How to Get Started with Polars for Data Analysis

Polars is a Python DataFrame library built on a Rust engine with lazy evaluation and multi-core execution. Install it with pip install polars, load a file with pl.read_csv(), and chain .filter(), .group_by(), and .agg() calls. For files over 100 MB, use pl.scan_csv() to defer execution until .collect(). On a 10 million row dataset, Polars completes a group-by aggregation in 0.22 seconds; pandas takes 1.8 seconds on the same task.

Why Polars Is Getting Traction in 2026

Polars 1.0 launched in late 2024, and 2026 has seen accelerating adoption as data teams hit memory and runtime limits with standard pandas workflows. The library addresses a specific constraint: data workloads have grown faster than the single-threaded, NumPy-backed execution model that pandas was built around. Polars uses Apache Arrow in-memory format, builds query plans before executing, and parallelizes operations across all available CPU cores by default.

Published benchmarks from the H2O.ai group-by test suite show Polars completing a group-by on 10 million rows in 0.22 seconds versus 1.8 seconds for pandas. On a 1 GB CSV file, Polars reads and loads data in 1.6 seconds using 0.18 GB of memory; pandas requires 8.2 seconds and 1.4 GB. Sort operations at the same scale show an even wider gap: Polars finishes in 0.29 seconds versus 3.4 seconds for pandas.

Pandas 3.0, released in late 2025, narrowed part of this gap by making PyArrow-backed strings the default column format. Group-by operations on string columns now run 2 to 4 times faster than Pandas 2.x. For workloads under one million rows, the practical difference is modest. At larger scales, Polars holds a substantial performance and memory advantage.

Installation

Install Polars as a single package with no system dependencies:

pip install polars

For Excel file support, add the optional packages:

pip install polars openpyxl xlsxwriter

Polars bundles its own Arrow implementation and does not require pyarrow separately, though installing it adds interoperability with other Arrow-based tools. Verify the install:

import polars as pl
print(pl.__version__)

The current stable API is 1.x as of mid-2026. The API is stable across 1.x patch versions.

Reading Data

Polars reads CSV, Parquet, JSON, NDJSON, and other common formats:

import polars as pl

# Load CSV eagerly (executes immediately)
df = pl.read_csv("sales.csv")

# Inspect schema and first rows
print(df.schema)
print(df.head())

# Auto-parse date columns
df = pl.read_csv("sales.csv", try_parse_dates=True)

# Load Parquet
df = pl.read_parquet("sales.parquet")

Polars infers column types automatically. Integers map to Int64, floats to Float64, and strings to Utf8. Parquet reads are faster than CSV because the format stores data in columnar binary form with type information embedded.

Filtering and Selecting Columns

Polars uses an expression syntax centered on pl.col(). You build column references as expressions and pass them to methods:

# Filter rows where revenue exceeds 10,000
high_revenue = df.filter(pl.col("revenue") > 10_000)

# Select specific columns
subset = df.select(["customer_id", "revenue", "region"])

# Chain filter and select in one expression
result = (
    df
    .filter(pl.col("revenue") > 10_000)
    .select(["customer_id", "revenue", "region"])
)

# Multiple conditions
west_active = df.filter(
    (pl.col("region") == "West") & (pl.col("status") == "active")
)

The & operator means AND; | means OR. Expressions are composable and can reference multiple columns in a single filter.

Group-By and Aggregation

Group-by aggregations name their output columns explicitly with .alias(), preventing naming ambiguity in downstream steps:

summary = (
    df
    .group_by("region")
    .agg([
        pl.col("revenue").sum().alias("total_revenue"),
        pl.col("revenue").mean().alias("avg_revenue"),
        pl.col("customer_id").n_unique().alias("unique_customers")
    ])
    .sort("total_revenue", descending=True)
)

Multiple aggregate expressions inside a single .agg() call run in parallel where possible. On a 10 million row dataset with five groups, this pattern completes in under 0.25 seconds on a mid-range laptop CPU.

Lazy Evaluation

Polars has two execution modes. pl.read_csv() executes immediately and returns an eager DataFrame. pl.scan_csv() returns a LazyFrame that stores a query plan without running it. Execution happens when you call .collect().

Lazy mode is faster for most analysis workflows because Polars can optimize the plan before executing it. It pushes filter operations early in the query, drops unused columns before reading them from disk, and parallelizes independent branches automatically.

result = (
    pl.scan_csv("large_sales.csv")
    .filter(pl.col("status") == "active")
    .group_by("region")
    .agg(pl.col("revenue").sum().alias("total"))
    .sort("total", descending=True)
    .collect()
)

To inspect the query plan before running:

plan = pl.scan_csv("large_sales.csv").filter(pl.col("status") == "active")
print(plan.explain())

For files over 500 MB, using scan_csv instead of read_csv reduces peak memory usage by 30 to 60 percent in most cases, based on community benchmarks.

Handling Missing Data

Polars represents null values consistently as null across all column types. Pandas mixes NaN (for float columns) and None (for object columns), which causes type inference issues. In Polars, null handling is uniform:

# Count nulls per column
print(df.null_count())

# Drop rows with any null
df_clean = df.drop_nulls()

# Drop nulls in specific columns only
df_clean = df.drop_nulls(subset=["revenue", "customer_id"])

# Fill nulls with a constant
df_filled = df.with_columns(pl.col("revenue").fill_null(0))

# Fill nulls with the column median
df_filled = df.with_columns(
    pl.col("revenue").fill_null(pl.col("revenue").median())
)

Adding Derived Columns

Use .with_columns() to add or transform columns without mutating the original DataFrame:

df = df.with_columns([
    (pl.col("revenue") * 0.08).alias("tax"),
    (pl.col("revenue") / pl.col("units_sold")).alias("revenue_per_unit")
])

Multiple expressions inside one .with_columns() call run in parallel if they are independent. Splitting them into separate calls forces sequential execution.

Working with Dates

Polars provides a .dt accessor for date and datetime columns:

df = df.with_columns([
    pl.col("sale_date").dt.year().alias("year"),
    pl.col("sale_date").dt.month().alias("month"),
    pl.col("sale_date").dt.weekday().alias("weekday")
])

df_2025 = df.filter(pl.col("year") == 2025)

For non-standard date formats, parse explicitly before using the .dt accessor:

df = df.with_columns(
    pl.col("sale_date").str.to_date("%d/%m/%Y")
)

Converting to and from Pandas

Machine learning libraries and most visualization tools expect pandas DataFrames. Conversion is one call:

# Polars to pandas
pandas_df = df.to_pandas()

# Pandas to Polars
polars_df = pl.from_pandas(pandas_df)

The conversion copies data. For large datasets, keep processing in Polars through the transformation steps and convert only at the final output or plotting step. If you prefer to run the same kind of grouping, filtering, and aggregation without writing code, VSLZ handles it from a file upload with no setup required.

When Polars Makes Sense

Polars performs well when datasets are over 100 MB, when group-by, sort, and join operations dominate the workflow, or when memory is constrained. Polars typically requires 2 to 4 times the dataset size in RAM; pandas needs 5 to 10 times.

Pandas remains the better default for small exploratory notebooks, for code that feeds directly into scikit-learn or statsmodels, and for teams that have not yet hit performance constraints. The two libraries can coexist in the same project since conversion is straightforward.

Summary

To analyze data with Polars: install with pip install polars, use pl.scan_csv() for files over 100 MB, chain .filter(), .group_by(), and .agg() expressions to build the query, and call .collect() to run it. Export results with df.write_csv() or df.write_parquet(). Full API documentation is at pola.rs.