Palestine: \\nIslamic Relief: https:/...</td>\n",
       "      <td>[withloveleena]</td>\n",
       "      <td>2023-10-13 19:33:46+00:00</td>\n",
       "      <td>146838</td>\n",
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       "      <td>Friday</td>\n",
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       "      <th>2</th>\n",
       "      <td>lM7NA5r_5sA</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Moving Day, Saying Goodbye &amp; Settling into the...</td>\n",
       "      <td>Click my link here to get 10% off your first m...</td>\n",
       "      <td>[withloveleena]</td>\n",
       "      <td>2023-09-30 15:11:00+00:00</td>\n",
       "      <td>105564</td>\n",
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       "      <th>3</th>\n",
       "      <td>-FHMbW7FFQk</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Empty House Tour! Our Dream Home | Transitiona...</td>\n",
       "      <td>00:00 - Intro\\n1:15 - Entry Hall\\n1:48 - Forma...</td>\n",
       "      <td>[withloveleena]</td>\n",
       "      <td>2023-09-28 15:28:04+00:00</td>\n",
       "      <td>367101</td>\n",
       "      <td>11336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1190</td>\n",
       "      <td>PT35M2S</td>\n",
       "      <td>hd</td>\n",
       "      <td>true</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0 days 00:35:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ssh6HNaE3Nw</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>We Are MOVING! Packing Our Entire House + New ...</td>\n",
       "      <td>I am so happy I could partner with Castlery to...</td>\n",
       "      <td>[withloveleena]</td>\n",
       "      <td>2023-09-17 14:57:49+00:00</td>\n",
       "      <td>107381</td>\n",
       "      <td>3013</td>\n",
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       "      <th>...</th>\n",
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       "      <td>NXVBUYbSdvk</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Oman Travel Diary!</td>\n",
       "      <td>Come Explore Anantara Al Jabal Al Akhdar with ...</td>\n",
       "      <td>[oman travel, oman travel review, anantara al ...</td>\n",
       "      <td>2017-12-23 14:55:52+00:00</td>\n",
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       "      <td>Saturday</td>\n",
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       "      <th>336</th>\n",
       "      <td>VaGdgIPKF0w</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>What I Wore and What We Did in Los Cabos! | Vlog</td>\n",
       "      <td></td>\n",
       "      <td>[hijab tutorial, withloveleena, los cabos, cab...</td>\n",
       "      <td>2017-11-11 20:29:24+00:00</td>\n",
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       "      <td>false</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0 days 00:12:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>LhGnXmEoRPw</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>NYC Vlog! | What I REALLY think about NYFW</td>\n",
       "      <td></td>\n",
       "      <td>[hijab fashion, hijab, nyfw, new york fashion ...</td>\n",
       "      <td>2017-10-08 22:25:28+00:00</td>\n",
       "      <td>129288</td>\n",
       "      <td>2730</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Sunday</td>\n",
       "      <td>0 days 00:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>b0MsXTAd1b8</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Honeymoon Travel Diary</td>\n",
       "      <td>We had the best time on our honeymoon to the M...</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-03-09 19:47:36+00:00</td>\n",
