youtube_Channel_data_Analysis.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/top-5000-youtube-channels.csv\")"
      ],
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        "id": "3PEFJGz4fNu0"
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      "execution_count": 5,
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      "source": [
        "**1. Display all row except the last 5 row using head method**"
      ],
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        "data.head(-5)"
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      "execution_count": 6,
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              "         Rank Grade                      Channel name Video Uploads  \\\n",
              "0         1st  A++                             Zee TV         82757   \n",
              "1         2nd  A++                           T-Series         12661   \n",
              "2         3rd  A++         Cocomelon - Nursery Rhymes           373   \n",
              "3         4th  A++                          SET India         27323   \n",
              "4         5th  A++                                WWE         36756   \n",
              "...       ...   ...                               ...           ...   \n",
              "4990  4,991st   B+      Ho Ngoc Ha's Official Channel           208   \n",
              "4991  4,992nd   B+               Toys to Learn Colors            11   \n",
              "4992  4,993rd   B+                              КАZКА            25   \n",
              "4993  4,994th   B+   United CUBE (CUBE Entertainment…          1055   \n",
              "4994  4,995th   B+                      Wings Marathi          1735   \n",
              "\n",
              "     Subscribers  Video views  \n",
              "0       18752951  20869786591  \n",
              "1       61196302  47548839843  \n",
              "2       19238251   9793305082  \n",
              "3       31180559  22675948293  \n",
              "4       32852346  26273668433  \n",
              "...          ...          ...  \n",
              "4990         --     127185704  \n",
              "4991      663114    141933264  \n",
              "4992      131766     74304638  \n",
              "4993     1586835    371299166  \n",
              "4994     1099659    346175699  \n",
              "\n",
              "[4995 rows x 6 columns]"
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              "      <td>4,994th</td>\n",
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              "      <td>1586835</td>\n",
              "      <td>371299166</td>\n",
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              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>4,995th</td>\n",
              "      <td>B+</td>\n",
              "      <td>Wings Marathi</td>\n",
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              "      <td>1099659</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          \"1,502nd\",\n          \"2,587th\",\n          \"2,654th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"A++ \",\n          \"A+ \",\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4993,\n        \"samples\": [\n          \"Discovery Kids Brasil\",\n          \"Duki\",\n          \"ChildishGambinoVEVO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2286,\n        \"samples\": [\n          \"3909\",\n          \"16487\",\n          \"554\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4612,\n        \"samples\": [\n          \"31996\",\n          \"2525853\",\n          \"85910\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2003843972,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          343839616,\n          762590058,\n          172266742\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "**2. Display all row Except the first 5 row **"
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        "id": "T7jUxvG0hlZ5"
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      "execution_count": 7,
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            "text/plain": [
              "         Rank Grade           Channel name Video Uploads Subscribers  \\\n",
              "5         6th  A++              Movieclips         30243    17149705   \n",
              "6         7th  A++              netd müzik          8500    11373567   \n",
              "7         8th  A++   ABS-CBN Entertainment        100147    12149206   \n",
              "8         9th  A++         Ryan ToysReview          1140    16082927   \n",
              "9        10th  A++             Zee Marathi         74607     2841811   \n",
              "...       ...   ...                    ...           ...         ...   \n",
              "4995  4,996th   B+          Uras Benlioğlu           706     2072942   \n",
              "4996  4,997th   B+       HI-TECH MUSIC LTD           797     1055091   \n",
              "4997  4,998th   B+             Mastersaint           110     3265735   \n",
              "4998  4,999th   B+          Bruce McIntosh          3475       32990   \n",
              "4999  5,000th   B+               SehatAQUA           254       21172   \n",
              "\n",
              "      Video views  \n",
              "5     16618094724  \n",
              "6     23898730764  \n",
              "7     17202609850  \n",
              "8     24518098041  \n",
              "9      2591830307  \n",
              "...           ...  \n",
              "4995    441202795  \n",
              "4996    377331722  \n",
              "4997    311758426  \n",
              "4998     14563764  \n",
              "4999     73312511  \n",
              "\n",
              "[4995 rows x 6 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rank</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Channel name</th>\n",
              "      <th>Video Uploads</th>\n",
              "      <th>Subscribers</th>\n",
              "      <th>Video views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6th</td>\n",
              "      <td>A++</td>\n",
              "      <td>Movieclips</td>\n",
              "      <td>30243</td>\n",
              "      <td>17149705</td>\n",
              "      <td>16618094724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7th</td>\n",
              "      <td>A++</td>\n",
              "      <td>netd müzik</td>\n",
              "      <td>8500</td>\n",
              "      <td>11373567</td>\n",
              "      <td>23898730764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8th</td>\n",
              "      <td>A++</td>\n",
              "      <td>ABS-CBN Entertainment</td>\n",
              "      <td>100147</td>\n",
              "      <td>12149206</td>\n",
              "      <td>17202609850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9th</td>\n",
