Indonesia's_Trending_YouTube_Video_Project.ipynb

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      "source": [
        "# Import necessary packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import datetime\n",
        "from datetime import datetime\n",
        "import math\n",
        "from scipy.stats import mannwhitneyu\n",
        "from itertools import combinations\n",
        "import scipy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "llSlsAzY1qa1"
      },
      "outputs": [],
      "source": [
        "# Code to read csv file into Colaboratory:\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_4m2CFkG-k-"
      },
      "source": [
        "The data used for this project is represents Indonesia's trending YouTube videos, containing of 148,149 rows across 27 columns. Each row represents a distinct video whereas each row contains that video's characteristic. Firstly, we look at the first five rows of the data in order to to gain familiarity with the data. "
      ]
    },
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        "outputId": "a5b0f670-65ac-4c80-b520-e63b124498fb"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-3a5bac5aa143>:9: DtypeWarning: Columns (0,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('trending.csv')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  video_id          publish_time                channel_id  \\\n",
              "0      NaN  2021-02-05T09:00:34Z  UCEf_Bc-KVd7onSeifS3py9g   \n",
              "1      NaN  2021-02-04T15:54:08Z  UCYEa4_MC7OFjcWrO6SO-u8g   \n",
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              "\n",
              "                                               title  \\\n",
              "0                        aespa 에스파 'Forever (약속)' MV   \n",
              "1  [FULL] Siapa di Balik Kudeta AHY? | Dua Sisi t...   \n",
              "2     CABRIOLET CHALLENGE: TANTANGAN MENGGODA (7/12)   \n",
              "3                        With Windah Basudara & Hans   \n",
              "4  🤯 LATE COMEBACK DRAMA! | HIGHLIGHTS | Granada ...   \n",
              "\n",
              "                                         description thumbnail_url  \\\n",
              "0  aespa's new single \"Forever\" is out!\\nListen a...           NaN   \n",
              "1  Jakarta, https://www.tvonenews.com - Ketua umu...           NaN   \n",
              "2  Road Party Season 2: Cabriolet Challenge\\n\\nEp...           NaN   \n",
              "3  Join this channel to get access to perks:\\nhtt...           NaN   \n",
              "4  With just two minutes to play, Barça looked to...           NaN   \n",
              "\n",
              "   thumbnail_width  thumbnail_height            channel_name  \\\n",
              "0              NaN               NaN                  SMTOWN   \n",
              "1              NaN               NaN  Indonesia Lawyers Club   \n",
              "2              NaN               NaN                Motomobi   \n",
              "3              NaN               NaN                      yb   \n",
              "4              NaN               NaN            FC Barcelona   \n",
              "\n",
              "                                                tags  ...  caption  \\\n",
              "0                                                NaN  ...     True   \n",
              "1  ['tvone', 'tv one', 'tvonenews', 'indonesia la...  ...    False   \n",
              "2  ['MotoMobi', 'Mobil', 'motor', 'review', 'indo...  ...    False   \n",
              "3                                                NaN  ...    False   \n",
              "4  ['FC Barcelona', 'برشلونة،', 'Fútbol', 'FUTBOL...  ...    False   \n",
              "\n",
              "  license_status allowed_region blocked_region       view      like  dislike  \\\n",
              "0           True            NaN            NaN  7806846.0  857902.0  24078.0   \n",
              "1           True            NaN            NaN   885038.0    6765.0   1263.0   \n",
              "2           True            NaN            NaN   889708.0   47895.0    532.0   \n",
              "3           True            NaN            NaN  1006854.0   91973.0   3967.0   \n",
              "4           True            NaN            NaN  6275035.0  218131.0   4289.0   \n",
              "\n",
              "   favorite  comment                     trending_time  \n",
              "0         0  94961.0  2021-02-07 05:46:51.832614+00:00  \n",
              "1         0   6365.0  2021-02-07 05:46:51.832649+00:00  \n",
              "2         0   8785.0  2021-02-07 05:46:51.832664+00:00  \n",
              "3         0  12957.0  2021-02-07 05:46:51.832678+00:00  \n",
              "4         0  12799.0  2021-02-07 05:46:51.832730+00:00  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th>video_id</th>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Provide the link to share the file/folder located on Google Drive.\n",
        "link ='https://drive.google.com/file/d/1b23_N_uLP3SltdPy6L90iQO-silRCPlI/view?usp=share_link'\n",
        "# Extract the ID of the file\n",
        "id = link.split(\"/\")[-2]\n",
        "# Create and load the dataset\n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('trending.csv') \n",
        "# Store the dataset in a dataframe\n",
        "df = pd.read_csv('trending.csv')\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiCGW3uFOU2x"
      },
      "source": [
        "In doing this project, we would like to know several things, which are listed below.\n",
        "\n",
        "\n",
        "1.   Exploratory data analysis\n",
        "2.   How do the comments corelate to dislike and like count?\n",
        "3.   How do like, dislike, and comments differ from each other?\n",
        "4.   How do publish time of the video affect views and comments?\n",
        "5.   What is the average video duration?\n",
        "6.   On average, how long after uploading will the video be marked as trending?\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziFDihaQgAx3"
      },
      "source": [
        "We make a copy dataframe for the raw dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdH_xTwT9JCQ"
      },
      "source": [
        "## 1. Exploratory data analysis\n",
        "\n",
        "First, we perform exploratory data analysis to obtain a sense of what the data looks like. Exploratory data analysis is mainly used to observe initial trends or patterns of the data, where we plot figures and graphs that we think would be valuable information. EDA also helps to identify the quality of data, the distribution of data, and the presence of any outliers or missing values.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVye98n3RMRx"
      },
      "source": [
        "We use `df.info()` to get a concise summary of a DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZlY330z9v5C",
        "outputId": "5dc7b191-8ba0-4d99-e2b0-1d4ed651aabd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 148149 entries, 0 to 148148\n",
            "Data columns (total 27 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   video_id           95027 non-null   object \n",
            " 1   publish_time       148149 non-null  object \n",
            " 2   channel_id         148149 non-null  object \n",
            " 3   title              148149 non-null  object \n",
            " 4   description        142622 non-null  object \n",
            " 5   thumbnail_url      95027 non-null   object \n",
            " 6   thumbnail_width    95027 non-null   float64\n",
            " 7   thumbnail_height   95027 non-null   float64\n",
            " 8   channel_name       148149 non-null  object \n",
            " 9   tags               127609 non-null  object \n",
            " 10  category_id        148149 non-null  int64  \n",
            " 11  live_status        148149 non-null  object \n",
            " 12  local_title        148149 non-null  object \n",
            " 13  local_description  142622 non-null  object \n",
            " 14  duration           148149 non-null  object \n",
            " 15  dimension          148149 non-null  object \n",
            " 16  definition         148149 non-null  object \n",
            " 17  caption            148149 non-null  bool   \n",
            " 18  license_status     148149 non-null  bool   \n",
            " 19  allowed_region     5077 non-null    object \n",
            " 20  blocked_region     5621 non-null    object \n",
            " 21  view               148139 non-null  float64\n",
            " 22  like               147021 non-null  float64\n",
            " 23  dislike            53630 non-null   float64\n",
            " 24  favorite           148149 non-null  int64  \n",
            " 25  comment            147703 non-null  float64\n",
            " 26  trending_time      148149 non-null  object \n",
            "dtypes: bool(2), float64(6), int64(2), object(17)\n",
            "memory usage: 28.5+ MB\n"
          ]
        }
      ],
      "source": [
        " df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_KLBHLuC2un"
      },
      "source": [
        "## 1.1. Description of the dataset column\n",
        "\n",
        "In here, we first describe the column definitions, thereby allowing us to gain a clear understanding of the variables present in the dataset. It helps us interpret the data correctly and know what each column represents. This understanding is crucial for making meaningful interpretations and drawing accurate conclusions from the data.\n",
        "\n",
        "*   `video_id`: The unique identifier for each video distinguished by id\n",
        "*   `publish_time`: The publish date and time of the video\n",
        "*   `channel_id`: The unique identifier of the channel that published the video\n",
        "*   `title`: The title of the video\n",
        "*   `description`: The description of the video\n",
        "*   `channel_name`: The name of the channel that published the video\n",
        "*   `tags`: The tags associated with the video\n",
        "*   `category_id`: The category identifier for the video\n",
        "*   `live_status`: Whether the video is live or not\n",
        "*   `local_title`: The localized title of the video\n",
        "*   `local_description`: The localized description of the video\n",
        "*   `duration`: The duration of the video\n",
        "*   `dimension`: The dimension of the video\n",
        "*   `definition`: The definition of the video\n",
        "*   `caption`: Whether the video has caption or not\n",
        "*   `license_status`: Whether the video has a license or not\n",
        "*   `allowed_region`: The region where the video is allowed to be viewed\n",
        "*   `blocked_region`: The region where the video is blocked from being viewed\n",
        "*   `view`: The number of views the video has received\n",
        "*   `like`: The number of likes the video has received\n",
        "*   `dislike`: The number of dislikes the video has received\n",
        "*   `favorite`: The number of times the video has been added to a favorite list\n",
        "*   `comment`: The number of comments the video has received\n",
        "*   `trending_time`: The date and time the video became trending\n",
        "*   `title_sentiment`: The sentiment score of the video title"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. Null Values Handling\n",
        "\n",
        "Firstly, we check the extent of missing values to assess data quality, handle missing data, ensure proper preprocessing, maintain analysis integrity, and be aware of data completeness. It helps evaluate the reliability of the dataset, determine appropriate strategies for missing data, and prevent biases in analysis."
      ],
      "metadata": {
        "id": "hXKuZhwBVYI8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wcmV1Ai9vwG",
        "outputId": "e3aed56e-d86f-4473-cb00-33024da34e0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "video_id              53122\n",
              "publish_time              0\n",
              "channel_id                0\n",
              "title                     0\n",
              "description            5527\n",
              "thumbnail_url         53122\n",
              "thumbnail_width       53122\n",
              "thumbnail_height      53122\n",
              "channel_name              0\n",
              "tags                  20540\n",
              "category_id               0\n",
              "live_status               0\n",
              "local_title               0\n",
              "local_description      5527\n",
              "duration                  0\n",
              "dimension                 0\n",
              "definition                0\n",
              "caption                   0\n",
              "license_status            0\n",
              "allowed_region       143072\n",
              "blocked_region       142528\n",
              "view                     10\n",
              "like                   1128\n",
              "dislike               94519\n",
              "favorite                  0\n",
              "comment                 446\n",
              "trending_time             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Checking whether the data has missing or null values\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97oiymggWffm"
      },
      "source": [
        "For the `tags` and `local_description`, we can fill the null value with 'No Tags' and 'No Description' since it's possible for a trending video to not have a tags nor a description."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qGoZgDmvV8Ld"
      },
      "outputs": [],
      "source": [
        "# Fill the missing values so the accuracy of the analysis and lead to better insights.\n",
        "df['tags'] = df['tags'].fillna('No Tags')\n",
        "df['local_description'] = df['local_description'].fillna('No Description')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MY8oFFaGVN6F"
      },
      "source": [
        "Same goes for the `allowed_region` and `blocked_region`. It's possible for a trending video to not have a certain allowed region or available in all country, so we fill the null value with 'All Region'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iCNj1spw9vp6"
      },
      "outputs": [],
      "source": [
        "df['allowed_region'] = df['allowed_region'].fillna('All Region') \n",
        "df['blocked_region'] = df['blocked_region'].fillna('All Region')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRbw5fSCZE7P"
      },
      "source": [
        "Considering the data represents trending videos, it would not make sense to fill the data with zeros, and since the distribution of `view`, `like`, \n",
        "`dislike` and `comment` data is right-skewed, the median is often a better measure of central tendency than the mean. Therefore, we fill the missing value with median instead of the mean.\n",
        "\n",
        "Initially, we examine the histograms of the `view`, `like`, `dislike`, and `comment` columns.\n",
        "\n",
        "### 1.3.1. Views"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "_YXhRO6lF6M6",
        "outputId": "85c4759a-8dd8-4271-f02a-edcd5ecc6191"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view = df[df['view'] < 15000000]\n",
        "\n",
        "plt.hist(view['view'],bins=50)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our histogram, results conform that the `view` column is right-skewed, thereby making it appropriate to fill the values with its median."
      ],
      "metadata": {
        "id": "kk_z2Cs5JwoS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bTkbrhAw9vjy"
      },
      "outputs": [],
      "source": [
        "df['view'] = df['view'].fillna(df['view'].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.2. Likes Count\n",
        "\n",
        "Akin to the `Views` column, we render the histogram regarding the `like` column to gain a more representative visual understanding of its spread."
      ],
      "metadata": {
        "id": "k-GafWimJf9c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "09HRdm2o9vFG",
        "outputId": "f4a7353e-fee6-4556-8621-cc06871eb8b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "like = df[df['like'] < 100000]\n",
        "\n",
        "plt.hist(like['like'],bins=50)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mRnLOwjZIe74"
      },
      "outputs": [],
      "source": [
        "df['like'] = df['like'].fillna(df['like'].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.3. Dislikes Count\n",
        "\n",
        "Similar to the previous two columns, we generate a histogram for the `dislike` column to gain a more comprehensive visual understanding of its distribution."
      ],
      "metadata": {
        "id": "2XUKG6jpKGqm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "7UkoY5ntIkc-",
        "outputId": "8b2fe2ad-6493-4f17-c554-5d76c229de4b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dislike = df[df['dislike'] < 1000]\n",
        "\n",
        "plt.hist(dislike['dislike'],bins=50)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XU3BEX01IuqK"
      },
      "outputs": [],
      "source": [
        "df['dislike'] = df['dislike'].fillna(df['dislike'].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.4. Comments Count\n",
        "\n",
        "Finally, we render a histogram for the `comment` column to confirm whether our suspicions regarding right-skewed data is true."
      ],
      "metadata": {
        "id": "VSeAB2VeK-FO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Z7wk7324JAai",
        "outputId": "a4e070d1-91c8-46f8-93ed-f7b42cc964eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "comment = df[df['comment'] < 10000]\n",
        "\n",
        "plt.hist(comment['comment'],bins=50)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now understand the data is indeed right-skewed, hence filling them with median is the appropriate course of action."
