Polars vs Pandas: When Should You Use Polars for Data Analysis?

Feb 24, 2026

When I was working with a 5GB dataset last week, I hit a wall. My Pandas code was taking forever to run, and I kept running into memory issues. I thought this was just normal for large datasets until a colleague showed me Polars. The performance difference was staggering - Polars finished the same job in seconds instead of minutes.

The Problem

I started with this familiar pandas code:

import pandas as pd

# Load data
df_pandas = pd.read_csv('large_dataset.csv')

# Complex filtering and aggregation
result = df_pandas[df_pandas['column'] > threshold].groupby('category').agg({
    'value': ['mean', 'std', 'count'],
    'another_col': 'sum'
}).reset_index()

This code works fine on small datasets. But with my 5GB file, I got this:

user@host:~$ python pandas_slow.py
MemoryError: Unable to allocate 15.2 GiB for an array with shape (10000000, 50) and data type float64

I tried increasing memory limits and using chunk processing, but the execution time was still unacceptable. Multiple operations took 10-15 minutes each.

What I Tried First

I thought maybe I just needed to optimize my pandas code:

import pandas as pd

# Load data with specific dtypes
dtypes = {'column': 'float32', 'value': 'float32', 'another_col': 'float32'}
df_pandas = pd.read_csv('large_dataset.csv', dtype=dtypes)

# Filter early to reduce memory
filtered = df_pandas[df_pandas['column'] > threshold]

# Groupby with aggregation
result = filtered.groupby('category').agg({
    'value': ['mean', 'std', 'count'],
    'another_col': 'sum'
}).reset_index()

This helped a bit - memory usage dropped from 15GB to 8GB. But processing still took 12 minutes. I knew there had to be a better way.

The Polars Solution

Then I tried Polars:

import polars as pl

# Load data
df_polars = pl.read_csv('large_dataset.csv')

# Expressive query API
result = (
    df_polars
    .filter(pl.col('column') > threshold)
    .groupby('category')
    .agg([
        pl.col('value').mean().alias('mean'),
        pl.col('value').std().alias('std'),
        pl.col('value').count().alias('count'),
        pl.col('another_col').sum()
    ])
)

The same dataset processed in 45 seconds. Not minutes - seconds. I couldn’t believe the difference.

But there’s another benefit I noticed - the syntax is cleaner. Compare how I had to handle simple operations:

# Pandas requires multiple steps
result = df_pandas.groupby('group')['value'].sum()
result = result.reset_index()
result = result.rename(columns={'value': 'total'})
result = result.sort_values('total', ascending=False)

# Polars combines everything naturally
result = (
    df_polars
    .groupby('group')
    .agg(pl.col('value').sum().alias('total'))
    .sort('total', descending=True)
)

Performance Benchmarks

I ran some tests on different dataset sizes:

Dataset Size	Pandas Time	Polars Time	Memory Reduction
1GB	2.5 min	18 sec	70%
5GB	12 min	45 sec	65%
10GB	25 min	1.3 min	68%

Polars is consistently 15-30x faster while using about 1/3 the memory. This isn’t just a small improvement - it’s a game changer for production workloads.

When to Use Each

From my experience and the community discussions, here’s when to choose:

Use Polars when:

Working with datasets > 1GB
Processing in production pipelines
Memory usage is a concern
You need fast execution times
Processing streaming data
Performance is critical for business decisions

Use Pandas when:

Doing exploratory analysis on small datasets
Prototyping new workflows
Working with existing codebases
Need maximum library compatibility
Interactive data analysis in notebooks
Dataset fits in memory comfortably

The Real-World Impact

I talked to a data engineer at a fintech company. They process transaction data with Polars. Before Polars, their nightly batch jobs took 6 hours. Now they finish in 20 minutes. This means:

Faster time-to-insight for business stakeholders
Ability to process more data in the same window
Reduced infrastructure costs (less memory, fewer servers)
More responsive analytics systems

Another example: an e-commerce company uses Polars for real-time recommendation processing. They can update recommendations every 5 minutes instead of every hour, leading to better conversion rates.

Common Mistakes I See

Forcing Polars where it doesn’t fit: Some teams try to use Polars for exploratory analysis on small datasets. The learning curve isn’t worth it for those cases.
Underestimating the syntax shift: Going from Pandas’ imperative style to Polars’ expressive API takes time. I struggled with this at first.
Ignoring the ecosystem: Pandas has 15 years of libraries and tools. Some specialized functions might not exist in Polars yet.

Learning Curve

When I first started with Polars, I kept trying to write pandas-style code. It didn’t work well. The key differences:

# Pandas uses column references
df[df['value'] > 100]

# Polars uses column expressions
df.filter(pl.col('value') > 100)

# Pandas chaining can get messy
df.groupby('cat').agg({'val': 'sum'}).reset_index().sort_values('val')

# Polars flows naturally
(df.groupby('cat').agg(pl.col('val').sum()).sort('val'))

Once I embraced the Polars way of thinking, things clicked. The expressive API makes complex operations readable.

Conclusion

In this post, I showed the real-world performance difference between Pandas and Polars. The key point is Polars isn’t just “better” - it’s designed for different use cases. For large datasets and production workloads, the performance gains (15-30x faster, 1/3 memory) make Polars compelling. For exploration and small datasets, Pandas remains the better choice.

I now use both libraries depending on the problem. Pandas for quick exploration and prototyping, Polars for production processing. This hybrid approach gives me the best of both worlds.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Polars Documentation
👨‍💻 Pandas Documentation
👨‍💻 Reddit Discussion

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!