              "      <td>A++</td>\n",
              "      <td>Ryan ToysReview</td>\n",
              "      <td>1140</td>\n",
              "      <td>16082927</td>\n",
              "      <td>24518098041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10th</td>\n",
              "      <td>A++</td>\n",
              "      <td>Zee Marathi</td>\n",
              "      <td>74607</td>\n",
              "      <td>2841811</td>\n",
              "      <td>2591830307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>4,996th</td>\n",
              "      <td>B+</td>\n",
              "      <td>Uras Benlioğlu</td>\n",
              "      <td>706</td>\n",
              "      <td>2072942</td>\n",
              "      <td>441202795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>4,997th</td>\n",
              "      <td>B+</td>\n",
              "      <td>HI-TECH MUSIC LTD</td>\n",
              "      <td>797</td>\n",
              "      <td>1055091</td>\n",
              "      <td>377331722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>4,998th</td>\n",
              "      <td>B+</td>\n",
              "      <td>Mastersaint</td>\n",
              "      <td>110</td>\n",
              "      <td>3265735</td>\n",
              "      <td>311758426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>4,999th</td>\n",
              "      <td>B+</td>\n",
              "      <td>Bruce McIntosh</td>\n",
              "      <td>3475</td>\n",
              "      <td>32990</td>\n",
              "      <td>14563764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>5,000th</td>\n",
              "      <td>B+</td>\n",
              "      <td>SehatAQUA</td>\n",
              "      <td>254</td>\n",
              "      <td>21172</td>\n",
              "      <td>73312511</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4995 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\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",
              "      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-2cad1d77-44cd-4359-9f33-546b6a1510d3 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-2cad1d77-44cd-4359-9f33-546b6a1510d3');\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-67700935-de28-4344-9c76-00e188bf1208\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67700935-de28-4344-9c76-00e188bf1208')\"\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",
              "      --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",
              "      --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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-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",
              "    }\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-67700935-de28-4344-9c76-00e188bf1208 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",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4995,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4995,\n        \"samples\": [\n          \"90th\",\n          \"3,042nd\",\n          \"1,503rd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"A++ \",\n          \"A+ \",\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4988,\n        \"samples\": [\n          \"Breakfast Club Power 105.1 FM\",\n          \"OyuncaX TV\",\n          \"Hannah Stocking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2282,\n        \"samples\": [\n          \"22339\",\n          \"2245\",\n          \"21065\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4607,\n        \"samples\": [\n          \"2247670\",\n          \"688075\",\n          \"366432\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1808839414,\n        \"min\": 75,\n        \"max\": 24518098041,\n        \"num_unique_values\": 4995,\n        \"samples\": [\n          242240716,\n          222905707,\n          80161302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Find the shape of our dataset**"
      ],
      "metadata": {
        "id": "j-NxckdahuFA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of rows: {data.shape[0]}\")\n",
        "print(f\"Number of Column: {data.shape[1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32aH3E75htRY",
        "outputId": "9f74b3d2-ab55-4ec2-cd0e-7c34d6bef74c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows: 5000\n",
            "Number of Column: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Get information about our dataset like total number rows, columns,data type of each column**"
      ],
      "metadata": {
        "id": "aZxnjCJ_iHYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mFkDvzZiFhx",
        "outputId": "970a4640-a6bc-47be-fe71-01b41a925e3f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rank           5000 non-null   object\n",
            " 1   Grade          5000 non-null   object\n",
            " 2   Channel name   5000 non-null   object\n",
            " 3   Video Uploads  5000 non-null   object\n",
            " 4   Subscribers    5000 non-null   object\n",
            " 5   Video views    5000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 234.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Get overall statistics about the data**"
      ],
      "metadata": {
        "id": "OYCPLOGsiUWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8c1T3mb2iS0B",
        "outputId": "4e641552-0019-461e-8641-c44ced6faa2f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Video views\n",
              "count  5.000000e+03\n",
              "mean   1.071449e+09\n",
              "std    2.003844e+09\n",
              "min    7.500000e+01\n",
              "25%    1.862329e+08\n",
              "50%    4.820548e+08\n",
              "75%    1.124368e+09\n",
              "max    4.754884e+10"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Video views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.071449e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003844e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.500000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.862329e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.820548e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.124368e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.754884e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0a437691-f621-44c7-906b-8930b4336bcf 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-0a437691-f621-44c7-906b-8930b4336bcf');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\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",
              "    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",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