       "      <td>224832</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>hd</td>\n",
       "      <td>false</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0 days 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>1EX75H3Khdg</td>\n",
       "      <td>With Love, Leena</td>\n",
       "      <td>Q&amp;A With My Sisters!</td>\n",
       "      <td>I hope you enjoy getting to know my sisters an...</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-11-30 01:35:30+00:00</td>\n",
       "      <td>1271488</td>\n",
       "      <td>18555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310</td>\n",
       "      <td>PT13M11S</td>\n",
       "      <td>hd</td>\n",
       "      <td>false</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0 days 00:13:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>340 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        video_id      channelTitle  \\\n",
       "0    3mSl6cxcGcw  With Love, Leena   \n",
       "1    Z-JVZO8Bvk0  With Love, Leena   \n",
       "2    lM7NA5r_5sA  With Love, Leena   \n",
       "3    -FHMbW7FFQk  With Love, Leena   \n",
       "4    Ssh6HNaE3Nw  With Love, Leena   \n",
       "..           ...               ...   \n",
       "335  NXVBUYbSdvk  With Love, Leena   \n",
       "336  VaGdgIPKF0w  With Love, Leena   \n",
       "337  LhGnXmEoRPw  With Love, Leena   \n",
       "338  b0MsXTAd1b8  With Love, Leena   \n",
       "339  1EX75H3Khdg  With Love, Leena   \n",
       "\n",
       "                                                 title  \\\n",
       "0    Cooking with Baba Asad | Our Thoughts on Pales...   \n",
       "1                              We Stand with Palestine   \n",
       "2    Moving Day, Saying Goodbye & Settling into the...   \n",
       "3    Empty House Tour! Our Dream Home | Transitiona...   \n",
       "4    We Are MOVING! Packing Our Entire House + New ...   \n",
       "..                                                 ...   \n",
       "335                                 Oman Travel Diary!   \n",
       "336   What I Wore and What We Did in Los Cabos! | Vlog   \n",
       "337         NYC Vlog! | What I REALLY think about NYFW   \n",
       "338                             Honeymoon Travel Diary   \n",
       "339                               Q&A With My Sisters!   \n",
       "\n",
       "                                           description  \\\n",
       "0    Palestinian Kufta Recipe//\\nIngredients: \\n2 l...   \n",
       "1    Donate to Palestine: \\nIslamic Relief: https:/...   \n",
       "2    Click my link here to get 10% off your first m...   \n",
       "3    00:00 - Intro\\n1:15 - Entry Hall\\n1:48 - Forma...   \n",
       "4    I am so happy I could partner with Castlery to...   \n",
       "..                                                 ...   \n",
       "335  Come Explore Anantara Al Jabal Al Akhdar with ...   \n",
       "336                                                      \n",
       "337                                                      \n",
       "338  We had the best time on our honeymoon to the M...   \n",
       "339  I hope you enjoy getting to know my sisters an...   \n",
       "\n",
       "                                                  tags  \\\n",
       "0                                      [withloveleena]   \n",
       "1                                      [withloveleena]   \n",
       "2                                      [withloveleena]   \n",
