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Exploring Mars: Scraping and Analyzing News and Weather Data

This repository showcases a project that leverages Beautiful Soup, Splinter, and Pandas for data scraping, analysis, and visualization of Mars news and weather data.

Project Overview

This project comprises two main components: the first part involves news scraping, while the second part focuses on collecting Mars weather data as a data frame and performing analysis on it.

Part 1: Scraping Mars News: Unveiling the Mysteries

In this part, we'll gather the Mars news titles and summaries with Python, Splinter, and Beautiful Soup, saving them in a user-friendly JSON file for easy access. The news data will be sourced from the website: Mars News

Part 2: Unearthing Mars Weather Data: Gathering and Analyzing the Climate

We'll explore the Mars climate using Python, Splinter, Beautiful Soup, and Pandas visualizations. Highlights include:

  • First, we'll scrape Mars weather data Mars weather and transform it into a convenient data frame.

  • After analyzing the data frame turns out that Mars, like Earth, has approximately 12 months in a Martian year.

  • There is 1867 Martian (not Earth) days' worth of data in the scraped dataset.

  • Monthly weather on Mars: line1 On Mars, on average, the months of the Martian calendar equivalent to the 8th and 9th months are the coldest.

  • Coldest and hottest months at Curiosity's location.: line2 On Mars, the 8th month of the Martian calendar is typically the coldest, while the 3rd month tends to be the hottest on average.

  • Average Martian monthly pressure: line3 Average atmospheric pressure is lowest in the sixth month and highest in the ninth.

  • Estimating Earth days in a Martian year using peak-to-peak daily minimum temperatures: line3 Mars' year is approximately 675 days based on the plot's peak-to-peak distance of around 1425-750, which is 675 days.

  • Exporting the scraped Mars weather dataframe to a CSV file.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please submit a pull request or open an issue on the GitHub repository.

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