The Fastest Way to Save Data: Polars and Parquet (2026 Guide)

You’ve been taught to use .csv files for everything. This is fine for small files, but for data science in 2026, it’s slow and inefficient. The modern standard is Parquet (.parquet). In this Polars Parquet Guide, you’ll learn about a much faster and more efficient way to work with data.

Why is Parquet Better than CSV?

1. Speed: It’s dramatically faster to read and write because it’s columnar. (It reads just the columns you need, instead of scanning every row like a CSV).
2. Size: It’s highly compressed. A 1GB CSV might be 100MB as a Parquet file.
3. Types: It saves your data types! 1 stays an int, 2027-01-01 stays a date. A CSV saves everything as a string.

Polars is built to work perfectly with Parquet.

Step 1: Installation

Polars needs an “engine” to handle Parquet files. The most common is pyarrow.

pip install polars[pyarrow]

Step 2: How to Write Parquet

Let’s take a CSV and convert it to Parquet.

import polars as pl

# 1. Read the "slow" CSV
df = pl.read_csv("my_large_data.csv")

# 2. Write to the "fast" Parquet
# You can also set 'compression' (e.g., 'snappy', 'zstd')
df.write_parquet("my_fast_data.parquet")

Step 3: How to Read Parquet

This is the easy part.

# This is much faster than read_csv
df = pl.read_parquet("my_fast_data.parquet")

print(df.head())

For any serious data pipeline, Parquet is the standard. CSV is only for importing/exporting with old tools (like Excel).