Polars vs Pandas (2025): Why Everyone Is Switching to Polars

For years, pandas was the default choice for working with tabular data in Python. But in 2025, a new player is taking over serious data workloads: Polars — a blazing-fast DataFrame library written in Rust.

In many benchmarks, Polars is reported to be 5–10× faster on typical operations and can reach 10–100× speedups on some workloads, while also using much less memory compared to pandas.^[1][2] For analysts and engineers working with large datasets, that’s a game-changer.

What Is Pandas?

Pandas is the most popular Python library for data analysis. It provides:

• DataFrame and Series data structures
• Easy CSV, Excel, SQL, JSON reading
• Powerful indexing, grouping, and joins
• Huge ecosystem, tutorials, and community support

For small to medium datasets and exploratory analysis, pandas is still an excellent choice. But it starts struggling when:

• Data gets large (millions of rows)
• Operations become complex
• You need to fully use all CPU cores

What Is Polars?

Polars is a modern DataFrame library built in Rust that focuses on:

• High performance (10×–100× faster in many tasks)^[1][2][3]
• Low memory usage thanks to Apache Arrow columnar format
• Multithreading by default – uses all your CPU cores
• Lazy evaluation – optimizes entire query plans before running

As of late 2025, Polars has tens of millions of monthly downloads and is used widely for analytics and AI/ML pipelines.^[4]

Basic Syntax: Pandas vs Polars

Reading a CSV file in pandas:

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())

Reading a CSV in Polars:

import polars as pl

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

At a basic level, Polars feels familiar to pandas users – but under the hood, it behaves very differently.

Speed: Why Polars Is So Much Faster

Polars achieves its performance advantage through a few key ideas:

• Rust backend: compiled, memory-safe, and very fast.
• Vectorized & parallel execution: uses all CPU cores, even for complex queries.^[1][2][3]
• Apache Arrow memory model: efficient columnar format shared by many modern tools.^[0][9]
• Lazy evaluation: Polars can see the full query chain and optimize it like a SQL engine before executing.^[1][2][3]

In benchmarks, Polars often finishes operations that take pandas several seconds or minutes in a fraction of the time. For very large data, that difference can be the line between “works on my laptop” and “crashes or hangs”.^{[0][2][3][12]}

Memory Usage: Working with Bigger Data

Pandas usually needs around 5–10× the dataset size in RAM to run heavy operations, while Polars often needs around 2–4× thanks to better memory layout and optimization.^[0][9][12]

This means Polars:

• Can handle larger datasets on the same machine
• Is less likely to crash with “out of memory” errors
• Is more energy-efficient for the same workload^[9][12][10]

Lazy vs Eager: A Different Way of Thinking

Pandas is primarily eager: each operation runs immediately.

# pandas (eager)
df = pd.read_csv("data.csv")
df = df[df["score"] > 80]
df = df.groupby("category")["score"].mean()

Polars supports both eager and lazy modes. In lazy mode, it builds a query plan and optimizes it:

import polars as pl

lazy_df = (
    pl.scan_csv("data.csv")                 # lazy scan
    .filter(pl.col("score") > 80)
    .group_by("category")
    .agg(pl.col("score").mean())
)

result = lazy_df.collect()  # plan is optimized and then executed

This SQL-like, expression-based style lets Polars perform aggressive optimizations that pandas simply can’t do in general.^[8][12]

Polars vs Pandas: Which One Should You Use?

When Pandas Is Still a Great Choice

• Small to medium-sized datasets that comfortably fit in RAM
• Quick one-off analysis or notebooks
• When you rely heavily on the pandas ecosystem and extensions
• When all collaborators already know pandas well

When Polars Is the Better Choice

• Large datasets where pandas feels slow or crashes
• CPU-heavy transformations, joins, and group-bys
• Data pipelines feeding ML / deep learning models
• Building analytics or AI services that must be fast and efficient

In practice, many teams now use both: pandas for quick experiments, and Polars where performance and scalability matter.^[3][12]

Quick Migration Tips (Pandas → Polars)

• Start by rewriting one heavy step (like a big group-by or join) in Polars.
• Use pl.from_pandas(df) and df.to_pandas() to bridge between libraries.
• Learn the expression API (e.g., pl.col(), filter(), agg()) — that’s where Polars shines.
• Use lazy mode (scan_csv, collect()) for full pipelines.

Conclusion

Pandas is not “dead” — it’s still a fantastic library. But for large-scale, performance-critical data work in 2025, Polars is becoming the default choice for many teams.

If your notebooks feel slow, your scripts hit memory limits, or you are building AI/data products that must run fast, it’s worth giving Polars a serious try.