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              "      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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-280ea885-c45d-49d6-bace-bdfb5696f8d8 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",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16579448521.042719,\n        \"min\": 75.0,\n        \"max\": 47548839843.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1071449400.15,\n          482054780.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. Data cleaning (replace \"--\" to NaN)**"
      ],
      "metadata": {
        "id": "uOsQ4FkAiqzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "IXo8kdVQieS7",
        "outputId": "c37ebd8a-2e61-4c68-91f2-c8f04d072fa0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Rank Grade                Channel name Video Uploads Subscribers  \\\n",
              "0    1st  A++                       Zee TV         82757    18752951   \n",
              "1    2nd  A++                     T-Series         12661    61196302   \n",
              "2    3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n",
              "3    4th  A++                    SET India         27323    31180559   \n",
              "4    5th  A++                          WWE         36756    32852346   \n",
              "5    6th  A++                   Movieclips         30243    17149705   \n",
              "6    7th  A++                   netd müzik          8500    11373567   \n",
              "7    8th  A++        ABS-CBN Entertainment        100147    12149206   \n",
              "8    9th  A++              Ryan ToysReview          1140    16082927   \n",
              "9   10th  A++                  Zee Marathi         74607     2841811   \n",
              "10  11th   A+              5-Minute Crafts          2085    33492951   \n",
              "11  12th   A+              Canal KondZilla           822    39409726   \n",
              "12  13th   A+             Like Nastya Vlog           150     7662886   \n",
              "13  14th   A+                        Ozuna            50    18824912   \n",
              "14  15th   A+                   Wave Music         16119    15899764   \n",
              "15  16th   A+                  Ch3Thailand         49239    11569723   \n",
              "16  17th   A+              WORLDSTARHIPHOP          4778    15830098   \n",
              "17  18th   A+              Vlad and Nikita            53         --    \n",
              "18  19th   A+                      Badabun          3060    23603062   \n",
              "19  20th   A+            WorkpointOfficial         24287    17687229   \n",
              "\n",
              "    Video views  \n",
              "0   20869786591  \n",
              "1   47548839843  \n",
              "2    9793305082  \n",
              "3   22675948293  \n",
              "4   26273668433  \n",
              "5   16618094724  \n",
              "6   23898730764  \n",
              "7   17202609850  \n",
              "8   24518098041  \n",
              "9    2591830307  \n",
              "10   8587520379  \n",
              "11  19291034467  \n",
              "12   2540099931  \n",
              "13   8727783225  \n",
              "14  10989179147  \n",
              "15   9388600275  \n",
              "16  11102158475  \n",
              "17   1428274554  \n",
              "18   5860444053  \n",
              "19  14022189654  "
            ],
            "text/html": [
              "\n",
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              "      <td>17149705</td>\n",
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              "      <th>6</th>\n",
              "      <td>7th</td>\n",
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              "      <td>9th</td>\n",
              "      <td>A++</td>\n",
              "      <td>Ryan ToysReview</td>\n",
              "      <td>1140</td>\n",
              "      <td>16082927</td>\n",
              "      <td>24518098041</td>\n",
              "    </tr>\n",
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              "      <th>9</th>\n",
              "      <td>10th</td>\n",
              "      <td>A++</td>\n",
              "      <td>Zee Marathi</td>\n",
              "      <td>74607</td>\n",
              "      <td>2841811</td>\n",
              "      <td>2591830307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11th</td>\n",
              "      <td>A+</td>\n",
              "      <td>5-Minute Crafts</td>\n",
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              "      <td>12th</td>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      const quickchartButtonEl =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          \"1,502nd\",\n          \"2,587th\",\n          \"2,654th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"A++ \",\n          \"A+ \",\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4993,\n        \"samples\": [\n          \"Discovery Kids Brasil\",\n          \"Duki\",\n          \"ChildishGambinoVEVO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2286,\n        \"samples\": [\n          \"3909\",\n          \"16487\",\n          \"554\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4612,\n        \"samples\": [\n          \"31996\",\n          \"2525853\",\n          \"85910\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2003843972,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          343839616,\n          762590058,\n          172266742\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.replace(\"--\",np.nan,regex = True)"
      ],
      "metadata": {
        "id": "EXWiuhrfi3Fe"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "JVWrC4qgjMTO",
        "outputId": "8f9db649-98bb-432c-af2c-ba5d1ef9a663"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Rank Grade                Channel name Video Uploads Subscribers  \\\n",
              "0    1st  A++                       Zee TV         82757    18752951   \n",
              "1    2nd  A++                     T-Series         12661    61196302   \n",
              "2    3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n",
              "3    4th  A++                    SET India         27323    31180559   \n",
              "4    5th  A++                          WWE         36756    32852346   \n",
              "5    6th  A++                   Movieclips         30243    17149705   \n",
              "6    7th  A++                   netd müzik          8500    11373567   \n",
              "7    8th  A++        ABS-CBN Entertainment        100147    12149206   \n",
              "8    9th  A++              Ryan ToysReview          1140    16082927   \n",
              "9   10th  A++                  Zee Marathi         74607     2841811   \n",
              "10  11th   A+              5-Minute Crafts          2085    33492951   \n",
              "11  12th   A+              Canal KondZilla           822    39409726   \n",
              "12  13th   A+             Like Nastya Vlog           150     7662886   \n",
              "13  14th   A+                        Ozuna            50    18824912   \n",
