How to Get Started with Polars for Data Analysis

Polars is a DataFrame library for Python built in Rust. Unlike pandas, it runs operations in parallel across all available CPU cores, evaluates query plans lazily to cut unnecessary work, and stores data in Apache Arrow columnar format. The practical result: 5x faster CSV reads, 5-12x faster group-by operations, and 87% lower memory consumption on large files compared to pandas. Independent benchmarks on a 100-million-row dataset show Polars completing aggregations 54x faster than pandas. You can install it with one command and use it alongside existing Python workflows immediately.

Installing Polars

Polars requires Python 3.9 or higher. Install it with pip:

pip install polars

To enable faster CSV and JSON I/O, install the optional dependencies:

pip install polars[io]

Verify the installation:

import polars as pl
print(pl.__version__)

As of April 2026, the latest stable release is in the 1.x series. The API has been stable since version 0.20, so most tutorials and documentation from 2024 onward remain accurate.

Loading Data

Polars reads CSV files using pl.read_csv(). For a 500MB sales transactions file that takes 14 seconds in pandas, Polars typically completes the same read in under 3 seconds.

import polars as pl

df = pl.read_csv("sales_data.csv")
print(df.head())
print(df.shape)   # (rows, columns)
print(df.schema)  # column names and types

For large files where you only need a subset of columns, pass a columns argument to avoid loading the entire file into memory:

df = pl.read_csv("sales_data.csv", columns=["date", "region", "revenue", "units"])

Polars also reads Parquet, JSON, Excel, and Arrow files natively:

df = pl.read_parquet("data.parquet")
df = pl.read_excel("report.xlsx")

Filtering Data with Expressions

The biggest mental shift moving from pandas to Polars is expressions. In pandas, you filter with boolean masks. In Polars, you use pl.col() expressions inside .filter().

# pandas equivalent: df[df["revenue"] > 10000]
filtered = df.filter(pl.col("revenue") > 10_000)

# Multiple conditions
filtered = df.filter(
    (pl.col("revenue") > 10_000) & (pl.col("region") == "North")
)

# Filter by date range
filtered = df.filter(
    pl.col("date").is_between(
        pl.date(2025, 1, 1), pl.date(2025, 12, 31)
    )
)

Expressions in Polars are composable and run in parallel. A filter on a 10-million-row dataset that takes 2.3 seconds in pandas typically runs in under 0.4 seconds in Polars because each CPU core processes a slice of the data simultaneously.

Selecting and Transforming Columns

Use .select() to choose columns and compute new ones in the same step:

result = df.select([
    pl.col("date"),
    pl.col("revenue"),
    (pl.col("revenue") / pl.col("units")).alias("avg_unit_price"),
    pl.col("region").str.to_uppercase().alias("region_upper"),
])

To add new columns while keeping all existing ones, use .with_columns():

df = df.with_columns([
    (pl.col("revenue") * 0.15).alias("tax"),
    pl.col("date").str.to_date("%Y-%m-%d").alias("parsed_date"),
])

String operations like .str.to_uppercase(), .str.contains(), and .str.replace() are available via the .str accessor. Date and time operations are available via the .dt accessor.

Grouping and Aggregating

Group-by operations are where Polars shows the most dramatic speed advantage. The syntax closely mirrors SQL:

summary = df.group_by("region").agg([
    pl.col("revenue").sum().alias("total_revenue"),
    pl.col("revenue").mean().alias("avg_revenue"),
    pl.col("units").sum().alias("total_units"),
    pl.len().alias("transaction_count"),
])

print(summary.sort("total_revenue", descending=True))

Group by multiple columns:

monthly = df.group_by(["region", "product_category"]).agg([
    pl.col("revenue").sum(),
    pl.col("units").sum(),
]).sort(["region", "product_category"])

On a 5-million-row dataset in independent benchmarks, pandas completed a group-by with three aggregations in 8.9 seconds. Polars completed the same operation in 0.7 seconds, a 12x improvement, because it distributes hash-based aggregation across all available cores.

Sorting Data

Sorting is one of pandas' biggest bottlenecks because it relies on single-threaded NumPy sort. Polars uses a parallelized sort algorithm and runs up to 11x faster on large datasets.

# Sort descending by a single column
sorted_df = df.sort("revenue", descending=True)

# Sort by multiple columns with mixed direction
sorted_df = df.sort(["region", "revenue"], descending=[False, True])

# Top 10 rows by revenue
top_10 = df.sort("revenue", descending=True).head(10)

Joining DataFrames

Polars supports all standard join types: inner, left, right, outer, cross, and semi. Performance is 3-8x faster than pandas on large datasets due to parallel hash joins.

customers = pl.read_csv("customers.csv")
orders = pl.read_csv("orders.csv")

# Inner join on a shared column name
merged = orders.join(customers, on="customer_id", how="inner")

# Left join where column names differ between tables
merged = orders.join(
    customers,
    left_on="cust_id",
    right_on="id",
    how="left"
)

Polars handles duplicate column names automatically by appending a suffix. Unlike pandas, there is no index to manage during joins, which eliminates a common source of shape-mismatch errors when working with multi-table datasets.

Lazy Evaluation for Large Files

For very large datasets that strain available RAM, Polars provides lazy evaluation via scan_csv() instead of read_csv(). Lazy mode builds a query plan and applies optimizations before reading any data:

result = (
    pl.scan_csv("large_file.csv")
    .filter(pl.col("revenue") > 10_000)
    .group_by("region")
    .agg(pl.col("revenue").sum())
    .sort("revenue", descending=True)
    .limit(10)
    .collect()  # executes the full optimized plan here
)

With lazy evaluation, Polars reads only the columns and rows needed for the final output. A query that selects 3 of 50 columns will scan only those 3 columns from disk, cutting memory use by 80-90% on wide files compared to eager loading.

Exporting Results

Write results back to CSV, Parquet, or Excel:

result.write_csv("output.csv")
result.write_parquet("output.parquet")
result.write_excel("output.xlsx")

Parquet is the recommended format for intermediate files because it preserves data types, compresses efficiently, and reads back significantly faster than CSV. A 500MB CSV typically compresses to 80-150MB in Parquet and reads 10x faster on subsequent loads.

Working Alongside Pandas

Polars is not an all-or-nothing replacement. You can convert freely between the two:

# Polars to pandas
pandas_df = polars_df.to_pandas()

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

This makes it practical to use Polars for performance-critical parts of a pipeline (reading large CSVs, aggregations, joins) while keeping pandas where ecosystem compatibility matters (scikit-learn, matplotlib, legacy code that only accepts pandas DataFrames).

For teams that want to skip local setup entirely, VSLZ handles CSV and spreadsheet analysis through a plain-English prompt interface, outputting charts, summaries, and filtered tables without any Python configuration.

Practical Migration Strategy

Start with new scripts rather than rewriting existing ones. Apply Polars to your slowest ETL jobs first, where pandas is producing memory errors or taking more than 30 seconds per run. Use scan_csv() and lazy mode from the beginning for any file over 1GB. The expression API takes roughly one working session to get comfortable with if you have existing pandas experience, and the official migration guide at docs.pola.rs covers every common pandas pattern with a direct Polars equivalent.