      ],
      "metadata": {
        "id": "IxLAm2vSLGvq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PkfGRSs-JUfZ"
      },
      "outputs": [],
      "source": [
        "df['comment'] = df['comment'].fillna(df['comment'].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJ7r7vvwVELl"
      },
      "source": [
        "### 1.4. Datetime Parsing\n",
        "\n",
        "It is common to have date and time data stored as strings or objects. As in this case, it is necessary to convert these strings or objects to a datetime format to perform operations such as sorting, filtering, or arithmetic calculations.\n",
        "\n",
        "Initially, we examine the string pattern to determine the format required for its conversion into a datetime representation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uJWXb-2U7He",
        "outputId": "808c7719-d744-4da3-cd00-ea3a877f632f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         2021-02-05T09:00:34Z\n",
              "1         2021-02-04T15:54:08Z\n",
              "2         2021-02-06T03:00:22Z\n",
              "3         2021-02-05T20:26:08Z\n",
              "4         2021-02-03T23:14:54Z\n",
              "                  ...         \n",
              "148144    2023-04-02T06:11:43Z\n",
              "148145    2023-03-31T11:30:10Z\n",
              "148146    2023-04-01T07:59:57Z\n",
              "148147    2023-03-31T00:00:12Z\n",
              "148148    2023-04-02T10:00:19Z\n",
              "Name: publish_time, Length: 148149, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df['publish_time']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGk_kLngS1iJ"
      },
      "source": [
        "The `publish_time` timestamp is in [Zulu](https://www.techtarget.com/whatis/definition/Zulu-Zulu-time) format (military name for UTC).\n",
        "\n",
        "We use `datetime.strptime()` method to convert the `publish_time` into a `datetime64` format, using a list comprehension."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4IsohGWaXZ0",
        "outputId": "61ad626c-1317-4c7d-af76-d1b8e2a21e4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2021-02-05 09:00:34\n",
              "1        2021-02-04 15:54:08\n",
              "2        2021-02-06 03:00:22\n",
              "3        2021-02-05 20:26:08\n",
              "4        2021-02-03 23:14:54\n",
              "                 ...        \n",
              "148144   2023-04-02 06:11:43\n",
              "148145   2023-03-31 11:30:10\n",
              "148146   2023-04-01 07:59:57\n",
              "148147   2023-03-31 00:00:12\n",
              "148148   2023-04-02 10:00:19\n",
              "Name: publish_time, Length: 148149, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['publish_time'] = [datetime.strptime(str(i), '%Y-%m-%dT%H:%M:%SZ') for i in df['publish_time']]\n",
        "df['publish_time']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkTghiFr9ZMi"
      },
      "source": [
        "Another datetime column in our data pertains to the column `trending_time`. For this, we can instantly use `pd.to_datetime()` to convert the `trending_time` into a datetime64 format.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SuryynI-XYxT",
        "outputId": "666b950d-84ff-4f2f-db98-e704fc9fa88c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2021-02-07 05:46:51.832614+00:00\n",
              "1        2021-02-07 05:46:51.832649+00:00\n",
              "2        2021-02-07 05:46:51.832664+00:00\n",
              "3        2021-02-07 05:46:51.832678+00:00\n",
              "4        2021-02-07 05:46:51.832730+00:00\n",
              "                       ...               \n",
              "148144   2023-04-18 06:04:24.338378+00:00\n",
              "148145   2023-04-18 06:04:24.338388+00:00\n",
              "148146   2023-04-18 06:04:24.338398+00:00\n",
              "148147   2023-04-18 06:04:24.338408+00:00\n",
              "148148   2023-04-18 06:04:24.338700+00:00\n",
              "Name: trending_time, Length: 148149, dtype: datetime64[ns, UTC]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df['trending_time'] = pd.to_datetime(df['trending_time'])\n",
        "df['trending_time']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZbkodA-ZMUHQ"
      },
      "source": [
        "Subsequently, in order to construct a Correlation Matrix matrix for Pearson Analysis, certain adjustments need to be made to certain columns to ensure their inclusion. The first column to process is `definition`, where we label-encoded the column since it only has 2 different values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rj7eK2owMK9B",
        "outputId": "7bd790ec-2d4d-4770-ad1c-80d90ced5985"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hd    147545\n",
              "sd       604\n",
              "Name: definition, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df['definition'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fo8hDa9XLlYJ"
      },
      "outputs": [],
      "source": [
        "definition_dict = {'hd':1, 'sd':0}\n",
        " \n",
        "# Map to a column\n",
        "df['definition'] =df['definition'].map(definition_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcAsXcJC0-gP"
      },
      "source": [
        "To generate a correlation matrix with numeric values only, a list comprehension was thereby employed to calculate the count of `blocked_region` occurrences per video. This approach allows us to obtain the necessary numeric data for constructing the correlation matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LpExBb9JiE2l"
      },
      "outputs": [],
      "source": [
        "df['blocked_region'] = [0 if i == 'All Region' else len(i) for i in df['blocked_region']] \n",
        "\n",
        "# Verify results\n",
        "# We can use `assert` so that it raises an exception if df['blocked_region'] was not successfully type-casted into integers.\n",
        "assert isinstance(df['blocked_region'].sum(), (int, np.integer))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9CHQqpBPEfS"
      },
      "source": [
        "Then we check if there is any numeric column that contains the same value in the entire dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8ZaSsp0IWYm",
        "outputId": "f05a63bc-e2eb-4b0a-a972-4348b7ae8584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    148149\n",
            "Name: favorite, dtype: int64\n",
            "480.0    95027\n",
            "Name: thumbnail_width, dtype: int64\n",
            "360.0    95027\n",
            "Name: thumbnail_height, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.favorite.value_counts())\n",
        "print(df.thumbnail_width.value_counts())\n",
        "print(df.thumbnail_height.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZqw-XCwA4ET"
      },
      "source": [
        "We drop columns that are not needed such as `thumbnail_url` since is unique for all videos also `thumbnail_width`, `thumbnail_height` and `favorite` and since they has the same value throughout the data, and hence would not convey anything."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rVxY2WeEmEnx"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['thumbnail_url','thumbnail_width', 'thumbnail_height','favorite'], axis=1) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ha2l_sIQP4Zr"
      },
      "source": [
        "For `category_id` we notice that the `dtypes` is `int` not category, so to proceed, we must convert the integer type to a category type."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H_mXvv1EGE_a"
      },
      "outputs": [],
      "source": [
        "df['category_id'] = df['category_id'].astype('category')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWmiVeKcYNF5"
      },
      "source": [
        "Regarding the `category_id`, we encode the ID to a new column `category` to facilitate understanding of each category. This `category_dict` was sourced from the accompanying JSON file within the [Kaggle page](https://www.kaggle.com/datasets/syahrulhamdani/indonesias-trending-youtube-video-statistics?datasetId=1143485&sortBy=dateRun&tab=profile&select=category.json)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QMPcLyXmrfgN"
      },
      "outputs": [],
      "source": [
        "category_dict = {1:'Film & Animation',\n",
        "                  2:'Autos & Vehicles',\n",
        "                  10:'Music',\n",
        "                  15 :'Pets & Animals',\n",
        "                  17:'Sports',\n",
        "                  18:'Short Movies',\n",
        "                  19:'Travel & Events',\n",
        "                  20:'Gaming',\n",
        "                  21:'Videoblogging',\n",
        "                  22 :'People & Blogs',\n",
        "                  23:'Comedy',\n",
        "                  24:'Entertainment',\n",
        "                  25:'News & Politics',\n",
        "                  26 :'Howto & Style',\n",
        "                  27:'Education',\n",
        "                  28:'Science & Technology',\n",
        "                  30:'Movie',\n",
        "                  31:'Anime/Animation',\n",
        "                  32:'Action/Adventure',\n",
        "                  33:'Classics',\n",
        "                  34:'Comedy',\n",
        "                  35:'Documentary',\n",
        "                  36:'Drama',\n",
        "                  37:'Family',\n",
        "                  38:'Foreign',\n",
        "                  39:'Horror',\n",
        "                  40:'Sci-Fi/Fantasy',\n",
        "                  41:'Thriller',\n",
        "                  42:'Shorts',\n",
        "                  43:'Shows',\n",
        "                  44:'Trailers'}\n",
        " \n",
        "# map to a column\n",
        "df['category'] = df['category_id'].map(category_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we can call `value_counts()` in `df['category']` to profie the data and gain a better understanding of the range and spread of values within each category."
      ],
      "metadata": {
        "id": "ypEQvDtRQYSJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AddNiWwXs3lo",
        "outputId": "2c41371f-ffba-4e32-ba40-c5b4c5323b58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entertainment           42022\n",
              "Music                   35583\n",
              "People & Blogs          16886\n",
              "Gaming                  13174\n",
              "Sports                  12601\n",
              "News & Politics          7368\n",
              "Film & Animation         4936\n",
              "Comedy                   4545\n",
              "Autos & Vehicles         3460\n",
              "Howto & Style            2201\n",
              "Science & Technology     1807\n",
              "Education                1144\n",
              "Pets & Animals           1060\n",
              "Travel & Events           898\n",
              "Name: category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df['category'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbjJnFhi7Z3P"
      },
      "source": [
        "To identify a better proportion apart from numbers, we create a pie chart that accurately represented the distribution of category IDs in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZxHPGoIAX0Mi",
        "outputId": "bc33ae2c-6ee7-4f8c-a68c-2c0f24300857"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.18.2.min.js\"></script>                <div id=\"579b9e7e-a681-481e-8df9-97ce1bf9469e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"579b9e7e-a681-481e-8df9-97ce1bf9469e\")) {                    Plotly.newPlot(                        \"579b9e7e-a681-481e-8df9-97ce1bf9469e\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"label=%{label}<br>value=%{value}<extra></extra>\",\"labels\":[\"Entertainment\",\"Music\",\"People & Blogs\",\"Gaming\",\"Sports\",\"News & Politics\",\"Film & Animation\",\"Comedy\",\"Autos & Vehicles\",\"Howto & Style\",\"Science & Technology\",\"Education\",\"Pets & Animals\",\"Travel & 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by category\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('579b9e7e-a681-481e-8df9-97ce1bf9469e');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = px.pie(df, names=df['category'].value_counts().index,values=df['category'].value_counts().values, title='Trending by category')\n",
        "ax.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKNPRvLwYxRM"
      },
      "source": [
        "Same applies to `license_status`. We create a pie chart that accurately represented the distribution of license status in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "3OysnNw-mEsT",
        "outputId": "e1cf8d3c-8c02-49c9-a0fb-b11b6b11cffb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.pie(df['license_status'].value_counts().values, labels=df['license_status'].value_counts().index,autopct='%1.1f%%')\n",
        "plt.title('Video with license')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on point number 2, when performing a Pearson's Correlation Analysis, it is preferable for the variables involved to exhibit a normal distribution. However, in contrast, when conducting a Mann-Whitney U test, it is necessary for the variables to be non-normally distributed. In this dataset, the raw data itself demonstrates a right-skewed distribution. Therefore, for the purpose of the Mann-Whitney U test, we will retain the non-normalized data."
      ],
      "metadata": {
        "id": "-9R6Zt4krYA2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OEZz8jtQjukf"
      },
      "outputs": [],
      "source": [
        "df_new = df.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXIBtA_XnwFl"
      },
      "source": [
        "## 2. Pearson Correlation Analysis\n",
        "### How do the comments correlate to dislike and like counts?\n",
        "The first step is to calculate the Pearson correlation coefficient. The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables.\n",
        "\n",
        "One of the assumptions of the Pearson correlation analysis is that both variables should be normally distributed. From EDA, we know that this data is right-skewed, where it is now appropriate to harness the logarithmic transformation to normalise the data. The logarithmic transformation works by compressing its range and reducing the influence of extreme values. This transformation helps to approximate a normal or near-normal distribution for the log-transformed data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQQK7mO2jIof",
        "outputId": "a4db3c22-1362-41ed-a5e8-965007ce5c1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-8d29f4d3ad2a>:2: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in log\n",
            "\n",
            "<ipython-input-30-8d29f4d3ad2a>:3: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in log\n",
            "\n",
            "<ipython-input-30-8d29f4d3ad2a>:4: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in log\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Apply log normalization\n",
        "df['like'] = [np.log(i) for i in df['like']]\n",
        "df['dislike'] = [np.log(i) for i in df['dislike']]\n",
        "df['comment'] = [np.log(i) for i in df['comment']]\n",
        "# Uncomment to visualize the variable\n",
        "# plt.hist(df['like'],bins=50)\n",
        "# plt.show()\n",
        "\n",
        "# plt.hist(df['dislike'],bins=40)\n",
        "# plt.show()\n",
        "\n",
        "# plt.hist(df['comment'],bins=50)\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After completing the data adjustment during the exploratory data analysis (EDA) phase, our next step is to construct a correlation matrix."
      ],
      "metadata": {
        "id": "EyarlLEkPASs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We use corr() function to find the correlation among the columns in the Dataframe using ‘pearson’ keyword argument. \n",
        "coef = df.corr(method='pearson', min_periods=1) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nokjJonOwH4",
        "outputId": "f3c8a34f-144d-4cc4-ca13-dad65d80db5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-dcb2f6fb045c>:2: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the correlation matrix has been constructed, we can employ a heatmap to visually represent the correlations within the matrix."
      ],
      "metadata": {
        "id": "nrGTiV6WPGLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(30,20))   \n",
        "sns.heatmap(df.corr(), ax = ax,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nHCRgQV8PFl4",
        "outputId": "f4de35a0-ca2e-49bc-aeb7-764e66ad7fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-61-738cd31e9b05>:2: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x2000 with 2 Axes>"
            ],
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          },
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWX2sWEuoBbm"
      },
      "source": [
        "### Calculating t-score\n",
        "After calculating the Pearson correlation coefficient between two variables, the next step is to determine if the correlation is statistically significant. To do this, we need to calculate the t-score using the formula below.\n",
        "\n",
        "[t-score](https://www.statology.org/t-test-for-correlation/) = r√(n-2)/(1-r2)\n",
        "\n",
        "Where:\n",
        "\n",
        "*   r : correlation coefficient \n",
        "*   n : sample size\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8LAKljrLTTUv"
      },
      "outputs": [],
      "source": [
        "def t_score(coef):\n",
        "  return float(coef)*(math.sqrt((float(384)-float(2))/(1-(float(coef))**float(2))))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can reshape the correlation matrix and automatically calculate the  t-scores as to not do it manually\n",
        "coef_mask = coef.where(np.triu(np.ones(coef.shape)).astype(np.bool)) # Mask the correlation matrix so that no duplicates are found\n",
        "coef_mask = coef_mask.stack().reset_index() # Flatten a triangular-shaped correlation to represent a pairwise correlation\n",
        "coef_mask.columns = ['Var1', 'Var2', 'Coef'] # Rename columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMyMTnF3Xp1V",
        "outputId": "1921ab68-c6e6-4291-e7e2-cd4fec023719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-b30ac1b93ebb>:2: DeprecationWarning:\n",
            "\n",
            "`np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coef_mask = coef_mask[coef_mask['Coef'] != 1] # Drop rows where coef is 1.0000\n",
        "coef_mask = coef_mask[(coef_mask['Var1'] == 'comment') | (coef_mask['Var1'] == 'like') | (coef_mask['Var1'] == 'dislike')]\n",
        "coef_mask['t-score'] = coef_mask['Coef'].apply(t_score)\n",
        "coef_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Ngds5k4tpl_B",
        "outputId": "bee35155-7a6c-4c56-b5aa-4e148dd4c23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Var1     Var2      Coef    t-score\n",
              "31     like  dislike  0.529599  12.202711\n",
              "32     like  comment  0.751395  22.256323\n",
              "34  dislike  comment  0.362420   7.600131"
            ],
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we specifically intend to perform Pearson analysis solely on the `like`,`comment` and `dislike` columns, we proceed by parsing the dataframe to include only the required columns."
      ],
      "metadata": {
        "id": "_3710Wzppfh2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Rq4dXY-TTXB",
        "outputId": "dd6d9051-2b7d-47f6-e0a0-3d952966bc43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "comment and like t-score is 22.256301707261763\n",
            "comment and dislike t-score is 7.600129451968456\n",
            "dislike and like t-score is 12.202699713188558\n"
          ]
        }
      ],
      "source": [
        "print('comment and like t-score is ' + str(t_score(.751395)))\n",
        "print('comment and dislike t-score is ' + str(t_score(.362420)))\n",
        "print('dislike and like t-score is ' + str(t_score(.529599)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyJhIreLKkOr"
      },
      "source": [
        " ### Calculate each coefficients' p-value\n",
        "\n",
        " Once we have the t-score, we can find the corresponding p-value associated with a t-score using the `scipy.stats.t.sf()` with a significance level of 95% \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similarly we can automatically compute the p-values from the previously-made `coef_mask`.\n",
        "def compute_pval(t_score):\n",
        "  return scipy.stats.t.sf(abs(t_score), 382)*2\n",
        "\n",
        "coef_mask['pval'] = coef_mask['t-score'].apply(compute_pval)\n",
        "coef_mask "
      ],
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        "colab": {
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          "height": 143
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Var1     Var2      Coef    t-score          pval\n",
              "31     like  dislike  0.529599  12.202711  3.797797e-29\n",
              "32     like  comment  0.751395  22.256323  5.788219e-71\n",
              "34  dislike  comment  0.362420   7.600131  2.310747e-13"
            ],
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              "      <th>31</th>\n",
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    {
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      "execution_count": null,
      "metadata": {
        "id": "dOS_Y5yETTbM"
      },
      "outputs": [],
      "source": [
        "p1 = scipy.stats.t.sf(abs(t_score(.751395)), 382)*2\n",
        "p2 = scipy.stats.t.sf(abs(t_score(.362420)), 382)*2\n",
        "p3 = scipy.stats.t.sf(abs(t_score(.529599)), 382)*2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "7cf9c81b-1b59-4a5a-f398-436a3b0b4656"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P value for comment and like is 5.789421960080116e-71\n",
            "P value for comment and dislike is 2.3107679378681204e-13\n",
            "P value for dislike and like 3.798177831813624e-29\n"
          ]
        }
      ],
      "source": [
        "print('P value for comment and like is ' + str(p1))\n",
        "print('P value for comment and dislike is ' + str(p2))\n",
        "print('P value for dislike and like ' + str(p3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ucN3laIdPFx"
      },
      "source": [
        "Based on the results we discovered that the highest correlation and the most significant factor is the number of comments and likes with 0.751395 correlation coefficient and 5.789421960080116 x 10^-71. p-value.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMWWtztPo30D"
      },
      "source": [
        "## 3. How do Like, Dislike, and Comments differ from each other?\n",
        "### Mann-Whitney U Test\n",
        ">  The Mann-Whitney U Test is a nonparametric test used to test whether two samples are likely to derive from the same population when the sample distributions are not normally distributed.\n",
        "\n",
        "The `scipy.stats.mannwhitneyu()` function in Python can be used to perform the Mann-Whitney U test.\n",
        "\n",
        "It is worth noting that the Mann-Whitney U test has the following null and alternate hypoteheses:\n",
        "\n",
        "* *Null hypothesis:* the two populations have identical distributions.\n",
        "* *Alternative hypothesis:* the two populations have different medians, but otherwise are identical."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fzUthbDU0FM5"
      },
      "outputs": [],
      "source": [
        "def MWU(data1,data2):\n",
        "  stat, p_value = mannwhitneyu(data1, data2)\n",
        "  print('Statistics=%.2f, p=%.2f' % (stat, p_value))\n",
        "  # We use level of significance = 95%\n",
        "  alpha = 0.05\n",
        "  # Conclusion\n",
        "  if p_value < alpha:\n",
        "      print('Reject Null Hypothesis (Significant difference between two samples)')\n",
        "  else:\n",
        "      print('Do not Reject Null Hypothesis (No significant difference between two samples)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjST7SQ7kcqn"
      },
      "source": [
        "Considering Mann-Whitney U Test is a test that may be used when the sample not normally distributed, we therefore use the data before the normalization process, which was saved in `df_new`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fiwbg5F0FPS",
        "outputId": "07f0fcf1-1c0a-4759-dbba-431edb8fa2b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Statistics=2326481664.00, p=0.00\n",
            "Reject Null Hypothesis (Significant difference between two samples)\n",
            "Statistics=16176731155.50, p=0.00\n",
            "Reject Null Hypothesis (Significant difference between two samples)\n",
            "Statistics=559628412.50, p=0.00\n",
            "Reject Null Hypothesis (Significant difference between two samples)\n"
          ]
        }
      ],
      "source": [
        "mwu_1 = MWU(df_new['comment'],df_new['like'])\n",
        "mwu_2 = MWU(df_new['comment'],df_new['dislike'])\n",
        "mwu_3 = MWU(df_new['dislike'],df_new['like'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the above results, we have significant evidence that the null hypotheses can be rejected. That is, none of the comments, like, and dislike have identical distributions due to all tests returning 0 p-values. Conversely, we infer that they all come from differing distributions."
