Lazy Evaluation in Polars vs Immediate Execution in Pandas

A common scenario in data processing is chaining many operations.
In Pandas, each step runs immediately. Polars offers a lazy mode that builds the query first, then executes when you call .collect().

Example data

import pandas as pd
import polars as pl

data = {"name": ["Alice", "Bob", "Charlie"], "score": [85, 92, 78]}
df_pd = pd.DataFrame(data)
df_pl = pl.DataFrame(data)

Pandas: eager execution

result_pd = df_pd[df_pd["score"] > 80][["name"]]
print(result_pd)

#     name
# 0   Alice
# 1     Bob

Each step executes immediately.

Polars: lazy evaluation

lazy_query = (
    df_pl.lazy()
    .filter(pl.col("score") > 80)
    .select("name")
)

print(lazy_query)       # shows query plan
print(lazy_query.collect())

# shape: (2, 1)
# ┌───────┐
# │ name  │
# │ ---   │
# │ str   │
# ╞═══════╡
# │ Alice │
# │ Bob   │
# └───────┘

Conclusion

• Pandas: operations are eager — run step by step.
• Polars: lazy mode builds a plan, optimized before execution.
• Lazy evaluation is efficient for complex pipelines and large data.