IMDB_Movie_Scraping.ipynb

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        "<a href=\"https://colab.research.google.com/github/anilsolanki2645/WebScarping/blob/main/IMDB_Movie_Scraping.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "\n",
        "## Scraping IMDB website at a time to fetch top 250 movies data sorted by Rating."
      ],
      "metadata": {
        "id": "7Ufx9aFeitr7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Mounts Google Drive"
      ],
      "metadata": {
        "id": "8JC1tlED7cWH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Importing all necessary libraries:"
      ],
      "metadata": {
        "id": "aRCs8hR9jPro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "from time import sleep\n",
        "from random import randint"
      ],
      "metadata": {
        "id": "KPcCRHux4NTx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* Movie Data Scraping and Storage Initialization"
      ],
      "metadata": {
        "id": "5hQ0WhC8jcw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Declaring the headers\n",
        "headers = {\"Accept-Language\": \"en-US,en;q=0.5\"}\n",
        "\n",
        "#declaring the list of empty variables, So that we can append the data overall\n",
        "movie_name = []\n",
        "year = []\n",
        "time=[]\n",
        "rating=[]\n",
        "metascore =[]\n",
        "votes = []\n",
        "description = []"
      ],
      "metadata": {
        "id": "BRy5IWQB4X3v"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Page Range Initialization for Movie Scraping: 1 to 250"
      ],
      "metadata": {
        "id": "PsfSY7l78ndd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of values and passing it in the url for dynamic webpages\n",
        "pages = np.arange(1,250,250)"
      ],
      "metadata": {
        "id": "S98lGuRg4csC"
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      "execution_count": 4,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "### collect and parse the information using requests and BeautifulSoup\n",
        "* Iterates through a list of pages, sends HTTP requests to IMDb's top movie listings, and extracts information such as movie name, year of release, runtime, rating, Metascore, votes, and description. The script uses BeautifulSoup for web scraping, random delays to avoid overwhelming the server, and headers to mimic a web browser. Extracted data is appended to respective lists, and at the end, a Pandas DataFrame is created to organize the movie information for further analysis or presentation.\n"
      ],
      "metadata": {
        "id": "s-HFZ_nRyDsh"
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      "cell_type": "code",
      "source": [
        "# Iterate through pages\n",
        "for page in pages:\n",
        "    # Set user-agent header to mimic a web browser\n",
        "    headers = {\n",
        "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',\n",
        "    }\n",
        "\n",
        "    print(page)\n",
        "\n",
        "    # Make a request to IMDb\n",
        "    page_response = requests.get(\"https://www.imdb.com/search/title/?sort=user_rating,desc&groups=top_1000&count=250\", headers=headers)\n",
        "\n",
        "    # Parse the HTML content using BeautifulSoup\n",
        "    soup = BeautifulSoup(page_response.text, 'html.parser')\n",
        "\n",
        "    # Find movie data containers\n",
        "    movie_data = soup.findAll('div', {'class': 'sc-43986a27-0 gUQEVh'})\n",
        "\n",
        "    # Pause to avoid overwhelming the server\n",
        "    # sleep(randint(2, 8))\n",
        "\n",
        "    # Print request status code for debugging\n",
        "    # print(\"Request status code:\", page_response.status_code)\n",
        "\n",
        "    # Iterate through movie data\n",
        "    for store in movie_data:\n",
        "        # Extract information\n",
        "        name = store.find('h3', class_='ipc-title__text').text\n",
        "        movie_name.append(name)\n",
        "\n",
        "        year_of_release = store.find('span', class_='sc-43986a27-8 jHYIIK dli-title-metadata-item').text\n",
        "        year.append(year_of_release)\n",
        "\n",
        "        runtime = store.findAll('span', class_='sc-43986a27-8 jHYIIK dli-title-metadata-item')[1].text\n",
        "        time.append(runtime)\n",
        "\n",
        "        # Handle the rating element gracefully\n",
        "        rate = store.find('span', class_=\"ipc-rating-star ipc-rating-star--base ipc-rating-star--imdb ratingGroup--imdb-rating\").text.replace('\\n', '').split('\\xa0')[0]\n",
        "        rating.append(rate)\n",
        "\n",
        "        meta = store.find('span', class_=\"sc-b0901df4-0 bcQdDJ metacritic-score-box\").text if store.find('span', class_=\"sc-b0901df4-0 bcQdDJ metacritic-score-box\") else \"*****\"\n",
        "        metascore.append(meta)\n",
        "\n",
        "        # Extracting the votes\n",
        "        value = store.findNext(\"div\", class_=\"sc-53c98e73-0 kRnqtn\").text.split('Votes')[-1]\n",
        "        votes.append(value)\n",
        "\n",
        "        # Extracting the movie description\n",
