Youtube_Comments_Senitmental_Analysis.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "#If you just want to skip the problematic lines and continue reading the rest of the file, you can use:on_bad_lines='skip'\n",
        "\n",
        "yt = pd.read_csv(r\"UScomments.csv\", on_bad_lines='skip')\n",
        "yt"
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            "<ipython-input-2-628d6f4515fb>:3: DtypeWarning: Columns (2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  yt = pd.read_csv(r\"UScomments.csv\", on_bad_lines='skip')\n"
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              "           video_id                                       comment_text likes  \\\n",
              "0       XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1       XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2       XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3       XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4       XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "...             ...                                                ...   ...   \n",
              "691395  EoejGgUNmVU                                             Лучшая     1   \n",
              "691396  EoejGgUNmVU  qu'est ce que j'aimerais que tu viennes à Roan...     0   \n",
              "691397  EoejGgUNmVU                          Ven a mexico! 😍 te amo LP     0   \n",
              "691398  EoejGgUNmVU                                    Islığı yeter...     0   \n",
              "691399  EoejGgUNmVU  Kocham tą piosenkę😍❤❤❤byłam zakochana po uszy ...     0   \n",
              "\n",
              "       replies  \n",
              "0            0  \n",
              "1            0  \n",
              "2            0  \n",
              "3            0  \n",
              "4            0  \n",
              "...        ...  \n",
              "691395       0  \n",
              "691396       0  \n",
              "691397       0  \n",
              "691398       0  \n",
              "691399       0  \n",
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              "      video_id                                       comment_text likes  \\\n",
              "0  XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1  XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2  XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3  XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4  XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "5  XpVt6Z1Gjjo                            #1 on trending AYYEEEEE     3   \n",
              "6  XpVt6Z1Gjjo                               The end though 😭👍🏻❤️     4   \n",
              "7  XpVt6Z1Gjjo                               #1 trending!!!!!!!!!     3   \n",
              "8  XpVt6Z1Gjjo                        Happy one year vlogaversary     3   \n",
              "9  XpVt6Z1Gjjo  You and your shit brother may have single hand...     0   \n",
              "\n",
              "  replies  \n",
              "0       0  \n",
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              "  <div id=\"df-1c0f1085-58ff-4f7b-8dd3-e77483fd0a80\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video_id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>likes</th>\n",
              "      <th>replies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>Logan Paul it's yo big day ‼️‼️‼️</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>I've been following you from the start of your...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>Say hi to Kong and maverick for me</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>MY FAN . attendance</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>trending 😉</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>#1 on trending AYYEEEEE</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>The end though 😭👍🏻❤️</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>#1 trending!!!!!!!!!</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>Happy one year vlogaversary</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>You and your shit brother may have single hand...</td>\n",
              "      <td>0</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1c0f1085-58ff-4f7b-8dd3-e77483fd0a80');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Cleaning\n",
        "\n",
        "yt.isnull() #It will shows the missing values in tables (Data Frame) as bool"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RGEhl177CDOv",
        "outputId": "af9908cf-7671-430a-e0bc-4caf174e8e37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        video_id  comment_text  likes  replies\n",
              "0          False         False  False    False\n",
              "1          False         False  False    False\n",
              "2          False         False  False    False\n",
              "3          False         False  False    False\n",
              "4          False         False  False    False\n",
              "...          ...           ...    ...      ...\n",
              "691395     False         False  False    False\n",
              "691396     False         False  False    False\n",
              "691397     False         False  False    False\n",
              "691398     False         False  False    False\n",
              "691399     False         False  False    False\n",
              "\n",
              "[691400 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72e2d113-8acb-43d5-9ba6-edcfe904855c\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video_id</th>\n",
              "      <th>comment_text</th>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>691395</th>\n",
              "      <td>False</td>\n",
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              "      <th>691396</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>691397</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691398</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>691399</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>691400 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-72e2d113-8acb-43d5-9ba6-edcfe904855c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72e2d113-8acb-43d5-9ba6-edcfe904855c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-79780ffa-a398-4823-a087-20b94a018596\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79780ffa-a398-4823-a087-20b94a018596')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79780ffa-a398-4823-a087-20b94a018596 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here the data are more than 10000, hence isnull() is not use here\n",
        "# So we can use sum with isnull\n",
        "\n",
        "yt.isnull().sum() #It will shows how any missing values in each colums"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "Zigwxg7hCSta",
        "outputId": "b8259b1a-4532-4044-8925-350e0e5efeb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "video_id         0\n",
              "comment_text    26\n",
              "likes            0\n",
              "replies          0\n",
              "dtype: int64"
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>video_id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comment_text</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>likes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>replies</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If the missing values are less we can use dropna\n",
        "\n",
        "yt.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "21ewoBD_CvS5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Now I am again checking for the missing value, this time is null\n",
        "\n",
        "yt.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "hoM7eH6GC-It",
        "outputId": "802355a0-01ee-447b-ff54-63f2eede1b85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "video_id        0\n",
              "comment_text    0\n",
              "likes           0\n",
              "replies         0\n",
              "dtype: int64"
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            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>video_id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>comment_text</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>likes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>replies</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 31
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking rows X Columns now\n",
        "\n",
        "yt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vEYjMbsjENFU",
        "outputId": "b3a6bf50-becc-431f-f608-9be4adcab60b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           video_id                                       comment_text likes  \\\n",
              "0       XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1       XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2       XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3       XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4       XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "...             ...                                                ...   ...   \n",
              "691395  EoejGgUNmVU                                             Лучшая     1   \n",
              "691396  EoejGgUNmVU  qu'est ce que j'aimerais que tu viennes à Roan...     0   \n",
              "691397  EoejGgUNmVU                          Ven a mexico! 😍 te amo LP     0   \n",
              "691398  EoejGgUNmVU                                    Islığı yeter...     0   \n",
              "691399  EoejGgUNmVU  Kocham tą piosenkę😍❤❤❤byłam zakochana po uszy ...     0   \n",
              "\n",
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              "691397       0  \n",
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              "691399       0  \n",
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              "[691374 rows x 4 columns]"
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              "<p>691374 rows × 4 columns</p>\n",
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              "\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-a9f6f172-ceae-4be3-ba30-021ede02b62f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a9f6f172-ceae-4be3-ba30-021ede02b62f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0ce9de8-7405-416a-b43c-f529227df7cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bb7bbbf9-e670-4f40-869d-b1ed29b2c37e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('yt')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bb7bbbf9-e670-4f40-869d-b1ed29b2c37e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('yt');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# droping duplicate\n",
        "\n",
        "yt = yt.drop_duplicates()"
      ],
      "metadata": {
        "id": "eXd7KjITDh6o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3BTjGMAFD9Ld",
        "outputId": "dbcf311f-ff67-498f-a148-b3117ba42739"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           video_id                                       comment_text likes  \\\n",
              "0       XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1       XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2       XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3       XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4       XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "...             ...                                                ...   ...   \n",
              "691359  EoejGgUNmVU     Lost on you ! 😍\\nOH My God I love this song !😭     0   \n",
              "691366  EoejGgUNmVU                                      I  LOVE  LP❤️     0   \n",
              "691392  EoejGgUNmVU                              *L O S T  O N  Y O U*     1   \n",
              "691393  EoejGgUNmVU                                                 <3     0   \n",
              "691398  EoejGgUNmVU                                    Islığı yeter...     0   \n",
              "\n",
              "       replies  \n",
              "0            0  \n",
              "1            0  \n",
              "2            0  \n",
              "3            0  \n",
              "4            0  \n",
              "...        ...  \n",
              "691359       0  \n",
              "691366       0  \n",
              "691392       0  \n",
              "691393       0  \n",
              "691398       0  \n",
              "\n",
              "[478691 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4426e545-f566-4c29-9af4-2eed685a5999\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video_id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>likes</th>\n",
              "      <th>replies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>Logan Paul it's yo big day ‼️‼️‼️</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>I've been following you from the start of your...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>Say hi to Kong and maverick for me</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>MY FAN . attendance</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XpVt6Z1Gjjo</td>\n",
