Pandas vs. Polar Bears

Introduction

Let’s say you are in the middle of a data project, dealing with huge data sets and trying to find as many patterns as possible as quickly as possible. You reach for the usual data manipulation tool, but what if there is a more suitable tool that will improve the outcome of your work? You turn to the lesser-known data processor Polars, which has entered the market only recently but remains a worthy contender for the borderline Pandas library. This article will help you understand pandas vs polars, how and when to use it, and show you the strengths and weaknesses of each data analysis tool.

Learning outcomes

• Understand the fundamental differences between Pandas and Polars.
• Learn more about the performance benchmarks for both libraries.
• Explore the unique features and functionality of each tool.
• Discover the areas in which each library excels.
• Learn about future developments and community support for Pandas and Polars.

What is Pandas?

Pandas is a robust library for data analysis and manipulation in Python. It offers data containers such as DataFrames and Series, which allow users to perform various analyses on the available data with relative simplicity. Pandas functions as a very flexible library built around an extremely rich set of functions; it also has a tight coupling with other data analysis libraries.

Key features of Pandas:

• DataFrames and Series for structured data manipulation.
• Extensive I/O capabilities (read/write from CSV, Excel, SQL databases, etc.).
• Extensive functionality for data cleansing, transformation and aggregation.
• Integration with NumPy, SciPy and Matplotlib.
• Extensive community support and documentation.

Example:

import pandas as pd

data = {'Name': ('Alice', 'Bob', 'Charlie'),
        'Age': (25, 30, 35),
        'City': ('New York', 'Los Angeles', 'Chicago')}
df = pd.DataFrame(data)
print(df)

Production:

      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

What is Polars?

Polars is a high-performance DataFrame library designed for speed and efficiency. It leverages Rust for its core computations, allowing it to handle large datasets with impressive speed. Polars aims to provide a fast and memory-efficient alternative to Pandas without sacrificing functionality.

Main features of the Polars:

• Ultra-fast performance thanks to Rust-based implementation.
• Lazy evaluation for optimized query execution.
• Memory efficiency through copy-free data handling.
• Parallel computing capabilities.
• Support for Arrow data format for interoperability.

Example:

import polars as pl

data = {'Name': ('Alice', 'Bob', 'Charlie'),
        'Age': (25, 30, 35),
        'City': ('New York', 'Los Angeles', 'Chicago')}
df = pl.DataFrame(data)
print(df)

Production:

shape: (3, 3)
┌─────────┬─────┬────────────┐
│ Name    ┆ Age ┆ City       │
│ ---     ┆ --- ┆ ---        │
│ str     ┆ i64 ┆ str        │
╞═════════╪═════╪════════════╡
│ Alice   ┆  25 ┆ New York   │
│ Bob     ┆  30 ┆ Los Angeles│
│ Charlie ┆  35 ┆ Chicago    │
└─────────┴─────┴────────────┘

Performance comparison

Performance is a critical factor when choosing a data manipulation library. Polars often outperforms Pandas in terms of speed and memory usage thanks to its Rust-based backend and efficient execution model.

Reference example:
Let's compare the time it takes to perform a simple grouping operation on a large dataset.

Pandas:

import pandas as pd
import numpy as np
import time

# Create a large DataFrame
df = pd.DataFrame({
    'A': np.random.randint(0, 100, size=1_000_000),
    'B': np.random.randint(0, 100, size=1_000_000),
    'C': np.random.randint(0, 100, size=1_000_000)
})

start_time = time.time()
result = df.groupby('A').sum()
end_time = time.time()
print(f"Pandas groupby time: {end_time - start_time} seconds")

Polar:

import polars as pl
import numpy as np
import time

# Create a large DataFrame
df = pl.DataFrame({
    'A': np.random.randint(0, 100, size=1_000_000),
    'B': np.random.randint(0, 100, size=1_000_000),
    'C': np.random.randint(0, 100, size=1_000_000)
})

start_time = time.time()
result = df.groupby('A').agg(pl.sum('B'), pl.sum('C'))
end_time = time.time()
print(f"Polars groupby time: {end_time - start_time} seconds")

Output example:

Pandas groupby time: 1.5 seconds
Polars groupby time: 0.2 seconds

Advantages of pandas

• Mature ecosystem: Pandas, on the other hand, have been around for quite some time and as such have a stable and lush environment.
• Extensive documentation: Flexible, complete and accompanied by good documentation.
• Wide adoption: Active user community, has a very large fan base and is widely used in the data science field.
• Integration: They have impressive compatibility and interoperability with other top-level libraries such as NumPy, SciPy, and Matplotlib.

Advantages of polar fleece

• Performance: Polars is optimized for speed and can handle large data sets more efficiently.
• Memory efficiency: It uses memory more efficiently, making it suitable for big data applications.
• Parallel processing: Supports parallel processing, which can significantly speed up calculations.
• Lazy evaluation: Execute operations only when necessary, optimizing the query plan for better performance.

When to use Pandas and Polars

Now let's see how to use pandas and polaroids.

Pandas

• When working with small to medium sized data sets.
• When you need extensive data manipulation capabilities.
• When you need integration with other Python libraries.
• When working in an environment with extensive Pandas support and resources.

Polar

• When working with large data sets that require high performance.
• When you need efficient use of memory.
• When working on tasks that can benefit from parallel processing.
• When you need lazy evaluation to optimize query execution.

Key differences between Pandas and Polars

Now let's look at the following table for Pandas vs Polars.

Additional use cases

Pandas:

• Time series analysis: Best suited for time series data manipulation, it incorporates specific functions that allow resampling, moving windows, and time zone conversion.
• Data cleansing: It also includes powerful procedures to handle missing values, duplicates, and data type conversions.
• Fusion and union: Data merging, joining, and concatenating functions: Features that allow passing data from different sources through a wide range of manipulations.

Polar:

• Big Data Processing: Efficiently handles large data sets that would be cumbersome in Pandas, thanks to its optimized execution model.
• Stream processing: Suitable for real-time data processing applications where performance and memory efficiency are critical.
• Batch processing: Ideal for batch processing tasks in data pipelines, leveraging its parallel processing capabilities to accelerate calculations.

Conclusion

If heavy computational operations are retained, Pandas is better suited for log-based calculations and vice versa for Polars. Data manipulation in Pandas is rich, flexible, and well supported, making it a reasonable and suitable choice in many data science contexts. While Pandas offers increased speed compared to NumPy, there is a high-performance data structure known as Polars, especially when working with large data sets and memory-consuming operations. We appreciate these differences and advantages and believe that it is valuable to understand the criteria based on which you want to make a decision on which study program is best for you.

Frequent questions