Alternatives to CSV to Speed Up Your Pandas DataFrames
Save time by switching to Feather and Parquet
If you work with Pandas and large datasets in Python, you may have noticed that loading and saving data can become quite slow. Pandas dataframes are powerful, but reading and writing them from disk in formats like CSV can be a bottleneck, especially for big data tasks.
In this article, we’ll explore how to speed up your Pandas workflows by using the Parquet and Feather file formats instead of CSV. These columnar data formats can drastically reduce reading and writing times compared to row-based formats like CSV.
The Benefits of Columnar Data Formats
Formats like CSV store data row-by-row on disk. This row-based storage works fine for smaller datasets but becomes inefficient as the data grows larger. That’s because reading and writing have to process one full row at a time, even if you only need a single column.
Columnar formats like Parquet and Feather instead organize data by column. This allows much more efficient access — your program only needs to read the specific columns it needs.
This column-oriented storage provides major speed benefits:
- Reading only needed columns from disk skips unneeded…