              "      <td>trending 😉</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>691359</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>Lost on you ! 😍\\nOH My God I love this song !😭</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691366</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>I  LOVE  LP❤️</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691392</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>*L O S T  O N  Y O U*</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691393</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>&lt;3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691398</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>Islığı yeter...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>478691 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4426e545-f566-4c29-9af4-2eed685a5999')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4426e545-f566-4c29-9af4-2eed685a5999 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4426e545-f566-4c29-9af4-2eed685a5999');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
              "  <div id=\"id_63ec8877-0647-4831-91d0-2f6338700b89\">\n",
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              "    </style>\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.notebook.generateWithVariable('yt');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(yt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHylGn7qL0fe",
        "outputId": "af102d2f-35ce-4786-92b8-526062cf6c1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analysis -1 (sentiment analysis)\n",
        "\n",
        "#Example:\n",
        "#------------------------------------------------------\n",
        "# User-1 (\"Good Content, enjoyed jokes\") --> [0,1]\n",
        "# User-2 (\"Not liked, wast of time\") --> [-1,0]\n",
        "\n",
        "# So here >1 is as positive one , 0 - is Neutral , -1 is Negative one\n",
        "\n",
        "# Here it is called as \"Polarity\" [-1,1]\n",
        "\n",
        "# To Analysis text we need to install some additional Package\n",
        "\n",
        "# 1. textblob"
      ],
      "metadata": {
        "id": "3UWl3k4HEcFb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install textblob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXma7j7lGHbM",
        "outputId": "8f0f640a-a629-4198-bf74-59bd1f3726dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (0.17.1)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.10/dist-packages (from textblob) (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (2024.9.11)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (4.66.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob"
      ],
      "metadata": {
        "id": "KjBeZbbvGWoK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Single sentence analysis (Warm-up)\n",
        "\n",
        "TextBlob(\"Logan Paul it's yo big day ‼️‼️‼️\t\").sentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvfqxCOQGseA",
        "outputId": "ab9f8aa8-2f8d-4eec-cbbc-8cacd64ed292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sentiment(polarity=0.0, subjectivity=0.1)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To get the polarity alone\n",
        "\n",
        "TextBlob(\"Logan Paul it's yo big day ‼️‼️‼️\t\").sentiment.polarity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5B3RKnGHJPn",
        "outputId": "170423e7-35ad-47e2-ccc9-dd474fcd8aa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking total shape (rows and colums of the give data set)\n",
        "\n",
        "yt.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pzx9vcLIwBk",
        "outputId": "23b9de5a-e641-4c02-a636-623f2b2ef50c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(478691, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If you want to analysis only particualr range from the given data\n",
        "\n",
        "sample_df = yt[0:1000] # It takes only 1000 rows from 700K rows of data set"
      ],
      "metadata": {
        "id": "xLu5ggskINEm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RR3ISbvmIiO7",
        "outputId": "5349b961-ed57-4f0b-96cf-675506f09fe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(yt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "LxxdLJ3nN29Y",
        "outputId": "f1d6a3df-7bb4-48b7-cf4b-8e2965a85fda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To execute sentiment polarity for values in data set we need to do FOR function\n",
        "\n",
        "from textblob import TextBlob\n",
        "\n",
        "polarity = []  # Reinitialize the list\n",
        "\n",
        "for comment in yt[\"comment_text\"]:\n",
        "    try:\n",
        "        polarity.append(TextBlob(comment).sentiment.polarity)\n",
        "    except:\n",
        "        polarity.append(0)\n",
        "\n",
        "# Assign the polarity values back to the DataFrame\n",
        "yt['polarity'] = polarity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rd3hf5ZOJH2C",
        "outputId": "ce544c39-0222-425b-de91-e2b5915fd58d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-a6138958f203>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  yt['polarity'] = polarity\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(polarity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w__RbpucKY6U",
        "outputId": "8acdc6ec-8157-443b-eb9d-6b96335e25ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "478691"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now need to add this polarity in DF (yt)\n",
        "\n",
        "yt['polarity'] = polarity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "Ob1AdtKEKb04",
        "outputId": "703fd679-be8a-4e8a-d089-66037a46d8c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'str' object does not support item assignment",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-bbc41f01ebe1>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Now need to add this polarity in DF (yt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0myt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'polarity'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpolarity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(yt))  # This should return <class 'pandas.core.frame.DataFrame'>\n",
        "\n",
        "#If the output is str again we need to import the data and do polarity to get (yt as DF)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TV7ULd8LSkE",
        "outputId": "db84caf1-fc7e-4355-8b2b-6c59fcbb7668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(yt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvkL8_BWMGyr",
        "outputId": "5088e6fb-ea99-4b9a-890f-cc6a829d9147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yt.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MRwt1zFfOlzz",
        "outputId": "68ea6dd2-174b-4bdc-fee8-5c774d0208bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      video_id                                       comment_text likes  \\\n",
              "0  XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1  XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2  XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3  XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4  XpVt6Z1Gjjo                                         trending 😉     3   \n",
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              "  replies  polarity  \n",
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              "1       0       0.0  \n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Wordcloud Analysis\n",
        "\n",
        "# Graphical representation of Text frequency\n",
        "\n",
        "# Helps to display all the possible +ve keywords / keyword have +ve polarity(like: Super, Good, Noce, Awesome..)"
      ],
      "metadata": {
        "id": "Kh0yrWYMxOL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter1 = yt[\"polarity\"] ==1"
      ],
      "metadata": {
        "id": "qA6kYSyJyCWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive_comment = yt[filter1] # it will show only the positive value"
      ],
      "metadata": {
        "id": "pvdzssKQyZ9i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter2 = yt[\"polarity\"] ==-1"
      ],
      "metadata": {
        "id": "uhqQ5sr6zMan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "negative_comment = yt[filter2]"
      ],
      "metadata": {
        "id": "1uPCa6cpzOh5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive_comment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jOJ0iGAtznEW",
        "outputId": "3b5adeba-f756-48f4-adb4-62c6ba550d02"
      },
      "execution_count": null,
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              "           video_id                                       comment_text likes  \\\n",
              "64      XpVt6Z1Gjjo                                    yu are the best     1   \n",
              "156     cLdxuaxaQwc  Power is the disease.  Care is the cure.  Keep...     0   \n",
              "227     WYYvHb03Eog  YAS Can't wait to get it! I just need to sell ...     0   \n",
              "307     sjlHnJvXdQs                                  This is priceless     0   \n",
              "319     sjlHnJvXdQs                                Summed up perfectly     0   \n",
              "...             ...                                                ...   ...   \n",
              "690702  n530GFrdPY8  LOVE IT BRAD!!! Making me wish I lived in the ...     2   \n",
              "690790  Hlt3rA-oDao                             Absolutely brilliant!!     0   \n",
              "690908  CN4YL1KPDAA   Do more in ear earphone reviews again!!!!!!!!!!!     0   \n",
              "691002  4kmSATD25iY  Another awesome video! (i also did a taste tes...     0   \n",
              "691393  EoejGgUNmVU                                                 <3     0   \n",
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              "       replies  polarity  \n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690702</th>\n",
              "      <td>n530GFrdPY8</td>\n",
              "      <td>LOVE IT BRAD!!! Making me wish I lived in the ...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690790</th>\n",
              "      <td>Hlt3rA-oDao</td>\n",
              "      <td>Absolutely brilliant!!</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690908</th>\n",
              "      <td>CN4YL1KPDAA</td>\n",
              "      <td>Do more in ear earphone reviews again!!!!!!!!!!!</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691002</th>\n",
              "      <td>4kmSATD25iY</td>\n",
              "      <td>Another awesome video! (i also did a taste tes...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691393</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>&lt;3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13211 rows × 5 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
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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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('positive_comment')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "positive_comment",
              "summary": "{\n  \"name\": \"positive_comment\",\n  \"rows\": 13211,\n  \"fields\": [\n    {\n      \"column\": \"video_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1895,\n        \"samples\": [\n          \"yDDDJC3AYtA\",\n          \"b-znn2eQL08\",\n          \"g85RGiOgUtI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11325,\n        \"samples\": [\n          \"This look is absolutely beautiful Christen!! \\ud83d\\udc95\\ud83d\\udc95 I love this!! And can't wait for your makeup line!! \\ud83d\\udc4f\\ud83c\\udffd\\ud83d\\udc4f\\ud83c\\udffd\",\n          \"In Latin rapper Maluma and Daddy Yankee are the best\",\n          \"Thats an awesome build!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"likes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 140,\n        \"samples\": [\n          172,\n          13,\n          \"118\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"replies\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          41,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"polarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To install wordcloud\n",
        "\n",
        "!pip install wordcloud"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtrxOGEuzsQR",
        "outputId": "83fd5b68-fa35-4c65-8fc7-568464eb7627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.10/dist-packages (1.9.3)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.10/dist-packages (from wordcloud) (1.26.4)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from wordcloud) (10.4.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from wordcloud) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud, STOPWORDS #STOPWORDS is used to remove meaningless words (is, are, it..)"
