Comparing
Powerful data structures for data analysis, time series, and statistics
Blazingly fast DataFrame library
| Metric | pandas | polars |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner | Beginner |
| Weekly downloads | 0 | 0↑ |
| Latest version | v3.0.2 | v1.39.3 |
| Recipes | 1 | 2↑ |
pandas only
Shared
polars only
Use pandas for data cleaning, exploration, and transformation: reading CSV/Excel/JSON files, computing statistics, reshaping data before feeding it to a model, or generating reports. It's the standard tool for the 'data wrangling' phase of any data science or analytics project.
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.
Pandas struggles with datasets larger than a few GB in RAM. For big data, use Polars (faster, lower memory) or Dask (distributed pandas). For pure numerical math without labels, NumPy is more appropriate.
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.
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polars