Pandas makes it easy to load, inspect, and save data in a variety of formats. This hub covers how to import data from CSV, Excel, JSON, SQL, and more — and how to export cleaned and transformed DataFrames for later use. You’ll also find techniques for customizing delimiters, handling large files, and troubleshooting common I/O issues.
📥 Input & Output Topics
- Reading and Writing Data from/to Different Formats – Handle CSV, Excel, SQL, JSON, Parquet, and more.
- Web Scraping with Pandas – Load data directly from HTML tables and web sources.
🔄 Useful I/O Tutorials
- Read JSON Files
- Write to an Existing Excel File
- Handle Parquet Files
- Write CSV with Custom Formatting
- Save DataFrame as Text File
- Handle Missing Values in CSV Import
- Custom CSV Delimiters and Quotes
- Write to CSV Without Index
🧯 Common I/O Issues
- Fix AttributeError: No Attribute ‘to_csv’
- Fix Compression NotImplementedError
- Fix Attribute Errors in Import
- Working with Compressed Files
📚 Use Cases
Use Case 1: Importing survey data? Use CSV handling and custom export techniques.
Use Case 2: Working with large log files? Try compressed file support and formatted CSV output.
Use Case 3: Saving for machine learning? Export using Parquet or text file formats.