        "        describe = store.findNext('div', class_='ipc-html-content ipc-html-content--base sc-53c98e73-1 gMTWhH dli-plot-container').text\n",
        "        description_ = describe.replace('\\n', '') if len(describe) > 1 else '*****'\n",
        "        description.append(description_)\n"
      ],
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            "1\n"
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      "cell_type": "markdown",
      "source": [
        "* Creating and Displaying Movie Data DataFrame\n"
      ],
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      "cell_type": "code",
      "source": [
        "# Creating a Pandas DataFrame to store movie data with columns for Movie Name, Year of Release, Watch Time, Movie Rating, Metascore of Movie, Votes, and Description\n",
        "movie_list = pd.DataFrame({\n",
        "    \"Movie Name\": movie_name,\n",
        "    \"Year of Release\": year,\n",
        "    \"Watch Time\": time,\n",
        "    \"Movie Rating\": rating,\n",
        "    \"Meatscore of movie\": metascore,\n",
        "    \"Votes\": votes,\n",
        "    \"Description\": description\n",
        "})\n",
        "\n",
        "# Displaying the movie list DataFrame\n",
        "movie_list\n"
      ],
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      "execution_count": 6,
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              "                                           Movie Name Year of Release  \\\n",
              "0                         1. The Shawshank Redemption            1994   \n",
              "1                                    2. The Godfather            1972   \n",
              "2                                  3. The Dark Knight            2008   \n",
              "3                                 4. Schindler's List            1993   \n",
              "4    5. The Lord of the Rings: The Return of the King            2003   \n",
              "..                                                ...             ...   \n",
              "245                                      246. Stalker            1979   \n",
              "246                             247. 12 Years a Slave            2013   \n",
              "247                                  248. Gran Torino            2008   \n",
              "248          249. Lock, Stock and Two Smoking Barrels            1998   \n",
              "249                                      250. Warrior            2011   \n",
              "\n",
              "    Watch Time Movie Rating Meatscore of movie      Votes  \\\n",
              "0       2h 22m          9.3                 82  2,830,101   \n",
              "1       2h 55m          9.2                100  1,972,590   \n",
              "2       2h 32m          9.0                 84  2,811,521   \n",
              "3       3h 15m          9.0                 95  1,422,109   \n",
              "4       3h 21m          9.0                 94  1,937,891   \n",
              "..         ...          ...                ...        ...   \n",
              "245     2h 42m          8.1                 85    141,957   \n",
              "246     2h 14m          8.1                 96    730,822   \n",
              "247     1h 56m          8.1                 72    804,410   \n",
              "248     1h 47m          8.1                 66    607,410   \n",
              "249     2h 20m          8.1                 71    491,172   \n",
              "\n",
              "                                           Description  \n",
              "0    Over the course of several years, two convicts...  \n",
              "1    Don Vito Corleone, head of a mafia family, dec...  \n",
              "2    When the menace known as the Joker wreaks havo...  \n",
              "3    In German-occupied Poland during World War II,...  \n",
              "4    Gandalf and Aragorn lead the World of Men agai...  \n",
              "..                                                 ...  \n",
              "245  A guide leads two men through an area known as...  \n",
              "246  In the antebellum United States, Solomon North...  \n",
              "247  After a Hmong teenager tries to steal his priz...  \n",
              "248  Eddy persuades his three pals to pool money fo...  \n",
              "249  The youngest son of an alcoholic former boxer ...  \n",
              "\n",
              "[250 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>Movie Name</th>\n",
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              "      <th>Movie Rating</th>\n",
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              "      <td>1. The Shawshank Redemption</td>\n",
              "      <td>1994</td>\n",
              "      <td>2h 22m</td>\n",
              "      <td>9.3</td>\n",
              "      <td>82</td>\n",
              "      <td>2,830,101</td>\n",
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              "      <td>2. The Godfather</td>\n",
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              "      <td>2h 55m</td>\n",
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              "      <td>5. The Lord of the Rings: The Return of the King</td>\n",
              "      <td>2003</td>\n",
              "      <td>3h 21m</td>\n",