      ],
      "metadata": {
        "id": "61lFG01qzzA7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set(STOPWORDS)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uwDPfEbu0Nig",
        "outputId": "3b208097-9138-40b4-88c6-6d3bfcf56091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a',\n",
              " 'about',\n",
              " 'above',\n",
              " 'after',\n",
              " 'again',\n",
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              " 'also',\n",
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              " \"aren't\",\n",
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              " 'ever',\n",
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              " 'from',\n",
              " 'further',\n",
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              " 'had',\n",
              " \"hadn't\",\n",
              " 'has',\n",
              " \"hasn't\",\n",
              " 'have',\n",
              " \"haven't\",\n",
              " 'having',\n",
              " 'he',\n",
              " \"he'd\",\n",
              " \"he'll\",\n",
              " \"he's\",\n",
              " 'hence',\n",
              " 'her',\n",
              " 'here',\n",
              " \"here's\",\n",
              " 'hers',\n",
              " 'herself',\n",
              " 'him',\n",
              " 'himself',\n",
              " 'his',\n",
              " 'how',\n",
              " \"how's\",\n",
              " 'however',\n",
              " 'http',\n",
              " 'i',\n",
              " \"i'd\",\n",
              " \"i'll\",\n",
              " \"i'm\",\n",
              " \"i've\",\n",
              " 'if',\n",
              " 'in',\n",
              " 'into',\n",
              " 'is',\n",
              " \"isn't\",\n",
              " 'it',\n",
              " \"it's\",\n",
              " 'its',\n",
              " 'itself',\n",
              " 'just',\n",
              " 'k',\n",
              " \"let's\",\n",
              " 'like',\n",
              " 'me',\n",
              " 'more',\n",
              " 'most',\n",
              " \"mustn't\",\n",
              " 'my',\n",
              " 'myself',\n",
              " 'no',\n",
              " 'nor',\n",
              " 'not',\n",
              " 'of',\n",
              " 'off',\n",
              " 'on',\n",
              " 'once',\n",
              " 'only',\n",
              " 'or',\n",
              " 'other',\n",
              " 'otherwise',\n",
              " 'ought',\n",
              " 'our',\n",
              " 'ours',\n",
              " 'ourselves',\n",
              " 'out',\n",
              " 'over',\n",
              " 'own',\n",
              " 'r',\n",
              " 'same',\n",
              " 'shall',\n",
              " \"shan't\",\n",
              " 'she',\n",
              " \"she'd\",\n",
              " \"she'll\",\n",
              " \"she's\",\n",
              " 'should',\n",
              " \"shouldn't\",\n",
              " 'since',\n",
              " 'so',\n",
              " 'some',\n",
              " 'such',\n",
              " 'than',\n",
              " 'that',\n",
              " \"that's\",\n",
              " 'the',\n",
              " 'their',\n",
              " 'theirs',\n",
              " 'them',\n",
              " 'themselves',\n",
              " 'then',\n",
              " 'there',\n",
              " \"there's\",\n",
              " 'therefore',\n",
              " 'these',\n",
              " 'they',\n",
              " \"they'd\",\n",
              " \"they'll\",\n",
              " \"they're\",\n",
              " \"they've\",\n",
              " 'this',\n",
              " 'those',\n",
              " 'through',\n",
              " 'to',\n",
              " 'too',\n",
              " 'under',\n",
              " 'until',\n",
              " 'up',\n",
              " 'very',\n",
              " 'was',\n",
              " \"wasn't\",\n",
              " 'we',\n",
              " \"we'd\",\n",
              " \"we'll\",\n",
              " \"we're\",\n",
              " \"we've\",\n",
              " 'were',\n",
              " \"weren't\",\n",
              " 'what',\n",
              " \"what's\",\n",
              " 'when',\n",
              " \"when's\",\n",
              " 'where',\n",
              " \"where's\",\n",
              " 'which',\n",
              " 'while',\n",
              " 'who',\n",
              " \"who's\",\n",
              " 'whom',\n",
              " 'why',\n",
              " \"why's\",\n",
              " 'with',\n",
              " \"won't\",\n",
              " 'would',\n",
              " \"wouldn't\",\n",
              " 'www',\n",
              " 'you',\n",
              " \"you'd\",\n",
              " \"you'll\",\n",
              " \"you're\",\n",
              " \"you've\",\n",
              " 'your',\n",
              " 'yours',\n",
              " 'yourself',\n",
              " 'yourselves'}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(yt[\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "TTGTEmkE0ao1",
        "outputId": "aec429c9-147e-4227-cf7d-2b3159ce2232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool=False) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To avoid this error we need to converet series into str\n",
        "\n",
        "wordcloud = WordCloud(stopwords = set(STOPWORDS)).generate(positive_comment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "wWq6GUJw01bV",
        "outputId": "90f3e34d-1d04-4e0c-909f-7801476ecfed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "expected string or bytes-like object",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-8d78d2969a9a>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# We need to convert this series to str for STOPWORDS function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mwordcloud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstopwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSTOPWORDS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive_comment\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \"\"\"\n\u001b[0;32m--> 642\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_from_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_generated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_text\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    621\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \"\"\"\n\u001b[0;32m--> 623\u001b[0;31m         \u001b[0mwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    624\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_from_frequencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mprocess_text\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    583\u001b[0m         \u001b[0mregexp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregexp\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregexp\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m         \u001b[0mwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregexp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    586\u001b[0m         \u001b[0;31m# remove 's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         words = [word[:-2] if word.lower().endswith(\"'s\") else word\n",
            "\u001b[0;32m/usr/lib/python3.10/re.py\u001b[0m in \u001b[0;36mfindall\u001b[0;34m(pattern, string, flags)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     Empty matches are included in the result.\"\"\"\n\u001b[0;32m--> 240\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfinditer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected string or bytes-like object"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting the series to str for using STOPWORDS function\n",
        "\n",
        "total_positive_comment = ''.join(positive_comment[\"comment_text\"])"
      ],
      "metadata": {
        "id": "8H5Jg_b41_2b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(total_positive_comment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IfrYv0H2AVV",
        "outputId": "34c212da-f87a-4753-e181-7259197a4fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud = WordCloud(stopwords = set(STOPWORDS)).generate(total_positive_comment)"
      ],
      "metadata": {
        "id": "uKww6fGg2TGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(wordcloud)\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "5Kc9Xw272Z7K",
        "outputId": "74b9b901-9931-4dfd-d533-8e97796e44a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 399.5, 199.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now wordcloud for bad comments\n",
        "\n",
        "# Converting series to str\n",
        "\n",
        "total_negative_comment = ''.join(negative_comment[\"comment_text\"])"
      ],
      "metadata": {
        "id": "UJ6-gXEm2u7M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud_neg = WordCloud(stopwords= set(STOPWORDS)).generate(total_negative_comment)"
      ],
      "metadata": {
        "id": "T_TVmiws2-ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(wordcloud_neg)\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "b0OPadLJ3cGG",
        "outputId": "70ae4b0a-1b83-43f1-fd5e-54b3c69b0ed1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 399.5, 199.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yt"
