Handling large CSV files is a common task for data scientists and machine learning engineers, but it can often become a bottleneck in terms of performance and productivity. Polars, a high-performance DataFrame library in Python, offers a solution that significantly enhances efficiency, particularly when working with large datasets. In this guide, we’ll explore how to use Polars to efficiently read and manipulate CSV files, and compare its performance to pandas, demonstrating why Polars is an excellent choice for scaling your workflows.

Setting Up the Environment and Creating CSV Files

Let’s start by setting up our environment and creating sample CSV files using both pandas and Polars. We’ll use the California Housing dataset from scikit-learn as our data source.

import polars as pl
import pandas as pd
from sklearn.datasets import fetch_california_housing
import time

# Fetch the California Housing dataset
california_housing = fetch_california_housing()

# Pandas approach
pd_df = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)
pd_df.to_csv('california_housing_pandas.csv', index=False)

# Polars approach
pl_df = pl.DataFrame(california_housing.data, schema=california_housing.feature_names)
pl_df.write_csv('california_housing_polars.csv')

print(f"Pandas DataFrame shape: {pd_df.shape}")
print(f"Polars DataFrame shape: {pl_df.shape}")

After running this code, you’ll have two CSV files: one created by pandas and another by Polars.

Pandas DataFrame shape: (20640, 8)
Polars DataFrame shape: (20640, 8)

Both DataFrames have the same shape, confirming that we’re working with identical datasets.

Comparing Read Performance: Polars vs Pandas

Now that we have our CSV files, let’s compare the performance of Polars and pandas when reading these files. We’ll use a simple timing function to measure the execution time for each library.

def time_function(func, *args, **kwargs):
    start_time = time.time()
    result = func(*args, **kwargs)
    end_time = time.time()
    return result, end_time - start_time

# Reading with Pandas
_, pandas_time = time_function(pd.read_csv, 'california_housing_pandas.csv')

# Reading with Polars
_, polars_time = time_function(pl.read_csv, 'california_housing_pandas.csv')

print(f"Pandas read time: {pandas_time:.4f} seconds")
print(f"Polars read time: {polars_time:.4f} seconds")
print(f"Polars is {pandas_time/polars_time:.2f}x faster than Pandas")

The output will show the time taken by each library to read the CSV file and calculate the speed difference. It’s important to note that performance can vary depending on your specific hardware, system configuration, and even between different runs. 

Pandas read time: 0.0409 seconds
Polars read time: 0.0223 seconds
Polars is 1.84x faster than Pandas

In my run, I found that Polars performed 1.84x faster than pandas for a dataset that has about 20,000 rows. 

Basic CSV Reading with Polars

Let’s explore how to perform basic CSV reading operations with Polars. We’ll read the entire CSV file and take a look at its structure and contents.

# Read the CSV file using Polars
df = pl.read_csv('california_housing_polars.csv')
print(f"Shape of the DataFrame: {df.shape}")
print(df.head())

This code reads the entire CSV file into a Polars DataFrame. The output will show the shape of the DataFrame (number of rows and columns) and the first few rows of data.

Polars provides a clean, tabular output that’s easy to read and understand.

Selective Column Reading and Lazy Evaluation

One of Polars’ powerful features is its ability to selectively read columns and perform lazy evaluation. This is particularly useful when dealing with large datasets where you only need a subset of the data or want to perform complex operations efficiently.

# Read only the 'MedInc' and 'AveRooms' columns
df = pl.read_csv('california_housing_polars.csv', columns=['MedInc', 'AveRooms'])
print(df.head())

# Read the CSV file lazily
lazy_df = pl.scan_csv('california_housing_polars.csv')

# Define operations
result = (
    lazy_df
    .filter(pl.col('MedInc') > 5)
    .select(['MedInc', 'AveRooms', 'Latitude', 'Longitude'])
    .sort('MedInc', descending=True)
    .limit(5)
)

# Execute the query and collect the results
final_df = result.collect()
print(final_df)

In this final section, we demonstrate two features. First, we selectively read only two columns from the CSV file, which can significantly reduce memory usage and improve performance when working with large datasets.

Then, we showcase lazy evaluation using pl.scan_csv(). This allows us to define a series of operations (filtering, selecting columns, sorting, and limiting results) without immediately executing them. The operations are only performed when we call collect(), at which point Polars optimizes the query for efficient execution.

The second output of this code will display the result of our lazy evaluation query: the top 5 rows with median income greater than 5, sorted in descending order.

This output illustrates the effectiveness of lazy evaluation in Polars. We’ve efficiently filtered, selected, sorted, and limited our data without loading the entire dataset into memory. This approach is particularly beneficial when working with large CSV files, as it allows for complex operations to be performed with minimal memory overhead and optimized execution.

Conclusion

Polars provides a robust and efficient alternative to pandas for handling CSV files, especially when managing large datasets. Its performance advantages, such as faster read operations, selective column reading, and lazy evaluation, can significantly streamline your data processing tasks. By incorporating Polars into your workflow, you can reduce time spent on data loading and focus more on the analysis itself. As you explore Polars further, you’ll uncover additional ways to optimize your workflows and handle large-scale data with greater ease and efficiency.