              "14  15th   A+                   Wave Music         16119    15899764   \n",
              "15  16th   A+                  Ch3Thailand         49239    11569723   \n",
              "16  17th   A+              WORLDSTARHIPHOP          4778    15830098   \n",
              "17  18th   A+              Vlad and Nikita            53         NaN   \n",
              "18  19th   A+                      Badabun          3060    23603062   \n",
              "19  20th   A+            WorkpointOfficial         24287    17687229   \n",
              "\n",
              "    Video views  \n",
              "0   20869786591  \n",
              "1   47548839843  \n",
              "2    9793305082  \n",
              "3   22675948293  \n",
              "4   26273668433  \n",
              "5   16618094724  \n",
              "6   23898730764  \n",
              "7   17202609850  \n",
              "8   24518098041  \n",
              "9    2591830307  \n",
              "10   8587520379  \n",
              "11  19291034467  \n",
              "12   2540099931  \n",
              "13   8727783225  \n",
              "14  10989179147  \n",
              "15   9388600275  \n",
              "16  11102158475  \n",
              "17   1428274554  \n",
              "18   5860444053  \n",
              "19  14022189654  "
            ],
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              "      <th>8</th>\n",
              "      <td>9th</td>\n",
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              "      <td>1140</td>\n",
              "      <td>16082927</td>\n",
              "      <td>24518098041</td>\n",
              "    </tr>\n",
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              "      <th>9</th>\n",
              "      <td>10th</td>\n",
              "      <td>A++</td>\n",
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              "      <td>74607</td>\n",
              "      <td>2841811</td>\n",
              "      <td>2591830307</td>\n",
              "    </tr>\n",
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              "      <th>10</th>\n",
              "      <td>11th</td>\n",
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              "      <td>5-Minute Crafts</td>\n",
              "      <td>2085</td>\n",
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              "      <th>11</th>\n",
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              "      <td>Canal KondZilla</td>\n",
              "      <td>822</td>\n",
              "      <td>39409726</td>\n",
              "      <td>19291034467</td>\n",
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              "      <th>12</th>\n",
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              "      <td>Like Nastya Vlog</td>\n",
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              "    }\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-c8d28028-1409-47cc-8eec-40b409e9d04e 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-c8d28028-1409-47cc-8eec-40b409e9d04e');\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-95c5b11d-7560-401b-a06c-f3051a1cff96\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95c5b11d-7560-401b-a06c-f3051a1cff96')\"\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",
              "      --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",
              "      --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-95c5b11d-7560-401b-a06c-f3051a1cff96 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",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          \"1,502nd\",\n          \"2,587th\",\n          \"2,654th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"A++ \",\n          \"A+ \",\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4993,\n        \"samples\": [\n          \"Discovery Kids Brasil\",\n          \"Duki\",\n          \"ChildishGambinoVEVO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2285,\n        \"samples\": [\n          \"39806\",\n          \"309\",\n          \"63\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4611,\n        \"samples\": [\n          \"5608514\",\n          \"929669\",\n          \"1007128\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2003843972,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          343839616,\n          762590058,\n          172266742\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. Check null value in this dataset**"
      ],
      "metadata": {
        "id": "JfWYiEuyjinK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(data.isnull().sum()/len(data))*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "G4ELQg8ijO5M",
        "outputId": "79e05ce6-2d5f-4af8-d3aa-164b8acdc964"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rank             0.00\n",
              "Grade            0.00\n",
              "Channel name     0.00\n",
              "Video Uploads    0.12\n",
              "Subscribers      7.74\n",
              "Video views      0.00\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grade</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Channel name</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Video Uploads</th>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subscribers</th>\n",
              "      <td>7.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Video views</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "ChRfarZejoB6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "W5nABStnj6H3",
        "outputId": "6888e1ed-2d62-4174-bc9a-490198749e92"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(axis = 0,inplace = True)"
      ],
      "metadata": {
        "id": "3-dhORAxj-rM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WUdIzUxKkYZX",
        "outputId": "90fb8590-ae9b-4989-a75a-20692cef1eab"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. Data Cleaning"
      ],
      "metadata": {
        "id": "BsMkx4qvk1Dd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "7SfA1HXMkoO9",
        "outputId": "2c4270e9-7278-49de-9f69-c16f078f458c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Rank Grade                Channel name Video Uploads Subscribers  \\\n",
              "0  1st  A++                       Zee TV         82757    18752951   \n",
              "1  2nd  A++                     T-Series         12661    61196302   \n",
              "2  3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n",
              "3  4th  A++                    SET India         27323    31180559   \n",
              "4  5th  A++                          WWE         36756    32852346   \n",
              "\n",
              "   Video views  \n",
              "0  20869786591  \n",
              "1  47548839843  \n",
              "2   9793305082  \n",
              "3  22675948293  \n",