      ],
      "metadata": {
        "id": "N7_IjMFoWAvO"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXyRBoo4lwQu"
      },
      "source": [
        "##4. How do publish time of the video affect views and comments?\n",
        "First we want to look at the graph for `publish_time` and `view`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(df['publish_time'],df['view'])\n",
        "plt.xlabel('Video\\'s Publish Time')\n",
        "plt.ylabel('Total Views')\n",
        "plt.xticks( rotation=25 )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKQx_2Ctl6aG"
      },
      "source": [
        "Since we want to know whether a certain hour or day can yield more views, we pull the hour from `publish_time` and save it as `publish_time_hour`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YA8uF28EQDHF"
      },
      "outputs": [],
      "source": [
        "df['publish_time_hour'] = [i.hour for i in df['publish_time']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tl5GkaeCmOcK"
      },
      "source": [
        "We employ the `groupby()` function to perform data aggregation, grouping the sum of views based on the hour."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3GzhqsJQDI_"
      },
      "outputs": [],
      "source": [
        "view_per_hour = df.groupby('publish_time_hour')['view'].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fY387LW3m8ic"
      },
      "source": [
        "Subsequently, we generate a plot using the grouped data, allowing us to visualize the distribution of views across different hours and identify the hours that accumulate higher view counts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "BeriLh2xSqpu",
        "outputId": "09532407-eab1-46eb-ff1f-8a9457fb15ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(view_per_hour)\n",
        "start, end = ax.get_xlim()\n",
        "ax.xaxis.set_ticks(np.arange(0, 24, 1))\n",
        "ax.set_title('Indonesia\\'s Trending YouTube Views per Hour')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzajPG9-nbU3"
      },
      "source": [
        "Based on the observed graph, it is evident that videos uploaded at 9 A.M. consistently receive the highest number of views compared to other hours.\n",
        "\n",
        "To determine the highest views based on the day, we conducted a similar analysis by extracting the day from the `publish_time` and storing it as `publish_time_day`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARqiwO30SqtR",
        "outputId": "b9b826d8-4482-475d-a4e3-66c5f11b30bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         4\n",
              "1         3\n",
              "2         5\n",
              "3         4\n",
              "4         2\n",
              "         ..\n",
              "148144    6\n",
              "148145    4\n",
              "148146    5\n",
              "148147    4\n",
              "148148    6\n",
              "Name: publish_time_day, Length: 148149, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "df['publish_time_day'] = [i.weekday() for i in df['publish_time']]\n",
        "df['publish_time_day']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQU7UAnqrXQy"
      },
      "source": [
        "Similarly, we employed the groupby() function to aggregate the data and group the sum of views based on the day."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69pxpD7DSqwC",
        "outputId": "b8a8ccf7-6085-4ee3-8d2c-5355c07dda79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "publish_time_day\n",
              "0    7.334391e+10\n",
              "1    6.255452e+10\n",
              "2    6.059686e+10\n",
              "3    5.437619e+10\n",
              "4    1.135852e+11\n",
              "5    5.493073e+10\n",
              "6    6.069513e+10\n",
              "Name: view, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "view_per_day = df.groupby('publish_time_day')['view'].sum()\n",
        "view_per_day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZyLtV6NrgS0"
      },
      "source": [
        "Finally, we plotted the data that was grouped by the day to identify the day that received the highest views."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "bL_MSznSSqzV",
        "outputId": "5c0667bd-f5e3-4876-f42b-8ab32607eb49"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xIhQKRYv3O3/+PADgD3/4Q5Nr//jjjybXHjp0KHJycnDhwgUcPnwYMpkMcXFxJknKgQMHEB8fb0zyJ0yYgPj4eDz11FPw9/fHxIkT8dVXX7U6MWnu97Nbt26oqKhAQUEBCgoKoNVqsWLFiibxz5gxA0DTv43m/r5uVVlZGQDAzc3NeGzNmjXo168f1Go1vL294evri++//77J/ylTp07FoUOHjL+jGzduRG1tLaZMmWK2+OyBVdWMlJeXIyoqCjNnzmxSbNUaer0eTk5OeO6557Bp06Zmz6muroavry9effXVJks1LdWN/pOS8vqCIACoTxZkMhm2b9/e7LnXJ0jvvfcepk+fjm3btuHHH3/Ec889Z5yjb6kGYvHixXjttdcwc+ZM/OMf/4CXl5cxkbzZf5bPP/88xo4di61bt2Lnzp147bXXkJCQgJ9//hkxMTGtfQma1dynNoPBgHvvvRd//etfm71Pt27dTL6+2WtrLh11ndvV0ifNxsXaHfE4jR+jLb/nLYmNjUWPHj3w5Zdf4uWXX8aXX34JQRCMSUpLxN/zzz//HAEBAU2+f/1ybnH0af/+/UhLS0P//v2NBZr//e9/UVZWhmPHjpkUdDo5OWH//v3Ys2cPvv/+e+zYsQMbNmzAH/7wB/z444+3/X+QGP8TTzzRbB0bAGONy/UxmcvJkyehUCiMCc7atWsxffp0PPTQQ/jLX/4CPz8/KBQKJCQk4OLFiyb3nThxIl544QV88cUXePnll7F27VoMGDCgTR8GyMqSkVGjRmHUqFEtfr+6uhqvvPIKvvzyS2i1WvTp0wdvv/02hg8fDgBwcXHBsmXLAACHDh1qdiVJeHg4PvjgAwBoUsVtzcLCwrB7926UlZWZ/Kd49uzZJuc1dxwAUlNT4ePjAxcXlzZdOzIyEoIgICIiosmbbXP69u2Lvn374tVXX8Xhw4cRHx+P5cuX45///Gez53/99de4++678dlnn5kc12q18PHxaVV8f/7zn/HnP/8Z58+fR3R0NN577z2sXbu2dU+wDSIjI1FWVmYcCbld4s9L/GR8veZ+hrdzHYPBgPT0dJNPuRcuXDDb47f0Oyd+H7g2YtP4b7etIye38jjnz583+TR+4cIFGAwGY6FzW3/PWzJ58mS89tprOH78ONatW4euXbsaR5NaEhkZCQDw8/O76e9WaGgoQkNDceDAAaSlpRmn/u666y4sWLAAGzduhF6vx1133WVyP7lcjnvuuQf33HMP3n//fSxevBivvPIK9uzZc9NrNvf7ee7cOTg7OxtHdd3c3KDX6832t9FamZmZ2LdvH+Li4owjI19//TU6d+6MzZs3mySdixYtanJ/Ly8vjBkzBl988QUmT56MQ4cONSlEp5uzqmmam5k3bx6OHDmC9evX4/jx43jsscdw//33N/uHYG9Gjx6Nuro6YzIG1H8K/PDDD03OCwwMRHR0NNasWWPyH/XJkyfx448/YvTo0W2+9iOPPAKFQoG///3vTT5pC4Jg7Cip0+lQV1dn8v2+fftCLpejurq6xcdXKBRNHnfjxo3Izs6+YVwVFRVNlmZGRkbCzc3thte7HePHj8eRI0ea7Uaq1WqbPP+buf7ndf3w8a5du8xaZyHWvnz00Ucmxxv//tyq0aNH4+jRozhy5IjxWHl5OVasWIHw8HBj/Yv4pnt9bY1er8eKFSvadD13d3f4+Pg0qdFp/Pyut3TpUpOvxecufkBq7e/5zYijIK+//jqSk5NvOioC1P983N3dsXjxYtTW1jb5fkFBgcnXQ4cOxc8//4yjR48ak5Ho6Gi4ubnhrbfegpOTk0mNR1FRUZPHjI6OBoBW/a0cOXLEpNYiKysL27Ztw3333QeFQgGFQoFHH30UmzZtwsmTJ28av7kUFRVh0qRJ0Ov1Jg3xxJGe63+Ov/76q8nv5/WmTJmC06dP4y9/+Yux9ofaxqpGRm4kMzMTq1atQmZmpnEp54svvogdO3Zg1apVWLx4scQRSmvs2LGIj4/HSy+9hEuXLqFXr17YvHlzszUV//73vzFq1CjExcXhySefNC7t1Wg0t7SvSWRkJP75z39i4cKFuHTpEh566CG4ubkhPT0dW7ZswdNPP40XX3wRP//8M+bNm4fHHnsM3bp1Q11dHT7//HPjf1QteeCBB/Dmm29ixowZGDx4ME6cOIEvvvgCnTt3vmFc586dwz333IPx48ejV69ecHBwwJYtW5CXl9du/5n85S9/wTfffIMHHngA06dPR2xsLMrLy3HixAl8/fXXuHTpUqtGc66XkJCAMWPGYMiQIZg5cyaKiorw4Ycfonfv3sa58NsVGxuLRx99FEuWLMHVq1eNS3vPnTsHoOVpj9Z66aWX8OWXX2LUqFF47rnn4OXlhTVr1iA9PR2bNm0y1i707t0bd955JxYuXGhcTrl+/fo2J3EA8NRTT+Gtt97CU089hQEDBmD//v3G59Oc9PR0PPjgg7j//vtx5MgRrF27Fo8//jiioqIAtP73/GYiIiIwePBgbNu2DQBalYy4u7tj2bJlmDJlCvr374+JEyfC19cXmZmZ+P777xEfH4///e9/xvOHDh2KL774AjKZzDhto1AoMHjwYOzcuRPDhw83WSr75ptvYv/+/RgzZgzCwsKQn5+Pjz76CJ06dTIpOm5Jnz59MHLkSJOlvQDw97//3XjOW2+9hT179mDQoEGYNWsWevXqhaKiIiQlJeGnn35qNiFqi3PnzmHt2rUQBAE6nQ4pKSnYuHEjysrK8P777+P+++83nvvAAw9g8+bNePjhhzFmzBikp6dj+fLl6NWrV7N/U2PGjIG3tzc2btyIUaNGwc/P77ZitUsdvn7HTAAIW7ZsMX793XffCQAEFxcXk5uDg4PJMjbRtGnThHHjxt3wGsOGDRPmz59v3sBvQ0tLe11cXJqcKy5HvN7Vq1eFKVOmCO7u7oJGoxGmTJliXE7beInjTz/9JMTHxwtOTk6Cu7u7MHbsWOH06dPNXqPxcr+Wll9u2rRJGDJkiPFn06NHD2Hu3LnC2bNnBUEQhLS0NGHmzJlCZGSkoFarBS8vL+Huu+8WfvrpJ5PHaW5p75///GchMDBQcHJyEuLj44UjR44Iw4YNu2HXzcLCQmHu3LlCjx49BBcXF0Gj0QiDBg0Svvrqqxbv05yWlvaOGTOm2fNLS0uFhQsXCl26dBGUSqXg4+MjDB48WHj33XeFmpoaQRCuLT1tbtkxmllKu2nTJqFnz56CSqUSevXqJWzevLnZDqyN79uWn2F5ebkwd+5cwcvLS3B1dRUeeugh49LGt95668Yv0nVauubFixeFP/7xj4KHh4egVquFgQMHCt99912T+1+8eFEYMWKEoFKpBH9/f+Hll18Wdu3a1aalvYJQ3/X0ySefFDQajeDm5iaMHz9eyM/Pb/E1On36tPDHP/5RcHNzEzw9PYV58+YJlZWVTa51s9/z1li6dKkAQBg4cGCz37/REueRI0cKGo1GUKvVQmRkpDB9+nSTZbWCIAinTp0SAAg9e/Y0Of7Pf/5TACC89tprJsd3794tjBs3TggKChKUSqUQFBQkTJo0qckS9eagYVnt2rVrha5duwoqlUqIiYlp9meVl5cnzJ07VwgJCREcHR2FgIAA4Z577hFWrFhh8hzRihYAjWMQb3K5XPDw8BBiYmKE+fPnC6dOnWpyvsFgEBYvXiyEhYUZ4/3uu+9u2NVYXOq9bt26VsdF18gEwcIq1FpJJpNhy5YtxhUxGzZswOTJk3Hq1KkmxVSurq5NirqmT58OrVZ7wxU5w4cPR3R0NOf/iFqQnJyMmJgYrF27tlWf4Mn+yGQyzJ0712Rkxha98MIL+Oyzz5CbmwtnZ2epw7E6NjNNExMTA71ej/z8fOMcKBGZT2VlZZMVDEuWLIFcLm9S7EhkT6qqqrB27Vo8+uijTERukVUlI2VlZSbV++np6UhOToaXlxe6deuGyZMnY+rUqXjvvfcQExODgoIC7N69G/369TO2rT59+jRqampQVFSE0tJSJCcnA7hWjAXAeKysrAwFBQVITk6GUqlsUxMpIlvzzjvvIDExEXfffTccHBywfft2bN++HU8//TRCQkKkDo+ow+Xn5+Onn37C119/jatXr2L+/PlSh2S9pJ4nagtxrrDxTawfqKmpEV5//XUhPDxccHR0FAIDA4WHH35YOH78uPExxDbdjW/Xa+77lrL7KZFUfvzxRyE+Pl7w9PQUHB0dhcjISOGNN94QamtrpQ6NLBhu0ordmonvSX5+fsKHH34odThWzWprRoiIiMg22FSfESIiIrI+TEaIiIhIUlZRwGowGHDlyhW4ublxF0QiIiIrIQgCSktLERQUZGxe2ByrSEauXLnCan0iIiIrlZWV1eJmp4CVJCPi5kVZWVlwd3eXOBoiIiJqDZ1Oh5CQEOP7eEusIhkRp2bc3d2ZjBAREVmZm5VYsICViIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiMhK7D6Th59T86QOg8jsrGLXXiIie1dUXoNnPk8EABx9ZQS8XJQSR0RkPhwZISKyAkkZxagzCKgzCDiWWSx1OERmxWSEiMgKJF6XgCRmMBkh28JkhIjICiRdl4AkcWSEbAyTESIiC1enN+D45RLj1ylZJajVGySMiMi8mIwQEVm41NxSVNbq4aZ2gLvaAZW1eqTmlEodFpHZMBkhIrJwYo1ITKgn+od5NhwrkjIkIrNiMkJEZOHEGpH+oR6IDW1IRjK1EkZEZF7sM0JEZOGuJSOeUMhl9ce4ooZsCJMRIiILll9ahayiSshkQHSoB+QyGeQyIFtbidySKgRo1FKHSHTbOE1DRGTBkjK0AIBufm5wVzvCVeWAHgHu9d/jEl+yEUxGiIgsmNhttX+Yh/FYrLGIlckI2QYmI0REFkwc/YhpKFwFmIyQ7WEyQkRkoWrqDEhpaHYmJiBAfSErAJy6UoKqWr0ksRGZE5MRIiILdTpHh5o6AzycHdHZx8V4PMTLCT6uKtTqBZzMLrnBIxBZByYjREQWSly+GxPiAZlMZjwuk8kQ21BDwqkasgVMRoiILNT1/UUaY90I2ZI2JyP79+/H2LFjERQUBJlMhq1bt97w/JycHDz++OPo1q0b5HI5nn/++VsMlYjIvogjI9fXi4jEY0mZxRAEoUPjIjK3Nicj5eXliIqKwtKlS1t1fnV1NXx9ffHqq68iKiqqzQESEdmj3JIqXCmpglwGRIV4NPl+7yANHBUyFJbVIKuosuMDJDKjNndgHTVqFEaNGtXq88PDw/HBBx8AAFauXNnWyxER2SVxiqZ7gDtcVE3/q1Y7KtAnWINjmVokZhYh1Nu5o0MkMhuLrBmprq6GTqczuRER2RNxiqZ/qEeL5xg3zWPdCFk5i0xGEhISoNFojLeQkBCpQyIi6lCJmS3Xi4iuFbFqOyIkonZjkcnIwoULUVJSYrxlZWVJHRIRUYeprtPjVHb9iHBzK2lE/RuSkbO5OpRV13VIbETtwSKTEZVKBXd3d5MbEZG9OJmtQ43eAC8XJcJuUAvi765GsIcTDAKQkqXtuACJzMwikxEiIntm3Bwv1LTZWXPYb4RsQZtX05SVleHChQvGr9PT05GcnAwvLy+EhoZi4cKFyM7Oxv/93/8Zz0lOTjbet6CgAMnJyVAqlejVq9ftPwMiIhsjJhb9b1AvIooN88Q3KVeYjJBVa3My8vvvv+Puu+82fr1gwQIAwLRp07B69Wrk5OQgMzPT5D4xMTHGfycmJmLdunUICwvDpUuXbjFsIiLbJAjCDTuvNnZ98zODQYBcfuORFCJL1OZkZPjw4Tfs9rd69eomx9gdkIioda6UVCFPVw2FXIZ+nTQ3Pb9HgBucHBUorarDhYIydPN364AoicyLNSNERBZE7C/SK9Adzsqbf150UMgRFaIxuS+RtWEyQkRkQRJb0eysMRaxkrVjMkJEZEGMK2laUbwqMiYjmUxGyDoxGSEishBVtXqcunLzZmeNxYTUn5tWUI6i8pp2iY2oPTEZISKyECeyS1BnEODrpkInT6dW38/TRYlIXxcA10ZWiKwJkxEiIgtxfb3IzZqdNSaOpCQxGSErxGSEiMhCXNupt/VTNCIWsZI1YzJCRGQB6pudaQG0rXhVJCYjKVklqNUbzBkaUbtjMkJEZAEuF1eisKwajgoZ+gbfvNlZY5G+rnBXO6CyVo/UnNJ2iJCo/TAZISKyAOL0Sq8gDdSOijbfXy6XGUdUEjOKzBobUXtjMkJEZAGSMtve7Kyxa0WsWjNERNRxmIwQEVmAtmyO1xIWsZK1YjJCRCSxipo6nGmo84i9heJVUVSIB+QyIFtbidySKnOFR9TumIwQEUksJasEeoOAAHc1gjxa3+ysMVeVA3oEuANgvxGyLkxGiIgkZpyiCfO47cfiVA1ZIyYjREQSO2aGehGRmNBwZISsCZMRIiIJ3W6zs8ZiQ70AACezS1BVq7/txyPqCExGiIgkdOlqBYrKa6BUyNE7yP22Hy/Eywk+rirU6gWczC4xQ4RE7Y/JCBGRhMT9aPoEu0Pl0PZmZ43JZDLENkzVsG6ErAWTESIiCZmjv0hjLGIla8NkhIhIQmLCcDv9RRq7vhOrIAhme1yi9sJkhIhIImXVdTiXV9/szBzFq6I+wRo4KmQoLKtGVlGl2R6XqL0wGSEikkhKlhYGAQj2cIK/u9psj6t2VKBPw86/iZncNI8sH5MRIiKJiMWrMbexOV5LYkNZN0LWg8kIEZFEEjPNXy8iulbEqjX7YxOZG5MRIiIJGAwCjonNzsy4kkYk1qCczdWhrLrO7I9PZE5MRoiIJJBWWI6SylqoHOToGXj7zc4a83dXI9jDCQahvjaFyJIxGSEikoDYXySqkweUDu3zXzH7jZC1YDJCRCQBY/GqGXbqbQmTEbIWTEaIiCTQHp1XGxOTkaTMYhgMbH5GlovJCBFRB9NV1eJ8fhmA9k1GegS4wclRgdKqOlwsKGu36xDdLiYjREQdLDlTC0EAQr2c4eumarfrOCjkiAppaH7GqRqyYExGiIg6mJgY9G+HZmeNsW6ErAGTESKiDmasF2mHZmeNGZORTCYjZLmYjBARdSCDQUByQ9+P9qwXEcWE1F8jraAcxeU17X49olvBZISIqANdKChDaVUdnBwV6BHg1u7X83RRorOvCwDgWBZHR8gyMRkhIupAYu1GVIgGDoqO+S+Ym+aRpWMyQkTUgZIy2r+/SGMsYiVLx2SEiKgDdUSzs8bEZCQlqwS1ekOHXZeotZiMEBF1EG1FDS4WlAPomJU0okhfV7irHVBZq0dqTmmHXZeotZiMEBF1kGOZWgBAhI8LvFyUHXZduVyGmNBrreGJLA2TESKiDiImAjEd0OysMdaNkCVjMkJE1EGkqBcRMRkhS8ZkhIioA+gNApIbpmliO7BeRBQV4gG5DMjWViK3pKrDr090I0xGiIg6wNncUpTX6OGqckA3//ZvdtaYq8oBPQLcAbBuhCwPkxEiog4gJgBRIRoo5DJJYugf5lEfC6dqyMK0ORnZv38/xo4di6CgIMhkMmzduvWm99m7dy/69+8PlUqFLl26YPXq1bcQKhGR9ZKyXkTETfPIUrU5GSkvL0dUVBSWLl3aqvPT09MxZswY3H333UhOTsbzzz+Pp556Cjt37mxzsERE1kpc1tuR/UUaiw31AgCczC5BVa1esjiIGnNo6x1GjRqFUaNGtfr85cuXIyIiAu+99x4AoGfPnjh48CD+85//YOTIkW29PBGR1blaVo30woZmZyHSJSMhXk7wcVWhsKwaJ7NLMCDcS7JYiK7X7jUjR44cwYgRI0yOjRw5EkeOHGnxPtXV1dDpdCY3IiJrJY6KRPq6QOPsKFkcMpkM/Rt6nHCJL1mSdk9GcnNz4e/vb3LM398fOp0OlZWVzd4nISEBGo3GeAsJCWnvMImI2o0l1IuIxLoRrqghS2KRq2kWLlyIkpIS4y0rK0vqkIiIbpk4CiFFf5HGrjU/00IQBImjIarX5pqRtgoICEBeXp7Jsby8PLi7u8PJyanZ+6hUKqhUqvYOjYio3dXpDTh+uQSAtMWroj7BGjgqZCgsq0ZWUSVCvZ2lDomo/UdG4uLisHv3bpNju3btQlxcXHtfmohIcqm5pais1cNN7YAuvq5ShwO1owJ9gjUAgMTMIomjIarX5mSkrKwMycnJSE5OBlC/dDc5ORmZmZkA6qdYpk6dajx/9uzZSEtLw1//+lekpqbio48+wldffYUXXnjBPM+AiMiCXdsczxNyiZqdNSbWrrCIlSxFm5OR33//HTExMYiJiQEALFiwADExMXj99dcBADk5OcbEBAAiIiLw/fffY9euXYiKisJ7772HTz/9lMt6icguiG/4/SXYqbclxiLWDK20gRA1aHPNyPDhw29Y9NRcd9Xhw4fj2LFjbb0UEZHVs6SVNCIxGUnN1aGsug6uqnYvHyS6IYtcTUNEZAsKSuuLRGUyINqCRkb83dUI9nCCQQBSsrRSh0PEZISIqL2IoyLd/Nzgrpau2Vlzri3xZd0ISY/JCBFROxF3xxV3y7Uk7MRKloTJCBFRO7l+JY2liQ2r35fmWGYxDAY2PyNpMRkhImoHNXXXNTuzwGSkR6AbnBwV0FXV4WJBmdThkJ1jMkJE1A7O5OhQXWeAh7MjOvu4SB1OE44KOaJCGpqfcaqGJMZkhIioHYhv8DEhHhbT7KwxFrGSpWAyQkTUDiyxv0hjxk6s3MGXJMZkhIioHRzL1AKwjM3xWiIW1qYVlKO4vEbiaMieMRkhIjKz3JIqZGsrIZcBUSEeUofTIi8XJTr71tezHMvi6AhJh8kIEZGZiVM03QPcLb7Veiw3zSMLwGSEiMjMkixwc7yWsIiVLAGTESIiM7OG4lWRWNOSklWCOr1B4mjIXjEZISIyo+o6PU5m6wBcG3WwZF18XeGmdkBlrR6puaVSh0N2iskIEZEZnczWoUZvgJeLEmHezlKHc1NyuezaEl9O1ZBEmIwQEZnRscxr9SIymWU2O2uMdSMkNSYjRERmZMmb47WEyQhJjckIEZGZCIJgfEO3hnoRUVSIB+QyIFtbiTxdldThkB1iMkJEZCZXSqqQp6uGQi5Dv04aqcNpNVeVA7oHuAO4tiyZqCMxGSEiMhPxjbxnoBuclZbd7Kyx2DAPAJyqIWkwGSEiMhNr6i/SmLFuhJvmkQSYjBARmUlSw+Z41lQvIooN9QIAnMwuQVWtXuJoyN4wGSEiMoOqWj1OZZcAsM6RkRAvJ/i4KlGrF3Cy4XkQdRQmI0REZnAiuwR1BgE+rip08nSSOpw2k8muNT9L4lQNdTAmI0REZnD95njW0uysMfYbIakwGSEiMgNxNMEa60VE15IRLQRBkDgasidMRoiIblN9szMtgGu74FqjPsEaOCpkKCyrRlZRpdThkB1hMkJEdJsuF1eisKwaDnIZ+gZbT7OzxtSOCvQOqo8/MbNI4mjInjAZISK6TeIUTe9gDdSOComjuT3iVE1Sw0gPUUdgMkJEdJsSrytetXYsYiUpMBkhIrpN1tx5tTExGUnN1aGsuk7iaMheMBkhIroNFTV1OJNTCsC6i1dF/u5qBHs4wSAAKVlaqcMhO8FkhIjoNhy/XAK9QUCAuxpBGrXU4ZhFf07VUAdjMkJEdBuM9SJh1tvsrLHYhtoXdmKljsJkhIjoNhyzoXoRUWxY/aZ5SRnFMBjY/IzaH5MRIqJbJAiCcafeGBtKRnoEusHJUQFdVR0uFpRJHQ7ZASYjRES3KONqBYrKa6BUyNEn2F3qcMzGUSFHVEhD8zPWjVAHYDJCRHSLxDfqPsHuUDlYd7OzxsRpJyYj1BGYjBAR3SJb6i/SmLETK4tYqQMwGSEiukVivYgt9BdpTKyBuVhQjuLyGomjIVvHZISI6BaUVdfhbK4OwLVRBFvi5aJEZ18XAMCxLI6OUPtiMkJEdAtSsrQwCECwhxP83W2j2VljsawboQ7CZISI6BYkNbxBx9jA5ngtYSdW6ihMRoiIboEtF6+KxOmnlKwS1OkNEkdDtozJCBFRGxkMAo41bCJni/Uioi6+rnBTO6CyVo/U3FKpwyEbdkvJyNKlSxEeHg61Wo1Bgwbh6NGjLZ5bW1uLN998E5GRkVCr1YiKisKOHTtuOWAiIqmlFZZDW1ELlYMcPQNtp9lZY3K5jP1GqEO0ORnZsGEDFixYgEWLFiEpKQlRUVEYOXIk8vPzmz3/1Vdfxccff4wPP/wQp0+fxuzZs/Hwww/j2LFjtx08EZEUxCmafp00UDrY9gBzLOtGqAO0+a/o/fffx6xZszBjxgz06tULy5cvh7OzM1auXNns+Z9//jlefvlljB49Gp07d8acOXMwevRovPfee7cdPBGRFGxxc7yWcGSEOkKbkpGamhokJiZixIgR1x5ALseIESNw5MiRZu9TXV0Ntdp02ZuTkxMOHjzY4nWqq6uh0+lMbkREliIpQwvANpudNRYVooFcBmRrK5Gnq5I6HLJRbUpGCgsLodfr4e/vb3Lc398fubm5zd5n5MiReP/993H+/HkYDAbs2rULmzdvRk5OTovXSUhIgEajMd5CQkLaEiYRUbvRVdXiXH59Mac9jIy4qR3RPaC+LiaJoyPUTtp9svODDz5A165d0aNHDyiVSsybNw8zZsyAXN7ypRcuXIiSkhLjLSsrq73DJCJqleRMLQQBCPFygq+bSupwOkRsmAcATtVQ+2lTMuLj4wOFQoG8vDyT43l5eQgICGj2Pr6+vti6dSvKy8uRkZGB1NRUuLq6onPnzi1eR6VSwd3d3eRGRGQJ7KG/SGPGIlZumkftpE3JiFKpRGxsLHbv3m08ZjAYsHv3bsTFxd3wvmq1GsHBwairq8OmTZswbty4W4uYiEhC4uZ4ttxfpDEx8TqZXYKqWr3E0ZAtavM0zYIFC/DJJ59gzZo1OHPmDObMmYPy8nLMmDEDADB16lQsXLjQeP6vv/6KzZs3Iy0tDQcOHMD9998Pg8GAv/71r+Z7FkREHcBgEOxqJY0o1MsZPq5K1OoFnLpSInU4ZIMc2nqHCRMmoKCgAK+//jpyc3MRHR2NHTt2GItaMzMzTepBqqqq8OqrryItLQ2urq4YPXo0Pv/8c3h4eJjtSRARdYQLBWUoraqDk6MCPQLcpA6nw8hk9c3Pfjydh8SMYsSGeUkdEtmYNicjADBv3jzMmzev2e/t3bvX5Othw4bh9OnTt3IZIiKLIq4miQrRwEFh283OGosNu5aMEJmbff01ERHdBnssXhVd68SqhSAIEkdDtobJCBFRK4mjAvaYjPQJ1sBRIUNhWTWyiiqlDodsDJMRIqJW0FbU4GJBOQAgJtRD2mAkoHZUoHeQBsC1ESIic2EyQkTUCseytACACB8XeLvaR7OzxrhpHrUXJiNERK1wrOEN2B5HRURMRqi9MBkhImqFRDsuXhWJyUhqrg5l1XUSR0O2hMkIEdFN6A0Ckhs6r9pzMuLvrkawhxMMApDSMG1FZA5MRoiIbuJcXinKa/RwUSrQ3Y6anTWnf8PoCHfwJXNiMkJEdBNijUR0qAcUcpnE0UgrtqFmhpvmkTkxGSEiugl7bnbWmNgKPimjGAYDm5+ReTAZISK6iWOsFzHqEegGJ0cFdFV1uFhQJnU4ZCOYjBAR3UBReQ3SC+232Vljjgo5+nWqb37GJb5kLkxGiIhuQCzUjPR1gYezUuJoLIO4xJedWMlcmIwQEd0A60WaYvMzMjcmI0REN2BMRsKYjIhiGhKziwXlKC6vkTgasgVMRoiIWlCnNyAlqwTAtdEAArxclOjs6wIAOJbF0RG6fUxGiIhakJpbispaPdzUDuji6yp1OBZFnLbiVA2ZA5MRIqIWiFM00SEekNt5s7PGjEWsGVppAyGbwGSEiKgF4koaFq82JSYjyVla1OkNEkdD1o7JCBFRC5Iamp2xXqSpLr6ucFM7oLJWj9TcUqnDISvHZISIqBkFpdXILKqATFa/Jw2ZkstlrBshs2EyQkTUDLFepKufK9zVjhJHY5mYjJC5MBkhImoGm53dHDuxkrkwGSEiasaxhlUibHbWsqgQDeQy4HJxJfJ0VVKHQ1aMyQgRUSM1dQakXNYC4MjIjbipHdE9wB3AtZVHRLeCyQgRUSNncnSorjNA4+SIzj4uUodj0WLDPACwboRuD5MRIqJGrtWLsNnZzRiLWFk3QreByQgRUSNifxFO0dycWMR6KluHqlq9xNGQtWIyQkTUiLHzKotXbyrUyxk+rkrU6A04daVE6nDISjEZISK6Tp6uCtnaSshlQFSIh9ThWDyZjM3P6PYxGSEiuo44KtI9wB2uKgeJo7EO4lQNkxG6VUxGiIiuc33xKrVOf2MyooUgCBJHQ9aIyQgR0XUSuVNvm/UN1sBRIUNhWTUuF1dKHQ5ZISYjREQNquv0OJmtA8Di1bZQOyrQO0gDgFM1dGuYjBARNTh1RYcavQFeLkqEeztLHY5VYd0I3Q4mI0REDYxLekM9IJOx2VlbMBmh28FkhIiogVi8GsN6kTYTa2xSc3Uoq66TOBqyNkxGiIgaJIk79TIZabMAjRrBHk4wCMDxLK3U4ZCVYTJCRATgirYSuboqKOQyRIVopA7HKvXnVA3dIiYjRES49gbaM9ANzko2O7sVsQ29WbhpHrUVkxEiIlzf7IxTNLcqNswLQH0hsMHA5mfUekxGiIjAnXrNoUegG9SOcuiq6nCxoEzqcMiKMBkhIrtXVavH6YYdZ2PZ7OyWOSrkiOrkAeDaSBNRazAZISK7dyK7BLV6AT6uKnTydJI6HKvGfiN0K5iMEJHdY7Mz82EyQreCyQgR2T1j8SqnaG6b2DDuYkE5istrJI6GrMUtJSNLly5FeHg41Go1Bg0ahKNHj97w/CVLlqB79+5wcnJCSEgIXnjhBVRVVd1SwERE5iQIgrF4lfUit8/LRYnOPi4AgGNZHB2h1mlzMrJhwwYsWLAAixYtQlJSEqKiojBy5Ejk5+c3e/66devw0ksvYdGiRThz5gw+++wzbNiwAS+//PJtB09EdLsuF1eioLQaDnIZ+gaz2Zk5iCNMYkdboptpczLy/vvvY9asWZgxYwZ69eqF5cuXw9nZGStXrmz2/MOHDyM+Ph6PP/44wsPDcd9992HSpEk3HU0hIuoI4hRN7yB3qB0VEkdjG1g3Qm3VpmSkpqYGiYmJGDFixLUHkMsxYsQIHDlypNn7DB48GImJicbkIy0tDT/88ANGjx7d4nWqq6uh0+lMbkRE7UEsXuXmeOYjJiPJWVrU6Q0SR0PWoE09jwsLC6HX6+Hv729y3N/fH6mpqc3e5/HHH0dhYSGGDBkCQRBQV1eH2bNn33CaJiEhAX//+9/bEhoR0S1hvYj5dfF1hZvaAaVVdUjNLUUfTn/RTbT7apq9e/di8eLF+Oijj5CUlITNmzfj+++/xz/+8Y8W77Nw4UKUlJQYb1lZWe0dJhHZoYqaOpzOqR955Uoa85HLZcaRJk7VUGu0aWTEx8cHCoUCeXl5Jsfz8vIQEBDQ7H1ee+01TJkyBU899RQAoG/fvigvL8fTTz+NV155BXJ503xIpVJBpVK1JTQiojY7frkEeoMAf3cVgjRqqcOxKbGhnth/rgBJmcWYNjhc6nDIwrVpZESpVCI2Nha7d+82HjMYDNi9ezfi4uKavU9FRUWThEOhqC8SEwRupERE0hGLV2PDPNnszMxYxEpt0eZ9shcsWIBp06ZhwIABGDhwIJYsWYLy8nLMmDEDADB16lQEBwcjISEBADB27Fi8//77iImJwaBBg3DhwgW89tprGDt2rDEpISKSgrj0lJvjmV9UiAZyWf3S6TxdFfzdOfJELWtzMjJhwgQUFBTg9ddfR25uLqKjo7Fjxw5jUWtmZqbJSMirr74KmUyGV199FdnZ2fD19cXYsWPxr3/9y3zPgoiojeqbnXElTXtxUzuie4A7zuTokJRRjFF9A6UOiSyYTLCCuRKdTgeNRoOSkhK4u7tLHQ4R2YBLheUY/u5eKBVynPj7fVA5cKTW3F7ZcgJf/JqJp4ZE4NUHekkdDkmgte/f3JuGiOySOCrSJ9idiUg7EetGxNeaqCVMRojILhk3x+MUTbsRk5GT2TpU1eoljoYsGZMRIrJLiWLxKvuLtJtQL2f4uCpRozfg1JUSqcMhC8ZkhIjsTll1Hc7mNjQ748hIu5HJZMbXl0t86UaYjBCR3TmepYVBAII9nBDAZmftqj/7jVArMBkhIrtzbUmvh7SB2IFrRaxaNrqkFjEZISK7I35K5xRN++sbrIGjQoaC0mpcLq6UOhyyUExGiMiuCIKAY1laACxe7QhqRwV6B9Xv2supGmoJkxEisitpheXQVtRC5SBHr0A2UewI3KeGbobJCBHZlaSGN8R+nTRQOvC/wI7AFTV0M/xLJCK7wmZnHa9/mAcAIDVXh/LqOmmDIYvEZISI7Iq4Uy83x+s4gRonBHs4wSAAKQ31OkTXYzJCRHZDV1WLc/mlAK59WqeOwX4jdCNMRojIbqRkaSEIQIiXE/zc2OysI8U29HRJ5KZ51AwmI0RkN9hfRDriyEhSRjEMBjY/I1NMRojIbiRlagEwGZFCz0B3qB3l0FXVIa2wTOpwyMIwGSEiu2AwCDjWMEUQy2ZnHc5RIUdUJw8ArBuhppiMEJFduFBQhtKqOjg5KtAjwE3qcOwSm59RS5iMEJFduL7ZmYOC//VJgckItYR/kURkF4zNzjhFIxmxt8vFgnJoK2okjoYsCZMRIrILYvFqLItXJePlokRnHxcAwLGGnwcRwGSEiOyAtqIGF/LrV3DENPS7IGmw+Rk1h8kIEdm8Yw0tyMO9neHtqpI2GDvHuhFqDpMRIrJ5x9jszGKIyUhylhZ1eoPE0ZClYDJCRDbP2OyMxauS6+LrCje1Aypr9UjNLZU6HLIQTEaIyKbpr2t2xpER6cnlMuOqGk7VkIjJCBHZtHN5pSiv0cNFqUB3NjuzCOKKpiRumkcNmIwQkU0T3/CiQz2gkMskjoYAFrFSU0xGiMimJWVoAXCKxpJEhWgglwGXiyuRp6uSOhyyAHadjJRX12HLscsQBG5nTWSrklgvYnHc1I7o5l8/ZZbE0RGCHScjBoOAP3+Vghc2pODlLSdQU8clZkS2pqi8BumF5QDY7MzScKqGrme3yYhMVv+fk0wGfHk0C49/8gsKSqulDouIzEhcRRPp6wIPZ6XE0dD1xGSERawE2HUyIsMzwyKxcvodcFM74PeMYoz730GczC6ROjQiMhNO0VguMRk5ma1DVa1e4mhIanabjIju7u6HrXPj0dnXBVdKqvDossPYlpwtdVhEZAbiFACbnVmeUC9n+LgqUaM34NQVfgi0d3afjABApK8rts6Nx93dfVFdZ8D89cl4e0cq9AYWthJZqzq9ASlZ9W9yHBmxPDIZm5/RNUxGGrirHfHptDswe1gkAGDZ3ouY9X+/Q1dVK3FkRHQrUnNLUVmrh5vKAV39XKUOh5rBIlYSMRm5jkIuw0ujeuCDidFQOcjxc2o+Hlp6CGkFZVKHRkRtdOy6ZmdyNjuzSNeKWLVssWDnmIw0Y1x0ML6ePRiBGjXSCsoxbukh7D2bL3VYRNQGidyp1+L1DdbAUSFDQWk1LhdXSh0OSYjJSAv6dtLgm3lDMCDME6VVdZi5+jd8vO8is3ciK8Gdei2f2lGB3kEaAJyqsXdMRm7A102FL2YNwsQ7QmAQgITtqVjwVQqXoRFZuMKyamQWVUAmA6JDPKQOh26gP4tYCUxGbkrloEDCI33x5rjeUMhl2HIsG+M/PoLcEu6nQGSpxBbjXf1coXFylDgauhEWsRLAZKRVZDIZpsaF4/MnB8LT2RHHL5dg7P8O8o+HyEIlstmZ1egf5gEASM3Voby6TtpgSDJMRtpgcKQPvpk3BD0C3FBQWo1JK37BV79lSR0WETVyjDv1Wo1AjROCPZxgEICULK3U4ZBEmIy0UYiXMzbNGYz7ewegRm/AXzcdxxvfnEKdnhvtEVmCWr0Bx7O1AFi8ai36c6rG7jEZuQUuKgd8NLk/XhjRDQCw+vAlTF15FMXlNRJHRkRncnSoqjVA4+SIzj4uUodDrdC/YUflRG6aZ7eYjNwiuVyG+SO6YvkTsXBWKnD44lWMW3oIZ3NLpQ6NyK6Jn65j2OzMahibn2UUw8BtOOzSLSUjS5cuRXh4ONRqNQYNGoSjR4+2eO7w4cMhk8ma3MaMGXPLQVuS+/sEYPOzgxHi5YTMogo8/NEh7DiZK3VYRHbL2F+E9SJWo2egO9SOcuiq6pBWyI7X9qjNyciGDRuwYMECLFq0CElJSYiKisLIkSORn998h9LNmzcjJyfHeDt58iQUCgUee+yx2w7eUvQIcMc3c4dgcKQ3Kmr0mL02ER/8dJ4ZPpEExGW9sawXsRqOCjmiOnkAYN2IvWpzMvL+++9j1qxZmDFjBnr16oXly5fD2dkZK1eubPZ8Ly8vBAQEGG+7du2Cs7OzTSUjAODposT/zRyI6YPDAQD/+ekc5q5L4lI1og6Up6tCtrYSchkQxWZnVoX9Ruxbm5KRmpoaJCYmYsSIEdceQC7HiBEjcOTIkVY9xmeffYaJEyfCxaXlwrLq6mrodDqTmzVwUMjxxoO98c6j/eCokGH7yVw8uuwwsooqpA6NyC6IoyLd/N3gqnKQOBpqC3ZitW9tSkYKCwuh1+vh7+9vctzf3x+5uTevkzh69ChOnjyJp5566obnJSQkQKPRGG8hISFtCVNy4+8Iwfqn74SPqwqpuaV48H8HcfhiodRhEdm8JLHZGadorI74M7tYUA5tBVcm2psOXU3z2WefoW/fvhg4cOANz1u4cCFKSkqMt6ws62ssFhvmhW//FI9+nTQorqjFlM+OYs3hS9xoj6gdicWrsSxetTpeLkrjUuxjDT9Hsh9tSkZ8fHygUCiQl5dncjwvLw8BAQE3vG95eTnWr1+PJ5988qbXUalUcHd3N7lZo0CNE756Jg4PRQdBbxCw6JtTWLj5BGrq2CCNyNyq6/Q4cbkEAEdGrBWbn9mvNiUjSqUSsbGx2L17t/GYwWDA7t27ERcXd8P7bty4EdXV1XjiiSduLVIrpXZU4D8TovHy6B6Qy4D1v2Xh8U9+QUFptdShEdmUU1d0qNEb4OWiRLi3s9Th0C1gEav9avM0zYIFC/DJJ59gzZo1OHPmDObMmYPy8nLMmDEDADB16lQsXLiwyf0+++wzPPTQQ/D29r79qK2MTCbD03dFYuX0O+CmdsDvGcV48H8HjZ/iiOj2icWr/UM9IJOx2Zk1EotYk7O03GLDzrQ5GZkwYQLeffddvP7664iOjkZycjJ27NhhLGrNzMxETk6OyX3Onj2LgwcPtmqKxpYN7+6HbXPj0dnXBTklVfjj8sPYlpwtdVhENkGsM4hhvYjV6urnCjeVAypr9UhlN2u7IhOsoKJSp9NBo9GgpKTEautHrqerqsX8L49hz9kCAMDsYZH4y8juULB1NdEtu3PxbuTqqvDlrDsRF2l/I7C2YurKo9h/rgBvjuuNqXHhUodDt6m179/cm0YC7mpHfDrtDswZHgkAWL7vIp5a8xt0VbUSR0Zkna5oK5Grq4JCLkNUiEbqcOg2xLLfiF1iMiIRhVyGv93fAx9MjIbKQY49Zwvw0NJDuFjAfRmI2krsL9Iz0A3OSjY7s2b9wzwAMBmxN0xGJDYuOhhfzx6MQI0aaQXleGjpIew52/w+P0TUvKQMLQBujmcLokM8IJMBl4srkaerkjoc6iBMRixA304afDNvCAaEeaK0qg4zV/+Gj/ddZIM0olZKFDuvMhmxem5qR3T3dwNwbYUU2T4mIxbC102FdbPuxKSBIRAEIGF7Kl7YkIyqWr3UoRFZtKpaPU5faWh2xmTEJoj9RsTpN7J9TEYsiNJBjsUP98Wb43pDIZdha/IVjP/4CHJKKqUOjchincwuQa1egI+rCiFeTlKHQ2bA5mf2h8mIhZHJZJgaF461Tw6Cp7Mjjl8uwdgPDyExo0jq0IgsknFzPDY7sxniCNfJbB1Hh+0EkxELFRfpjW/mDUGPADcUllVj4opfsOG3TKnDIrI44qdn7kdjO8K8neHtokSN3oBTV9ip2h4wGbFgIV7O2DRnMEb1CUCtXsDfNp3AG9+cQi3bJBMBAARBMO7Uy3oR2yGTybhpnp1hMmLhXFQOWPp4fyy4txsAYPXhS5i28iiKy2skjoxIepeLK1FQWg0HuQz9OrHZmS0xFrE2LNsm28ZkxArI5TI8d09XfDwlFi5KBQ5fvIoHlx5Eaq5O6tCIJCXWi/QOcofaUSFxNGROxiLWzGK2ObADTEasyMjeAdj8bDxCvZyRVVSJRz46jB0nc6UOi0gyYh8Kbo5ne/oGa+Agl6GgtBqXi7mi0NYxGbEy3QPcsG1uPOK7eKOiRo/ZaxOx5KdzMBj4yYHsj7FehMWrNkftqEDv4PqpN9aN2D4mI1bI00WJNTMGYkZ8OABgyU/n8ewXSSivrpM2MKIOVFmjx5mc+qnKWCYjNomb5tkPJiNWykEhx6KxvfHOH/tBqZBjx6lcPLrsMDKvVkgdGlGHOH5ZizqDAH93FYI0aqnDoXbATqz2g8mIlRs/IARfPn0nfFxVSM0txYNLD+LwhUKpwyJqd9fvR8NmZ7ZJ3MH3TI6OI782jsmIDYgN88S3f4pHv04aaCtqMWXlUaw5fIkV6GTTuFOv7QvUOCFIo4ZBAFKytFKHQ+2IyYiNCNQ44atn4vBwTDD0BgGLvjmFlzadQHUdWymT7REEAccy2XnVHrD5mX1gMmJD1I4KvD8+Ci+P7gG5DNjwexYe/+RXFJRWSx0akVllFlXgankNlAo5+gS7Sx0OtaPr+42Q7WIyYmNkMhmevisSK6ffATe1AxIzivHg/w7i+GWt1KERmY34Kbl3sDtUDmx2ZsvEZORYppYtDGwYkxEbNby7H7bNjUdnXxfklFThseVHsC05W+qwiMwi6briVbJtPQPdoXaUo6SyFmmFZVKHQ+2EyYgN6+zriq1z4/GHHn6orjNg/vpkJGw/Az0/XZCVE4tX2V/E9jkq5OjXyQMA60ZsGZMRG+eudsQnUwfg2eGRAICP96XhyTW/oaSyVuLIiG5NeXWdcV8mjozYh1gWsdo8JiN2QCGX4a/398B/J8VA7SjH3rMFePijQ7hYwCFPsj4pWVoYBCBIo0YAm53ZBXZitX1MRuzIg1FB+Hr2YARq1EgrKMdD/zuEPan5UodF1CZJXNJrd8Sf9cWCcmgraiSOhtoDkxE70ydYg2/mDcEd4Z4ora7DzDW/Yfm+i2yQRlbDuDkep2jshpeLEp19XADUr6oh28NkxA75uqnwxVN3YtLAUAgC8Nb2VDy/IRlVtWyQRpZNEASOjNipGE7V2DQmI3ZK6SDH4of74B/jesNBLsO25Ct4bPkRXNFWSh0aUYvSCsuhraiFykGOXoFsdmZPWMRq25iM2DGZTIYpceH4/MlB8HR2xInsEjz4v0P4/VKR1KERNSup4Y2oXycNlA7878ueiMlIcpYWdXqDxNGQufGvmRAX6Y1v5g1BjwA3FJZVY9Inv2D90UypwyJqgvUi9qurnyvcVA6orNUjNbdU6nDIzJiMEAAgxMsZm+YMxqg+AajVC3hp8wks2nYStfwEQhZEHBmJYTJid+RyGWIaRkeSuE+NzWEyQkYuKgd8NLk/FtzbDQCw5kgGpn52FMXlXEpH0tNV1eJcfv0n4v5hHtIGQ5LoH+oBgHUjtojJCJmQyWR47p6uWDElFi5KBY6kXcWDSw8aO14SSSUlSwtBAEK8nODnxmZn9ohFrLaLyQg1677eAdj8bDxCvZyRVVSJRz46jB0nc6QOi+yYuB8N60XsV3SIB2Qy4HJxJfJ1VVKHQ2bkIHUAZLm6B7jhm3nxmLfuGA5eKMTstUno7OOCCPHme+3fAe5qyGQyqUMmG5bInXrtnpvaEd393ZCaW4qkzGLc3ydQ6pDITJiM0A15OCuxesYdWPxDKlYeSkdaYTnSCsubnOfkqEC4j4tJsiJ+7emilCBysiUGg4BjTEYI9VM1qbmlSMxgMmJLmIzQTTko5Hh9bC88M6wzLuSXIb2w3OSWWVSBylo9zuTocCanaW2Jh7OjMUHp3JCkiF87K/krSDd3saAMpVV1cHJUoEegm9ThkIT6h3rii18zWTdiY/hOQK3m766Gv7sa8V18TI7X6g3IKqpokqSkF5Yjp6QK2opaHMvUNrunRIC7+tqUj/e16Z8QT2c2tSIjcSlnv04aOCr4e2HPxCLWk9k6VNXqoXZUSBwRmQOTEbptjgo5Ovu6orOva5PvVdTU4VJhBS5drU9O0grKkV5YP7pSXFGLXF0VcnVVOJJ21eR+CrkMIZ5OJtM9ET6uiPB1QaC7GnI561PsifgpmPvRUJi3M7xdlLhaXoNTV0oQG+YldUhkBkxGqF05Kx3QK8gdvYKa7iOiragxGUVJKyxHekH9vytr9bh0tQKXrlYAZwtM7qdykJvUpURcV6vi5aJkIa0NYudVEslkMvQP88Su03lIytAyGbERTEZIMh7OSsSEKpt00xQEAfml1Q2jKNdGUtIKy5F5tQLVdQak5pY22xLaXe2ACF9XRHg7G0dSxDoVVxV/3a1RSUUtLuSXAbjW9IrsW2xDMpKYUYxZUgdDZsH/ncniyGQyY31KXKS3yffq9AZkaytNRlHE25WSSuiq6pCSpUVKlrbJ4/q5qZqs+Ons64IQL2eoHDjvbKmOZdVP0YR7O8PbVSVxNGQJxBGyxMxiCILA0VAbwGSErIqDQo4wbxeEebvg7u6m36uq1SPjagXSC8tMkpVLV8tRWFaD/NJq5JdW42i66a7EchnQydO5SaIS4eOCIA8nKFifIilxPxpO0ZCoXycNHOQyFJRW43JxJUK8nKUOiW4TkxGyGWpHBboHuKF7QNOlnyWVtbh0fW1KYbnx67LqOmQWVSCzqAL7z5nWpygd5Ajzcjau8jEW0vq4wMeV9SkdQawXiWHxKjVQOyrQO1iDlCwtEjOKmYzYACYjZBc0To6ICvFAVIiHyXFBEFBQVt1kyie9sBwZVytQU2fA+fwynG+oWbieq8rBZBSls++1olp3tWMHPTPbpjcISG6YcovlyAhdJzbUEylZWiRlFuOhmGCpw7FqZ3NL8eXRTLw0qodkS6WZjJBdk8lk8HNTw89NjUGdTetT9AYBV4z1KQ3N3hqmgS4XV6Ksug4nsktwIrukyeP6uKpwby9/PHNXZ4T7uHTU07E55/NLUVZdBxelotkRL7JfsWGeWHkonc3PbpHBIODn1HysOpyOQxfqWyv0CnTH+DtCJInnlpKRpUuX4t///jdyc3MRFRWFDz/8EAMHDmzxfK1Wi1deeQWbN29GUVERwsLCsGTJEowePfqWAydqbwq5DCFezgjxcsawbr4m36uq1SOrqMJkykf8d0FpNQrLqvHl0Uxs+C0To/sGYvawSPQJ1kj0TKyX+EYTFeLB2h0y0T/MAwBwJkeH8uo6uHC1XKuUVddh4+9ZWH34EjKuVgCor5u7v08AegY2bcHQUdr809uwYQMWLFiA5cuXY9CgQViyZAlGjhyJs2fPws/Pr8n5NTU1uPfee+Hn54evv/4awcHByMjIgIeHhzniJ5KE2lGBrv5u6Orf9NN6aVUtjl8uwWcH0/Fzaj6+O56D747n4K5uvpgzLBJ3dvZirUkrcadeakmgxglBGjWulFQhJUuLwY06Q5OpzKsVWH34Er76PQtl1XUA6lshTBoYiilxYejkKW3dTZuTkffffx+zZs3CjBkzAADLly/H999/j5UrV+Kll15qcv7KlStRVFSEw4cPw9Gxfh49PDz89qImsmBuakfEd/FBfBcfnMnRYfm+i/g25Qr2nyvA/nMFiAn1wJxhkRjR05+dZG9C3BwvlsWr1Iz+YZ64cjwHiRnFTEaaIQgCjqRdxapDl/DTmTwIQv3xSF8XTI+PwKP9gy1mf7A2RVFTU4PExEQsXLjQeEwul2PEiBE4cuRIs/f55ptvEBcXh7lz52Lbtm3w9fXF448/jr/97W9QKJovlKmurkZ1dbXxa52u6eZrRNagZ6A7PpgYgz/f2x2fHEjDht+zcCxTi6c/T0QXP1fMHhaJcdFB3G+lGUXlNcYdomPY7IyaERvmie+O5xj3LqJ6VbV6fJN8BSsPpZs0hxzWzRcz4sNxV1dfi/sg1KZkpLCwEHq9Hv7+/ibH/f39kZqa2ux90tLS8PPPP2Py5Mn44YcfcOHCBTz77LOora3FokWLmr1PQkIC/v73v7clNCKLFurtjH881AfP3dMVqw6l4/MjGbiQX4YXN6bg/R/PYtZdnTHhjhCL+ZRiCcRRkc6+LvBwVkocDVkiccQsKVMLg0GwuDfYjpanq8LaXzKw7tdMXC2vAQA4OSrwaGwwpg+OQBe/pvuHWYp2/5/PYDDAz88PK1asgEKhQGxsLLKzs/Hvf/+7xWRk4cKFWLBggfFrnU6HkBBpKnyJzMnXTYW/3t8Ds4dHYt2vmfj0QDqulFTh79+exn93n8f0wRGYNjiMb764tlMvl/RSS3oGukPtKEdJZS3SCsvQxc8+V1ylZGmx6lA6vj+Rg1p9/VxMsIcTpsaFYeIdodA4W36rgTYlIz4+PlAoFMjLyzM5npeXh4CAgGbvExgYCEdHR5MpmZ49eyI3Nxc1NTVQKpv+p6tSqaBSse0z2S53tSNmD4vE9MHh2JR0GR/vS0NmUQX+89M5fLz/Ih4fGIonh0YgUOMkdaiSMRavsl6EWuCokKNfJw8cTS9CYkaxXSUjdXoDdpzKxapDl0yWN98R7okZ8RG4r5c/HKxo+rdNkSqVSsTGxmL37t3GYwaDAbt370ZcXFyz94mPj8eFCxdgMBiMx86dO4fAwMBmExEie6J2VGDyoDD8/Odh+HBSDHoGuqOiRo9PD6bjrnf24K9fp+BiQdOGa7auTm9AymUtAK6koRsTp2rspd+ItqIGy/ZexF3v7MG8dceQmFEMR4UMj8QE49t5Q7Bx9mCM7htoVYkIcAvTNAsWLMC0adMwYMAADBw4EEuWLEF5eblxdc3UqVMRHByMhIQEAMCcOXPwv//9D/Pnz8ef/vQnnD9/HosXL8Zzzz1n3mdCZMUcFHKMjQrCA/0Cse9cAZbtvYhf04vw1e+XsTHxMu7vHYA5wyPRr5OH1KF2iNTcUlTU6OGmckBXC57nJumJ03jitgG26nxeKVYdvoTNSZdRVVv/4d7HVYnHB4XhiTtD4eemljjC29PmZGTChAkoKCjA66+/jtzcXERHR2PHjh3GotbMzEzI5dcyspCQEOzcuRMvvPAC+vXrh+DgYMyfPx9/+9vfzPcsiGyETCbD8O5+GN7dD4kZxVi29yJ+OpOH7Sdzsf1kLuK7eOPZ4V0wONLbpnuViMWr0aEedl+USDcmTuNdyC+DtqLGpuqtDAYB+84VYOWhdBw4X2g83ivQHTPiwzE2Kkiy9u3mJhMEceWx5dLpdNBoNCgpKYG7u3Qd4oikcC6vFMv3XcS25CvQG+r/XPt10mDOsEjc1zvAJjuTvrAhGVuOZWP+PV3xwr3dpA6HLNzd7+5FemE5Vk2/A3f3aNp809qUV9dhU9JlrD50ybi8XS4D7u3lj5nxERgYYT2NE1v7/s11hEQWrpu/G94fH40F93bDpwfSsf63TBy/XII5XyShs68LZt8ViYdigqF0sK454hsRV9KweJVao3+oJ9ILy5GYUWzVyUhWUQX+78glrP8tC6VV9V1S3VQOmHBHCKYNDrfp3YmZjBBZiU6eznjjwd740x+6YM3hS1h9+BLSCsrx103H8f6uc3hqaAQmDQy1+j06CsuqjXtmRDfaZZmoObFhntiUdNkqi1gFQcDR9CKsOnQJP57ORcPgJyJ8XDB9cDgeje0EVyv/m24N23+GRDbG21WFBfd1x9PDIvHlr5n49GAacnVV+Of3Z/DhzxcwbXA4pg8Oh5eLdc6dJzW8oXTzd4XGyfL7I5D0xBU1KZe1qNMbrGIlSXWdHt+m5GDVoXScunKty/jQrj6YER+O4d387KpeiskIkZVyVTlg1l2dMXVwGLYey8byfWlILyzHf3efxyf70zBxYAieGtoZwR7W1atEXBXBJb3UWl39XOGmckBpdR1Sc0steofs/NIqfPFLJr74NQOFZfVdUtWOcjwc0wkz4sPRrZnNN+0BkxEiK6dyUGDCHaH4Y2wIdp7KxbK9F3EiuwSrDl3C50cyMC46GLOHdW52h2FLJI6MMBmh1pLLZYgO9cCB84VIyiy2yGTkZHYJVh5Kx3cpOajR1y/NDdSoMSUuDJPuCIWnlY5kmguTESIboZDLMLpvIEb1CcChC1exbN8FHLpwFZuSLmNT0mXc18sfc4ZHIsaC3+Rr9QYcz9YCAPqHeUgaC1mX2DBPHDhfiMSMYkyNC5c6HAD1zft2nc7DqkOXcPRSkfF4/1APzBwSgZG9A7hJZgMmI0Q2RiaTYUhXHwzp6oPkLC2W772Inadz8ePpPPx4Og93dvbCnOFdcFdXH4tbHngmR4eqWgM0To7o7MNmZ9R6ltSJtaSiFht+z8SawxnI1lYCABzkMozpF4gZ8REszG4GkxEiGxYd4oHlU2JxIb8MK/ZfxJZj2fglrQi/pB1F7yB3zBkeiVF9Ai2mV4k4RRPDZmfURtEhHpDJgMvFlcjXVcHPveM7kl4sKMPqQ5fwdeJlVNbqAQBeLko8PjAUU+LC4C9BTNaCyQiRHeji54p3/hiF50d0w2cH0/Hl0UycuqLDvHXHEO59Fk/fFYlHY4OhcpC2m2Mii1fpFrmpHdHd3w2puaVIyizG/X0CO+S6giBg//lCrDqUjr1nC4zHewS4YUZ8OMZFB9tMl9T2xGSEyI4EeTjhtQd6Yd7dXfB/RzKw+nA6Ll2twMtbTmDJT+fw5JAIPD4oFG5qaZbUsniVbkf/ME+k5pYiMaP9k5GKmjpsTsrG6sOXcCG/fjNLmQy4p4c/Zg4JR1xn296ywdyYjBDZIU8XJeaP6IpZd0Vg/dEsfHIgDTklVUjYnor/7bmAqXFhmBEfAR9XVYfFlK+rQra2EnIZEBVieashyPLFhnpi3a+Z7Vo3kq2trO+SejQLJZW1AOqX2Y8fEIJpg8MQ5u3Sbte2ZUxGiOyYs9IBM4dE4Ik7w/BNyhUs33cRF/LLsHTPRXx6IB0T7gjBrKGdO6QNtdgCvpu/m2QjM2TdxCLWk9k6VNfpzTbtKAgCEjOKserQJew4lWvcIyrM2xnTB4fjj7Gd+Dt7m5iMEBGUDnL8MbYTHokJxq4zefho70WkZGnxf0cy8MWvmRjbLxCzh0eiR0D7bVQpfprlfjR0q8K8neHtosTV8hqczNYZk5NbVVNnwPcnrmDVoUs4frnEeHxwpDdmxkfg7h5+FlP8be2YjBCRkVwuw8jeAbivlz9+SSvCR3sv4MD5QmxNvoKtyVdwTw8/zBkeiQHhXma/Njuv0u2SyWToH+aJXafzkJRRfMvJSGFZNdb9monPf8lAQWk1gPqE/eHoYMwYEt6uSbm9YjJCRE3IZDLERXojLtIbJ7NLsGzfRfxwIge7U/OxOzUfd4R74tnhXTC8u69ZivRq6gw4kV3/yfN2P82SfesfWp+MJGYUY1Yb73v6ig6rDqVjW8oV1NTVd0n1d1dhalw4Jg0Mtdr9nqwBkxEiuqE+wRosfbw/0gvLsWL/RWxKzMZvl4oxY/Vv6BHghjnDIzGmb+BtbU526koJauoM8HJRItzbdrdJp/ZnbH6WWQxBEG6aLOsNAn46k4dVh9LxS9q1LqlRIR6YGR+O0X0D2SW1AzAZIaJWifBxQcIj/fD8iG5YeTAda3/JQGpuKeavT8a7P9b3KnksttMt9VQQ60ViQjy4HJJuS79OGjjIZSgorcbl4soWi691VbX46rcsrDlyCVlF9V1SxS0VZsSHc7qwgzEZIaI28XdXY+Honnh2eBd8/sslrDpU/5/5a1tP4oOfzmFGfP3qHI1T61cXHBPrRThFQ7dJ7ahA72ANUrK0SMosbpKMpBeWY83hS9j4exbKa+q7pHo4Oxq7pAZqrGuXa1vBZISIbonG2RHz/tAVTw7pjI2JWfh4XxqytZX4986zWL73IibfGYaZQ8Lh53bzFtjisl5+GiVziA31REqWFokZxRgXHQxBEHDowlWsOpSOn8/mQ6hfmYuufq6YOSQCD0UHw0nJLqlSYjJCRLfFSakwFvh9d/wKlu29iHN5ZVi+7yJWHkrHH2M74Zm7OrfYDOqKthI5JVVQyGVsdkZm0T/MAysPAUfTi/Dl0UysOpSOc3llxu//oYcfZsZHIL4Lu6RaCiYjRGQWjgo5Ho7phHFRwdhzNh8f7b2IxIxirPs1E+uPZmJMvyDMHtYZvYNMEw5xVKRHgBuclfwviW6fWMSamluKhZtPAABclAo8NiAE0waHI8KHXVItDf/yicis5HIZ7unpj3t6+uNoehGW7b2APWcL8G3KFXybcgXDuvlizvBIDIrwgkwmQ1KGFgCnaMh8AjVO6OrnivP5ZQjxcsK0uHCMvyME7uySarGYjBBRuxkY4YWBEQNx+ooOH++/iG9TrmDfuQLsO1eA/qEemDO8CxIbRkbYX4TMac3MgcgqqsCAcC92SbUCMkEQS3ksl06ng0ajQUlJCdzd2fmOyFplXq3AigMX8dXvl41NpUT7/3I3QtljhMimtPb9m51ciKjDhHo7458P9cWhv/0Bzw6PhJuqfnA2UKNGiBeXVBLZK46MEJFkdFW12HEiF72C3NEnmCtpiGxNa9+/WTNCRJJxVzti/B0hUodBRBLjNA0RERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJyip27RUEAUD9VsRERERkHcT3bfF9vCVWkYyUlpYCAEJCuNU4ERGRtSktLYVGo2nx+zLhZumKBTAYDLhy5Qrc3Nwgk8nM9rg6nQ4hISHIysqCu7u72R7XVvH1aj2+Vq3H16r1+Fq1Hl+r1mvP10oQBJSWliIoKAhyecuVIVYxMiKXy9GpU6d2e3x3d3f+srYBX6/W42vVenytWo+vVevxtWq99nqtbjQiImIBKxEREUmKyQgRERFJyq6TEZVKhUWLFkGlUkkdilXg69V6fK1aj69V6/G1aj2+Vq1nCa+VVRSwEhERke2y65ERIiIikh6TESIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpKUXScjS5cuRXh4ONRqNQYNGoSjR49KHZJF2r9/P8aOHYugoCDIZDJs3bpV6pAsUkJCAu644w64ubnBz88PDz30EM6ePSt1WBZr2bJl6Nevn7HrY1xcHLZv3y51WBbvrbfegkwmw/PPPy91KBbpjTfegEwmM7n16NFD6rAsVnZ2Np544gl4e3vDyckJffv2xe+//97hcdhtMrJhwwYsWLAAixYtQlJSEqKiojBy5Ejk5+dLHZrFKS8vR1RUFJYuXSp1KBZt3759mDt3Ln755Rfs2rULtbW1uO+++1BeXi51aBapU6dOeOutt5CYmIjff/8df/jDHzBu3DicOnVK6tAs1m+//YaPP/4Y/fr1kzoUi9a7d2/k5OQYbwcPHpQ6JItUXFyM+Ph4ODo6Yvv27Th9+jTee+89eHp6dnwwgp0aOHCgMHfuXOPXer1eCAoKEhISEiSMyvIBELZs2SJ1GFYhPz9fACDs27dP6lCshqenp/Dpp59KHYZFKi0tFbp27Srs2rVLGDZsmDB//nypQ7JIixYtEqKioqQOwyr87W9/E4YMGSJ1GIIgCIJdjozU1NQgMTERI0aMMB6Ty+UYMWIEjhw5ImFkZEtKSkoAAF5eXhJHYvn0ej3Wr1+P8vJyxMXFSR2ORZo7dy7GjBlj8v8WNe/8+fMICgpC586dMXnyZGRmZkodkkX65ptvMGDAADz22GPw8/NDTEwMPvnkE0lisctkpLCwEHq9Hv7+/ibH/f39kZubK1FUZEsMBgOef/55xMfHo0+fPlKHY7FOnDgBV1dXqFQqzJ49G1u2bEGvXr2kDsvirF+/HklJSUhISJA6FIs3aNAgrF69Gjt27MCyZcuQnp6OoUOHorS0VOrQLE5aWhqWLVuGrl27YufOnZgzZw6ee+45rFmzpsNjcejwKxLZgblz5+LkyZOcq76J7t27Izk5GSUlJfj6668xbdo07Nu3jwnJdbKysjB//nzs2rULarVa6nAs3qhRo4z/7tevHwYNGoSwsDB89dVXePLJJyWMzPIYDAYMGDAAixcvBgDExMTg5MmTWL58OaZNm9ahsdjlyIiPjw8UCgXy8vJMjufl5SEgIECiqMhWzJs3D9999x327NmDTp06SR2ORVMqlejSpQtiY2ORkJCAqKgofPDBB1KHZVESExORn5+P/v37w8HBAQ4ODti3bx/++9//wsHBAXq9XuoQLZqHhwe6deuGCxcuSB2KxQkMDGyS+Pfs2VOSaS27TEaUSiViY2Oxe/du4zGDwYDdu3dzvppumSAImDdvHrZs2YKff/4ZERERUodkdQwGA6qrq6UOw6Lcc889OHHiBJKTk423AQMGYPLkyUhOToZCoZA6RItWVlaGixcvIjAwUOpQLE58fHyT9gPnzp1DWFhYh8dit9M0CxYswLRp0zBgwAAMHDgQS5YsQXl5OWbMmCF1aBanrKzM5FNFeno6kpOT4eXlhdDQUAkjsyxz587FunXrsG3bNri5uRnrjzQaDZycnCSOzvIsXLgQo0aNQmhoKEpLS7Fu3Trs3bsXO3fulDo0i+Lm5tak7sjFxQXe3t6sR2rGiy++iLFjxyIsLAxXrlzBokWLoFAoMGnSJKlDszgvvPACBg8ejMWLF2P8+PE4evQoVqxYgRUrVnR8MFIv55HShx9+KISGhgpKpVIYOHCg8Msvv0gdkkXas2ePAKDJbdq0aVKHZlGae40ACKtWrZI6NIs0c+ZMISwsTFAqlYKvr69wzz33CD/++KPUYVkFLu1t2YQJE4TAwEBBqVQKwcHBwoQJE4QLFy5IHZbF+vbbb4U+ffoIKpVK6NGjh7BixQpJ4pAJgiB0fApEREREVM8ua0aIiIjIcjAZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkn9PyBkdRC2uPuyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(view_per_day)\n",
        "start, end = ax.get_xlim()\n",
        "ax.xaxis.set_ticks(np.arange(0, 7, 1))\n",
        "ax.set_title('Indonesia\\'s Trending YouTube Views per Day')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEhGEignr3fx"
      },
      "source": [
        "In Python, the value 0 in the `weekday()` function corresponds to [Monday](https://pythontic.com/datetime/date/weekday). Based on the graph above, we can conclude that the videos uploaded on Friday received the highest number of views compared to other days."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKN1lmMI9KXt"
      },
      "source": [
        "## 5. What is the average video duration?  \n",
        "First, we want to see the data for the duration column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oN_TY1HcwMEo",
        "outputId": "b3d95225-826e-49a9-a2dd-b6c489282931"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           PT5M7S\n",
              "1         PT42M30S\n",
              "2         PT46M43S\n",
              "3         PT45M59S\n",
              "4          PT5M12S\n",
              "            ...   \n",
              "148144       PT45M\n",
              "148145      PT6M8S\n",
              "148146       PT51S\n",
              "148147     PT8M10S\n",
              "148148     PT16M5S\n",
              "Name: duration, Length: 148149, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df['duration']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vg3wZ3squ-EZ"
      },
      "source": [
        "We notice every data contains the string 'PT', we're going to strip these to ease the parsing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BPyu8fT2HEx9"
      },
      "outputs": [],
      "source": [
        "df['duration'] = df['duration'].apply(lambda x: str(x).strip('PT'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ASqTrevu9LW"
      },
      "source": [
        "The column `duration` contains data from day to second with different combination in each row, so before we format the data to datetime we need to know every possible combination that could appear to prevent unconverted data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpONr-_oUsGS",
        "outputId": "e1c3c1f3-e426-43bf-89f6-461edf1d1e37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('D',), ('H',), ('M',), ('S',)]\n",
            "[('D', 'H'), ('D', 'M'), ('D', 'S'), ('H', 'M'), ('H', 'S'), ('M', 'S')]\n",
            "[('D', 'H', 'M'), ('D', 'H', 'S'), ('D', 'M', 'S'), ('H', 'M', 'S')]\n",
            "[('D', 'H', 'M', 'S')]\n"
          ]
        }
      ],
      "source": [
        "for i in range(1,5):\n",
        "  print(list(combinations('DHMS', i)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzMN8lOXyE2s"
      },
      "source": [
        "After we find every possible combination from day to second, we make a function that loop each combination and parse the data as a datetime object for row that match the combination. Also we equalize the data to a minute format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Md46SGaJFCQ"
      },
      "outputs": [],
      "source": [
        "# Make a function to change the duration column values\n",
        "def duration_dt(x):\n",
        "  if ('DT' in x) & ('H' in x) & ('M' in x) & ('S' in x):\n",
        "    for i in (('DT', ':'), ('H', ':'), ('M', ':'), (\"S\", '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%H:%M:%S').day)*1440\n",
        "    hours = (pd.to_datetime(x,format='%d:%H:%M:%S').hour)*60\n",
        "    minut = pd.to_datetime(x,format='%d:%H:%M:%S').minute\n",
        "    second = (pd.to_datetime(x,format='%d:%H:%M:%S').second)/60\n",
        "    return  float(days+hours+minut+second)\n",
        "  elif ('DT' in x) & ('H' in x) & ('M' in x):\n",
        "    for i in (('DT', ':'), ('H', ':'), ('M', '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%H:%M').day)*1440\n",
        "    hours = (pd.to_datetime(x,format='%d:%H:%M').hour)*60\n",
        "    minut = pd.to_datetime(x,format='%d:%H:%M').minute\n",
        "    return  float(days+hours+minut)\n",
        "  elif ('DT' in x) & ('H' in x) & ('S' in x):\n",
        "    for i in (('DT', ':'), ('H', ':'), (\"S\", '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%H:%S').day)*1440\n",
        "    hours = (pd.to_datetime(x,format='%d:%H:%S').hour)*60\n",
        "    second = (pd.to_datetime(x,format='%d:%H:%S').second)/60\n",
        "    return  float(days+hours+second)\n",
        "  elif ('DT' in x) & ('M' in x) & ('S' in x):\n",
        "    for i in (('DT', ':'), ('M', ':'), (\"S\", '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%H:%M:%S').day)*1440\n",
        "    minut = pd.to_datetime(x,format='%d:%H:%M:%S').minute\n",
        "    second = (pd.to_datetime(x,format='%d:%H:%M:%S').second)/60\n",
        "    return  float(days+hours+minut+second)\n",
        "  elif ('DT' in x) & ('H' in x):\n",
        "    for i in (('DT', ':'), ('H', '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%H').day)*1440\n",
        "    hours = (pd.to_datetime(x,format='%d:%H').hour)*60\n",
        "    return  float(days+hours)\n",
        "  elif ('DT' in x) & ('M' in x):\n",
        "    for i in (('DT', ':'),('M', '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%M').day)*1440\n",
        "    minut = pd.to_datetime(x,format='%d:%M').minute\n",
        "    return  float(days+minut)\n",
        "  elif ('DT' in x) & ('S' in x):\n",
        "    for i in (('DT', ':'), (\"S\", '')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%S').day)*1440\n",
        "    second = (pd.to_datetime(x,format='%d:%S').second)/60\n",
        "    return  float(days+hours+second)\n",
        "  elif ('H' in x) & ('M' in x) & ('S' in x):   \n",
        "    for r in ((\"H\", \":\"), (\"M\", \":\"), (\"S\", \"\")):\n",
        "      x = x.replace(*r)\n",
        "    hours = (pd.to_datetime(x,format='%H:%M:%S').hour)*60\n",
        "    minut = pd.to_datetime(x,format='%H:%M:%S').minute\n",
        "    second = (pd.to_datetime(x,format='%H:%M:%S').second)/60\n",
        "    return  float(hours+minut+second)\n",
        "  elif ('H' in x) & ('M' in x) :\n",
        "    for r in ((\"H\", \":\"), (\"M\", \"\")):\n",
        "      x = x.replace(*r)\n",
        "    hours = (pd.to_datetime(x,format='%H:%M').time().hour)*60\n",
        "    minut = pd.to_datetime(x,format='%H:%M').time().minute\n",
        "    return float(hours+minut)\n",
        "  elif ('H' in x) & ('S' in x):\n",
