YouTube Web Scraping with Google API: Project Overview

Welcome to the YouTube Web Scraping project! This repository demonstrates how to use the Google YouTube Data API alongside Python libraries like Pandas, NumPy, and Matplotlib for data extraction, manipulation, and visualization. Below, you'll find details on how to set up and use the project, including prerequisites, installation, and usage instructions.

Table of Contents

1. Introduction
2. Prerequisites
3. Installation
4. Usage
5. Project Structure
6. Contributing
7. License

Introduction

This project leverages the Google YouTube Data API to scrape video information from YouTube. Using Pandas for data manipulation, NumPy for numerical operations, and Matplotlib for visualizations, you can analyze trends, statistics, and other insights from YouTube data.

Prerequisites

To run this project, you will need:

• Python 3.7 or higher
• Google API Key: Obtain an API key from the Google Cloud Console.
• Required Python Libraries:
  • google-api-python-client
  • pandas
  • numpy
  • matplotlib

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/youtube-web-scraping.git
   cd youtube-web-scraping

2. Create a Virtual Environment (Optional but recommended)

   python -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`

3. Install Required Libraries

   pip install -r requirements.txt

4. Obtain and Set Up API Key

   • Go to the Google Cloud Console.
   • Create a new project or use an existing one.
   • Enable the YouTube Data API v3.
   • Create an API key and add it to your environment variables or a configuration file.

Usage

1. Configure API Key

   Set your API key in the config.py file or export it as an environment variable:

   # config.py
   API_KEY = 'YOUR_API_KEY_HERE'

   Or export it as an environment variable:

   export YOUTUBE_API_KEY='YOUR_API_KEY_HERE'

2. Run the Scraper

   Execute the main script to start scraping data:

   python scraper.py

   By default, the script will fetch video data based on predefined parameters. You can customize the search queries and parameters in scraper.py.

3. Analyze and Visualize Data

   After running the scraper, you can analyze and visualize the data using the provided Jupyter notebooks:

   jupyter notebook analysis.ipynb

   This notebook contains examples of how to use Pandas, NumPy, and Matplotlib to perform data analysis and create visualizations.

Project Structure

• scraper.py: Main script for scraping YouTube data using the Google API.
• config.py: Configuration file for API keys and settings.
• analysis.ipynb: Jupyter Notebook for analyzing and visualizing scraped data.
• requirements.txt: List of required Python libraries.
• README.md: This file.

Contributing

We welcome contributions to this project! If you have suggestions, improvements, or bug fixes, please open an issue or submit a pull request. Make sure to follow the coding standards and include appropriate tests with your contributions.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Feel free to reach out if you have any questions or need further assistance. Happy scraping and analyzing!