Pandas v/s Polars in Python

Hello, friends! Today, we’ll explore the performance differences between Polars and Pandas in Python. When working with large datasets, both libraries have their advantages and trade-offs. Recently, I started using Polars for my analysis, and I’d love to share some key differences with you!

Using Polars instead of Pandas has several advantages, especially when working with large datasets or performance-critical applications. Here’s why Polars can be a better choice:

1. Speed & Performance

• Multi-threaded Execution: Polars is built from the ground up using Rust and designed to leverage multi-threading. This makes it significantly faster than Pandas, which is mostly single-threaded.
• Vectorized Computation: Polars uses Apache Arrow under the hood, which allows for SIMD (Single Instruction, Multiple Data) optimizations, resulting in faster computations.

2. Memory Efficiency

• Zero Copy Operations: Unlike Pandas, Polars minimizes memory usage by avoiding unnecessary copies when performing operations like filtering, grouping, or sorting.
• Lazy Execution: Polars can use lazy evaluation, meaning operations are only executed when needed, optimizing memory usage and execution plans automatically.

3. Scalability

• Handles Larger-than-RAM Data: Polars efficiently processes large datasets that don’t fit into RAM by using out-of-core processing, whereas Pandas can struggle with memory constraints.
• Streaming Support: It can process data in chunks, making it possible to analyze large files without loading them entirely into memory.

4. Expressive & Intuitive API

• Chaining Operations: Polars provides a more readable and SQL-like API for chaining operations, reducing the need for intermediate variables.
• Built-in Query Optimization: Lazy evaluation allows Polars to optimize query execution, whereas Pandas executes operations immediately, leading to potential inefficiencies.

5. Better Handling of Missing Values

• Polars has more explicit and consistent handling of null ( None or NaN ) values, reducing unexpected behavior that sometimes occurs in Pandas.

6. Stronger Data Type Support

• Arrow-Based DataFrame: Since Polars is built on Apache Arrow, it supports better data types like categoricals, timestamps, and large integers without performance degradation.
• Strict Schema Enforcement: Unlike Pandas, which allows implicit type changes, Polars enforces schema consistency, reducing errors in data pipelines.

7. Better Parallelization of GroupBy & Joins

• Efficient GroupBy & Joins: Polars parallelizes GroupBy and Join operations, making them significantly faster than Pandas, which processes them sequentially.

When Should You Still Use Pandas?

• If you’re working with small datasets (<100K rows) and don’t need high performance.
• If you rely on Pandas-specific libraries (e.g., scikit-learn , statsmodels ) that don’t yet support Polars.
• If you’re comfortable with the Pandas API and don’t want to learn a new framework.

Conclusion

If performance, memory efficiency, and scalability are critical, Polars is the better choice. But for quick exploratory data analysis and legacy codebases, Pandas is still very useful.

Here is the sample code you can try running.

import pandas as pd
import polars as pl
import numpy as np
import time
# Generate 100 million rows
N = 100_000_000
data = {
"A": np.random.randint(0, 100, N),  # Random integers between 0 and 100
"B": np.random.rand(N) * 100,  # Random float values between 0 and 100
"C": np.random.choice(["X", "Y", "Z"], N)  # Random categorical values
}
# Convert to Pandas DataFrame
df_pandas = pd.DataFrame(data)
# Convert to Polars LazyFrame (Optimized for large datasets)
df_polars = pl.LazyFrame(data)
# Measure execution time for Pandas
start_time = time.time()
df_pandas_filtered = df_pandas[(df_pandas["A"] > 50) & (df_pandas["B"] < 50)]
df_pandas_grouped = df_pandas_filtered.groupby("C")["B"].mean()
pandas_time = time.time() - start_time
# Measure execution time for Polars (Lazy Execution)
start_time = time.time()
df_polars_filtered = df_polars.filter((pl.col("A") > 50) & (pl.col("B") < 50))
df_polars_grouped = df_polars_filtered.group_by("C").agg(pl.col("B").mean())
df_polars_grouped.collect()  # This triggers execution
polars_time = time.time() - start_time
# Print execution times
print(f"Pandas execution time: {pandas_time:.4f} seconds")
print(f"Polars execution time (LazyFrame Optimized): {polars_time:.4f} seconds")

Result

Pandas execution time: 16.6483 seconds Polars execution time (LazyFrame Optimized): 4.0098 seconds

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