https://github.com/shivabajelan/web_scraping_mars_new_and_weather_data_summary
I used BeautifulSoup and automated browsing to extract information about Mars from two different sources. In Part 1, I scraped titles and preview text from Mars news articles, while in Part 2, I scraped and analysed Mars weather data to gain insights into the planet's climate patterns.
https://github.com/shivabajelan/web_scraping_mars_new_and_weather_data_summary
beautifulsoup python scraper web
Last synced: 7 months ago
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I used BeautifulSoup and automated browsing to extract information about Mars from two different sources. In Part 1, I scraped titles and preview text from Mars news articles, while in Part 2, I scraped and analysed Mars weather data to gain insights into the planet's climate patterns.
- Host: GitHub
- URL: https://github.com/shivabajelan/web_scraping_mars_new_and_weather_data_summary
- Owner: Shivabajelan
- License: mit
- Created: 2024-02-10T09:40:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-12T04:33:05.000Z (over 1 year ago)
- Last Synced: 2025-01-15T16:04:51.972Z (9 months ago)
- Topics: beautifulsoup, python, scraper, web
- Language: Jupyter Notebook
- Homepage:
- Size: 210 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Web_Scraping_Mars_New_and_Weather_Data_Summary
#### This project focused on extracting and analysing Mars news articles and weather data from websites using web scraping techniques.
## Overview
### The goal of this project was to:
#### 1.Scrape titles and preview text from Mars news articles.
##### 2.Scrape and analyse Mars weather data in an HTML table.
## Tools and Technologies
#### Python
#### Jupyter Notebook
##### Beautiful Soup
##### Splinter
##### Pandas
# Project Structure
## Part 1: Mars News Scraping
#### 1.Used automated browsing with Splinter to visit the Mars news site.
#### 2.Created a Beautiful Soup object to parse the HTML content.
#### 3.Extracted the titles and preview text of the news articles.
#### 4.Stored the scraping results in a list of dictionaries with 'title' and 'preview' keys.
#### 5.Printed the list in the Jupyter Notebook.
#### 6.Stored the scraped data in a JSON file for easier sharing if needed in future.
## Part 2: Mars Weather Data Scraping and Analysis
#### 1.Used automated browsing with Splinter to visit the Mars Temperature Data Site.
#### 2.Created a Beautiful Soup object to scrape the data in the HTML table.
#### 3.Assembled the scraped data into a Pandas DataFrame with appropriate column headings.
#### 4.Examined and cast the data types to appropriate datetime, int, or float data types if necessary.
#### 5.Analysed the dataset to answer questions related to Martian months, days, temperature, and atmospheric pressure.
#### 6.Visualised the results using bar charts and line plots.
#### 7.Exported the DataFrame to a CSV file.
# Conclusion
##### This project demonstrated the ability to effectively use web scraping techniques to collect and analyze data from Mars-related websites. The findings provide valuable insights into Martian months, days, temperature, and atmospheric pressure.