How to Get Started with Polars for Data Analysis

Why Analysts Are Switching to Polars

Pandas has powered Python data analysis for over 15 years. It is stable, widely supported, and the default choice in most data science curricula. But it runs on a single CPU thread, which means it cannot take advantage of the multi-core processors that sit in every laptop sold in the last decade.

Polars was built to solve that. Written in Rust and released in stable form in 2024, it uses Apache Arrow as its in-memory column format and runs operations across all available CPU cores by default. The result is measurable: Polars runs filter and group-by operations 10 to 30 times faster than pandas on datasets above 1 million rows, according to benchmarks published by the Polars development team. For a 10 million row dataset, a group-by that takes 18 seconds in pandas completes in under 0.7 seconds in Polars.

By April 2026, Polars has more than 30,000 GitHub stars and ships with version 1.x releases that have been production-stable for nearly two years. It is a practical choice for analysts who work with large files daily and need faster turnaround without switching to a distributed system like Spark.

Installing Polars

Polars installs from PyPI with a single command:

pip install polars

No C compiler, no system dependencies, no additional configuration. After installation, verify it works:

import polars as pl
print(pl.__version__)

For Jupyter notebooks or Google Colab, use !pip install polars in a cell. The install typically completes in under 30 seconds.

Loading Data

The most common way to bring data into Polars is from a CSV file:

df = pl.read_csv("sales_data.csv")
print(df.head())

Polars infers column types automatically and reads files using all available CPU cores. A 2 million row CSV that takes roughly 3.5 seconds to load with pandas.read_csv() loads in about 0.3 seconds with pl.read_csv().

For Excel files, install the optional extras first:

pip install polars[xlsx2csv]

Then load with:

df = pl.read_excel("report.xlsx")

Polars also reads Parquet natively, which is useful if your data warehouse exports in that format:

df = pl.read_parquet("dataset.parquet")

If you want to skip the local setup entirely, platforms like VSLZ handle file uploads directly and return analysis from a plain-English prompt, which can be faster when the goal is a quick insight rather than a repeatable script.

Filtering Rows

In pandas, you filter rows using boolean indexing:

# pandas
result = df[df["region"] == "West"]

In Polars, the equivalent uses the expression API:

# polars
result = df.filter(pl.col("region") == "West")

The pl.col() pattern is consistent across the entire library. Once you learn it for filtering, the same shape appears in grouping, sorting, and aggregations, which reduces the number of syntax patterns you need to remember.

Multi-condition filters use the & and | operators:

result = df.filter(
    (pl.col("region") == "West") & (pl.col("revenue") > 10000)
)

Grouping and Aggregating

Grouping and summing revenue by region in pandas:

# pandas
df.groupby("region")["revenue"].sum()

In Polars:

# polars
df.group_by("region").agg(pl.col("revenue").sum())

The .agg() method accepts a list of expressions, so multiple aggregations stay in one block:

df.group_by("region").agg([
    pl.col("revenue").sum().alias("total_revenue"),
    pl.col("orders").count().alias("order_count"),
    pl.col("revenue").mean().alias("avg_revenue"),
])

This returns a clean DataFrame with the column names you specify, with no index reset required.

Using the Lazy API for Large Files

For files too large to load fully into memory, Polars offers a lazy execution mode. Instead of read_csv, use scan_csv:

lf = pl.scan_csv("large_dataset.csv")

result = (
    lf
    .filter(pl.col("status") == "completed")
    .group_by("month")
    .agg(pl.col("amount").sum())
    .collect()
)

The .collect() at the end triggers execution. Before that point, Polars builds a query plan and applies optimizations automatically, similar to how SQL databases handle query planning. For a 50 million row dataset, the lazy API typically uses 60 to 70 percent less memory than loading the full file with read_csv and then filtering in memory.

Adding Calculated Columns and String Operations

Adding a derived column:

df.with_columns(
    (pl.col("revenue") / pl.col("orders")).alias("revenue_per_order")
)

String operations follow the .str namespace, similar to pandas:

df.with_columns(
    pl.col("region").str.to_lowercase().alias("region_lower")
)

Extracting the year and month from a date string:

df.with_columns(
    pl.col("date").str.slice(0, 7).alias("year_month")
)

Sorting and Selecting Columns

Sorting by a column, descending:

df.sort("revenue", descending=True)

Selecting a subset of columns:

df.select(["date", "region", "revenue"])

Renaming a column:

df.rename({"revenue": "total_revenue"})

All operations can be chained in sequence without intermediate variable assignments:

result = (
    df
    .filter(pl.col("region") == "West")
    .sort("revenue", descending=True)
    .select(["date", "region", "revenue"])
    .head(10)
)

Exporting Results

To write a result to CSV:

result.write_csv("output.csv")

To write to Excel:

result.write_excel("output.xlsx")

To write Parquet for downstream use:

result.write_parquet("output.parquet")

All three methods are synchronous and write to disk immediately.

Next Steps

The Polars documentation at docs.pola.rs covers the full expression API, date and time handling, window functions, and joining DataFrames. For most day-to-day analysis tasks, the operations covered above handle the majority of what you need. The practical test is to take a script you already run in pandas and translate it line by line into Polars. Most translations are direct, and the performance difference becomes obvious on the first run against a file above a few hundred thousand rows.