CSV Read Performance — Pandas vs Polars

When working with data, reading CSV files is one of the most common operations.
Performance can make a big difference, especially with large datasets. Let’s see how Pandas and Polars compare.

Example setup

import pandas as pd
import polars as pl
import time

# Path to a large CSV file
csv_file = "large_dataset.csv"

Reading with Pandas

start = time.time()
df_pd = pd.read_csv(csv_file)
end = time.time()
print("Pandas time:", end - start)

Reading with Polars

start = time.time()
df_pl = pl.read_csv(csv_file)
end = time.time()
print("Polars time:", end - start)

Expected results

• Pandas is widely used but can be slower with very large files.
• Polars is designed for performance and often reads CSVs several times faster.

Conclusion

If speed matters (and it usually does for big data), Polars has a clear advantage for reading CSVs.
Still, Pandas remains a solid option for many workflows.

👉 Next step: we’ll explore GroupBy operations in Pandas and Polars.