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              "           video_id                                       comment_text likes  \\\n",
              "0       XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1       XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2       XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3       XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4       XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "...             ...                                                ...   ...   \n",
              "691359  EoejGgUNmVU     Lost on you ! 😍\\nOH My God I love this song !😭     0   \n",
              "691366  EoejGgUNmVU                                      I  LOVE  LP❤️     0   \n",
              "691392  EoejGgUNmVU                              *L O S T  O N  Y O U*     1   \n",
              "691393  EoejGgUNmVU                                                 <3     0   \n",
              "691398  EoejGgUNmVU                                    Islığı yeter...     0   \n",
              "\n",
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              "691366       0  \n",
              "691392       0  \n",
              "691393       0  \n",
              "691398       0  \n",
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              "[478691 rows x 4 columns]"
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              "      <td>Logan Paul it's yo big day ‼️‼️‼️</td>\n",
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              "    <tr>\n",
              "      <th>691359</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>Lost on you ! 😍\\nOH My God I love this song !😭</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>691366</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
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              "      <th>691392</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
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              "    <tr>\n",
              "      <th>691398</th>\n",
              "      <td>EoejGgUNmVU</td>\n",
              "      <td>Islığı yeter...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Emoji Analysis\n",
        "\n",
        "!pip install emoji"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYJaD0x3XhNP",
        "outputId": "262b8107-93e4-47f0-827c-2d384fb4dd2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting emoji\n",
            "  Downloading emoji-2.13.2-py3-none-any.whl.metadata (5.8 kB)\n",
            "Downloading emoji-2.13.2-py3-none-any.whl (553 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m553.2/553.2 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: emoji\n",
            "Successfully installed emoji-2.13.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import emoji\n",
        "\n",
        "emoji.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vCq6fw-EYn1O",
        "outputId": "e2d9c04b-c111-493b-81bb-e26078362a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.13.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yt[\"comment_text\"].head(6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "VHGqYOgSY1wH",
        "outputId": "1a613850-105e-48a3-a98c-567d6508d218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                    Logan Paul it's yo big day ‼️‼️‼️\n",
              "1    I've been following you from the start of your...\n",
              "2                   Say hi to Kong and maverick for me\n",
              "3                                  MY FAN . attendance\n",
              "4                                           trending 😉\n",
              "5                              #1 on trending AYYEEEEE\n",
              "Name: comment_text, dtype: object"
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logan Paul it's yo big day ‼️‼️‼️</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I've been following you from the start of your...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Say hi to Kong and maverick for me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MY FAN . attendance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>trending 😉</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>#1 on trending AYYEEEEE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = \"trending 😉\""
      ],
      "metadata": {
        "id": "QtOAJW2TH2Oq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emoji_test = []\n",
        "\n",
        "for char in sample:\n",
        "  if char in emoji.EMOJI_DATA:\n",
        "    emoji_test.append(char)"
      ],
      "metadata": {
        "id": "IS0Xc_p-H7VP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(emoji_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8dUb1u7IabD",
        "outputId": "6fda82b8-f0c2-4557-8ddc-e2424c4034b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['😉']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "UJawEaAqJGM3",
        "outputId": "1a49e7d5-cd2e-47ba-80af-2d5b5b8967af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           video_id                                       comment_text likes  \\\n",
              "0       XpVt6Z1Gjjo                  Logan Paul it's yo big day ‼️‼️‼️     4   \n",
              "1       XpVt6Z1Gjjo  I've been following you from the start of your...     3   \n",
              "2       XpVt6Z1Gjjo                 Say hi to Kong and maverick for me     3   \n",
              "3       XpVt6Z1Gjjo                                MY FAN . attendance     3   \n",
              "4       XpVt6Z1Gjjo                                         trending 😉     3   \n",
              "...             ...                                                ...   ...   \n",
              "691359  EoejGgUNmVU     Lost on you ! 😍\\nOH My God I love this song !😭     0   \n",
              "691366  EoejGgUNmVU                                      I  LOVE  LP❤️     0   \n",
              "691392  EoejGgUNmVU                              *L O S T  O N  Y O U*     1   \n",
              "691393  EoejGgUNmVU                                                 <3     0   \n",
              "691398  EoejGgUNmVU                                    Islığı yeter...     0   \n",
              "\n",
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              "[478691 rows x 4 columns]"
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              "\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-36d695b4-94c9-44f6-a436-8d8389936bd2 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('yt')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('yt');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yt"
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emoji_final = []\n",
        "\n",
        "# Yella comments-aiyum iterate pannra (for-loop-la)\n",
        "for comment in yt[\"comment_text\"]:\n",
        "    # Oru comment-ku ulla ellaa characters-aiyum check panna\n",
        "    for char in comment:\n",
        "        if char in emoji.EMOJI_DATA:  # If character emoji list-la irundha\n",
        "            emoji_final.append(char)  # Append pannra\n",
        "\n",
        "# Ippo emoji_final-la ellaa emojis-um irukum, all comments-lendhu"
      ],
      "metadata": {
        "id": "gDEAs1CeJJcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emoji_final[0:10] #To print 1st 10 emojis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8-mmQSsJhdu",
        "outputId": "05f2944f-4076-458c-8ec0-29b1d66216ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['‼', '‼', '‼', '😉', '😭', '👍', '🏻', '❤', '😍', '💋']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now I need a count of each emoji's count\n",
        "\n",
        "from collections import Counter"
      ],
      "metadata": {
        "id": "WSG7wGLvK5tr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(emoji_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKLQG_jyLq4G",
        "outputId": "1fb71e74-ba0b-470e-be10-e3a885ba1adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'‼': 148,\n",
              "         '😉': 728,\n",
              "         '😭': 6155,\n",
              "         '👍': 3789,\n",
              "         '🏻': 2488,\n",
              "         '❤': 22180,\n",
              "         '😍': 22145,\n",
              "         '💋': 733,\n",
              "         '💙': 1958,\n",
              "         '👈': 96,\n",
              "         '😂': 27287,\n",
              "         '🔥': 6288,\n",
              "         '👻': 216,\n",
              "         '💎': 265,\n",
              "         '😆': 840,\n",
              "         '😁': 1605,\n",
              "         '😒': 432,\n",
              "         '👏': 4005,\n",
              "         '😊': 2386,\n",
              "         '👌': 2228,\n",
              "         '🤔': 1172,\n",
              "         '💔': 492,\n",
              "         '👎': 549,\n",
              "         '🍎': 38,\n",
              "         '😤': 308,\n",
              "         '😢': 2006,\n",
              "         '👫': 27,\n",
              "         '💘': 535,\n",
              "         '😻': 893,\n",
              "         '💖': 4242,\n",
              "         '🌟': 464,\n",
              "         '😮': 873,\n",
              "         '👇': 115,\n",
              "         '😳': 276,\n",