       "3                                      [withloveleena]   \n",
       "4                                      [withloveleena]   \n",
       "..                                                 ...   \n",
       "335  [oman travel, oman travel review, anantara al ...   \n",
       "336  [hijab tutorial, withloveleena, los cabos, cab...   \n",
       "337  [hijab fashion, hijab, nyfw, new york fashion ...   \n",
       "338                                               None   \n",
       "339                                               None   \n",
       "\n",
       "                  publishedAt  viewCount  likeCount  favouriteCount  \\\n",
       "0   2023-11-08 21:05:46+00:00     152795       9552             NaN   \n",
       "1   2023-10-13 19:33:46+00:00     146838      13569             NaN   \n",
       "2   2023-09-30 15:11:00+00:00     105564       3078             NaN   \n",
       "3   2023-09-28 15:28:04+00:00     367101      11336             NaN   \n",
       "4   2023-09-17 14:57:49+00:00     107381       3013             NaN   \n",
       "..                        ...        ...        ...             ...   \n",
       "335 2017-12-23 14:55:52+00:00     167615       3608             NaN   \n",
       "336 2017-11-11 20:29:24+00:00     152726       3647             NaN   \n",
       "337 2017-10-08 22:25:28+00:00     129288       2730             NaN   \n",
       "338 2017-03-09 19:47:36+00:00     224832       5161             NaN   \n",
       "339 2016-11-30 01:35:30+00:00    1271488      18555             NaN   \n",
       "\n",
       "     commentCount  duration definition caption pushblishDayName  \\\n",
       "0            1212  PT54M45S         hd    true        Wednesday   \n",
       "1            2143    PT8M4S         hd   false           Friday   \n",
       "2             196  PT12M50S         hd    true         Saturday   \n",
       "3            1190   PT35M2S         hd    true         Thursday   \n",
       "4             198  PT13M36S         hd    true           Sunday   \n",
       "..            ...       ...        ...     ...              ...   \n",
       "335           156   PT1M20S         hd   false         Saturday   \n",
       "336           158  PT12M16S         hd   false         Saturday   \n",
       "337           139   PT9M49S         hd   false           Sunday   \n",
       "338           190    PT3M7S         hd   false         Thursday   \n",
       "339          1310  PT13M11S         hd   false        Wednesday   \n",
       "\n",
       "       durationSecs  \n",
       "0   0 days 00:54:45  \n",
       "1   0 days 00:08:04  \n",
       "2   0 days 00:12:50  \n",
       "3   0 days 00:35:02  \n",
       "4   0 days 00:13:36  \n",
       "..              ...  \n",
       "335 0 days 00:01:20  \n",
       "336 0 days 00:12:16  \n",
       "337 0 days 00:09:49  \n",
       "338 0 days 00:03:07  \n",
       "339 0 days 00:13:11  \n",
       "\n",
       "[340 rows x 15 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vedio_df[['durationSecs','duration']]\n",
    "vedio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bcea06df-6d4e-4edb-8c1e-3b9dfdd15f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rimsha Fatima\\AppData\\Local\\Temp\\ipykernel_29424\\1807962729.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  plot = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 129392 (\\N{SMILING FACE WITH SMILING EYES AND THREE HEARTS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 128151 (\\N{GROWING HEART}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 128133 (\\N{NAIL POLISH}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 127995 (\\N{EMOJI MODIFIER FITZPATRICK TYPE-1-2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 10024 (\\N{SPARKLES}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\Rimsha Fatima\\pythonlab\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 129402 (\\N{FACE WITH PLEADING EYES}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "ax = sns.barplot(x='title', y='viewCount', data=vedio_df.sort_values('viewCount', ascending=False)[0:9])\n",