              "4  26273668433  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rank</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Channel name</th>\n",
              "      <th>Video Uploads</th>\n",
              "      <th>Subscribers</th>\n",
              "      <th>Video views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st</td>\n",
              "      <td>A++</td>\n",
              "      <td>Zee TV</td>\n",
              "      <td>82757</td>\n",
              "      <td>18752951</td>\n",
              "      <td>20869786591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2nd</td>\n",
              "      <td>A++</td>\n",
              "      <td>T-Series</td>\n",
              "      <td>12661</td>\n",
              "      <td>61196302</td>\n",
              "      <td>47548839843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3rd</td>\n",
              "      <td>A++</td>\n",
              "      <td>Cocomelon - Nursery Rhymes</td>\n",
              "      <td>373</td>\n",
              "      <td>19238251</td>\n",
              "      <td>9793305082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4th</td>\n",
              "      <td>A++</td>\n",
              "      <td>SET India</td>\n",
              "      <td>27323</td>\n",
              "      <td>31180559</td>\n",
              "      <td>22675948293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5th</td>\n",
              "      <td>A++</td>\n",
              "      <td>WWE</td>\n",
              "      <td>36756</td>\n",
              "      <td>32852346</td>\n",
              "      <td>26273668433</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-8954e550-8cf5-4089-be01-73e3b6e09441 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-8954e550-8cf5-4089-be01-73e3b6e09441');\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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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --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",
              "      --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-3b591474-a998-4cad-abd7-414714bbcf14 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",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4610,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          \"1,162nd\",\n          \"2,985th\",\n          \"1,489th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A+ \",\n          \"B+ \",\n          \"A \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4604,\n        \"samples\": [\n          \"\\u041c\\u0430\\u0448\\u0430 \\u0442\\u0430 \\u0412\\u0435\\u0434\\u043c\\u0456\\u0434\\u044c\",\n          \"Monsta\",\n          \"CoasterForce\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          \"651\",\n          \"1469\",\n          \"1312\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4608,\n        \"samples\": [\n          \"683574\",\n          \"515143\",\n          \"6199546\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2068705922,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1352179341,\n          516927832,\n          191254670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "RdeGQppMk5jk",
        "outputId": "91f23e74-ab87-44c7-a0de-56f4a89143b2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rank             object\n",
              "Grade            object\n",
              "Channel name     object\n",
              "Video Uploads    object\n",
              "Subscribers      object\n",
              "Video views       int64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grade</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Channel name</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Video Uploads</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subscribers</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Video views</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Rank\"] = data[\"Rank\"].str[0:-2]"
      ],
      "metadata": {
        "id": "6unt8sytlDdX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "bI5EjOWQlVh_",
        "outputId": "5480b034-5249-490b-fa01-7245b9365122"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Rank Grade                Channel name Video Uploads Subscribers  \\\n",
              "0    1  A++                       Zee TV         82757    18752951   \n",
              "1    2  A++                     T-Series         12661    61196302   \n",
              "2    3  A++   Cocomelon - Nursery Rhymes           373    19238251   \n",
              "3    4  A++                    SET India         27323    31180559   \n",
              "4    5  A++                          WWE         36756    32852346   \n",
              "\n",
              "   Video views  \n",
              "0  20869786591  \n",
              "1  47548839843  \n",
              "2   9793305082  \n",
              "3  22675948293  \n",
              "4  26273668433  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29401b85-4e10-4026-b2a9-5dfa123cd429\" 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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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A++</td>\n",
              "      <td>Zee TV</td>\n",
              "      <td>82757</td>\n",
              "      <td>18752951</td>\n",
              "      <td>20869786591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>A++</td>\n",
              "      <td>T-Series</td>\n",
              "      <td>12661</td>\n",
              "      <td>61196302</td>\n",
              "      <td>47548839843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A++</td>\n",
              "      <td>Cocomelon - Nursery Rhymes</td>\n",
              "      <td>373</td>\n",
              "      <td>19238251</td>\n",
              "      <td>9793305082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>A++</td>\n",
              "      <td>SET India</td>\n",
              "      <td>27323</td>\n",
              "      <td>31180559</td>\n",
              "      <td>22675948293</td>\n",
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              "      <td>36756</td>\n",
              "      <td>32852346</td>\n",
              "      <td>26273668433</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29401b85-4e10-4026-b2a9-5dfa123cd429')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
              "      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-29401b85-4e10-4026-b2a9-5dfa123cd429 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-29401b85-4e10-4026-b2a9-5dfa123cd429');\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",
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              "\n",
              "\n",
              "<div id=\"df-dd39965b-4207-4e60-b6da-2015979db80f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd39965b-4207-4e60-b6da-2015979db80f')\"\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",