              "         '😠': 378,\n",
              "         '😴': 142,\n",
              "         '😝': 298,\n",
              "         '™': 29,\n",
              "         '☺': 853,\n",
              "         '😔': 388,\n",
              "         '😩': 1050,\n",
              "         '😡': 344,\n",
              "         '🙏': 1237,\n",
              "         '😄': 1125,\n",
              "         '😬': 1274,\n",
              "         '☹': 140,\n",
              "         '🤷': 250,\n",
              "         '🤣': 1721,\n",
              "         '😃': 756,\n",
              "         '🏽': 1109,\n",
              "         '🍏': 50,\n",
              "         '😓': 204,\n",
              "         '🤦': 286,\n",
              "         '♀': 415,\n",
              "         '😞': 286,\n",
              "         '🏼': 1791,\n",
              "         '✌': 593,\n",
              "         '💀': 663,\n",
              "         '👀': 346,\n",
              "         '😱': 1587,\n",
              "         '👑': 511,\n",
              "         '🌮': 56,\n",
              "         '😀': 1315,\n",
              "         '💆': 18,\n",
              "         '🏿': 456,\n",
              "         '♂': 394,\n",
              "         '😅': 799,\n",
              "         '🐂': 11,\n",
              "         '💩': 669,\n",
              "         '💕': 3731,\n",
              "         '💓': 1136,\n",
              "         '💗': 1583,\n",
              "         '💝': 477,\n",
              "         '💜': 2588,\n",
              "         '😎': 1093,\n",
              "         '🎉': 514,\n",
              "         '😋': 410,\n",
              "         '😘': 4174,\n",
              "         '😐': 528,\n",
              "         '😕': 349,\n",
              "         '🤘': 459,\n",
              "         '🙌': 1526,\n",
              "         '😏': 492,\n",
              "         '😛': 190,\n",
              "         '🌳': 10,\n",
              "         '🌽': 28,\n",
              "         '🎃': 480,\n",
              "         '🚣': 14,\n",
              "         '🚂': 11,\n",
              "         '😑': 335,\n",
              "         '✨': 617,\n",
              "         '👅': 133,\n",
              "         '💦': 273,\n",
              "         '🌴': 44,\n",
              "         '😫': 550,\n",
              "         '😥': 464,\n",
              "         '💪': 495,\n",
              "         '🙂': 278,\n",
              "         '🙈': 238,\n",
              "         '😲': 374,\n",
              "         '🤢': 169,\n",
              "         '🎧': 144,\n",
              "         '🎶': 649,\n",
              "         '🎵': 230,\n",
              "         '🎼': 110,\n",
              "         '🎤': 279,\n",
              "         '♨': 38,\n",
              "         '💯': 1055,\n",
              "         '👊': 298,\n",
              "         '💏': 30,\n",
              "         '🙃': 191,\n",
              "         '🐸': 96,\n",
              "         '🏈': 25,\n",
              "         '💁': 132,\n",
              "         '🤡': 211,\n",
              "         '🙇': 43,\n",
              "         '👋': 147,\n",
              "         '💄': 137,\n",
              "         '🐐': 121,\n",
              "         '😇': 421,\n",
              "         '🐊': 13,\n",
              "         '🌎': 79,\n",
              "         '🖤': 577,\n",
              "         '🤗': 653,\n",
              "         '🙄': 448,\n",
              "         '😨': 251,\n",
              "         '😌': 223,\n",
              "         '🤑': 85,\n",
              "         '😗': 256,\n",
              "         '📱': 134,\n",
              "         '🌀': 14,\n",
              "         '🏾': 853,\n",
              "         '😶': 133,\n",
              "         '😖': 208,\n",
              "         '🙋': 223,\n",
              "         '🌹': 367,\n",
              "         '😵': 193,\n",
              "         '🌯': 35,\n",
              "         '😣': 274,\n",
              "         '💛': 1277,\n",
              "         '💞': 956,\n",
              "         '😽': 22,\n",
              "         '♥': 3400,\n",
              "         '😜': 402,\n",
              "         '🚩': 15,\n",
              "         '🆙': 9,\n",
              "         '🆒': 12,\n",
              "         '✔': 123,\n",
              "         '🙊': 112,\n",
              "         '😹': 166,\n",
              "         '🥀': 25,\n",
              "         '🍫': 137,\n",
              "         '👸': 95,\n",
              "         '😰': 192,\n",
              "         '💚': 986,\n",
              "         '🎊': 102,\n",
              "         '🎂': 122,\n",
              "         '🐈': 32,\n",
              "         '⌚': 16,\n",
              "         '👿': 122,\n",
              "         '💸': 203,\n",
              "         '💰': 438,\n",
              "         '🏀': 72,\n",
              "         '🎈': 486,\n",
              "         '🔪': 67,\n",
              "         '🚫': 40,\n",
              "         '🤠': 54,\n",
              "         '🖕': 659,\n",
              "         '👉': 144,\n",
              "         '✊': 186,\n",
              "         '🤴': 24,\n",
              "         '🐘': 16,\n",
              "         '❄': 187,\n",
              "         '🍦': 64,\n",
              "         '🅱': 142,\n",
              "         '😙': 903,\n",
              "         '💧': 29,\n",
              "         '❗': 83,\n",
              "         '😈': 317,\n",
              "         '🙁': 94,\n",
              "         '👽': 127,\n",
              "         '⚠': 55,\n",
              "         '👭': 43,\n",
              "         '🍓': 241,\n",
              "         '😯': 110,\n",
              "         '🎥': 55,\n",
              "         '☝': 109,\n",
              "         '❣': 392,\n",
              "         '🎀': 42,\n",
              "         '🍭': 73,\n",
              "         '💌': 65,\n",
              "         '🐼': 38,\n",
              "         '😚': 379,\n",
              "         '🌻': 52,\n",
              "         '🐇': 24,\n",
              "         '🐰': 28,\n",
              "         '🔸': 27,\n",
              "         '🚀': 64,\n",
              "         '🚔': 13,\n",
              "         '🚒': 20,\n",
              "         '🚑': 8,\n",
              "         '💃': 268,\n",
              "         '💟': 380,\n",
              "         '💨': 44,\n",
              "         '🎺': 14,\n",
              "         '👣': 89,\n",
              "         '👆': 51,\n",
              "         '👂': 21,\n",
              "         '👃': 24,\n",
              "         '🌭': 17,\n",
              "         '👐': 89,\n",
              "         '🌊': 51,\n",
              "         '🌞': 43,\n",
              "         '🦄': 286,\n",
              "         '🌬': 13,\n",
              "         '🌪': 21,\n",
              "         '⛈': 21,\n",
              "         '🌲': 32,\n",
              "         '😸': 46,\n",
              "         '👮': 19,\n",
              "         '👕': 21,\n",
              "         '👖': 29,\n",
              "         '👘': 13,\n",
              "         '👚': 8,\n",
              "         '👔': 8,\n",
              "         '⬇': 14,\n",
              "         '🍆': 81,\n",
              "         '😦': 88,\n",
              "         '😧': 108,\n",
              "         '😟': 96,\n",
              "         '😷': 91,\n",
              "         '😪': 179,\n",
              "         '🙍': 15,\n",
              "         '🙅': 133,\n",
              "         '🙆': 81,\n",
              "         '🙎': 11,\n",
              "         '😺': 55,\n",
              "         '😼': 18,\n",
              "         '😿': 120,\n",
              "         '😾': 19,\n",
              "         '🙀': 45,\n",
              "         '🙉': 33,\n",
              "         '👶': 41,\n",
              "         '👦': 67,\n",
              "         '👧': 138,\n",
              "         '👨': 188,\n",
              "         '👩': 242,\n",
              "         '👴': 8,\n",
              "         '👵': 9,\n",
              "         '💑': 117,\n",
              "         '👪': 12,\n",
              "         '👬': 28,\n",
              "         '👤': 7,\n",
              "         '👥': 11,\n",
              "         '👷': 12,\n",
              "         '💂': 14,\n",
              "         '👯': 41,\n",
              "         '👰': 45,\n",
              "         '🎅': 46,\n",
              "         '👼': 27,\n",
              "         '👱': 19,\n",
              "         '👲': 6,\n",
              "         '👳': 23,\n",
              "         '💇': 18,\n",
              "         '💅': 90,\n",
              "         '👹': 32,\n",
              "         '👺': 29,\n",
              "         '👾': 79,\n",
              "         '👄': 67,\n",
              "         '✋': 87,\n",
              "         '🔰': 12,\n",
              "         '👞': 13,\n",
              "         '👟': 31,\n",
              "         '👒': 9,\n",
              "         '🎩': 23,\n",
              "         '🎓': 14,\n",
              "         '👓': 16,\n",
              "         '👗': 19,\n",
              "         '👙': 34,\n",
              "         '👠': 35,\n",
              "         '👡': 8,\n",
              "         '👢': 15,\n",
              "         '👜': 7,\n",
              "         '💼': 14,\n",
