Polars in Practice: Migrating My Data Pipeline From Pandas (Part 2 of 3)

In Part 1, I described how pandas reached its limit and how Polars solved my immediate performance headaches. In this part, I’ll dig into the practical steps I used to convert a real data pipeline from pandas to Polars. I’ll share what made the migration worth it, the roadblocks I hit, and tips for making the switch smoothly.

When Should You Migrate?

You should consider switching your pipeline to Polars if:

• Your analysis on pandas takes minutes rather than seconds.
• You routinely hit memory errors with files over 500MB.
• You process similar files in batch, or you deploy pipelines to production.
• You run groupby operations, aggregations, joins, or pivot tables on large data.
• Your hardware (laptop, VM, or cloud) can’t be upgraded easily for cost or security reasons.

If you’re only doing quick exploratory work or rely heavily on pandas-specific plotting or extensions, the switch may not be urgent. But for pipeline speed, reliability, and scalability, Polars can be the clear winner.

How I Ported My Pipeline: Step by Step

1. Install and Load Data

First, install Polars:

pip install polars

Then load the data. Here’s a side-by-side of pandas and Polars:

# pandas
import pandas as pd
data_pd = pd.read_csv('large_file.csv')

# polars
import polars as pl
data_pl = pl.read_csv('large_file.csv')

Polars reads large files faster, and you’ll notice lower memory use too.

2. Data Cleaning and Type Handling

Pandas and Polars both support missing values, string cleaning, and data conversion, but the syntax differs slightly:

# pandas
data_pd['amount'] = data_pd['amount'].fillna(0).astype('float')

# polars
data_pl = data_pl.with_columns(
    pl.col('amount').fill_null(0).cast(pl.Float64)
)

Polars uses lazy evaluation, so you can chain many transformations efficiently.

3. Groupby and Aggregation Syntax

This was the key pain point for me in pandas:

# pandas
result_pd = data_pd.groupby(['user', 'month']).agg({'amount': 'sum', 'category': 'nunique'})

Equivalent logic in Polars:

# polars
result_pl = (
    data_pl
    .group_by(['user', 'month'])
    .agg([
        pl.col('amount').sum(),
        pl.col('category').n_unique()
    ])
)

Polars lets you name the results and run multiple aggregations inside a single agg() call.

4. Common Migration Gotchas

• Datetime Handling: Polars has explicit methods for .dt.year(), .dt.month(), etc. Convert date columns at load time if needed.
• Column Selection: Always use pl.col("column") in expressions rather than string names inside brackets.
• Method Names: Some methods are named differently (.n_unique() in Polars vs .nunique() in pandas), so check the docs.
• No in-place edits: Polars creates new DataFrames for transformations, which prevents accidental side effects.
• Missing plotting: For visuals, use matplotlib or Plotly, passing Pandas/NumPy arrays if needed.

5. Real Performance Comparison

Here’s what actually happened on my pipeline migration:

Operation	pandas (s)	Polars (s)
CSV Read	57	7
Groupby/Aggregate	311	19
Memory Usage(MB)	3520	940

This moved the pipeline run time from over 6 minutes to about 30 seconds.

Migration Tips

• Start by porting one step at a time and comparing results.
• Test with a sample of your real data, not toy datasets.
• Use assertions to verify outputs match between pandas and Polars.
• Keep old and new code side-by-side until confident in the migration.

If you run into specific errors, the Polars documentation is clear and direct, and the GitHub issues page is active.

When Should You Migrate? Key Triggers

• When batch runs in pandas regularly time out or fail for memory
• When you want to scale to much bigger files without investing in new hardware
• When you need to repeat data processing jobs quickly, with predictable run times
• When you want pipeline code that’s easy to maintain (Polars’ functional style helps)

For smaller, interactive projects or exploratory analysis, you might not see much gain. For automated pipelines and production, the benefits stack up fast.

What’s Next? (Coming in Part 3)

In the final post of the series, I’ll cover advanced Polars features for large-scale workflows:

• Lazy evaluation and query optimisation
• Out-of-core and streaming for “too big for RAM” files
• Integrating Polars in ETL pipelines
• Production lessons and monitoring

Let me know in the comments which migration problems you want me to tackle, or if you have a data pipeline horror story to share.