Polars vs pandas: Size and Speed Differences Explained

Polars typically consumes 15–30% less memory and executes analytical operations 2–10× faster than pandas by leveraging Apache Arrow columnar storage, compiled Rust kernels, and lazy evaluation instead of NumPy-based eager execution.

When working with large datasets in Python, the architectural differences between these libraries directly impact resource utilization and processing throughput. This technical comparison examines the specific size and speed differences when comparing Polars vs pandas, referencing the implementation details found in the pandas-dev/pandas source code.

Core Architectural Foundations

Data Storage and Memory Layout

In pandas/core/frame.py and pandas/core/series.py, pandas implements the DataFrame as a thin wrapper around an ordered dictionary of Series objects, where each column stores data as a NumPy ndarray. This design requires boxing scalar values as Python objects for certain dtypes (particularly object dtype), introducing per-element pointer indirection and memory overhead.

Polars adopts the Apache Arrow columnar format natively, where each column is an Arrow ChunkedArray stored in contiguous, cache-friendly buffers. This layout eliminates per-element Python object overhead and enables zero-copy data sharing without serialization costs.

Execution Engine Implementation

Pandas relies on vectorized NumPy operations orchestrated through pandas/util/_decorators.py, dispatching to C extensions like pandas/_libs/algos.pyx and pandas/_libs/ops.pyx for low-level arithmetic. However, the hashing and grouping algorithms in pandas/_libs/hashtable.c run single-threaded, and operations that cannot be expressed in NumPy fall back to Python loops, limiting parallelism.

Polars executes queries through a lazy evaluation graph compiled to native Rust code, automatically parallelizing workloads across CPU cores and utilizing SIMD instructions for numerical kernels.

Memory Footprint Comparison

Size Overhead Analysis

For a DataFrame containing 1 million rows and 10 int64 columns, both libraries store approximately 80 MiB of raw data. However, pandas adds 10–30% additional overhead per column due to Python object metadata and index structures maintained in pandas/core/series.py. Polars maintains the 80 MiB footprint without extra wrapping costs, typically resulting in a 15–30% smaller memory profile.

You can verify this difference using the following measurement code:

import pandas as pd
import polars as pl
import numpy as np

N = 1_000_000
df_pd = pd.DataFrame({
    "a": np.random.rand(N),
    "b": np.random.randint(0, 100, size=N),
    "c": np.random.choice(["x", "y", "z"], size=N)
})

df_pl = pl.DataFrame({
    "a": np.random.rand(N),
    "b": np.random.randint(0, 100, size=N),
    "c": np.random.choice(["x", "y", "z"], size=N)
})

print("pandas memory (MiB):", df_pd.memory_usage(deep=True).sum() / 2**20)
print("polars memory (MiB):", df_pl.estimated_size() / 2**20)

I/O Memory Pressure

During CSV ingestion, pandas/io/parsers.py reads data line-by-line in Python, building intermediate NumPy arrays that temporarily double memory usage. Polars streams data directly into Arrow buffers using Rust parsers, avoiding intermediate Python objects and reducing peak memory consumption during file loading.

Execution Speed Differences

Computational Throughput

Polars runs 2× to 10× faster than pandas for most analytical operations because its kernels execute as compiled Rust rather than interpreted Python or Cython. Group-by operations particularly benefit from multi-threaded Arrow hash tables, typically achieving 2–5× speedups over the single-threaded implementation in pandas/_libs/hashtable.c.

Benchmark Comparison

The performance gap becomes apparent in aggregation workloads:

import timeit

def pandas_groupby():
    return df_pd.groupby("c")["a"].mean()

def polars_groupby():
    return df_pl.groupby("c").agg(pl.col("a").mean())

print("pandas:", timeit.timeit(pandas_groupby, number=5))
print("polars:", timeit.timeit(polars_groupby, number=5))

Typical results show pandas completing in approximately 2.8 seconds versus 0.45 seconds for Polars on identical hardware.

Why These Performance Gaps Exist

Columnar vs. Hybrid Layout: Pandas' mixed-type approach (NumPy arrays plus Python objects) provides flexibility at the cost of per-element overhead, while Polars' strict Arrow format eliminates boxing costs.

Native Compilation: Pandas kernels in pandas/_libs/ are written in Cython or C but invoked from Python, incurring call overhead and limiting thread safety. Polars kernels are pure Rust, compiled ahead-of-time, and scheduled across threads without the Global Interpreter Lock.

Query Optimization: Pandas evaluates operations eagerly, allocating intermediate results for each step. Polars constructs a lazy execution plan that fuses operators and minimizes memory allocations and data movement.

Zero-Copy Architecture: Arrow's memory format allows Polars to memory-map Parquet and CSV files directly, whereas pandas parsers in pandas/io/parsers.py copy data into new NumPy arrays during ingestion.

Summary

• Memory Efficiency: Polars' Apache Arrow buffers eliminate Python object overhead, reducing memory footprint by 15–30% compared to pandas' NumPy-based storage in pandas/core/series.py.
• Processing Speed: Compiled Rust kernels and parallel execution make Polars 2–10× faster for aggregations, joins, and I/O operations.
• Architectural Trade-offs: Pandas prioritizes ecosystem integration and API maturity, while Polars optimizes for columnar analytics and throughput.
• I/O Performance: Polars streams data directly into memory without intermediate copies, unlike pandas' line-by-line Python parsing in pandas/io/parsers.py.

Frequently Asked Questions

Is Polars always faster than pandas?

No, pandas remains competitive for small datasets that fit comfortably in cache and operations that stay entirely within NumPy's vectorized kernels. Pandas also performs better when workflows require frequent Python object manipulation or tight integration with libraries like SciPy and Matplotlib that expect NumPy arrays as implemented in pandas/core/frame.py.

Why does pandas use more memory than Polars?

Pandas stores data in pandas/core/series.py as NumPy arrays wrapped with Python Series objects, adding per-column metadata and index structures. Additionally, object dtype columns store pointers to Python objects rather than raw values. Polars stores data in contiguous Arrow buffers without Python wrappers, eliminating this overhead.

Can I use Polars and pandas together in the same project?

Yes, both libraries provide zero-copy conversion methods. You can convert a Polars DataFrame to pandas using .to_pandas() and convert pandas to Polars using pl.from_pandas(), though these operations may involve memory copying depending on the data types involved.

Does Polars support all pandas operations?

Polars covers most core data manipulation operations but lacks some specialized pandas functionality, particularly in statistical modeling, time-series resampling, and the extensive ecosystem of pandas extensions. For workflows dependent on specific algorithms in pandas/_libs/algos.pyx or custom Python apply() functions with side effects, pandas remains the better choice.

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