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              "    </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",
              "      --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",
              "      --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",
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              "\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4610,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          \"1,162\",\n          \"2,985\",\n          \"1,489\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A+ \",\n          \"B+ \",\n          \"A \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4604,\n        \"samples\": [\n          \"\\u041c\\u0430\\u0448\\u0430 \\u0442\\u0430 \\u0412\\u0435\\u0434\\u043c\\u0456\\u0434\\u044c\",\n          \"Monsta\",\n          \"CoasterForce\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          \"651\",\n          \"1469\",\n          \"1312\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4608,\n        \"samples\": [\n          \"683574\",\n          \"515143\",\n          \"6199546\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2068705922,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1352179341,\n          516927832,\n          191254670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Pt3WjxJel9l7",
        "outputId": "86e4a3d5-8ece-4918-b6cf-716cfa0a175f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Rank Grade       Channel name Video Uploads Subscribers  Video views\n",
              "4995  4,996   B+      Uras Benlioğlu           706     2072942    441202795\n",
              "4996  4,997   B+   HI-TECH MUSIC LTD           797     1055091    377331722\n",
              "4997  4,998   B+         Mastersaint           110     3265735    311758426\n",
              "4998  4,999   B+      Bruce McIntosh          3475       32990     14563764\n",
              "4999  5,000   B+           SehatAQUA           254       21172     73312511"
            ],
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"4,997\",\n          \"5,000\",\n          \"4,998\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"HI-TECH MUSIC LTD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"797\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1055091\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 189097877,\n        \"min\": 14563764,\n        \"max\": 441202795,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          377331722\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Rank\"] = data[\"Rank\"].str.replace(\",\",\"\")"
      ],
      "metadata": {
        "id": "r8_wJIzkmrTw"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "sMnpjtkTmx1e",
        "outputId": "2d13c2e8-539a-4042-e323-8700ea6c95da"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Rank Grade       Channel name Video Uploads Subscribers  Video views\n",
              "4995  4996   B+      Uras Benlioğlu           706     2072942    441202795\n",
              "4996  4997   B+   HI-TECH MUSIC LTD           797     1055091    377331722\n",
              "4997  4998   B+         Mastersaint           110     3265735    311758426\n",
              "4998  4999   B+      Bruce McIntosh          3475       32990     14563764\n",
              "4999  5000   B+           SehatAQUA           254       21172     73312511"
            ],
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\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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              "    .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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-39598b6d-0540-45b0-b76e-33a756a8cc94 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-39598b6d-0540-45b0-b76e-33a756a8cc94');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
              "      --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",
              "      --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",
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              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-left-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",
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              "    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-8e3399c1-5a63-4009-b4a2-da864936df6c 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",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"4997\",\n          \"5000\",\n          \"4998\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"B+ \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"HI-TECH MUSIC LTD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"797\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1055091\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 189097877,\n        \"min\": 14563764,\n        \"max\": 441202795,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          377331722\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSpvha-em66J",
        "outputId": "d99d2846-12d0-4082-db97-ec9d3a285fb7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4610 entries, 0 to 4999\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rank           4610 non-null   object\n",
            " 1   Grade          4610 non-null   object\n",
            " 2   Channel name   4610 non-null   object\n",
            " 3   Video Uploads  4610 non-null   object\n",
            " 4   Subscribers    4610 non-null   object\n",
            " 5   Video views    4610 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 252.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Rank\"] = data[\"Rank\"].astype(int)"
      ],
      "metadata": {
        "id": "TpwKmOiMnLjG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWj0tWBsnOiH",
        "outputId": "99f463db-8954-4003-af4d-54fc627fe2aa"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4610 entries, 0 to 4999\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rank           4610 non-null   int64 \n",
            " 1   Grade          4610 non-null   object\n",
            " 2   Channel name   4610 non-null   object\n",
            " 3   Video Uploads  4610 non-null   object\n",
            " 4   Subscribers    4610 non-null   object\n",
            " 5   Video views    4610 non-null   int64 \n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 252.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**9. Data Cleaning [Video upload and Subscribers]**"
      ],
      "metadata": {
        "id": "48iwJR7XnXio"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "itn1SVVjnQBk",
        "outputId": "01c5a7c6-104f-4dd7-d5fe-cf655c02fabd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank Grade                Channel name Video Uploads Subscribers  \\\n",
              "0     1  A++                       Zee TV         82757    18752951   \n",
