This Simple Python project allows you to scrape product data from Amazon using BeautifulSoup, Requests, and Pandas. The scraper can extract information like product names, prices, ratings, and more from Amazon product pages.
- BeautifulSoup: For parsing HTML and extracting the required data from Amazon web pages.
- Pandas: For storing and manipulating the scraped data in a structured format (e.g., CSV or Excel).
- Requests: To make HTTP requests to Amazon and retrieve the HTML content of product pages.
- Scrape product details including name, price, rating, and availability.
- Store the scraped data in a Pandas DataFrame.
- Export the data to CSV for further analysis.
This project scrapes the GDP data of countries from the web and organizes it into a structured dataset for analysis. It uses Python and popular libraries like pandas, BeautifulSoup, and requests to extract, clean, and process the data.
- Web Scraping: Extracts GDP data of countries from a specified webpage.
- Data Cleaning: Processes and organizes raw data into a structured format using pandas.
- Exporting: Saves the cleaned data as a CSV file for further analysis.
- Customizable: The code can be adapted for other websites or datasets.
- pandas: Data manipulation and analysis.
- numpy: Numerical operations and handling missing data.
- requests: Sending HTTP requests to fetch web pages.
- BeautifulSoup: Parsing HTML and XML documents.
- StringIO: Handling string data as file-like objects.
This project involves analyzing crime data to uncover patterns, trends, and insights using Python and Jupyter Notebook.
- SQLite Magic: Write and execute SQL queries directly in Jupyter Notebook using
%sqlmagic commands. - Data Analysis: Clean, transform, and explore datasets using Python.
- pandas (Data manipulation)
- matplotlib and
seaborn(Data visualization) - sqlite3 (SQLite database interaction)
- ipython-sql (SQLite magic for inline SQL execution)
- SQLite: Manage datasets in a relational database format.