              "      <td>9.0</td>\n",
              "      <td>94</td>\n",
              "      <td>1,937,891</td>\n",
              "      <td>Gandalf and Aragorn lead the World of Men agai...</td>\n",
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              "      <td>8.1</td>\n",
              "      <td>85</td>\n",
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              "      <td>247. 12 Years a Slave</td>\n",
              "      <td>2013</td>\n",
              "      <td>2h 14m</td>\n",
              "      <td>8.1</td>\n",
              "      <td>96</td>\n",
              "      <td>730,822</td>\n",
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              "      <td>248. Gran Torino</td>\n",
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              "      <td>1h 56m</td>\n",
              "      <td>8.1</td>\n",
              "      <td>72</td>\n",
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              "      <td>8.1</td>\n",
              "      <td>66</td>\n",
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              "      <td>8.1</td>\n",
              "      <td>71</td>\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('movie_list');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Displaying the last three rows of the Movie Data DataFrame"
      ],
      "metadata": {
        "id": "BdrY-Nhv89re"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_list[-3:]"
      ],
      "metadata": {
        "id": "6U7Vi-nR5Ekj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "outputId": "126df9ff-6686-45a6-c38a-02f8d5ed5ac6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Movie Name Year of Release Watch Time  \\\n",
              "247                          248. Gran Torino            2008     1h 56m   \n",
              "248  249. Lock, Stock and Two Smoking Barrels            1998     1h 47m   \n",
              "249                              250. Warrior            2011     2h 20m   \n",
              "\n",
              "    Movie Rating Meatscore of movie    Votes  \\\n",
              "247          8.1                 72  804,410   \n",
              "248          8.1                 66  607,410   \n",
              "249          8.1                 71  491,172   \n",
              "\n",
              "                                           Description  \n",
              "247  After a Hmong teenager tries to steal his priz...  \n",
              "248  Eddy persuades his three pals to pool money fo...  \n",
              "249  The youngest son of an alcoholic former boxer ...  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Movie Name</th>\n",
              "      <th>Year of Release</th>\n",
              "      <th>Watch Time</th>\n",
              "      <th>Movie Rating</th>\n",
              "      <th>Meatscore of movie</th>\n",
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              "      <th>247</th>\n",
              "      <td>248. Gran Torino</td>\n",
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              "      <td>72</td>\n",
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              "      <td>249. Lock, Stock and Two Smoking Barrels</td>\n",
              "      <td>1998</td>\n",
              "      <td>1h 47m</td>\n",
              "      <td>8.1</td>\n",
              "      <td>66</td>\n",
              "      <td>607,410</td>\n",
              "      <td>Eddy persuades his three pals to pool money fo...</td>\n",
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              "    <tr>\n",
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              "      <td>250. Warrior</td>\n",
              "      <td>2011</td>\n",
              "      <td>2h 20m</td>\n",
              "      <td>8.1</td>\n",
              "      <td>71</td>\n",
              "      <td>491,172</td>\n",
              "      <td>The youngest son of an alcoholic former boxer ...</td>\n",
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              "  </tbody>\n",
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              "        document.querySelector('#df-cfa8ac6d-7702-495b-80be-06bce5288c99 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-cfa8ac6d-7702-495b-80be-06bce5288c99');\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",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\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-820e87ae-8831-4bab-8aed-8a33b34a3e5a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Checking the data type of the Movie Data DataFrame"
      ],
      "metadata": {
        "id": "Q1Bsw0Ia9EpN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(movie_list)"
      ],
      "metadata": {
        "id": "O7MVcD1S5fm1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08f86763-6266-4b33-9937-ecc19fced259"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Saving Movie Data DataFrame to Excel: IMDB_Movie_Ratings.xlsx"
      ],
      "metadata": {
        "id": "awUUV5DQ9SAM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_list.to_excel('/content/drive/MyDrive/Colab Notebooks/IMDB_Movie_Ratings.xlsx', header=True, index=False)"
      ],
      "metadata": {
        "id": "gVEgBTKW9W26"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}