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Polars

Polars is a modern DataFrame library that offers significant performance advantages over Pandas, especially for large datasets. We compare the basic operations between Polars and Pandas.

Basic DataFrame Creation and Reading

Pandas

import pandas as pd

# Read CSV
df = pd.read_csv("data.csv", sep=";", header=None, names=['city', 'temp'])

# Process in chunks for large files
reader = pd.read_csv("data.csv", sep=';', chunksize=1000000)
for chunk in reader:
    # Process chunk
    pass

Polars

import polars as pl

# Read CSV (lazy evaluation)
df = pl.scan_csv(
    "data.csv",
    separator=";",
    has_header=False,
    new_columns=["city", "temp"]
)

Key Differences

  • Lazy Evaluation:
    • Polars uses lazy evaluation by default with scan_csv() and .lazy()
    • Operations are only executed when you call .collect()
    • This allows Polars to optimize the query plan
  • Streaming:
# Polars streaming for large datasets
results = (
    df.lazy()
    .group_by("city")
    .agg([
        pl.col("temp").min(),
        pl.col("temp").max(),
        pl.col("temp").mean()
    ])
    .collect(streaming=True)  # Enable streaming
)

Performance Benefits

  • Better memory efficiency
  • Faster processing for large datasets
  • Built-in parallel processing
  • Column-oriented design

When to Use Polars

  • Large datasets that don't fit in memory Performance-critical applications
  • When you need parallel processing
  • Modern data pipeline development