How to Switch from Pandas to Polars for Faster Analysis

If your Pandas scripts are taking minutes to process CSV files with more than a million rows, switching to Polars can reduce that to seconds. Polars uses a Rust backend with SIMD optimization and multi-threaded execution by default. Independent benchmarks published in early 2026 show CSV reads running 2.5x to 11x faster than Pandas, and Excel file reads reaching 10x to 12x improvements on large files. This guide walks through the full migration from installation to production-ready workflows.

Installing Polars

Polars requires Python 3.8 or above. The base library installs with no NumPy or Pandas dependency:

pip install polars

For Excel file support or cloud storage access (S3, GCS):

pip install "polars[excel,cloud]"

If you need to convert between Polars and Pandas DataFrames or use NumPy arrays alongside Polars:

pip install "polars[numpy,pandas,pyarrow]"

Verify the installation:

import polars as pl
print(pl.__version__)

As of early 2026, Polars is on a stable 1.x release track with regular minor updates. Check the PyPI page for the current version before pinning in a requirements file.

Core Syntax Differences

The import convention is import polars as pl, mirroring import pandas as pd. Most operations map directly with different method names.

Reading a CSV:

# Pandas
df = pd.read_csv('sales_data.csv')

# Polars
df = pl.read_csv('sales_data.csv')

Selecting columns:

# Pandas
df[['region', 'revenue']]

# Polars
df.select(['region', 'revenue'])

Filtering rows:

# Pandas
df[df['revenue'] > 10000]

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

The pl.col() expression is the core pattern in Polars. Every column reference passes through it, which lets Polars analyze and optimize the full operation before any computation runs. This explicit expression model is what enables the query optimization covered in the next section.

Adding or transforming a column:

# Pandas
df['revenue_usd'] = df['revenue'] * 1.08

# Polars
df = df.with_columns((pl.col('revenue') * 1.08).alias('revenue_usd'))

Lazy Evaluation: The Core Performance Advantage

Polars has two execution modes. Eager mode (the default) runs immediately. Lazy mode queues a sequence of operations and optimizes the full query plan before executing anything.

To use lazy mode, call .lazy() after reading your file:

result = (
    pl.read_csv('sales_data.csv')
    .lazy()
    .filter(pl.col('region') == 'North America')
    .group_by('product')
    .agg(pl.col('revenue').sum().alias('total_revenue'))
    .sort('total_revenue', descending=True)
    .collect()
)

The .collect() call triggers execution. Between .lazy() and .collect(), Polars applies two key optimizations automatically:

• Predicate pushdown: filters are applied as early as possible, reducing the number of rows read
• Projection pushdown: only the columns the query references are loaded from disk

On large CSV files (500,000 rows or more), these optimizations alone can reduce read time by 40 to 60 percent compared to reading the full file and then filtering in memory.

Grouping and Aggregation

Aggregation syntax is where Polars diverges most from Pandas. In Polars, each output column is named explicitly using .alias(), which avoids the multi-level column index Pandas produces with multiple aggregations.

# Pandas
df.groupby('region')['revenue'].agg(['sum', 'mean', 'count'])

# Polars
df.group_by('region').agg([
    pl.col('revenue').sum().alias('revenue_sum'),
    pl.col('revenue').mean().alias('revenue_mean'),
    pl.col('revenue').count().alias('revenue_count'),
])

Multiple columns can be aggregated in a single .agg() call:

df.group_by(['region', 'quarter']).agg([
    pl.col('revenue').sum().alias('total_revenue'),
    pl.col('units_sold').sum().alias('total_units'),
    pl.col('customer_id').n_unique().alias('unique_customers'),
])

Joining DataFrames

Polars join syntax is explicit about join type:

# Pandas
merged = pd.merge(sales, customers, on='customer_id', how='left')

# Polars
merged = sales.join(customers, on='customer_id', how='left')

For joins on columns with different names in each DataFrame:

merged = sales.join(customers, left_on='cust_id', right_on='customer_id', how='inner')

Polars enforces strict type matching on join keys. If one side has the key as integers and the other as strings, the join fails with a schema error rather than silently producing wrong results.

Handling Missing Values

Polars uses null internally rather than NaN. The methods are similar in purpose but different in name:

# Drop rows with any null
df.drop_nulls()

# Fill nulls in a specific column
df.with_columns(pl.col('revenue').fill_null(0))

# Count nulls per column
df.null_count()

One practical difference from Pandas: Polars enforces type consistency strictly. If a column is loaded as integers and you try to fill nulls with a float, it raises a schema error. The fix is to cast first:

df.with_columns(pl.col('revenue').cast(pl.Float64).fill_null(0.0))

This strictness is intentional. It prevents the type coercion bugs that are common in Pandas workflows where a column silently becomes object dtype after a merge.

Reading Parquet Files

Polars has native Parquet support and performs particularly well on this format because it can leverage column pruning at the file level:

# Read a single file
df = pl.read_parquet('data.parquet')

# Read multiple partitioned files
df = pl.read_parquet('data/year=2025/*.parquet')

# Lazy read with column selection
df = (
    pl.scan_parquet('data/year=2025/*.parquet')
    .select(['date', 'region', 'revenue'])
    .filter(pl.col('revenue') > 5000)
    .collect()
)

pl.scan_parquet() is the lazy equivalent of pl.read_parquet(). Using it with column selection and filters means Polars never loads columns you do not reference, which matters when working with wide tables (50+ columns) where you only need a handful.

When to Switch and When to Stay

Switch to Polars when:

• Files are larger than 100,000 rows and operations are taking more than a few seconds
• You are running repeated batch jobs where speed compounds across many runs
• Memory pressure is a problem and you want lazy streaming execution

Stay with Pandas when:

• Downstream libraries output Pandas DataFrames directly (scikit-learn, statsmodels). Conversion is possible via .to_pandas() and pl.from_pandas(), but adds friction to every step
• The analysis is a one-off task on a small dataset where the learning curve is not worth it
• Your team is not Python-fluent. If the scripts are maintained by people who are not regular Python users, the additional syntax overhead is a real cost

For teams that want fast answers from their data without writing Python at all, VSLZ AI accepts file uploads and handles analysis from a plain English prompt, which sidesteps the Pandas versus Polars choice entirely.

Summary

Polars installs with a single pip command and covers the most common Pandas operations with modest syntax changes. The key shifts are: use pl.col() for every column reference, switch groupby to group_by() with explicit .alias() per output column, and adopt .lazy() mode for any file above a few hundred thousand rows. On datasets in the millions of rows, the performance difference is large enough that the migration pays back in the first week of use.