              "1     2  A++                     T-Series         12661    61196302   \n",
              "2     3  A++   Cocomelon - Nursery Rhymes           373    19238251   \n",
              "3     4  A++                    SET India         27323    31180559   \n",
              "4     5  A++                          WWE         36756    32852346   \n",
              "\n",
              "   Video views  \n",
              "0  20869786591  \n",
              "1  47548839843  \n",
              "2   9793305082  \n",
              "3  22675948293  \n",
              "4  26273668433  "
            ],
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [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",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c0098fac-3853-4a46-b39d-6a8c4cc7260c');\n",
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              "          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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --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",
              "      --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-8ece3bb8-7fa4-42c5-92ab-adb97d115f84 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",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4610,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1443,\n        \"min\": 1,\n        \"max\": 5000,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1162,\n          2985,\n          1489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A+ \",\n          \"B+ \",\n          \"A \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4604,\n        \"samples\": [\n          \"\\u041c\\u0430\\u0448\\u0430 \\u0442\\u0430 \\u0412\\u0435\\u0434\\u043c\\u0456\\u0434\\u044c\",\n          \"Monsta\",\n          \"CoasterForce\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          \"651\",\n          \"1469\",\n          \"1312\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4608,\n        \"samples\": [\n          \"683574\",\n          \"515143\",\n          \"6199546\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2068705922,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1352179341,\n          516927832,\n          191254670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "6PdfYAAWnhRC",
        "outputId": "3ab4c573-9379-4cd1-c858-ff85432149ab"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rank              int64\n",
              "Grade            object\n",
              "Channel name     object\n",
              "Video Uploads    object\n",
              "Subscribers      object\n",
              "Video views       int64\n",
              "dtype: object"
            ],
            "text/html": [
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              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
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    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Subscribers\"] = data[\"Subscribers\"].astype(\"int\")"
      ],
      "metadata": {
        "id": "QjHG9wdAnnEm"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1GeYgcQnt60",
        "outputId": "036ab068-3ff6-4428-b03d-5259d42f94eb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4610 entries, 0 to 4999\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rank           4610 non-null   int64 \n",
            " 1   Grade          4610 non-null   object\n",
            " 2   Channel name   4610 non-null   object\n",
            " 3   Video Uploads  4610 non-null   object\n",
            " 4   Subscribers    4610 non-null   int64 \n",
            " 5   Video views    4610 non-null   int64 \n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 252.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Video Uploads\"] = data[\"Video Uploads\"].astype(\"int\")"
      ],
      "metadata": {
        "id": "DCeCGXAOnwTf"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GA84z6doJgi",
        "outputId": "d0d7c6f5-899b-41bd-d722-dcf6e9b74832"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4610 entries, 0 to 4999\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rank           4610 non-null   int64 \n",
            " 1   Grade          4610 non-null   object\n",
            " 2   Channel name   4610 non-null   object\n",
            " 3   Video Uploads  4610 non-null   int64 \n",
            " 4   Subscribers    4610 non-null   int64 \n",
            " 5   Video views    4610 non-null   int64 \n",
            "dtypes: int64(4), object(2)\n",
            "memory usage: 252.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**10. Data Claeaning [Grade Column]**"
      ],
      "metadata": {
        "id": "rvwqhSrioM1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "3swt1mFooKiM",
        "outputId": "2134b398-4c61-4e73-c610-1a75b6f8843d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank Grade                Channel name  Video Uploads  Subscribers  \\\n",
              "0     1  A++                       Zee TV          82757     18752951   \n",
              "1     2  A++                     T-Series          12661     61196302   \n",
              "2     3  A++   Cocomelon - Nursery Rhymes            373     19238251   \n",
              "3     4  A++                    SET India          27323     31180559   \n",
              "4     5  A++                          WWE          36756     32852346   \n",
              "\n",
              "   Video views  \n",
              "0  20869786591  \n",
              "1  47548839843  \n",
              "2   9793305082  \n",
              "3  22675948293  \n",
              "4  26273668433  "
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1ca2728-6aac-4871-a51f-a85f8d31f959')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      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",
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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",
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              "        document.querySelector('#df-f1ca2728-6aac-4871-a51f-a85f8d31f959 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f1ca2728-6aac-4871-a51f-a85f8d31f959');\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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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\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",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    30% {\n",
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              "      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",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        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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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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          "metadata": {},
