Comparing
Fundamental package for array computing in Python
Blazingly fast DataFrame library
| Metric | numpy | polars |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner | Beginner |
| Weekly downloads | 0 | 0↑ |
| Latest version | v2.4.4 | v1.39.3 |
| Recipes | 1 | 2↑ |
numpy only
Shared
polars only
Use NumPy any time you're doing numerical computation: matrix math, statistics, signal processing, image data as pixel arrays, or feeding data into ML libraries. It's the foundation most scientific Python libraries (pandas, scipy, PyTorch) are built on.
Use Polars for data transformation on datasets over 500MB, in production data pipelines where speed matters, or anywhere you're hitting pandas memory limits. It's also a good choice for new projects even on small data — the API is cleaner and the performance headroom is free.
NumPy is not for general-purpose data manipulation with mixed types or strings — use pandas DataFrames there. For very large datasets that don't fit in RAM, look at Dask or CuPy (GPU arrays).
The ML ecosystem (scikit-learn, PyTorch, statsmodels) speaks NumPy arrays and pandas DataFrames. You'll need to call .to_pandas() or .to_numpy() at the handoff point, which adds friction. For quick exploratory work in a Jupyter notebook, pandas' ecosystem and familiarity often wins.
numpy
polars