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 Different Data Formats
Handle CSV, Excel, SQL, JSON, Parquet, and more with flexible import and export options. - Web Scraping with Pandas
Load data directly from HTML tables and web sources using Pandas reading capabilities.
🔄 Useful I/O Tutorials
🧯 Common I/O Issues
- Working with Compressed Files
- Serializing Pandas Objects with Pickle
- Fixed-Width Format Files
- Custom Parsers for Complex Text Files
📚 Real-World Use Cases
Use Case 1: Importing Survey DataImport survey responses from CSV files while handling missing values and custom delimiters. Use CSV export techniques to save cleaned and processed results for stakeholder review.
Use Case 2: Processing Large Log FilesWork with compressed log files using compression support and formatted output to generate readable reports and analytics summaries.
Use Case 3: Machine Learning Data PipelineExport your cleaned data using Parquet format for fast loading in ML frameworks, or use text file formats for compatibility with multiple tools.
Use Case 4: SQL Database IntegrationRead data directly from SQL databases and export Pandas DataFrames back to tables using SQL integration techniques for seamless data workflows.
📊 Format Comparison
| Format | Read Function | Write Function | Best For |
|---|---|---|---|
| CSV | pd.read_csv() |
.to_csv() |
Tabular data, wide compatibility |
| Excel | pd.read_excel() |
.to_excel() |
Business reports, formatted sheets |
| JSON | pd.read_json() |
.to_json() |
APIs, nested data structures |
| Parquet | pd.read_parquet() |
.to_parquet() |
Big data, fast I/O operations |
| SQL | pd.read_sql() |
.to_sql() |
Database storage, CRUD operations |
| HDF5 | pd.read_hdf() |
.to_hdf() |
Scientific computing, hierarchical data |