⚡ Pandas to Polars: 11 Essential Operations
Does your Pandas .groupby() take forever on large datasets? Polars is a DataFrame library written in Rust, designed for parallel execution. Here are the 11 key operations to migrate your workflow.
🔄 Key differences:
| Operation | Pandas | Polars |
|---|---|---|
| Filter | df[df['col'] > 5] | df.filter(pl.col('col') > 5) |
| New col | df['new'] = ... | df.with_columns(...) |
| GroupBy | .groupby().agg() | .group_by().agg() |
| Cast type | .astype() | .cast() |
| Nulls | NaN | null |
⚠️ Most important: Polars DataFrames are immutable. No inplace=True. Always reassign:
df = df.with_columns(pl.col("price").cast(pl.Float64))🚀 Next level: Explore the Lazy API with .lazy() and .collect() for automatic query optimization.
💡 Quick explanation
Pandas was created in 2008 and processes data single-threaded. Polars, created in Rust in 2021, automatically uses all your CPU cores. On datasets over 1 million rows, Polars can be 5-20x faster. Same code, much more speed!
More information at the link 👇
More in the following external reference.
Also published on LinkedIn.