          "execution_count": 35
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      ]
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    {
      "cell_type": "code",
      "source": [
        "data[\"Grade\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vo8WURLyoaMN",
        "outputId": "31e6eebf-70bf-4cf8-9d3e-203d5e555f18"
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      "execution_count": 36,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['A++ ', 'A+ ', 'A ', 'A- ', 'B+ '], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
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    {
      "cell_type": "code",
      "source": [
        "data[\"Grade\"] = data[\"Grade\"].map({'A++ ':5, 'A+ ':4, 'A ':3, 'A- ':2, 'B+ ':1})"
      ],
      "metadata": {
        "id": "hFVynRmxovb-"
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              "Rank              int64\n",
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              "Channel name     object\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**11. Find the Avearge View for each cahnnel**"
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        "id": "k-dWsIPmJX5V"
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      "execution_count": 42,
      "outputs": [
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          "data": {
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              "Index(['Rank', 'Grade', 'Channel name', 'Video Uploads', 'Subscribers',\n",
              "       'Video views'],\n",
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          "metadata": {},
          "execution_count": 42
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        "data['Average Views'] = data['Video views']/data['Video Uploads']"
      ],
      "metadata": {
        "id": "pVBSKuHCJjMm"
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      "execution_count": 44,
      "outputs": []
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      "execution_count": 45,
      "outputs": [
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              "      Rank  Grade                Channel name  Video Uploads  Subscribers  \\\n",
              "0        1      5                      Zee TV          82757     18752951   \n",
              "1        2      5                    T-Series          12661     61196302   \n",
              "2        3      5  Cocomelon - Nursery Rhymes            373     19238251   \n",
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              "4        5      5                         WWE          36756     32852346   \n",
              "...    ...    ...                         ...            ...          ...   \n",
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              "4999  5000      1                   SehatAQUA            254        21172   \n",
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              "\n",
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              "      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",
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              "    (() => {\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4610,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1443,\n        \"min\": 1,\n        \"max\": 5000,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1162,\n          2985,\n          1489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4604,\n        \"samples\": [\n          \"\\u041c\\u0430\\u0448\\u0430 \\u0442\\u0430 \\u0412\\u0435\\u0434\\u043c\\u0456\\u0434\\u044c\",\n          \"Monsta\",\n          \"CoasterForce\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video Uploads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17726,\n        \"min\": 1,\n        \"max\": 422326,\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          651,\n          1469,\n          1312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3927262,\n        \"min\": 301,\n        \"max\": 65995310,\n        \"num_unique_values\": 4608,\n        \"samples\": [\n          683574,\n          515143,\n          6199546\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Video views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2068705922,\n        \"min\": 75,\n        \"max\": 47548839843,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          1352179341,\n          516927832,\n          191254670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average Views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17520068.838935904,\n        \"min\": 53.55387743247168,\n        \"max\": 333012122.0,\n        \"num_unique_values\": 4610,\n        \"samples\": [\n          3094231.9016018305,\n          1420131.4065934066,\n          11250274.705882354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**12 . Find out top 5 channel with maximum number of video upload**"
      ],
      "metadata": {
        "id": "NdhBNZhkKHtp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLwBiOl4KCsV",
        "outputId": "84ec03eb-1c73-4573-a1be-478acd08e373"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rank', 'Grade', 'Channel name', 'Video Uploads', 'Subscribers',\n",
              "       'Video views', 'Average Views'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(\"Channel name\")['Video Uploads'].sum().sort_values(ascending = False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "NISaSg50KdKi",
        "outputId": "ebcd35dd-83b7-481d-df9b-79097a6f8e25"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Channel name\n",
              "AP Archive                 422326\n",
              "Various Artists - Topic    411006\n",
              "YTN NEWS                   355996\n",
              "SBS Drama                  335521\n",
              "GMA News                   269065\n",
              "Name: Video Uploads, dtype: int64"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Video Uploads</th>\n",
              "    </tr>\n",
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              "      <th>Channel name</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AP Archive</th>\n",
              "      <td>422326</td>\n",
              "    </tr>\n",
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              "      <th>Various Artists - Topic</th>\n",
              "      <td>411006</td>\n",
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              "      <td>355996</td>\n",
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              "      <td>335521</td>\n",
              "    </tr>\n",
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              "      <th>GMA News</th>\n",
              "      <td>269065</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4WqBixmJKyog"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pegKS3GsLEHF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}