    "plot = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x/1000) + 'K'))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5b97d162-5d42-4b9b-b8ed-d3ae05e34a93",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rimsha Fatima\\AppData\\Local\\Temp\\ipykernel_29424\\2098156171.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(x=vedio_df['channelTitle'], y=vedio_df['viewCount'], palette='pastel')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='channelTitle', ylabel='viewCount'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.violinplot(x=vedio_df['channelTitle'], y=vedio_df['viewCount'], palette='pastel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "586da912-9181-41c9-a497-1328e97f424c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='likeCount', ylabel='viewCount'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(1,2)\n",
    "sns.scatterplot(data = vedio_df, x = \"commentCount\", y = \"viewCount\", ax=ax[0])\n",
    "sns.scatterplot(data = vedio_df, x = \"likeCount\", y = \"viewCount\", ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7a976969-ce3c-42ce-9ced-fe7265d5cc20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='durationSecs', ylabel='Count'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LFy+OlpaWGD9+fG5MZWVlVFRURE1NTYpJAQAAAABIS6pHakdELF26NKqqqmL79u1RVFQUzz77bJx22mmxZMmSyM/Pj969e7cZ379//1i7dm278zU3N0dzc3PufmNj46GKDgAAAADAYXZQR2oPGTIkNmzYsNv2jRs3xpAhQw5orqFDh8aSJUti0aJFccMNN8TkyZNj+fLlBxMrIiJmzZoVvXr1yt0GDhx40HMBAAAAANC5HFSpvXr16ti1a9du25ubm+N3v/vdAc2Vn58fJ598cowaNSpmzZoVI0eOjAceeCDKyspix44dsXHjxjbj161bF2VlZe3ON3369Ni0aVPu9v777x9QHgAAAAAAOq8DOv3Ij370o9zPL774YvTq1St3f9euXTF//vwYNGjQxwrU2toazc3NMWrUqOjevXvMnz8/Jk6cGBERK1eujLq6uqiqqmr38QUFBVFQUPCxMgAAAAAA0DkdUKl9+eWXR0REXl5eTJ48uc2+7t27x6BBg+Lb3/72fs83ffr0uOSSS6KioiI2b94cTz75ZCxYsCBXmF933XUxderUKCkpieLi4rjpppuiqqoqxowZcyCxAQAAAAA4QhxQqd3a2hoREYMHD45f/vKXUVpa+rGefP369fH5z38+6uvro1evXjFixIh48cUX40//9E8jIuL++++PLl26xMSJE6O5uTkmTJgQDz/88Md6TgAAAAAAsuuASu0PrFq1qkOe/PHHH9/r/h49esTs2bNj9uzZHfJ8AAAAAABk20GV2hER8+fPj/nz58f69etzR3B/4IknnvjYwQAAAAAA4KMOqtSeOXNm3HXXXXH22WdHeXl55OXldXQuAAAAAADYzUGV2o8++mjMnTs3Pve5z3V0HgAAAAAAaFeXg3nQjh074k/+5E86OgsAAAAAAOzVQZXaX/rSl+LJJ5/s6CwAAAAAALBXB3X6ke3bt8djjz0WP/vZz2LEiBHRvXv3Nvvvu+++DgkHAAAAAAAfdlCl9ltvvRVnnnlmREQsW7aszT4XjQQAAAAA4FA5qFL7lVde6egcAAAAAACwTwd1Tm0AAAAAAEjDQR2p/elPf3qvpxl5+eWXDzoQAAAAAAC056BK7Q/Op/2BlpaWWLJkSSxbtiwmT57cEbkAAAAAAGA3B1Vq33///Xvcfuedd8aWLVs+ViAAAAAAAGhPh55Te9KkSfHEE0905JQAAAAAAJDToaV2TU1N9OjRoyOnBAAAAACAnIM6/cgVV1zR5n6SJFFfXx9vvPFG3H777R0SDAAAAAAAPuqgSu1evXq1ud+lS5cYOnRo3HXXXXHRRRd1SDAAAAAAAPiogyq158yZ09E5AAAAAABgnw6q1P7A4sWLo7a2NiIihg8fHp/4xCc6JBQAAAAAAOzJQZXa69evj89+9rOxYMGC6N27d0REbNy4MT796U/HvHnzom/fvh2ZEaBTqKuri4aGhrRj7NUH/9EIAAAAcKQ6qFL7pptuis2bN8fbb78dw4YNi4iI5cuXx+TJk+Ov//qv46mnnurQkABpq6uri8rKYdHUtC3tKPulpXlH2hEAAAAADomDKrVfeOGF+NnPfpYrtCMiTjvttJg9e7YLRQJHpIaGhmhq2hajr50RxeWD0o7TrvqlNbHsR4/Fzp07044CAAAAcEgcVKnd2toa3bt332179+7do7W19WOHAuisissHRUnF0LRjtKuxfnXaEQAAAAAOqS4H86CxY8fG1772tVizZk1u2+9+97u45ZZbYty4cR0WDgAAAAAAPuygSu2HHnooGhsbY9CgQXHSSSfFSSedFIMHD