Pandas with Rows (2022)
4 months ago
- #Python
- #Performance Optimization
- #Data Analysis
- The article discusses various methods to analyze flight delay data from American airports using Python and pandas.
- Initial naive approach with pandas leads to memory issues due to large dataset size (13GB).
- Pure Python solution processes data incrementally to avoid memory overload, taking ~7 minutes.
- PyPy interpreter reduces execution time to ~4 minutes and 40 seconds with slightly higher memory usage.
- Optimized pandas approach loads only necessary columns and specifies data types, completing in ~2 minutes and 45 seconds.
- Using PyArrow engine with pandas further reduces time to ~1 minute and 10 seconds by leveraging multithreading.
- Direct PyArrow usage cuts time to ~50 seconds and reduces memory peak to 7.5GB.
- Processing data year-by-year with PyArrow achieves ~37 seconds runtime and ~900MB memory usage.
- Multiprocessing with pandas reduces time to ~53 seconds but is slower than PyArrow solutions.
- Article suggests exploring other tools like R, Julia, Rust, or alternatives like Vaex and Polars for further optimization.