        "    for r in ((\"H\", \":\"), (\"S\", \"\")):\n",
        "      x = x.replace(*r)\n",
        "    hours = (pd.to_datetime(x,format='%H:%S').time().hour)*60\n",
        "    second = (pd.to_datetime(x,format='%H:%S').time().second)/60\n",
        "    return float(hours+second)\n",
        "  elif ('DT' in x) :\n",
        "    for i in (('DT', ':')):\n",
        "      x = x.replace(*i)\n",
        "    days = (pd.to_datetime(x,format='%d:%S').day)*1440\n",
        "    return  float(days)\n",
        "  elif ('H' in x):\n",
        "    for r in ((\"H\", \"\"),):\n",
        "      x = x.replace(*r)\n",
        "    return float((pd.to_datetime(x,format='%H').hour)*60)\n",
        "  elif ('M' in x) & ('S' in x):\n",
        "    for r in ((\"M\", \":\"\n",
        "    \n",
        "    ),(\"S\", \"\")):\n",
        "      x = x.replace(*r)\n",
        "    minute = pd.to_datetime(x,format='%M:%S').time().minute\n",
        "    second = (pd.to_datetime(x,format='%M:%S').time().second)/60\n",
        "    return float(minute+second)\n",
        "  elif ('M' in x):\n",
        "    for r in ((\"M\", \"\"),):\n",
        "      x = x.replace(*r)\n",
        "    return float(pd.to_datetime(x,format='%M').time().minute)\n",
        "  elif ('S' in x):\n",
        "    for r in ((\"S\", \"\"),):\n",
        "      x = x.replace(*r)\n",
        "    return float((pd.to_datetime(x,format='%S').time().second)/60)\n",
        "  else:\n",
        "    return x\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObMA9CH-yk3p"
      },
      "source": [
        "Using list comprehension we apply the function to every row in `duration` then we assign to a `duration_dt` column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-er4wCsbF0aV",
        "outputId": "347407c8-0bad-4e32-8442-f2633f65c99f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "df['duration_dt'] = [duration_dt(i) for i in df['duration']]\n",
        "df['duration_dt'].dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ThnzNUE0buO"
      },
      "source": [
        "Now that the data type is 'float64' we can directly use mean() function to find the mean of duration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJfYfm2IWY09",
        "outputId": "115d1fd2-6140-42ff-e862-f5c1365233d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19.406353513467295"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df['duration_dt'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8L9Jv4e8c_YC",
        "outputId": "c4c8a762-2971-4e56-c664-a576f6d632c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average duration of Indonesia's Trending YouTube Video is : 19.406353513467295 minutes\n"
          ]
        }
      ],
      "source": [
        "print('The average duration of Indonesia\\'s Trending YouTube Video is :', df['duration_dt'].mean(), 'minutes') "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gnVEsPR9KaF"
      },
      "source": [
        "##6. On average, how long after uploading will the video be marked as 'trending'?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prior to begin our analysis, we would like to ascertain whether both columns are marked with the same timezone."
      ],
      "metadata": {
        "id": "vCmjaUljhAIg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rpyZlR9fZF6",
        "outputId": "18afe5ec-d818-4a44-b569-33fe55d9923a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        2021-02-07 05:46:51.832614+00:00\n",
            "1        2021-02-07 05:46:51.832649+00:00\n",
            "2        2021-02-07 05:46:51.832664+00:00\n",
            "3        2021-02-07 05:46:51.832678+00:00\n",
            "4        2021-02-07 05:46:51.832730+00:00\n",
            "                       ...               \n",
            "148144   2023-04-18 06:04:24.338378+00:00\n",
            "148145   2023-04-18 06:04:24.338388+00:00\n",
            "148146   2023-04-18 06:04:24.338398+00:00\n",
            "148147   2023-04-18 06:04:24.338408+00:00\n",
            "148148   2023-04-18 06:04:24.338700+00:00\n",
            "Name: trending_time, Length: 148149, dtype: datetime64[ns, UTC]\n"
          ]
        }
      ],
      "source": [
        "print(df['trending_time'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['publish_time'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySZL1yLog0JI",
        "outputId": "14022eb6-0d8d-4e74-ed24-50bda8304d83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        2021-02-05 09:00:34\n",
            "1        2021-02-04 15:54:08\n",
            "2        2021-02-06 03:00:22\n",
            "3        2021-02-05 20:26:08\n",
            "4        2021-02-03 23:14:54\n",
            "                 ...        \n",
            "148144   2023-04-02 06:11:43\n",
            "148145   2023-03-31 11:30:10\n",
            "148146   2023-04-01 07:59:57\n",
            "148147   2023-03-31 00:00:12\n",
            "148148   2023-04-02 10:00:19\n",
            "Name: publish_time, Length: 148149, dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsMbMgP43QE4"
      },
      "source": [
        "We notice that the `trending_time` has a UTC timezone and the `publish_time` does not have any timezone written. \n",
        "\n",
        "So, we localize the `trending_time` using `dt.tz_localize()`.\n",
        "[`dt.tz_localize()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.tz_localize.html) is a method used to localize a timezone-naive datetime object to a specific timezone.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSlC4TbAohs9",
        "outputId": "070d2f8d-d4b0-417b-ea87-72e247cdf681"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2021-02-07 05:46:51.832614\n",
              "1        2021-02-07 05:46:51.832649\n",
              "2        2021-02-07 05:46:51.832664\n",
              "3        2021-02-07 05:46:51.832678\n",
              "4        2021-02-07 05:46:51.832730\n",
              "                    ...            \n",
              "148144   2023-04-18 06:04:24.338378\n",
              "148145   2023-04-18 06:04:24.338388\n",
              "148146   2023-04-18 06:04:24.338398\n",
              "148147   2023-04-18 06:04:24.338408\n",
              "148148   2023-04-18 06:04:24.338700\n",
              "Name: trending_time, Length: 148149, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "df['trending_time'] = df['trending_time'].dt.tz_localize(None)\n",
        "df['trending_time']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y30ezeaP4157"
      },
      "source": [
        "Once the `publish_time` and `trending_time` have been localized, we calculate the time difference between them by subtracting the `publish_time` from the `trending_time`. This allows us to determine the duration between when a video was published and when it started trending."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQ3pFJJDsoyx",
        "outputId": "f1b6d5f5-ebe4-4734-b25f-ad9d9b1d077a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1 days 20:46:17.832614\n",
              "1         2 days 13:52:43.832649\n",
              "2         1 days 02:46:29.832664\n",
              "3         1 days 09:20:43.832678\n",
              "4         3 days 06:31:57.832730\n",
              "                   ...          \n",
              "148144   15 days 23:52:41.338378\n",
              "148145   17 days 18:34:14.338388\n",
              "148146   16 days 22:04:27.338398\n",
              "148147   18 days 06:04:12.338408\n",
              "148148   15 days 20:04:05.338700\n",
              "Name: trending_gap, Length: 148149, dtype: timedelta64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "df['trending_gap'] = df['trending_time'] - df['publish_time']\n",
        "df['trending_gap']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above results, we then can call the `mean()` of the `trending_gap` column to extract the average time span for all 148,149 videos."
      ],
      "metadata": {
        "id": "qkM3JYNHYah_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xk8nG4hYfNGy",
        "outputId": "630165fd-3284-4f45-b94f-99b6613a9b05"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average trending video require 8 days 13:14:29.559902717\n"
          ]
        }
      ],
      "source": [
        "print('Average trending video require', df['trending_gap'].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 13:14:29.559902717 refer to 13 hours 14 minutes and 29 seconds. Hence on average, we infer that a video would take 8 days, 13 hours, 14 minutes, and 29 seconds to be trending."
      ],
      "metadata": {
        "id": "7TPL9y2dtOIj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Acknowledgements and Recommendations\n",
        "The dataset was uploaded to [Kaggle](https://www.kaggle.com/datasets/syahrulhamdani/indonesias-trending-youtube-video-statistics?select=trending.csv)\n",
        " by the user [Syahrul Hamdani](https://www.kaggle.com/syahrulhamdani) around June 2022.\n",
        "\n",
        "Original data is extracted using the YouTube API. Heavily inspired by the work of @rsrishav and @datasnaek with similar dataset mentioned earlier.\n",
        "\n",
        "For future analysis, it is advisable to dig deeper into the relationship between the video's title tags and the number of views. Additionally, employing machine learning techniques to forecast upcoming trending videos on YouTube would be beneficial.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "# Data License\n",
        "This data is licensed under [Creative Commons Attribution-Universal (CC0 1.0)](https://creativecommons.org/publicdomain/zero/1.0/). I hereby affirm that I do not possess any material nor monetary benefit by doing this project.\n",
        "\n",
