🐍 Python Projects Portfolio
This portfolio presents Ahmed Hegazy’s hands-on Python projects, showcasing expertise in data cleaning, API integration, and analytical problem-solving. These projects highlight skills in working with real-world datasets, performing advanced cleaning techniques, and integrating third-party APIs to extract actionable insights.
GitHub Link
📝 Brief Summary of the Data Cleaning and Analysis Process:
- Loaded Data: Imported 12 CSV files representing each month of 2023.
- Merged Data: Combined these files into a single dataframe for analysis.
- Datatype Conversion: Transformed
started_at and ended_at columns into datetime format.
- Column Renaming: Adjusted column names to enhance clarity and consistency.
- New Columns Added: Created additional columns to extract insights such as:
- Time, month, year, day of the week, and a combined month-year column.
- Ride Length Calculation: Determined ride durations in minutes.
- Data Cleaning: Removed rows with negative ride durations.
- Descriptive Analysis: Conducted statistical exploration of the data.
- Summary Export: Generated a summary file for further insights.
GitHub Link
📝 Key Steps in the Data Cleaning Process:
- Reading Data: Imported a CSV file named
New.csv containing cricket players’ performance statistics into a Pandas DataFrame.
- Renaming Columns: Simplified column names, e.g.,
Mat → matches_played, Inns → innings_batted.
- Null Value Handling:
- Identified null values in
balls_faced and batting_strike_rate.
- Replaced these null values with 0 for data consistency.
- Duplicate Removal: Eliminated duplicate rows to ensure uniqueness.
- Splitting Columns:
- Split the
Span column into Rooki_year (start year) and final_year (end year).
- Dropped the original
Span column as it was no longer needed.
- Dropping Irrelevant Data: Removed unnecessary rows and columns, including a row with all NaN values.
GitHub Link
📝 Project Highlights:
- API Integration: Set up a session with appropriate headers, including an API key, to securely interact with the CoinMarketCap API.
- Latest Listings: Fetched real-time cryptocurrency data using a GET request.
- Error Handling: Implemented safeguards against:
- Connection issues.
- Timeout errors.
- Excessive redirects.
- Readable Output: Parsed the API’s JSON response into a clear, structured format for easy interpretation.
📝 Project Highlights:
- Web Scraping: Utilized Python libraries such as BeautifulSoup and Requests to scrape a website’s data.
- Data Extraction: Collected information like:
- Titles, descriptions, and links from a list of items on a webpage.
- Data Cleaning: Cleaned extracted data by removing unnecessary HTML tags and formatting inconsistencies.
- CSV Export: Exported the cleaned data to a CSV file for easy future analysis.
- Error Handling: Implemented robust error handling to manage issues like missing elements, broken links, and network timeouts.
- Automation: Added functionality to periodically scrape the website, ensuring the data stays updated.