47Gxsb47ne/29EZAQAAAAAgIg7y9CMDBw6MN998M372s5/FihUrIiJi2LBhMX78+A4NBwAAAAAAH3ZAR2q//PLLcdppp0VjY2Pk5eXFn/7pn8ZNN90UN910U5xzzjkxfPjw+L//9/8eqqwAAAAAABzlDuhI7e985ztx/fXXR3Fx8W77evXqFX/1V38V9913X5x33nkdFhAA0lZbW5t2hP1SWloaFRUVaccAAACAQ+qASu1f//rX8T//5/9sd/9FF10U/+t//a+PHQoAOoOmTRsiIi8mTZqUdpT9UljYM1asqFVsAwAAcEQ7oFJ73bp10b179/Yn69Yt/uM//uNjhwKAzqBl2+aISOLMq6dF38GVacfZq8b61bHoiZnR0NCg1AYAAOCIdkCl9vHHHx/Lli2Lk08+eY/733rrrSgvL++QYADQWRT1q4iSiqFpxwAAAADiAC8U+Wd/9mdx++23x/bt23fb19TUFDNmzIg///M/77BwAAAAAADwYQd0pPa3vvWt+Nd//dc49dRT48Ybb4yhQ//zqLUVK1bE7NmzY9euXfE3f/M3hyQoAAAAAAAcUKndv3//eP311+OGG26I6dOnR5IkERGRl5cXEyZMiNmzZ0f//v0PSVAAAAAAADigUjsi4sQTT4x/+7d/iz/84Q/x7rvvRpIkccopp0SfPn0ORT4AAAAAAMg54FL7A3369IlzzjmnI7MAAAAAAMBeHdCFIgEAAAAAIE1KbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGRGt7QDAEe3urq6aGhoSDvGPtXW1qYdAQAAAIBQagMpqquri8rKYdHUtC3tKPutpXlH2hEAAAAAjmpKbSA1DQ0N0dS0LUZfOyOKywelHWev6pfWxLIfPRY7d+5MOwoAAADAUU2pDaSuuHxQlFQMTTvGXjXWr047AgAAAADhQpEAAAAAAGSIUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZnRLOwAA0HFqa2vTjrBPzc3NUVBQkHaMfSotLY2Kioq0YwAAAPARSm0AOAI0bdoQEXkxadKktKPsW15eRJKknWKfCgt7xooVtYptAACATkapDQBHgJZtmyMiiTOvnhZ9B1emHadd9UtrYtmPHuv0ORvrV8eiJ2ZGQ0ODUhsAAKCTUWoDwBGkqF9FlFQMTTtGuxrrV0dE588JAABA5+VCkQAAAAAAZIZSGwAAAACAzFBqAwAAAACQGUptAAAAAAAyQ6kNAAAAAEBmKLUBAAAAAMgMpTYAAAAAAJmh1AYAAAAAIDOU2gAAAAAAZIZSGwAAAACAzFBqAwAAAACQGUptAAAAAAAyQ6kNAAAAAEBmKLUBAAAAAMgMpTYAAAAAAJmh1AYAAAAAIDOU2gAAAAAAZEaqpfasWbPinHPOiWOPPTb69esXl19+eaxcubLNmO3bt8eUKVPiuOOOi6Kiopg4cWKsW7cupcQAAAAAAKQp1VK7uro6pkyZEgsXLoyXXnopWlpa4qKLLoqtW7fmxtxyyy3x/PPPxzPPPBPV1dWxZs2auOKKK1JMDQAAAABAWrql+eQvvPBCm/tz586Nfv36xeLFi+P888+PTZs2xeOPPx5PPvlkjB07NiIi5syZE8OGDYuFCxfGmDFj0ogNAAAAAEBKOtU5tTdt2hQRESUlJRERsXjx4mhpaYnx48fnxlRWVkZFRUXU1NSkkhEAAAAAgPSkeqT2h7W2tsbNN98cn/zkJ+P000+PiIi1a9dGfn5+9O7du83Y/v37x9q1a/c4T3NzczQ3N+fuNzY2HrLMAAAAAAAcXp3mSO0pU6bEsmXLYt68eR9rnlmzZkWvXr1yt4EDB3ZQQgAAAAAA0tYpSu0bb7wxfvzjH8crr7wSJ5xwQm57WVlZ7NixIzZu3Nhm/Lp166KsrGyPc02fPj02bdqUu73//vuHMjoAAAAAAIdRqqV2kiRx4403xrPPPhsvv/xyDB48uM3+UaNGRffu3WP+/Pm5bStXroy6urqoqqra45wFBQVRXFzc5gYAAAAAwJEh1XNqT5kyJZ588sn44Q9/GMcee2zuPNm9evWKwsLC6NWrV1x33XUxderUKCkpieLi4rjpppuiqqoqxowZk2Z0AAAAAABSkGqp/cgjj0RExIUXXthm+5w5c+ILX/hCRETcf//90aVLl5g4cWI0NzfHhAkT4uGHHz7MSQEAAAAA6AxSLbWTJNnnmB49esTs2bNj9uzZhyERAAAAAACdWae4UCQAAAAAAOwPpTYAAAAAAJmh1AYAAAAAIDOU2gAAAAAAZIZSGwAAAACAzFBqAwAAAACQGUptAAAAAAAyQ6kNAAAAAEBmKLUBAAAAAMiMbmkHAADorGpra9OOsF9KS0ujoqIi7RgAAACHhVIbAOAjmjZtiIi8mDRpUtpR9kthYc9YsaJWsQ0AABwVlNoAAB/Rsm1zRCRx5tXTou/gyrTj7FVj/epY9MTMaGhoUGoDAABHBaU2AEA7ivpVREnF0LRjAAAA8CEuFAkAAAAAQGYotQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaAAAAAABkhlIbAAAAAIDMUGoDAAAAAJAZSm0AAAAAADJDqQ0AAAAAQGYotQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaAAAAAABkRre0AwCHRl1dXTQ0NKQdY69qa2vTjgAAAABAxii14QhUV1cXlZXDoqlpW9pR9ktL8460IwAAAACQEUptOAI1NDREU9O2GH3tjCguH5R2nHbVL62JZT96LHbu3Jl2FAAAAAAyQqkNR7Di8kFRUjE07RjtaqxfnXYEAAAAADLGhSIBAAAAAMgMpTYAAAAAAJmh1AYAAAAAIDOU2gAAAAAAZIZSGwAAAACAzFBqAwAAAACQGUptAAAAAAAyQ6kNAAAAAEBmKLUBAAAAAMgMpTYAAAAAAJmh1AYAAAAAIDOU2gAAAAAAZEa3tAMAAPDx1dbWph1hn0pLS6OioiLtGAAAQMYptQEAMqxp04aIyItJkyalHWWfCgt7xooVtYptAADgY1FqAwBkWMu2zRGRxJlXT4u+gyvTjtOuxvrVseiJmdHQ0KDUBgAAPhalNgDAEaCoX0WUVAxNOwYAAMAh50KRAAAAAABkhlIbAAAAAIDMUGoDAAAAAJAZSm0AAAAAADJDqQ0AAAAAQGYotQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaAAAAAABkhlIbAAAAAIDMUGoDAAAAAJAZSm0AAAAAADJDqQ0AAAAAQGYotQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaAAAAAABkhlIbAAAAAIDMUGoDAAAAAJAZSm0AAAAAADJDqQ0AAAAAQGYotQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaAAAAAABkhlIbAAAAAIDMSLXUfvXVV+PSSy+NAQMGRF5eXjz33HNt9idJEnfccUeUl5dHYWFhjB8/Pt555510wgIAAAAAkLpUS+2tW7fGyJEjY/bs2Xvcf88998SDDz4Yjz76aCxatCiOOeaYmDBhQmzfvv0wJwUAAAAAoDPoluaTX3LJJXHJJZfscV+SJPGd73wnvvWtb8Vll10WERH/9E//FP3794/nnnsuPvvZzx7OqAAAAAAAdAKd9pzaq1atirVr18b48eNz23r16hWjR4+OmpqaFJMBAAAAAJCWVI/U3pu1a9dGRET//v3bbO/fv39u3540NzdHc3Nz7n5jY+OhCQgAwAGrra1NO8I+lZaWRkVFRdoxAACAdnTaUvtgzZo1K2bOnJl2DAAAPqRp04aIyItJkyalHWWfCgt7xooVtYptAADopDptqV1WVhYREevWrYvy8vLc9nXr1sWZZ57Z7uOmT58eU6dOzd1vbGyMgQMHHrKcAADsW8u2zRGRxJlXT4u+gyvTjtOuxvrVseiJmdHQ0KDUBgCATqrTltqDBw+OsrKymD9/fq7EbmxsjEWLFsUNN9zQ7uMKCgqioKDgMKUEAOBAFPWriJKKoWnHAAAAMizVUnvLli3x7rvv5u6vWrUqlixZEiUlJVFRURE333xz/Pf//t/jlFNOicGDB8ftt98eAwYMiMsvvzy90AAAAAAApCbVUvuNN96IT3/607n7H5w2ZPLkyTF37tz4xje+EVu3bo0vf/nLsXHjxvjUpz4VL7zwQvTo0SOtyAAAAAAApCjVUvvCCy+MJEna3Z+Xlxd33XVX3HXXXYcxFQAAAAAAnVWXtAMAAAAAAMD+6rQXigQAANpXV1cXDQ0NacfYL6WlpVFRUZF2DAAAjhBKbQAAyJi6urqorBwWTU3b0o6yXwoLe8aKFbWKbQAAOoRSGwAAMqahoSGamrbF6GtnRHH5oLTj7FVj/epY9MTMaGhoUGoDANAhlNoAAJBRxeWDoqRiaNoxAADgsHKhSAAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZ4UKRdCq1tbVpR9in0tLSqKioSDsGAAAAAByVlNp0Ck2bNkREXkyaNCntKPtUWNgzVqyoVWwDAAAAQAqU2nQKLds2R0QSZ149LfoOrkw7Trsa61fHoidmRkNDg1IbAAAAAFKg1KZTKepXESUVQ9OOAQAAAAB0Ui4UCQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGRGt7QDAABAZ1NbW5t2hL3q7PkAAOBQUmoDAMAfNW3aEBF5MWnSpLSj7JeW5h1pRwAAgMNOqQ0AAH/Usm1zRCRx5tXTou/gyrTjtKt+aU0s+9FjsXPnzrSjAADAYafUBgCAjyjqVxElFUPTjtGuxvrVaUcAAIDUuFAkAAAAAACZodQGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBnd0g4AWVRbW5t2hL3q7PkAgKNPFr6flJaWRkVFRdoxAADYB6U2HICmTRsiIi8mTZqUdpT90tK8I+0IAMBRLkvfnwoLe8aKFbWKbQCATk6pDQegZdvmiEjizKunRd/BlWnHaVf90ppY9qPHYufOnWlHAQCOcln5/tRYvzoWPTEzGhoalNoAAJ2cUhsOQlG/iiipGJp2jHY11q9OOwIAQBud/fsTAADZ4UKRAAAAAABkhlIbAAAAAIDMUGoDAAAAAJAZSm0AAAAAADLDhSIBAADocHV1ddHQ0JB2jP1SWloaFRUVaccAAPaTUhsAAIAOVVdXF5WVw6KpaVvaUfZLYWHPWLGiVrENABmh1AYAAKBDNTQ0RFPTthh97YwoLh+Udpy9aqxfHYuemBkNDQ1KbQDICKU2AAAAh0Rx+aAoqRiadgwA4AjjQpEAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMsOFIgEAAP6otrY27Qj71NzcHAUFBWnH2Kss/Dl+VBYyl5aWRkVFRdoxADKrrq4uGhoa0o6xT37f75tSGwAAOOo1bdoQEXkxadKktKPsW15eRJKknWK/tDTvSDvCPmXp776wsGesWFGr6AA4CHV1dVFZOSyamralHWWf/L7fN6U2AABw1GvZtjkikjjz6mnRd3Bl2nHaVb+0Jpb96LHM5Ny5c2faUfYpK3/3jfWrY9ETM6OhoUHJAXAQGhoaoqlpW4y+dkYUlw9KO067/L7fP0ptAACAPyrqVxElFUPTjtGuxvrVEZGdnFnS2f9MAegYxeWD/L4/ArhQJAAAAAAAmaHUBgAAAAAgMzJRas+ePTsGDRoUPXr0iNGjR8cvfvGLtCMBAAAAAJCCTl9q/+AHP4ipU6fGjBkz4s0334yRI0fGhAkTYv369WlHAwAAAADgMOv0pfZ9990X119/fXzxi1+M0047LR599NHo2bNnPPHEE2lHAwAAAADgMOuWdoC92bFjRyxevDimT5+e29alS5cYP3581NTU7PExzc3N0dzcnLu/adOmiIhobGw8tGE7oS1btkRExO9/szJ2NjelnGbvGut/ExERm373TnTvlpdymvbJ2bGykjMiO1nl7FhZyRmRnaxydrysZJWzY2UlZ0R2ssrZsbKSMyI7WRvX1kVExOLFi3P/1uusunTpEq2trWnH2C9ZySpnx8pKzojsZM1CzpUrV0ZE5+/JPvh9v2XLlqOyz/zgNSdJstdxecm+RqRozZo1cfzxx8frr78eVVVVue3f+MY3orq6OhYtWrTbY+68886YOXPm4YwJAAAAAEAHef/99+OEE05od3+nPlL7YEyfPj2mTp2au9/a2hq///3v47jjjou8vM77v+4dqbGxMQYOHBjvv/9+FBcXpx0HDjtrAKwDsAbAOgBrgKOdNUAWJUkSmzdvjgEDBux1XKcutUtLS6Nr166xbt26NtvXrVsXZWVle3xMQUFBFBQUtNnWu3fvQxWxUysuLvZLi6OaNQDWAVgDYB2ANcDRzhoga3r16rXPMZ36QpH5+fkxatSomD9/fm5ba2trzJ8/v83pSAAAAAAAODp06iO1IyKmTp0akydPjrPPPjvOPffc+M53vhNbt26NL37xi2lHAwAAAADgMOv0pfaVV14Z//Ef/xF33HFHrF27Ns4888x44YUXon///mlH67QKCgpixowZu52GBY4W1gBYB2ANgHUA1gBHO2uAI1lekiRJ2iEAAAAAAGB/dOpzagMAAAAAwIcptQEAAAAAyAylNgAAAAAAmaHUBgAAAAAgM5TaR5jZs2fHoEGDokePHjF69Oj4xS9+kXYk6BB33nln5OXltblVVlbm9m/fvj2mTJkSxx13XBQVFcXEiRNj3bp1beaoq6uLz3zmM9GzZ8/o169f3HbbbbFz587D/VJgv7366qtx6aWXxoABAyIvLy+ee+65NvuTJIk77rgjysvLo7CwMMaPHx/vvPNOmzG///3v45prroni4uLo3bt3XHfddbFly5Y2Y956660477zzokePHjFw4MC45557DvVLg/2yrzXwhS98YbfPhosvvrjNGGuALJs1a1acc845ceyxx0a/fv3i8ssvj5UrV7YZ01HfgRYsWBBnnXVWFBQUxMknnxxz58491C8P9ml/1sCFF16422fBV77ylTZjrAGy7JFHHokRI0ZEcXFxFBcXR1VVVfzkJz/J7fc5wNFKqX0E+cEPfhBTp06NGTNmxJtvvhkjR46MCRMmxPr169OOBh1i+PDhUV9fn7u99tpruX233HJLPP/88/HMM89EdXV1rFmzJq644orc/l27dsVnPvOZ2LFjR7z++uvxj//4jzF37ty444470ngpsF+2bt0aI0eOjNmzZ+9x/z333BMPPvhgPProo7Fo0aI45phjYsKECbF9+/bcmGuuuSbefvvteOmll+LHP/5xvPrqq/HlL385t7+xsTEuuuiiOPHEE2Px4sVx7733xp133hmPPfbYIX99sC/7WgMRERdffHGbz4annnqqzX5rgCyrrq6OKVOmxMKFC+Oll16KlpaWuOiii2Lr1q25MR3xHWjVqlXxmc98Jj796U/HkiVL4uabb44vfelL8eKLLx7W1wsftT9rICLi+uuvb/NZ8OH/nLQGyLoTTjgh7r777li8eHG88cYbMXbs2Ljsssvi7bffjgifAxzFEo4Y5557bjJlypTc/V27diUDBgxIZs2alWIq6BgzZsxIRo4cucd9GzduTLp3754888wzuW21tbVJRCQ1NTVJkiTJv/3bvyVdunRJ1q5dmxvzyCOPJMXFxUlzc/MhzQ4dISKSZ599Nne/tbU1KSsrS+69997cto0bNyYFBQXJU089lSRJkixfvjyJiOSXv/xlbsxPfvKTJC8vL/nd736XJEmSPPzww0mfPn3arINp06YlQ4cOPcSvCA7MR9dAkiTJ5MmTk8suu6zdx1gDHGnWr1+fRERSXV2dJEnHfQf6xje+kQwfPrzNc1155ZXJhAkTDvVLggPy0TWQJElywQUXJF/72tfafYw1wJGoT58+yT/8wz/4HOCo5kjtI8SOHTti8eLFMX78+Ny2Ll26xPjx46OmpibFZNBx3nnnnRgwYEAMGTIkrrnmmqirq4uIiMWLF0dLS0ub939lZWVUVFTk3v81NTVxxhlnRP/+/XNjJkyYEI2Njbn/4YYsWbVqVaxdu7bN+75Xr14xevToNu/73r17x9lnn50bM378+OjSpUssWrQoN+b888+P/Pz83JgJEybEypUr4w9/+MNhejVw8BYsWBD9+vWLoUOHxg033BAbNmzI7bMGONJs2rQpIiJKSkoiouO+A9XU1LSZ44Mx/h1BZ/PRNfCB73//+1FaWhqnn356TJ8+PbZt25bbZw1wJNm1a1fMmzcvtm7dGlVVVT4HOKp1SzsAHaOhoSF27drV5pdURET//v1jxYoVKaWCjjN69OiYO3duDB06NOrr62PmzJlx3nnnxbJly2Lt2rWRn58fvXv3bvOY/v37x9q1ayMiYu3atXtcHx/sg6z54H27p/f1h9/3/fr1a7O/W7duUVJS0mbM4MGDd5vjg319+vQ5JPmhI1x88cVxxRVXxODBg+O9996L//bf/ltccsklUVNTE127drUGOKK0trbGzTffHJ/85Cfj9NNPj4josO9A7Y1pbGyMpqamKCwsPBQvCQ7IntZARMTVV18dJ554YgwYMCDeeuutmDZtWqxcuTL+9V//NSKsAY4MS5cujaqqqti+fXsUFRXFs88+G6eddlosWbLE5wBHLaU2kAmXXHJJ7ucRI0bE6NGj48QTT4ynn37aByzAUeqzn/1s7uczzjgjRowYESeddFIsWLAgxo0bl2Iy6HhTpkyJZcuWtbmmCBxN2lsDH75OwhlnnBHl5eUxbty4eO+99+Kkk0463DHhkBg6dGgsWbIkNm3aFP/yL/8SkydPjurq6rRjQaqcfuQIUVpaGl27dt3tCrfr1q2LsrKylFLBodO7d+849dRT4913342ysrLYsWNHbNy4sc2YD7//y8rK9rg+PtgHWfPB+3Zvv/fLysp2u1jwzp074/e//721wRFpyJAhUVpaGu+++25EWAMcOW688cb48Y9/HK+88kqccMIJue0d9R2ovTHFxcUOHqBTaG8N7Mno0aMjItp8FlgDZF1+fn6cfPLJMWrUqJg1a1aMHDkyHnjgAZ8DHNWU2keI/Pz8GDVqVMyfPz+3rbW1NebPnx9VVVUpJoNDY8uWLfHee+9FeXl5jBo1Krp3797m/b9y5cqoq6vLvf+rqqpi6dKlbcqNl156KYqLi+O000477Pnh4xo8eHCUlZW1ed83NjbGokWL2rzvN27cGIsXL86Nefnll6O1tTX3D76qqqp49dVXo6WlJTfmpZdeiqFDhzrtApnz29/+NjZs2BDl5eURYQ2QfUmSxI033hjPPvtsvPzyy7udKqejvgNVVVW1meODMf4dQdr2tQb2ZMmSJRERbT4LrAGONK2trdHc3OxzgKNb2leqpOPMmzcvKSgoSObOnZssX748+fKXv5z07t27zRVuIau+/vWvJwsWLEhWrVqV/PznP0/Gjx+flJaWJuvXr0+SJEm+8pWvJBUVFcnLL7+cvPHGG0lVVVVSVVWVe/zOnTuT008/PbnooouSJUuWJC+88ELSt2/fZPr06Wm9JNinzZs3J7/61a+SX/3qV0lEJPfdd1/yq1/9KvnNb36TJEmS3H333Unv3r2TH/7wh8lbb72VXHbZZcngwYOTpqam3BwXX3xx8olPfCJZtGhR8tprryWnnHJKctVVV+X2b9y4Menfv3/yuc99Llm2bFkyb968pGfPnsn3vve9w/564aP2tgY2b96c3HrrrUlNTU2yatWq5Gc/+1ly1llnJaecckqyffv23BzWAFl2ww03JL169UoWLFiQ1NfX527btm3LjemI70D/7//9v6Rnz57JbbfdltTW1iazZ89OunbtmrzwwguH9fXCR+1rDbz77rvJXXfdlbzxxhvJqlWrkh/+8IfJkCFDkvPPPz83hzVA1n3zm99Mqqurk1WrViVvvfVW8s1vfjPJy8tLfvrTnyZJ4nOAo5dS+wjz3e9+N6moqEjy8/OTc889N1m4cGHakaBDXHnllUl5eXmSn5+fHH/88cmVV16ZvPvuu7n9TU1NyVe/+tWkT58+Sc+ePZO/+Iu/SOrr69vMsXr16uSSSy5JCgsLk9LS0uTrX/960tLScrhfCuy3V155JYmI3W6TJ09OkiRJWltbk9tvvz3p379/UlBQkIwbNy5ZuXJlmzk2bNiQXHXVVUlRUVFSXFycfPGLX0w2b97cZsyvf/3r5FOf+lRSUFCQHH/88cndd999uF4i7NXe1sC2bduSiy66KOnbt2/SvXv35MQTT0yuv/763f4z3xogy/b0/o+IZM6cObkxHfUd6JVXXknOPPPMJD8/PxkyZEib54C07GsN1NXVJeeff35SUlKSFBQUJCeffHJy2223JZs2bWozjzVAll177bXJiSeemOTn5yd9+/ZNxo0blyu0k8TnAEevvCRJksN3XDgAAAAAABw859QGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AADsxYUXXhg333xzKs+9YMGCyMvLi40bN6by/AAA0BkptQEAoBPYU3n