        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/4aqIj15baOaMjwlMhuaitx68tpGvaG6KOc8JOnIaEy//Xx72jbeWhPQr9WX5FU/iqIoiqLntjokc2LfLxRFURRF0ey03O3QLfX2s5FSpvQXR3rUFY0vi3wLoYmUqT96uVPRNLPXrq8o0vnFnnm145T0ma1VtjOgJOmJ/jF94mC3EmYq5/rfWFmsH1yzXuVpHkaQK+Vup35w9Xq9saoo53xMU/pB94i+1jpgW7/DkO7aUiX/1H77S/96QFEURVF0ZahDMiZGwqYNh+HxeDwej8en8R/dVKXqNHtP/agnpH3tw8sm30L5F4ej+tND3bbn7XYY2ntp/bzaq/K4dFON/X5ipsYfFpBP/Q7D0D9cWp92eWq+eB2G7r24Xo7JdnPM7zNHe5U07eu/qNSrjUXerOvD4/F4PB6PlzF7xpcmFI/H4/F4PN7GNxW59aktlbJjIJ7UH77cqVjSXBb5Ftr/66khHR+138frynK/bpw+8ynH+j+2uVIVaQYV9xzt1ZP9YznX75KhfZevKcjyRjvOD3i07/I1WeUz23dHEnrH0222g18uw9DXL6nLuj48Ho/H4/F4mcz4wuPxeDwen6P/lepipeM/20d0bDS+bPIttO+JJfXVNMvyJOktNYG86l/v9+jOTRW29baOxfXF1/ryyn97hV/vTrNZvhWxlJl2FpYV724o1XWVxXmd/6M9Y3p2MGxb946KIl0V9GddHx6Px+PxeLzr7IjYNPB4PB6Px+NtfLrBk1Aipf91uFumzGWT70L4e1v69YHGoLYGPLLiXXUl+uTBrpzrf0ed/RJHSfr39iGNJpM55ytJv7uuTGmeRTCDXwyE9ZnDPXp5JCJD0u71QX32/OqsyjoN6b0NJXq8b9bTJrPIN2qa+mprv64OrrGt/x21AT09MLasXg94PB6Px+OXr3edHQkzdfb/eDwej8fj8XN9icvQ9ZVFsuPfTg+pM5qcVia7+p2GVOt1Keh26JIyn4Jup4qdDg3EkzodTuj4WEx9saQG4gmlzNzyD7icqvY4taXYo8Yit5qKPGoZG1+q+PxQWN3RlPpiSY0lU1nnG0tJXz85oHsuqLXsh/MCHu2oKBpfkphD/6abTdcfS+qeY3051Tfp630uvW9NmW3d0/nYq926+3jfjPJfPtGvP9lUkfWG+P9jfVCfOdKrvlgi53z/35kRfXJzVM0lXllxY1Wxcn194fF4PB6PP3f9+IyvGUHh8Xg8Ho/HW/o/aKq03RjdlPSNM0M5139FuU93X1Cr8wMeVXqcchlz6w8nTXVFE3q8f0xffK1Xr45EM9Zf5HTod9cH9eGmoBp8bpW6rJ+S2BtLqiua0NdaB/T3JwcUn/PURmu/v3dUpmZ+zZrOHzRVTAx8ZVefx2FMDOpY89XWAfXEkhaRzPV/aENQ/gzTvVKm9NmjPbr7eO+c8mPJpDqjiawHvvxOQ7vXl+lLx/qyym868VRK9xzv0/2XNljWvaPCr1qvU13RRFb14fF4PB6PP7d9mhlfsyFOnDhx4sSJn8txj8PQe9PMGhqKJ/XcUHRWffb1l7gc+nxztXY3BuXL8JRBv9PQhiK3NhSV6Z11JfrCa736u9ZBDcZnzygadzdWFevzzdUT+0Glp8rjVJXHqb+9qE63rS3TJw5267G+sYz5vzIc0/HRmDYXWy93vKGySCUuh0YSs7+ITeds/TfXl6rEZnAunDT1nc4Ri1wyXz+Pw5FxXzZJOh2J676WAVldv8vLfDo/YD0Dy4531pXqr0/0K5ZKd5R1/o/3hzWcSNkOVv7W2vKJWWnL5/1BnDhx4sSJE1+ecYdMc/yH5kRwys9W4sSJEydOnPi5HF/nd2m93yU7/q51QKF4Mqv6/Q7p59c26o6mioyDXrMpdTn0l801+sfL6mVY1H9DVZF+cM26rAa9ZnNN0K8fbl+vS0u9afOXaSphpvTF12bPjjpLscuhNV63bfnZ9d/RFLStqyeW0JFQNK/rV+F2qDGLJznubRtUbzRhWf8fb7TfcN+O9X6Xyl3OrM9/uraMxtQRSdjW/RtrS+Uwltf7gzhx4sSJEye+POOO8UEwQyiKoiiKoum0xuOS32k9AydhmrrneH/W9f3HlWt1canPsq5s+bW6Ev2/K9aO5zRR71tqA/rhNevlyXEwbTo+h6H/fn2jrgoWZTyPf28f1kjCekpTsdOhNX5X2vKTWul1qjnNjKpvtg+PzxzL47oFPS7Vee0HLCf5ss31u6Lcr1vXZrc/2HTqfC4FPY6c85UMxU1T+84M2dbd6Hcr6Hbl1R8oiqIoip5b6jg7EoaiKIqiKGqv5wU8tvt7HR+Nj29knqEew5Q+11ytt9WWWNYjSfGUqbZwXAdHojoSimkwbrWv1Tg315foTdXFkil5HdIXmmvSDnoNxVM6Eorp4EhUJ8Nxi/28xil3O/WlbTXyGEba8xmJp3R8NGZZh9OQthR70paf1DqPS740e3A9cGooq3qsdHsw8wDjc4MRjSSSc8oHnA793cV1Gctb4TIMXVXuzzvvB07bD3wVOR1a53flVS+KoiiKoueWMuMLRVEURdGsdEeF/dMcxwd/MtezOeDRHRvsl80lTFPvP9Cu1/3shK5+vFVXPnZCl/60RftsBkGOj8XUNpaQDOmPNlbq0jL7QZ4HO0Z0yU+P66rHWnTVYy163c9adNMv2xSymLEVT5l6vG9M45H053ViLG7b5usri7Lqlyqvc2KQbS5d0YQODEeyqsdKr6vMvL/XSzb1X1dZpEtsZuadDtuf9yTXVxbnnffx0fi0Dexn4nZIFcz4QlEURVE0C3VNjYQJRVEURVHUXjem2SfqaCim8X0U0tezqyagUrf1csmBeFK/+lSbnh4Iz/j5SDym3znQroF4Uh/cUKGkaern/WP6+9YB/fuZYUlStdepOzfZD6j9V3dI73/ujEaTZwe5RhMp/bg7oeb9x/XEdRu03u/WcCKl+08O6L6WAZ2YM5PL+ryOhKKSrGew1XldWfVLndclt81MtcMjsYlD87tu2wLWm+9P58WhyJw8fQ7pHy9rsMzr2x0jemYwrM8116St98JSb1bnb6eHR2KqtVim6TIMVXsc8+oXFEVRFEXPDXXJ+jsWAAAAwAyqvU7b2LGxmLL5TvH2+hLbw77ZPqynB8OW9cRNU39xpEdXBf364mt9erBjfMBr8thNxR6Vua3zG4wn9dZftM04fjqnI3F9+nC33lFXot96/ozCSdP2WCvSzXyq8TqzqmejzZMhJak1nF3f2pFuFtwk/fHknDb+YFOF5aDTQDypTx7q0nWV9jMAJ7mgxDuv3FvDMd0g63Y2FnvmVTcAAACcG7imBsEAAAAA0rA1zebrHeGEMn2n8DkMvT7Ncsn9PaNp6+iJJrXjsVYlzLkHva7Mb7v/2DODkYy57Ts1rH9ps99TKh1nwvZPH9xW4s3YtiRdVGLft6+FYlnVYYXTUFYb/Y8kUjPacBmGPmizJPW7nSM6MhJLuxn/JCUuR965S1JbmmWkF2XZtwAAAHBuw4wvAAAAyApfmgGUcCqVcfbNtlJv2jp+aTPbazoJmZbHnJdmOd+hkWje9WZDzGIgbhKXYWRXb5pjYmb+uTUWZV7maJXD315cpyaLpa3HRmP68MudOeWzxu/SmYj94GA6omn6VoaY8QUAAAAZcY3vu2Bo/E9mKIqiKIqidmpP69j0faisy1+YZlaTNDG7J8/vJQ2+uUvyJjkxGsu73my0Nc2sJE12SYZ6/A7rfc8kqSOSyDt/R4brNsmZcHwqzwtLffq9DUHL4z57pEeheEqSaftEzNn4HY68829Pt4G+qQW9riiKoiiKrg51SMa4R1EURVEUTadpSE1+v0hT3m7z9qk6NJ/87EnOq97MmpKZPoEs6tmcZo+vqUoW4LpZ1f8nmyotSw3Ek+MPE5g4rjuWzLrqfPOPp+vapX4/oCiKoii6InR84wVTQlEURVEUTatpcBjKWD7TDKHxrenzzW+h6s2shmmkTyCLeoYTmQaRFua6za7/6qBPt64tmxMdjCd11c9aFEulpur1Z7F32GTVC3Fdl/z9gKIoiqLoilBmfKEoiqIomp2mYUORJ2P5V0aiaevYWuLNO7/2NHtIzafebHS9xV5YM8iinpNplkv6nMb88syCgMspn9PQVy6qt3xIwLc6Rsaf3Dmt3gqP/VM+ZzCP/i1x2S8BXfL3A4qiKIqiK0JdZ0fChKIoiqIoaqsJ0xzfrN0Cv8OQJjcitynfMhpLW8elpV4dHI6kzaPE5ZDDMDQUT874+dGQ/aDaBSXe8YMynF+tx6XuaGLqNLLtF3+asZnx49L3y5TaUOtxZZW/lQ7Gs9tUvsxtaHvQr0vLfHNio8mUPnO4e+55ZEp8gtbRWN75V7jTDK6ZyrteFEVRFEXPHXVNDIMBAAAApOXgSFQXl84dGJGkep9bmb5TDMRTem4woquDfsv4exrKtO/0cNo6/uy8ar21NqBff+a0joRiUz9/aTiqWMqUx2K20rUVRdpQ5Em7CX3A5dBLN27StztG9ImD3RqMZ7l/laQ1fvsZXy8PR5XNd610+2VVeVxZ1WFFbyyV1XElLqe+sK1uTv8lTekjL3fqVDgxJwdvmg35p5MwNadstpSnGfg6HZmbEwAAAMBsXGeHwQAAAADs6Y7aD86cX+JRNt8pvtUxbDvw9dbagN5VX6JvdVgPfl0V9OsDG4IKup06/MbNeqQrpL9r7dcPu0M6PhrVcCKlKovldw5D+sH29brh563qjs6dAeVzGPqXyxtU43XpAxuCemd9iR5sH9bfnujX4TQzySap89o/UbIrmlA2/XImzdMLG4tcWdVhx6lwXOvSDM5J0mfPr9EGiyWbh0NR/ceZIcv2q7NY6nhoJGpZNluqvfZt9GTZtwAAAHBuw4wvAAAAyIoTozGputgytrHIo2y+U3y/M6RPb61WsXPubCGvw9A3r1ynD77Yoe92jmg0mZJpSsUuh26qDegrF9UpMG3Pp121Ae2qDeivjvXp46926d/PDOmOpgrLds8PePXglev0/gPt6o0mlDQlpyHV+1z67Pk1eld96dSxtV6XPtRUoQ9sCOr1j7fq6YFw2nPalOaJjOMDbZn75bXRmG1sa8CbVR12vDIczTjwZTXoJUl//HKnhhOmZftrM9QpaWKWXf65Nwe8trHxPuN7LAAAAKSHGV8AAACQFU/2j+kDG4KWseYsZ3wdCkX0z22D+pDNAJXTkL52Sb0+c36VhuLjy/TK3A7VeV1yWuwNNpJIad/pQUmm/vRgl95ZV2I7IHNtZZFeunGjTofj0wa+3Cq12UD9/pMDem4wnPG80g18HRiKZCwvSe2RuKIp03Jj+S3FHpW5HeP7muXBC8MRvbU2kHO5f2wb1I97QrZxu2Wv03kmi/5LR5PNgFw0ZaozEp9X3QAAAHBuMPFNb/JLFoqiKIqiqLUeG40rlrIeaFjnd6vB57YsN11TpvSxV7v1ZP+YZT3S+NLENT63tpV4ta3EqzU+t+Wg11gypY+83KkXhsaXIw4nTP3poW7beiWp2OnQ1sB4vVsDXttBr+cGw9pzpFdJc/psJ2vdYjPwlTSlA0PhjOUlQ72xpKI2fet2GLqlvjSreqx0f8+oZb2Z+Ovj/Wnrvcpmyep0Hu8bzTrP2Xp1sEjVNstI4ylTA/GUZTkURVEURdHpOvFtbxlss4+iKIqi6LLWzmhcY0nrzdJdhqEPb6ywLDdbx5JJveeZU3nPYJrkY6926f+2Dcyo/19PDepPXumcV73PDYZ15c9O6Exkct8t+/N5Y3WxKmz2uhpNpnQmnLAsN1tPh+MK2/StJN3eWC7n5Pe3HK/bsdGoRhLZbXI/yZ8f7tZLw5NLPOfWu7OqWJUZ9vgaSaR0bPKJjnnk/ZvrymzrDidTOhXOfH1QFEVRFEWZ8YWiKIqiaFZ6KpxQT5qnD35wQ3BiD67M9bVHEtq2/7i+3jpgW58dr45E9cYnWvXVlsmyM+v/6+P9etsv2nQyzVMcrRhLpnTPsQRrbOoAACAASURBVD697RenJr4qZT6Pj2+uSltfx4w9vuw1ljL1/U77ZYXnl3in7dOV23UbiKfUEZm7qb8drwxH9dkjPbb1uQyHPr21OmM9HZHExKys3F9vRU7HjH3XZvPz/jGNJc2p41EURVEURe3UMf7NzhSKoiiKomg6jaVS+te2QdlR5nZqR7Ao6/raI3H93gvtuvJnJ/TzvjF1RRNKmKZl3YPxpA6ORPWZw9268mcnJpbvWddrytTDnSO66L+Pac+RHh0OjT/x0YqEaaojktAPu0Pa+USrPvpKpzqj8azyX+t36U3V9ntn/bR3VEOxZNb98bXWftu6yt1OXVzqzeu6DceTE09XzI5/PjUoM019WwPurJY5HgpFNZzD+U/XzUUelbutl6FK0r+0DeZVL4qiKIqi554a+vYrpgAAAACyYK3frVNvPs82/vXWAf3ei+051+t2GKr2OBV0O3VJmU+lLqdqvE51RBI6OBLVmUhcvbGkQjku2ZOkgMuhao9LWwIeNfrdqvG6lDBN9ceSemYwrM5IQt2xhGy22LLlfWvKtO+KtVN/T5zN6x9vSbuXmRXH37RFG232DPtp76hufKI1tyQn2FUb0MPXNGZ17B0vdeirLdaDcGv9bj18zfqsNra/6Rcn9UiX/Sy2dHxma7X+/Pway1hfLKk1PzxiuycaAAAAwHRcMjU+AwxFURRFUTSDnh6L6/G+MV1XWSQrbl1bpr861qtjoVhO9caTptojCbWHE3p1JFrQvEOJlELxmFrGYgWt90NNFTIse0E6GoqND3rlWO9/dYdsn3j5hqpi/WpNQP/VHcq53kc6Q3ptNGa7Ef90Prq5Uo92j6otElc4kZIMyecwFPQ49ZMdjdoa8Gas47VQbHzQK49+9ToNfXhjpW3dP+sbVTRpLov3A4qiKIqiy18dMjQOiqIoiqJoFvqf7cOyI+By6G8uqpexDPJcSH3v2jLbwT9J+q/ukbzq/X7XiG2dkvQnmyvldhh55X33sV6ZaWsfp6nIoxd2btJT1zXphzsa9cPtjXri+o068SvnZTXoZUq6+3hvzvlN6ie3VKkqzcb5P+oOLfn1R1EURVF05ajr7DcguyEy4sSJEydOnDjxs/q9jhH9zUV1suPayiJdUurTC0ORZZn/fONlbpf+9Dz7Te0l6dvtI+NFcqz/J90htYXjWj+1kf1Mrq8s1qWlPj0zELYsn67+77SP6Avbkgq60z+NURqf4XVJWebljFb0RBP6Tp7nX+Z26La15bZ1jyVT+l7HZN2510+cOHHixIkTP/fi7PEFAAAAOfOlC2r1sTRPNPxJz6je8cs2hZO578m13LltbZn+5fK1chjW8UMjUV360+OK5bkH1VtrA3okzX5cA/Gk6v8rvz2udlYV69HXb8grr2x54xOt2t87mlfZv9xWq09tsX9d/c2JPn3k5c58UwMAAIBzEKfe+6E/H//v8hqRI06cOHHixIkv3/iBwYh+d325il3WT97bWOzRYCylJ/tHl2X++ca3BNz63jWN8jqnHzOTm58+pZbReN7td0aSurm+VFVel2X9fuf4Q7n/e/LJljnU3zIWU2ORR5flOZsrE//QOqCvnJi+MX72+d1QFdDeyxps6+6MJPTbz51WKGHmVT9x4sSJEydO/NyMM+MLAAAAcsZhSPddXK/f32C9EbskdUUTuuaxE2odi9ses5JwGoa+c/U63VRbYnvM431jetOTrXnP9prkjzdV6v9caL+cNJYydfVjJyaWk+aG12HoF9dv1CVlvhlfEeeDKeknPSG9/Rdtec1E8zsd+tm1G3Rlud/2mL0nB/Q/X2gXX1wBAAAgFwx962W+PwAAAEDOVHmcanvLVvmd1rO+JGkonlTjj45qKJ5cxMwKj8OQPrmlWp/bVmt7TNI09Z6nT+lbHfab/2eL22Ho8es26uqg/UDQ6XBc2x87odPh3AcWg26nHrhird6aZhAvF77fOaLffO50XtfZZRj6/vZGvaUmYHtMOJnSxh8fVWckMZ80AQAA4BzEIRkaf/QSiqIoiqJo9tobT+mDL3YoYdr/Da3M7dRXL2lYFvnOR29vrEg76CVJ/9Q2qO92hgrSXjwlffzVzrQzx9b63fr7S9eozOPKuf6BeErvevqUPn2oW4PzGJQcjCf1iYNdevfTpzSUSOV1vh/ZXKU3VRfbtpEypbsOdaszmpx3v6IoiqIoeu6pU+/74MQeXwAAAAC5cSwU0/VVxWos8tgec1GpT2t8bv2kJ5R2kGw5YkjaWV2sr126RkVpZra1RxJ6//OnNVDAmW0nx+K6KlikrQGv7TFbAh41Bzz6z/ZhpXLs26Rp6rG+Uf1dS7+aijxa53fL7TDkMIyM5UYSKf1H+7De/ESr9veMKpnHdTUkfXRzlf7qwrq0bZ6JxHX7gTN5LaEEAAAAMPStV0wZst8bDEVRFEVRNI3W+Fw6tHOzKjxO2ZEypS8d69WnXu1a8nxz0Rsqi/X9a9YrYLOJ/yTv+mWbvt05UvD2S5wO/ey6prSb0cdTpv7t9JB+78V2xZNm3u2t8bu1pdijayr8urDEpy0Bj/wTm/iHk6ZeG43phcGInh0K67WRmM5E4/M6v0+dV6VPb61Ou1R2MJ7U5T89oZbR2LJ4PaAoiqIouvKUPb4AAABg3ty6tlwPXLE243FfbenXH7/cMe/N3xcahyF9sKlC/7u5VkG3/YCeJH35eJ/+5OWOBcvlV2oCemR7o1wZZmL9fWu/Pv5Kp4YTqQXLpRC4HYY+sqlSX7rAfvP+ST76SqfuOda7CFkBAADAamXiT2yTX6RQFEVRFEVz132nB/X5o70Zl7x9qKlC/3HlepW7ncsibystcjr1ueZaffnC+oyDXgdHotpzpGdB8/lx96je8/SptHlI0u9tqNDPrtuoMrdrQfOZj7oMQ9+4Yp2+sC3zoNe/nBrUl4/3Lou8URRFURRduerU+z7EHl8AAAAwbx7rG9WmYo8uTrMsT5LOL/HqnfWl+nn/qLqiy+spfbVel75x5Tr9zvqgnBlmWL00HNENP29Rf2zhn1h5fCymCrdLV6V5yqMk1flceltdQMdHYzo+GlvwvHLh0jKfvnHlOr2ltkSO9F2rn/SE9JvPnl72MwMBAABg+TMx8LX0I3AoiqIoiq5sTZmGHusb1SVlfm0q9kxFraj2uvS7jUGNJU0dCcUUTqaWNH+/06FfX1Om729v1EWl6QfupPGN59/z9GmdHIstSn4p09QjXSFtLvboojJf2r6t8br07oZSDSdSOhKKKTq18nFp+rfY6dTb6kr00NXrtTXgTZu7KenxvjHd9NRJhZOTg17L4/WNoiiKoujK1LN7fJnTfm4FceLEiRMnTpx4FvFip0M/fv0Gba8oSlPwLCfH4nrjEy3WM5QWIf9Kr1M/eX2TLskwoDRJdzShq392Qq1jsUXv31KXQ/uvbdLrytPP/JrkaCiqD73YoUd7QouS32wuKvHpX65Yq4tLfRlneUnSc4NhvfnJ1rOz6Jbh65s4ceLEiRMnvrLibG4PAAAABafc7dQ/XLZG724ozer4WMrUj7tD+vvWfv2gK6REhr3CCsGOiiJ9qKlC76wvzfjUxkme6h/TB15o1yvDkQXOzh6f09DXL12j31pXntXx8ZSpB9uH9fXWfv20d1QL3bOGpDdUFesDGyp0S0Op3NmMeEl6pGtEtzzdpkiSr6YAAABQOBj4AgAAgAWhzO3UP16+Rm+rLcl68MOU9NPeUX3haI9eGo6oO5pQobZ5chhStceli8t8+tR51XpDVXFWM7wkKWVKT/aP6bZnT6ktHC9MQvPkny5fq99cV5ZxL7LpHByJ6vYDZ/TSUERjyVTBBsEMSUVOhy4u8+n+y9ZoW4k367IpU/p2x7Bue+4Ug14AAABQcBj4AgAAgAXDaRi6c3OlvnhB5qf4TSdpmuqKJvXiUFjfPDOsb7YPKZRIZS5oQcDl0FtqAtrdGNQlZX7VeJ1y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)"
      ],
      "metadata": {
        "id": "B2HsYz18ZIVP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e_bAek1FwFfr"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}