              "         '🎒': 8,\n",
              "         '👝': 7,\n",
              "         '👛': 12,\n",
              "         '💳': 26,\n",
              "         '💵': 604,\n",
              "         '💴': 23,\n",
              "         '💶': 22,\n",
              "         '💷': 17,\n",
              "         '💱': 6,\n",
              "         '🔫': 99,\n",
              "         '💣': 141,\n",
              "         '💉': 13,\n",
              "         '💊': 9,\n",
              "         '🚬': 18,\n",
              "         '🔔': 27,\n",
              "         '🔕': 8,\n",
              "         '🚪': 54,\n",
              "         '🔬': 21,\n",
              "         '🔭': 7,\n",
              "         '🔮': 29,\n",
              "         '🔦': 7,\n",
              "         '🔋': 9,\n",
              "         '🔌': 13,\n",
              "         '📜': 6,\n",
              "         '📗': 5,\n",
              "         '📘': 10,\n",
              "         '📙': 6,\n",
              "         '📚': 45,\n",
              "         '📔': 6,\n",
              "         '📒': 7,\n",
              "         '📑': 4,\n",
              "         '📓': 6,\n",
              "         '📕': 7,\n",
              "         '📖': 16,\n",
              "         '📰': 8,\n",
              "         '📛': 9,\n",
              "         '🎄': 81,\n",
              "         '🎁': 69,\n",
              "         '🎆': 54,\n",
              "         '🎇': 141,\n",
              "         '🎍': 9,\n",
              "         '🎋': 8,\n",
              "         '📲': 20,\n",
              "         '📟': 8,\n",
              "         '☎': 11,\n",
              "         '📞': 10,\n",
              "         '📠': 7,\n",
              "         '📦': 12,\n",
              "         '✉': 16,\n",
              "         '📨': 8,\n",
              "         '📩': 7,\n",
              "         '📪': 6,\n",
              "         '📫': 7,\n",
              "         '📭': 8,\n",
              "         '📬': 7,\n",
              "         '📮': 6,\n",
              "         '📤': 5,\n",
              "         '📥': 7,\n",
              "         '📯': 5,\n",
              "         '📢': 14,\n",
              "         '📣': 28,\n",
              "         '📡': 9,\n",
              "         '💬': 11,\n",
              "         '💭': 30,\n",
              "         '✒': 12,\n",
              "         '✏': 10,\n",
              "         '📝': 14,\n",
              "         '📏': 15,\n",
              "         '📐': 8,\n",
              "         '📍': 6,\n",
              "         '📌': 9,\n",
              "         '📎': 9,\n",
              "         '✂': 11,\n",
              "         '💺': 4,\n",
              "         '💻': 25,\n",
              "         '💽': 18,\n",
              "         '💾': 7,\n",
              "         '💿': 1017,\n",
              "         '📆': 5,\n",
              "         '📅': 8,\n",
              "         '📇': 7,\n",
              "         '📋': 4,\n",
              "         '📁': 26,\n",
              "         '📂': 18,\n",
              "         '📃': 4,\n",
              "         '📄': 5,\n",
              "         '📊': 5,\n",
              "         '📈': 9,\n",
              "         '📉': 16,\n",
              "         '⛺': 8,\n",
              "         '🎡': 11,\n",
              "         '🎢': 11,\n",
              "         '🎠': 16,\n",
              "         '🎪': 7,\n",
              "         '🎨': 21,\n",
              "         '🎬': 25,\n",
              "         '📷': 19,\n",
              "         '📹': 18,\n",
              "         '🎦': 6,\n",
              "         '🎭': 17,\n",
              "         '🎫': 9,\n",
              "         '🎮': 21,\n",
              "         '🎲': 18,\n",
              "         '🎰': 5,\n",
              "         '🃏': 11,\n",
              "         '🎴': 8,\n",
              "         '🀄': 10,\n",
              "         '🎯': 22,\n",
              "         '📺': 13,\n",
              "         '📻': 9,\n",
              "         '📀': 61,\n",
              "         '📼': 8,\n",
              "         '🎻': 31,\n",
              "         '🎹': 134,\n",
              "         '🎷': 16,\n",
              "         '🎸': 66,\n",
              "         '〽': 7,\n",
              "         '🐕': 74,\n",
              "         '🐶': 241,\n",
              "         '🐩': 32,\n",
              "         '🐱': 61,\n",
              "         '🐀': 9,\n",
              "         '🐁': 8,\n",
              "         '🐭': 11,\n",
              "         '🐹': 11,\n",
              "         '🐢': 368,\n",
              "         '🐓': 89,\n",
              "         '🐔': 16,\n",
              "         '🐣': 65,\n",
              "         '🐤': 36,\n",
              "         '🐥': 101,\n",
              "         '🐦': 60,\n",
              "         '🐏': 23,\n",
              "         '🐑': 78,\n",
              "         '🐺': 271,\n",
              "         '🐃': 10,\n",
              "         '🐄': 22,\n",
              "         '🐮': 11,\n",
              "         '🐴': 19,\n",
              "         '🐗': 8,\n",
              "         '🐖': 20,\n",
              "         '🐷': 27,\n",
              "         '🐽': 14,\n",
              "         '🐍': 848,\n",
              "         '🐧': 23,\n",
              "         '🐨': 17,\n",
              "         '🐒': 35,\n",
              "         '🐵': 17,\n",
              "         '🐆': 8,\n",
              "         '🐯': 17,\n",
              "         '🐻': 55,\n",
              "         '🐫': 57,\n",
              "         '🐪': 7,\n",
              "         '🐳': 20,\n",
              "         '🐋': 14,\n",
              "         '🐟': 51,\n",
              "         '🐠': 15,\n",
              "         '🐡': 12,\n",
              "         '🐙': 27,\n",
              "         '🐚': 11,\n",
              "         '🐬': 20,\n",
              "         '🐌': 8,\n",
              "         '🐛': 14,\n",
              "         '🐜': 30,\n",
              "         '🐝': 257,\n",
              "         '🐞': 13,\n",
              "         '🐲': 17,\n",
              "         '🐉': 21,\n",
              "         '🐾': 57,\n",
              "         '🍸': 57,\n",
              "         '🍺': 46,\n",
              "         '🍻': 51,\n",
              "         '🍷': 47,\n",
              "         '🏡': 17,\n",
              "         '💍': 59,\n",
              "         '🤓': 104,\n",
              "         '🦀': 10,\n",
              "         '🕷': 31,\n",
              "         '🍨': 22,\n",
              "         '⛳': 10,\n",
              "         '🤤': 285,\n",
              "         '🎾': 14,\n",
              "         '🍯': 23,\n",
              "         '⛪': 14,\n",
              "         '🆘': 15,\n",
              "         '🦎': 10,\n",
              "         '🦁': 19,\n",
              "         '🐎': 17,\n",
              "         '🐅': 7,\n",
              "         '🌔': 6,\n",
              "         '🌚': 46,\n",
              "         '🌝': 25,\n",
              "         '🌛': 21,\n",
              "         '💫': 78,\n",
              "         '🌜': 6,\n",
              "         '🌓': 5,\n",
              "         '🌒': 9,\n",
              "         '🌑': 11,\n",
              "         '🌗': 5,\n",
              "         '🌍': 1801,\n",
              "         '🌕': 40,\n",
              "         '🌖': 5,\n",
              "         '🚤': 8,\n",
              "         '🚝': 6,\n",
              "         '🚡': 7,\n",
              "         '🏝': 6,\n",
              "         '🛤': 4,\n",
              "         '🌌': 59,\n",
              "         '🌠': 33,\n",
              "         '⛰': 3,\n",
              "         '🌁': 6,\n",
              "         '🌆': 6,\n",
              "         '🗼': 14,\n",
              "         '🗽': 19,\n",
              "         '🏗': 10,\n",
              "         '🏟': 8,\n",
              "         '🏯': 7,\n",
              "         '🏰': 5,\n",
              "         '🏘': 4,\n",
              "         '🚢': 10,\n",
              "         '🕌': 6,\n",
              "         '🕋': 16,\n",
              "         '🏢': 9,\n",
              "         '⛩': 4,\n",
              "         '🏬': 8,\n",
              "         '🏛': 4,\n",
              "         '🏨': 6,\n",
              "         '🏪': 5,\n",
              "         '🏣': 6,\n",
              "         '🖥': 10,\n",
              "         '⌨': 4,\n",
              "         '🕰': 6,\n",
              "         '⏱': 4,\n",
              "         '⚒': 2,\n",
              "         '🔝': 82,\n",
              "         '🔜': 15,\n",
              "         '🤥': 36,\n",
              "         '🕺': 47,\n",
              "         '🔐': 20,\n",
              "         '🌺': 62,\n",
              "         '🌸': 141,\n",
              "         '🤧': 89,\n",
              "         '💥': 247,\n",
              "         '🤙': 108,\n",
              "         '💐': 69,\n",
              "         '♓': 6,\n",
              "         '♣': 12,\n",
              "         '🖐': 20,\n",
              "         '🤐': 50,\n",
              "         '🔨': 12,\n",
              "         '🤚': 19,\n",
              "         '🍼': 36,\n",
              "         '🔴': 677,\n",
              "         '🔵': 632,\n",
              "         '🅾': 15,\n",
              "         '☑': 9,\n",
