How to Analyze Data Faster with Polars

Polars is a high-performance DataFrame library for Python. It is written in Rust, runs queries on multiple CPU cores by default, and consistently outperforms pandas on datasets larger than a few hundred megabytes. If your analysis is slow because you are waiting for a groupby or a filter to finish, Polars is worth a direct test. A 2025 benchmark published by Towards Data Science found Polars read an 800MB CSV file roughly 10 times faster than pandas on the same machine.

What Makes Polars Different from Pandas

Pandas loads data row-by-row in a single thread. Polars uses a columnar memory layout based on Apache Arrow and processes data in parallel across all available CPU cores without any configuration. You do not need to set up Dask, Spark, or any distributed system to get that speedup.

Polars also introduces a two-mode execution model. Eager execution runs operations immediately, just like pandas. LazyFrame defers execution, collects all operations into a query plan, and optimizes them before touching the data. For files larger than a few gigabytes, LazyFrame is the right starting point.

Installation

Open a terminal and run:

pip install polars

That is the complete setup. No C compiler, no Rust toolchain, no additional drivers. Polars ships as a pre-compiled binary wheel for Windows, macOS, and Linux.

Loading a CSV File

import polars as pl

df = pl.read_csv("sales_data.csv")
print(df.shape)      # (rows, columns)
print(df.dtypes)     # inferred types for each column
print(df.head(5))

Polars infers column types on the first pass. It correctly detects integers, floats, strings, and dates without you specifying a schema. If a column has mixed types, Polars raises a clear error rather than silently coercing values. That behavior is useful for catching data quality problems before they contaminate downstream analysis.

Filtering Rows

In pandas you use boolean masks. In Polars you use expressions inside a filter call:

# pandas style
filtered_pd = df[df['revenue'] > 10000]

# Polars style
filtered = df.filter(pl.col('revenue') > 10000)

Multiple conditions chain with standard operators:

filtered = df.filter(
    (pl.col('revenue') > 10000) & (pl.col('region') == 'North')
)

Selecting and Transforming Columns

Use select to pick columns and with_columns to add or modify them:

# Pick two columns
subset = df.select(['date', 'revenue', 'region'])

# Add a new column: margin as a percentage
df = df.with_columns(
    (pl.col('profit') / pl.col('revenue') * 100).alias('margin_pct')
)

The alias call names the resulting column. All transformations inside a single with_columns block run in parallel across cores.

Grouping and Aggregating

Groupby in Polars uses group_by followed by agg. The syntax is explicit, which makes it easier to read back after a week away from the code:

summary = (
    df.group_by('region')
    .agg([
        pl.col('revenue').sum().alias('total_revenue'),
        pl.col('revenue').mean().alias('avg_revenue'),
        pl.col('order_id').count().alias('order_count'),
    ])
    .sort('total_revenue', descending=True)
)
print(summary)

This produces a ranked table of regions by total revenue in a few lines. The equivalent pandas code requires groupby, agg, reset_index, and sort_values chained together, which is not difficult but adds steps and often trips up newer analysts on the reset_index requirement.

Using LazyFrame for Large Files

If your CSV is larger than available RAM, switch to scan_csv instead of read_csv. Polars will scan the file without loading it all at once, build a query plan from your operations, and return only the rows and columns you actually need:

result = (
    pl.scan_csv("large_dataset.csv")
    .filter(pl.col('status') == 'completed')
    .group_by('product_category')
    .agg(pl.col('amount').sum())
    .collect()
)

Between scan_csv and collect, Polars builds a logical plan and optimizes it. The filter runs before the aggregation, and columns not referenced in the query are never read from disk. On a 4GB CSV with 30 columns, this approach uses far less memory than loading the whole file into a pandas DataFrame.

Exporting Results

# Write to CSV
result.write_csv("summary_output.csv")

# Write to Parquet (smaller file, faster to reload)
result.write_parquet("summary_output.parquet")

# Convert to pandas if a downstream step requires it
result_pd = result.to_pandas()

The to_pandas conversion has near-zero cost because both libraries use Arrow format internally. If your existing charts or reporting tools expect a pandas DataFrame, converting at the final step is the practical approach.

Practical Performance Numbers

In a comparison published by Real Python, a groupby-and-aggregate on a 1 million row dataset completed in 0.08 seconds with Polars and 1.3 seconds with pandas, roughly a 16x difference. On datasets under 50,000 rows, the difference is negligible. The crossover point where Polars becomes meaningfully faster tends to be around 500MB of data or operations involving multiple chained transforms on wide tables.

If you want to skip the local Python setup entirely, VSLZ lets you upload the same CSV and ask analysis questions in plain English without installing anything.

What Polars Does Not Cover

Polars is not a replacement for every pandas use case. It does not have a built-in plotting API. Its Excel read support exists but is slower than CSV. And if your team has existing notebooks built on pandas idioms, the expression syntax requires a few hours to adjust to. The practical approach is to use Polars for the heavy lifting and convert the final result to pandas if you need a chart library or an API that expects the pandas format.

Summary

Polars is a practical upgrade for analysts who work with CSV and Parquet files that are too large for pandas to handle comfortably. Installation is one command. The expression syntax is consistent and readable. LazyFrame handles files that exceed available memory. For routine analysis on datasets between 100MB and a few gigabytes, Polars is the fastest pure-Python option available in 2026.