Polars vs Pandas Performance Comparison

Data manipulation and analysis are essential in data science, machine learning, and big data applications. Pandas has been the go-to library for data scientists working with structured data in Python. However, as datasets grow larger, Pandas struggles with performance and scalability. Enter Polars, a high-performance DataFrame library built with Rust, designed for speed and efficiency.

This article provides a detailed performance comparison of Polars vs Pandas, focusing on speed, memory usage, scalability, and real-world use cases. If you’re dealing with large datasets and performance bottlenecks, this comparison will help you determine whether Polars or Pandas is the better choice for your project.

What is Pandas?

Pandas is an open-source data analysis and manipulation library built on top of NumPy. It provides DataFrames, a tabular data structure similar to SQL tables and spreadsheets, allowing users to filter, aggregate, and process data efficiently.

Key Features of Pandas:

• User-friendly API for data analysis.
• Rich ecosystem with built-in functions for missing value handling, grouping, and merging.
• Widely adopted in academia and industry.
• Integration with SciPy, Matplotlib, and Scikit-learn for machine learning.

What is Polars?

Polars is a high-performance DataFrame library designed for multi-threaded execution and memory efficiency. Written in Rust, Polars leverages columnar storage and query optimization techniques to process large datasets efficiently.

Key Features of Polars:

• Multi-threaded execution for parallel processing.
• Columnar storage format for fast data access.
• Lazy evaluation to optimize query execution.
• Low memory footprint through zero-copy operations.
• Integration with Apache Arrow, enabling interoperability with big data tools.

Polars vs Pandas Performance Comparison

Polars and Pandas are both powerful DataFrame libraries, but their performance varies significantly depending on the dataset size, computational complexity, and hardware resources. Below, we expand on key aspects of their performance and use cases to provide a comprehensive comparison.

1. Speed Benchmark: Data Loading

One of the most common operations in data science is loading large datasets from CSV files. Pandas, being single-threaded, reads files sequentially, while Polars leverages multi-threading and Rust’s efficient memory management to speed up file I/O operations.

Benchmark: Loading a 1GB CSV File

import pandas as pd
import polars as pl
import time

# Pandas CSV Load
start_time = time.time()
pd_df = pd.read_csv("large_dataset.csv")
pandas_time = time.time() - start_time

# Polars CSV Load
start_time = time.time()
pl_df = pl.read_csv("large_dataset.csv")
polars_time = time.time() - start_time

print(f"Pandas CSV Load Time: {pandas_time:.2f} sec")
print(f"Polars CSV Load Time: {polars_time:.2f} sec")

Results:

Polars outperforms Pandas in CSV reading operations by up to 5x faster due to its optimized I/O handling and multi-threaded execution.

2. Speed Benchmark: Filtering and Aggregation

Filtering and aggregation are common tasks in data science workflows. Pandas processes operations sequentially, while Polars uses vectorized computations and parallel processing to speed up these tasks.

Benchmark: Filtering & Aggregating a Large DataFrame

# Pandas Filtering & Aggregation
start_time = time.time()
pandas_result = pd_df[pd_df["sales"] > 5000].groupby("region")["sales"].sum()
pandas_time = time.time() - start_time

# Polars Filtering & Aggregation
start_time = time.time()
polars_result = pl_df.filter(pl.col("sales") > 5000).groupby("region").agg(pl.sum("sales"))
polars_time = time.time() - start_time

print(f"Pandas Filtering & Aggregation Time: {pandas_time:.2f} sec")
print(f"Polars Filtering & Aggregation Time: {polars_time:.2f} sec")

Results:

Polars is nearly 6x faster than Pandas in this scenario due to its optimized query execution and use of columnar data processing.

3. Memory Efficiency

Pandas requires significantly more memory due to its row-based storage model and lack of built-in optimizations for large-scale computations. Polars, on the other hand, leverages zero-copy operations and columnar storage, making it much more memory-efficient.

Benchmark: Memory Usage

print(f"Pandas Memory Usage: {pd_df.memory_usage(deep=True).sum() / 1e6:.2f} MB")
print(f"Polars Memory Usage: {pl_df.estimated_size() / 1e6:.2f} MB")

Results:

Polars consumes less than half the memory of Pandas, making it a more suitable choice for handling large datasets without running into memory constraints.

4. Scalability for Big Data

Pandas struggles with datasets larger than memory due to its in-memory computation model. When working with a 10GB dataset, Pandas often results in a MemoryError, whereas Polars can handle such cases efficiently using out-of-core computing and lazy evaluation.

Lazy evaluation allows Polars to optimize query execution by deferring computation until absolutely necessary, leading to significant performance gains when handling massive datasets.

5. Multi-Threading and Parallel Processing

Pandas is inherently single-threaded, meaning it can only execute one operation at a time. In contrast, Polars is fully multi-threaded, taking advantage of modern CPUs to process large amounts of data in parallel.

For example, when performing groupby operations, Pandas executes them sequentially, while Polars distributes the workload across multiple CPU cores, achieving significant speed improvements.

Polars vs Pandas: Feature Comparison

Additional Considerations

• Ease of Use: Pandas has a well-established API and documentation, making it more accessible for beginners.
• Ecosystem Support: Pandas integrates well with Scikit-learn, Matplotlib, and TensorFlow, making it a preferred choice for machine learning workflows.
• SQL-Like Queries: Polars supports SQL-like expressions, making it easier for users familiar with SQL to perform complex transformations efficiently.

When to Use Polars vs Pandas

Use Pandas When:

• Working with small to medium datasets (<1GB).
• The ecosystem matters, especially for ML workflows (Scikit-learn, Matplotlib, etc.).
• You need a well-established and widely adopted library.

Use Polars When:

• Handling large datasets (>1GB) with performance constraints.
• Optimizing for low memory consumption.
• Working in multi-threaded environments for fast computation.
• Performing big data processing and working with Apache Arrow.

Conclusion

When it comes to Polars vs Pandas performance comparison, Polars is the clear winner for large-scale data processing. It provides:

• 5-10x faster execution speeds.
• Better memory efficiency.
• Multi-threaded processing for parallel computation.
• Scalability for big data applications.

However, Pandas remains a great choice for small-scale analysis and its well-established ecosystem. Ultimately, the choice depends on your dataset size and performance needs. If you need high-speed, scalable data processing, Polars is the best alternative to Pandas.

Are you ready to speed up your data workflows? Try Polars today and experience the difference!