              "         '🍣': 22,\n",
              "         '🍉': 68,\n",
              "         '🌵': 14,\n",
              "         '❌': 44,\n",
              "         '🍕': 219,\n",
              "         '🤨': 2,\n",
              "         '🤹': 7,\n",
              "         '↙': 9,\n",
              "         '©': 13,\n",
              "         '🍾': 83,\n",
              "         '⚰': 23,\n",
              "         '🏆': 90,\n",
              "         '🍝': 21,\n",
              "         '🍔': 239,\n",
              "         '🍴': 16,\n",
              "         '📿': 10,\n",
              "         '🌋': 25,\n",
              "         '🌈': 247,\n",
              "         '⚡': 113,\n",
              "         '🤞': 50,\n",
              "         '®': 31,\n",
              "         '🦋': 43,\n",
              "         '🍰': 61,\n",
              "         '🔊': 49,\n",
              "         '🌶': 23,\n",
              "         '☄': 25,\n",
              "         '🥗': 6,\n",
              "         '🍜': 54,\n",
              "         '🍱': 13,\n",
              "         '🥙': 6,\n",
              "         '🍖': 42,\n",
              "         '🍗': 320,\n",
              "         '🍪': 49,\n",
              "         '🍩': 52,\n",
              "         '☠': 110,\n",
              "         '☮': 11,\n",
              "         '🤕': 26,\n",
              "         '⭐': 145,\n",
              "         '🔒': 18,\n",
              "         '🥁': 15,\n",
              "         '💤': 25,\n",
              "         '🍁': 137,\n",
              "         '🦑': 10,\n",
              "         '🌙': 32,\n",
              "         '➡': 56,\n",
              "         '🥊': 14,\n",
              "         '🏃': 39,\n",
              "         '☕': 61,\n",
              "         '🚙': 10,\n",
              "         '🏋': 12,\n",
              "         '🖖': 23,\n",
              "         '♋': 9,\n",
              "         '🗣': 45,\n",
              "         '🤝': 10,\n",
              "         '🍚': 21,\n",
              "         '🏍': 7,\n",
              "         '🍑': 93,\n",
              "         '🥂': 25,\n",
              "         '🥑': 5,\n",
              "         '📸': 7,\n",
              "         '⛵': 10,\n",
              "         '🦅': 16,\n",
              "         '🚮': 31,\n",
              "         '🦆': 32,\n",
              "         '✅': 77,\n",
              "         '🛒': 4,\n",
              "         '🕵': 19,\n",
              "         '🔑': 22,\n",
              "         '🔉': 9,\n",
              "         '🍀': 26,\n",
              "         '🌷': 53,\n",
              "         '⁉': 12,\n",
              "         '✈': 32,\n",
              "         '⏫': 4,\n",
              "         '⚔': 7,\n",
              "         '🍂': 223,\n",
              "         '🦉': 6,\n",
              "         '🍃': 44,\n",
              "         '♍': 7,\n",
              "         '🏳': 33,\n",
              "         '🖱': 6,\n",
              "         '✴': 23,\n",
              "         '⛲': 9,\n",
              "         '✡': 4,\n",
              "         '🤸': 42,\n",
              "         '🍟': 38,\n",
              "         '👁': 62,\n",
              "         '🌼': 60,\n",
              "         '⬆': 15,\n",
              "         '💹': 3,\n",
              "         '▶': 33,\n",
              "         '🦇': 14,\n",
              "         '🕊': 29,\n",
              "         '☀': 43,\n",
              "         '🏅': 37,\n",
              "         '🎖': 8,\n",
              "         '🥇': 30,\n",
              "         '🥈': 7,\n",
              "         '🥉': 8,\n",
              "         '🍍': 115,\n",
              "         '🍳': 24,\n",
              "         '🍤': 17,\n",
              "         '🌇': 7,\n",
              "         '💮': 7,\n",
              "         '💲': 66,\n",
              "         '🍧': 16,\n",
              "         '🌐': 16,\n",
              "         '🥋': 7,\n",
              "         '〰': 290,\n",
              "         '🔞': 16,\n",
              "         '🎗': 12,\n",
              "         '🔆': 10,\n",
              "         '💢': 11,\n",
              "         '🚽': 16,\n",
              "         '🚨': 117,\n",
              "         '🍿': 39,\n",
              "         '📽': 9,\n",
              "         '💈': 7,\n",
              "         '🦌': 9,\n",
              "         '🦊': 9,\n",
              "         '🌾': 14,\n",
              "         '🐿': 7,\n",
              "         '☔': 7,\n",
              "         '🖊': 4,\n",
              "         '🌤': 10,\n",
              "         '⛅': 9,\n",
              "         '☃': 28,\n",
              "         '🕸': 9,\n",
              "         '↗': 11,\n",
              "         '🔽': 7,\n",
              "         '🥛': 12,\n",
              "         '🍋': 24,\n",
              "         '🖼': 4,\n",
              "         '🤛': 29,\n",
              "         '🗺': 15,\n",
              "         '🌄': 7,\n",
              "         '🌱': 13,\n",
              "         '⚓': 12,\n",
              "         '♦': 15,\n",
              "         '🗑': 9,\n",
              "         '🔙': 9,\n",
              "         '🤖': 17,\n",
              "         '🍬': 83,\n",
              "         '⛸': 8,\n",
              "         '⭕': 20,\n",
              "         '🍊': 37,\n",
              "         '🚲': 11,\n",
              "         '⚽': 65,\n",
              "         '◀': 17,\n",
              "         '☢': 6,\n",
              "         '🥔': 17,\n",
              "         '🗻': 5,\n",
              "         '💒': 11,\n",
              "         '☘': 7,\n",
              "         '💡': 13,\n",
              "         'ℹ': 6,\n",
              "         '🥒': 8,\n",
              "         '🍇': 64,\n",
              "         '🍒': 38,\n",
              "         '🍈': 22,\n",
              "         '🍐': 27,\n",
              "         '🍶': 23,\n",
              "         '🍅': 21,\n",
              "         '🔓': 9,\n",
              "         '⤵': 6,\n",
              "         '🍹': 22,\n",
              "         '🍄': 14,\n",
              "         '♠': 5,\n",
              "         '🤵': 7,\n",
              "         '⚫': 8,\n",
              "         '🗝': 6,\n",
              "         '🅿': 10,\n",
              "         '📧': 7,\n",
              "         '🏵': 7,\n",
              "         '🔅': 8,\n",
              "         '⛑': 26,\n",
              "         '🤒': 18,\n",
              "         '⬅': 15,\n",
              "         '🌏': 50,\n",
              "         '♊': 6,\n",
              "         '🈴': 7,\n",
              "         '🈯': 5,\n",
              "         '🈶': 5,\n",
              "         '🈳': 6,\n",
              "         '🌃': 9,\n",
              "         '🚾': 12,\n",
              "         '⏪': 4,\n",
              "         '❓': 15,\n",
              "         '♏': 8,\n",
              "         '♈': 7,\n",
              "         '♉': 5,\n",
              "         '♌': 6,\n",
              "         '♎': 9,\n",
              "         '♐': 7,\n",
              "         '♑': 8,\n",
              "         '♒': 5,\n",
              "         '🤜': 26,\n",
              "         '🚓': 7,\n",
              "         '🧀': 18,\n",
              "         '🍥': 18,\n",
              "         '⏰': 12,\n",
              "         '⛏': 4,\n",
              "         '🗡': 13,\n",
              "         '⛱': 5,\n",
              "         '✝': 73,\n",
              "         '🕯': 5,\n",
              "         '🕶': 12,\n",
              "         '🚇': 7,\n",
              "         '🔇': 6,\n",
              "         '🎛': 4,\n",
              "         '🎚': 4,\n",
              "         '🗯': 9,\n",
              "         '🏁': 18,\n",
              "         '💠': 8,\n",
              "         '🎙': 12,\n",
              "         '⏮': 3,\n",
              "         '🕴': 22,\n",
              "         '▪': 7,\n",
              "         '🥓': 10,\n",
              "         '🔼': 6,\n",
              "         '✍': 9,\n",
              "         '🤰': 8,\n",
              "         '🚋': 10,\n",
              "         '⛽': 6,\n",
              "         '♻': 8,\n",
              "         '🔱': 13,\n",
              "         '🍵': 23,\n",
              "         '🍮': 15,\n",
              "         '⬛': 141,\n",
              "         '⬜': 142,\n",
              "         '🔷': 103,\n",
              "         '🔶': 103,\n",
              "         '⚱': 6,\n",
              "         '🔍': 9,\n",
              "         '🎣': 11,\n",
              "         '🏄': 23,\n",
              "         '🦃': 9,\n",
              "         '🍌': 26,\n",
              "         '🏐': 29,\n",
              "         '▫': 5,\n",
              "         '🚶': 12,\n",
              "         '🥐': 9,\n",
              "         '❕': 13,\n",
              "         '📵': 7,\n",
              "         '🍠': 12,\n",
              "         '🍞': 15,\n",
              "         '🍢': 16,\n",
              "         '🍡': 40,\n",
              "         '⚾': 19,\n",
              "         '🏉': 26,\n",
              "         '🏌': 7,\n",
              "         '🏒': 6,\n",
              "         '⛷': 5,\n",
              "         '🏂': 9,\n",
              "         '🏏': 5,\n",
              "         '🏸': 7,\n",
