Polars vs. Pandas

Polars and Pandas solve the same problem: DataFrame manipulation. They just take different approaches.

Say we want to import a CSV of transaction data, filter for purchases over $100, and select just the customer ID and amount. In Pandas:

df = pd.read_csv("transactions.csv")
large_purchases = df[df["amount"] > 100][["customer_id", "amount"]]

And similarly in Polars:

import polars as pl

df = pl.read_csv("transactions.csv")
large_purchases = df.filter(pl.col("amount") > 100).select(["customer_id", "amount"])

Pandas often feels like accessing values in a Python dictionary: df["amount"], while Polars leans on explicit expressions: pl.col("amount") > 100.

Larger expressions tell Polars what we want, not just what to do right now. That gives Polars more freedom to optimize the work, especially in "lazy mode" (more on this later).

Pandas generally executes operations immediately. That's great for small, interactive analysis, but it can be less efficient in large pipelines.