How to Optimize Performance for Input/Output in Pandas
Optimizing Input/Output (I/O) performance in Pandas is absolutely crucial, especially when you’re wrestling with large datasets. Efficient I/O means your data loads faster, consumes less memory, and generally makes your data processing smoother and quicker.
The most significant factor influencing your I/O performance is the file format you choose for storing and reading your data. While CSV files are universal, human-readable, and simple, they’re text-based, slow to parse, inefficient at storing data types, and don’t offer built-in block compression. This often makes them the slowest choice for large files, so try to move away from them for repeated I/O if possible. (more…)