+J3/yJ1FfXx+9evXq0Oeqrq6OsWPHRklJSfTs2TNOOeWUmDx5cuzYsaNDnwcAAA4FpTYAABxCLS0tB/3Y/Pz8KCsri7y8vA7Ls3z58rj44ovj7LPPjldffTWWLl0a3/3udyM/Pz927drVYc8DAACHilIbAAD+aOvWrfH5z38+ioqKory8PL797W+32Z+XlxfPPfdcm229e/eOuXPnRkTE6tWrIy8vL37wgx/EBRdcED169Ijvf//7sWHDhrjqqqvi+OOPj549e8YZZ5wRTz31VG6OL3zhC1FdXR0PPPBA5OXlRV5eXqxevXqPpx/53//7f8fw4cOjoKAgBg0atFvGQYMGxd/93d/FtddeG8cee2xUVFTEY489ltv/05/+NMrKyuKee+6J008/PU466aS4+OKL4+///u+jsLAwN+61116L8847LwoLC2PgwIHx13/917F169bc/ubm5pg2bVoMHDgwCgoK4uSTT47HH388IiL+8Ic/xDXXXBN9+/aNwsLCOOWUU2LOnDkH9XcCAAAfpdQGAIA/uu2226K6ujp++MMfxk9/+tNYsGBBvPnmmwc8zze/+c342te+FrW1tTFhwoTYvn17jBo1Kv7P//k/sWzZsvjyl78cn/vc5+IXv/hFREQ88MADUVVVFddff33U19dHfX19DBw4cLd5Fy9eHH/5l38Zn/3sZ2Pp0qVx5513xu23354r1T/w7W9/O84+++z41a9+FV/96lfjhhtuiJUrV0ZERFlZWdTX18err77abv733nsvLr744pg4cWK89dZb8YMf/CBee+21uPHGG3NjPv/5z8dTTz0VDz74YNTW1sb3vve9KCoqioiI22+/PZYvXx4/+clPora2Nh555JEoLS094D9HAADYk25pBwAAgM5gy5Yt8fjjj8c///M/x7hx4yIi4h//8R/jhBNOOOC5br755rjiiivabLv11ltzP990003x4osvxtNPPx3nnntu9OrVK/Lz86Nnz55RVlbW7rz33XdfjBs3Lm6//faIiDj11FNj+fLlce+998YXvvCF3Lg/+7M/i69+9asRETFt2rS4//7745VXXomhQ4fGf/2v/zVefPHFuOCCC6KsrCzGjBkT48aNi89//vNRXFwcERGzZs2Ka665JneO71NOOSUefPDBuOCCC+KRRx6Jurq6ePrpp+Oll16K8ePHR0TEkCFDcs9fV1cXn/jEJ+Lss8+OiP88ehwAADqKI7UBACD+8+jkHTt2xOjRo3PbSkpKYujQoQc81wdl7gd27doVf/u3fxtnnHFGlJSURFFRUbz44otRV1d3QPPW1tbGJz/5yTbbPvnJT8Y777zT5nzYI0aMyP2cl5cXZWVlsX79+oiI6Nq1a8yZMyd++9vfxj333BPHH398/N3f/V0MHz486uvrIyLi17/+dcydOzeKiopytwkTJkRra2usWrUqlixZEl27do0LLrhgjzlvuOGGmDdvXpx55pnxjW98I15//fUDep0AALA3Sm0AANhPeXl5kSRJm217uhDkMccc0+b+vffeGw888EBMmzYtXnnllViyZElMmDAhduzYcUhydu/evc39vLy8aG1tbbPt+OOPj8997nPx0EMPxdtvvx3bt2+PRx99NCL+86j1v/qrv4olS5bkbr/+9a/jnXfeiZNOOqnNubf35JJLLonf/OY3ccstt8SaNWti3LhxbY5UBwCAj0OpDQAAEXHSSSdF9+7dY9GiRbltf/jDH+Lf//3fc/f79u2bO5o5IuKdd96Jbdu27XPun//853HZZZfFpEmTYuTIkTFkyJA280ZE5Ofntznaek+GDRsWP//5z3eb+9RTT42uXbvuM0d7+vTpE+Xl5bkLQZ511lmxfPnyOPnkk3e75efnxxlnnBGtra1RXV3d7px9+/aNyZMnxz//8z/Hd77znTYXqwQAgI/DObUBACAiioqK4rrrrovbbrstjjvuuOjXr1/8zd/8TXTp8v8fBzJ27Nh46KGHoqqqKnbt2hXTpk3b7ajoPTnllFPiX/7lX+L111+PPn36xH333Rfr1q2L0047LTdm0KBBsWjRoli9enUUFRVFSUnJbvN8/etfj3POOSf+9m//Nq688sqoqamJhx56KB5++OH9fp3f+973YsmSJfEXf/EXcdJJJ8X27dvjn/7pn+Ltt9+O7373uxHxn+fhHjNmTNx4443xpS99KY455phYvnx5vPTSS/HQQw/FoEGDYvLkyXHttdfGgw8+GCNHjozf/OY3sX79+vjLv/zLuOOOO2LUqFExfPjwaG5ujh//+McxbNiw/c4IAAB740htAAD4o3vvvTfOO++8uPTSS2P8+PHxqU99KkaNGpXb/+1vfzsGDhwY5513Xlx99dVx6623Rs+ePfc577e+9a0466yzYsKECXHhhRdGWVlZXH755W3G3HrrrdG1a9c47bTTom/fvns83/ZZZ50VTz/9dMybNy9OP/30uOOOO+Kuu+5qc5HIfTn33HNjy5Yt8ZWvfCWGDx8eF1xwQSxcuDCee+653DmyR4wYEdXV1fHv//7vcd5558UnPvGJuOOOO2LAgAG5eR555JH4L//lv8RXv/rVqKysjOuvvz53pHd+fn5Mnz49RowYEeeff3507do15s2bt98ZAQBgb/KSj54UEAAAAAAAOilHagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIDKU2AAAAAACZodQGAAAAACAzlNoAAAAAAGSGUhsAAAAAgMxQagMAAAAAkBlKbQAAAAAAMkOpDQAAAABAZii1AQAAAADIjP8PJyl+UohP7qUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "vedio_df['durationSecs'] = pd.to_numeric(vedio_df['durationSecs'], errors='coerce')\n",
    "sns.histplot(data=vedio_df[vedio_df['durationSecs'] < 10000], x=\"durationSecs\", bins=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "db208254-5d74-4a2d-8b06-a3c659bf1975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_df = pd.DataFrame(vedio_df['pushblishDayName'].value_counts())\n",
    "weekdays = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "day_df = day_df.reindex(weekdays)\n",
    "ax = day_df.reset_index().plot.bar(x='pushblishDayName', y='count', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c8d6c62-891b-4ccc-903b-ae924c2f31b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}