10-Day Weather Web Scraping

Project Overview

The 10-Day Weather Web Scraping project involves extracting weather forecasts for the next 10 days from a weather website using Python. The project utilizes web scraping techniques to gather data, which is then cleaned, processed, and visualized to provide a clear weather outlook.

Tools Used

• Python: Programming language used for web scraping and data analysis.
• BeautifulSoup: For parsing HTML and extracting data from web pages.
• Requests: For sending HTTP requests to access web page content.
• Pandas: For data manipulation and cleaning.

Project Steps

1. Web Scraping Setup

• Installed and configured the necessary libraries: BeautifulSoup, Requests, Pandas.
• Identified the target weather website and the structure of the HTML content to locate the necessary data fields (e.g., date, temperature, weather condition).

2. Data Extraction

• Sent HTTP requests to access the weather web page.
• Used BeautifulSoup to parse the HTML content and extract relevant data such as:
  • Dates of the forecast.
  • Daily temperatures (highs and lows).
  • Weather conditions (e.g., sunny, rainy, cloudy).
• Collected the data into a structured format for further processing.

3. Data Storage

• Stored the extracted data in a Pandas DataFrame for easy manipulation and analysis.
• Saved the data to a CSV file to ensure persistence and for future reference.

4. Data Cleaning

• Inspected the DataFrame for missing values, duplicates, and incorrect data types.
• Performed data cleaning tasks including:
  • Filling or dropping missing values.
  • Converting data types to appropriate formats (e.g., date, temperature).
  • Standardizing text data for consistency.

6. Insights and Findings

• Analyzed the visualizations to derive insights about the weather forecast.
• Identified patterns and trends, such as expected temperature ranges and prevalent weather conditions.
• Summarized findings in a clear and concise manner to provide a useful weather outlook.

Repository Structure

• web scraping weather/: Contains the web scraping script.
• weather data/: Stores the extracted and cleaned data files.
• README.md: Project description and overview.

How to Use

1. Clone the repository to your local machine.
2. Run the web scraping script located in the web scraping weather/ file:

Conclusion

This project demonstrates the use of web scraping to gather and analyze weather forecast data. By leveraging Python libraries for data extraction, cleaning, and visualization, the project provides a detailed and actionable 10-day weather outlook.

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Feel free to contribute to this project by suggesting improvements, reporting issues, or submitting pull requests. Happy scraping!