              "         '🎱': 53,\n",
              "         '🏓': 21,\n",
              "         '🤳': 8,\n",
              "         '🔻': 16,\n",
              "         '🔀': 7,\n",
              "         '⏩': 8,\n",
              "         '↘': 8,\n",
              "         '⏬': 4,\n",
              "         '🔺': 10,\n",
              "         '☁': 85,\n",
              "         '🏜': 6,\n",
              "         '🌅': 12,\n",
              "         '🌿': 22,\n",
              "         '⚪': 8,\n",
              "         '🌧': 11,\n",
              "         '🏞': 6,\n",
              "         '🏊': 16,\n",
              "         '🛐': 8,\n",
              "         '🥝': 9,\n",
              "         '🗨': 4,\n",
              "         '⚖': 10,\n",
              "         '🤶': 6,\n",
              "         '🌰': 49,\n",
              "         '🍲': 11,\n",
              "         '🍛': 16,\n",
              "         '🏹': 5,\n",
              "         '🛌': 14,\n",
              "         '🛏': 7,\n",
              "         '🛋': 6,\n",
              "         '🚿': 7,\n",
              "         '🛀': 11,\n",
              "         '🕐': 6,\n",
              "         '🚴': 9,\n",
              "         '🌩': 61,\n",
              "         '🌥': 4,\n",
              "         '🌫': 4,\n",
              "         '🥃': 12,\n",
              "         '🤬': 1,\n",
              "         '🚎': 4,\n",
              "         '🏫': 17,\n",
              "         '🚈': 5,\n",
              "         '🚍': 5,\n",
              "         '🥘': 12,\n",
              "         '🥄': 5,\n",
              "         '🥕': 7,\n",
              "         '⛄': 8,\n",
              "         '🌡': 7,\n",
              "         '🏺': 5,\n",
              "         '🛎': 11,\n",
              "         '🚰': 10,\n",
              "         '⛓': 4,\n",
              "         '🚗': 9,\n",
              "         '♿': 6,\n",
              "         '🅰': 17,\n",
              "         '🛰': 5,\n",
              "         '🔯': 47,\n",
              "         '⛎': 5,\n",
              "         '⚜': 13,\n",
              "         '⚕': 7,\n",
              "         '✳': 11,\n",
              "         '❔': 4,\n",
              "         '❇': 18,\n",
              "         '🚯': 7,\n",
              "         '🚼': 14,\n",
              "         '🚺': 8,\n",
              "         '🚻': 12,\n",
              "         '❎': 5,\n",
              "         '🈁': 7,\n",
              "         '📶': 5,\n",
              "         '↪': 10,\n",
              "         '↔': 5,\n",
              "         '🔛': 12,\n",
              "         '✖': 15,\n",
              "         '➰': 10,\n",
              "         '◾': 4,\n",
              "         '🔘': 6,\n",
              "         '🔲': 4,\n",
              "         '🕖': 5,\n",
              "         '🕕': 6,\n",
              "         '🕓': 7,\n",
              "         '🕛': 7,\n",
              "         '🕤': 7,\n",
              "         '🕞': 5,\n",
              "         '🏴': 7,\n",
              "         '🎌': 5,\n",
              "         '🖋': 6,\n",
              "         '🖇': 3,\n",
              "         '🖌': 3,\n",
              "         '🔎': 6,\n",
              "         '🔏': 4,\n",
              "         '🖍': 7,\n",
              "         '☪': 4,\n",
              "         '🎳': 13,\n",
              "         '🚌': 5,\n",
              "         '🚠': 7,\n",
              "         '🛴': 12,\n",
              "         '🚜': 4,\n",
              "         '🍽': 8,\n",
              "         '🌨': 16,\n",
              "         '🏠': 28,\n",
              "         '🚐': 4,\n",
              "         '🕢': 5,\n",
              "         '🕥': 5,\n",
              "         '☂': 3,\n",
              "         '🥖': 9,\n",
              "         '🥞': 8,\n",
              "         '🍘': 7,\n",
              "         '🌂': 5,\n",
              "         '🦐': 7,\n",
              "         '🦈': 10,\n",
              "         '🦂': 6,\n",
              "         '🦏': 4,\n",
              "         '🦍': 4,\n",
              "         '🤺': 3,\n",
              "         '⛹': 9,\n",
              "         '🤽': 8,\n",
              "         '🤼': 8,\n",
              "         '🤾': 6,\n",
              "         '🚵': 8,\n",
              "         '🏇': 5,\n",
              "         '🎽': 4,\n",
              "         '🎟': 16,\n",
              "         '🛵': 4,\n",
              "         '🚘': 8,\n",
              "         '🚃': 4,\n",
              "         '🚅': 5,\n",
              "         '🚊': 5,\n",
              "         '🛫': 4,\n",
              "         '🛬': 4,\n",
              "         '🚉': 4,\n",
              "         '🚖': 4,\n",
              "         '🚞': 7,\n",
              "         '🚁': 6,\n",
              "         '🛩': 3,\n",
              "         '🚆': 6,\n",
              "         '🚟': 4,\n",
              "         '🚄': 4,\n",
              "         '🛳': 4,\n",
              "         '🛥': 5,\n",
              "         '🛶': 3,\n",
              "         '⛴': 5,\n",
              "         '🕳': 3,\n",
              "         '🥚': 5,\n",
              "         '🍙': 9,\n",
              "         '🥅': 4,\n",
              "         '🏑': 3,\n",
              "         '🛍': 9,\n",
              "         '🎑': 5,\n",
              "         '🌉': 8,\n",
              "         '🗾': 4,\n",
              "         '🛁': 6,\n",
              "         '🛠': 5,\n",
              "         '🔩': 11,\n",
              "         '♾': 1,\n",
              "         '🉐': 7,\n",
              "         '🈵': 6,\n",
              "         '🔈': 7,\n",
              "         '⏳': 7,\n",
              "         '⌛': 7,\n",
              "         '➕': 14,\n",
              "         '🕍': 4,\n",
              "         '🤩': 3,\n",
              "         '⤴': 7,\n",
              "         '☯': 11,\n",
              "         '📳': 4,\n",
              "         '🌘': 4,\n",
              "         '🏤': 4,\n",
              "         '🏥': 6,\n",
              "         '🏦': 5,\n",
              "         '🏧': 4,\n",
              "         '🏩': 6,\n",
              "         '🏭': 12,\n",
              "         '🗿': 4,\n",
              "         '🔧': 11,\n",
              "         '🎿': 5,\n",
              "         'Ⓜ': 4,\n",
              "         '🚏': 3,\n",
              "         '🚕': 3,\n",
              "         '🚚': 4,\n",
              "         '🚛': 4,\n",
              "         '🛂': 4,\n",
              "         '🛃': 4,\n",
              "         '🛄': 7,\n",
              "         '🛅': 5,\n",
              "         '🚳': 6,\n",
              "         '🚷': 6,\n",
              "         '🚸': 5,\n",
              "         '🚥': 5,\n",
              "         '🚦': 5,\n",
              "         '🚧': 4,\n",
              "         '🔚': 4,\n",
              "         '🚱': 6,\n",
              "         '🆎': 5,\n",
              "         '🔠': 5,\n",
              "         '🔡': 5,\n",
              "         '🔢': 5,\n",
              "         '🔣': 5,\n",
              "         '🔤': 5,\n",
              "         '➿': 4,\n",
              "         '📴': 3,\n",
              "         '🚹': 5,\n",
              "         '🚭': 6,\n",
              "         '⛔': 11,\n",
              "         '🆗': 5,\n",
              "         ...})"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To show commonly:\n",
        "\n",
        "emoji_counts = Counter(emoji_final).most_common(5)"
      ],
      "metadata": {
        "id": "kJjGKlb9Mpry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now showing this counts in Bar chat\n",
        "\n",
        "# Split the emoji and their counts into separate lists for plotting\n",
        "emojis, counts = zip(*emoji_counts)\n",
        "\n",
        "# Set the font to 'DejaVu Sans' which supports emojis\n",
        "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
        "\n",
        "# Plotting the bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(emojis, counts, color='skyblue')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Emoji', fontsize=12)\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.title('Top 10 Most Common Emojis', fontsize=15)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "OpDbHFRPO6O-",
        "outputId": "7e6aaf75-a5d5-4f13-9338-72cc26333eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Multiple files (Refer udemy video)\n",
        "\n",
        "import os"
      ],
      "metadata": {
        "id": "Y_hyRZzgghqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.listdir(r\"/Users/srisuryam.k/Documents/Youtube_project_shan_singh_Udemy.rar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "OE4qQT9wgnLm",
        "outputId": "d8a87792-7c5d-46ab-8768-7f22be3a507c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/Users/srisuryam.k/Documents/'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-1fd07816be2b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"/Users/srisuryam.k/Documents/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/srisuryam.k/Documents/'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Which category has the maximum likes (Refer Udemy)"
      ],
      "metadata": {
        "id": "pT-_hD7fhnjC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}