When to Choose Pandas vs Polars?

Have you ever wondered whether to start a project with Pandas or Polars?
Both are powerful, but their strengths differ.

When Pandas shines

• Huge ecosystem and community.
• Well integrated with libraries like scikit-learn, statsmodels, matplotlib.
• Great for medium datasets that fit in memory.

When Polars shines

• Much faster for large datasets (multi-threaded engine).
• Lazy evaluation enables query optimization.
• Modern API with predictable performance.

Quick example

import pandas as pd
import polars as pl

data = {"id": [1, 2, 3], "value": [10, 20, 30]}
df_pd = pd.DataFrame(data)
df_pl = pl.DataFrame(data)

print(df_pd.groupby("id").sum())
print(df_pl.groupby("id").sum())

Conclusion

• Choose Pandas when ecosystem compatibility matters.
• Choose Polars for performance and scalability.
• Sometimes, the best setup is combining them: load/process with Polars, integrate with Pandas-based tools.