Pandas vs Polars vs PySpark: Which Python Library for Data Processing?

May 5, 2026

Cowrie

Dev @ Bswen

1. Purpose

In this post, I will demonstrate how to choose between Pandas, Polars, and PySpark for your data processing tasks. I’ll share my experience of hitting memory errors, slow performance, and overcomplicated setups—so you can avoid the same mistakes.

2. The Problem I Encountered

I was processing a 50GB CSV file with Pandas, and my Python script kept crashing:

MemoryError: Unable to allocate 12.5 GiB for an array with shape (50000000,) and data type float64

My first instinct was to increase memory. But then I realized: Pandas loads everything into memory. A 50GB file needs 50GB+ of RAM. My laptop only has 16GB.

Then I tried PySpark for the same task. The setup was complex:

# Installing Java
$ brew install openjdk@11

# Setting JAVA_HOME
$ export JAVA_HOME=$(/usr/libexec/java_home)

# Installing PySpark
$ pip install pyspark

After 30 minutes of configuration, my simple script finally ran—but for a 500MB test file, it took 45 seconds just to start up. Overkill.

That’s when I discovered Polars, the middle-ground sweet spot I had been missing.

3. Understanding the Three Libraries

3.1 Pandas: The Familiar Workhorse

Pandas Memory Model

Pandas loads the entire dataset into memory (RAM). If your file is 5GB, you need at least 5GB of free RAM—often more due to intermediate computations.

Pandas has been my go-to for years. It’s intuitive, well-documented, and perfect for quick analysis:

pandas_small_data.py

import pandas as pd

# Good for: datasets under 1GB, quick analysis
df = pd.read_csv('sales.csv')  # Loads entire file into memory

# Simple operations
df['total'] = df['price'] * df['quantity']
result = df.groupby('category')['total'].sum()

print(result)

The problem: When I tried this with a 50GB file, my system swapped to disk and became unresponsive.

3.2 Polars: The Speed Demon

Polars Lazy Evaluation

Polars uses lazy evaluation by default with scan_csv(). It builds a query plan and only executes when you call .collect(). This means it can optimize operations and use minimal memory.

Polars was the revelation. It’s 10-100x faster than Pandas on medium-sized data, thanks to its Rust-based engine and lazy evaluation:

polars_medium_data.py

import polars as pl

# Good for: datasets 1GB-100GB, faster processing
# Lazy evaluation - doesn't load until needed
result = (
    pl.scan_csv('sales.csv')  # scan = lazy, read = eager
    .filter(pl.col('price') > 100)
    .groupby('category')
    .agg(pl.col('quantity').sum())
    .collect()  # Execute only now
)

print(result)

When I ran this on my 50GB file, it processed in under 5 minutes—without loading the entire file into memory.

3.3 PySpark: The Distributed Giant

PySpark Overhead

PySpark requires a JVM (Java Virtual Machine) and has significant startup overhead. For small files, this overhead dominates. It shines when processing data distributed across multiple machines.

PySpark is designed for clusters. It distributes computation across nodes, making it ideal for truly big data:

pyspark_large_data.py

from pyspark.sql import SparkSession

# Good for: datasets >100GB, distributed processing
spark = SparkSession.builder \
    .appName('sales_analysis') \
    .getOrCreate()

# Distributed across cluster
df = spark.read.csv('sales.csv', header=True)

result = (
    df.filter(df.price > 100)
    .groupBy('category')
    .sum('quantity')
)

result.show()  # Triggers distributed computation

spark.stop()

The catch: For my single-machine setup with a 50GB file, PySpark added unnecessary complexity.

4. The Decision Framework

I created this decision tree based on my experience:

Data Size?          → Library Choice
─────────────────────────────────────
< 1GB               → Pandas (simplest)
1GB - 100GB         → Polars (fastest on single machine)
> 100GB             → PySpark (needs cluster)
Multiple machines?  → PySpark (distributed)
Need SQL?           → Any (all support SQL-like operations)

4.1 Performance Comparison Table

Library	Best For	Memory Model	Speed	Setup Complexity
Pandas	<1GB, prototyping	Single-machine, in-memory	Slow on large data	Simple (pip install pandas)
Polars	1GB-100GB	Single-machine, lazy evaluation	10-100x faster than Pandas	Simple (pip install polars)
PySpark	>100GB, distributed	Multi-node cluster	Scales horizontally	Complex (requires Java, cluster)

5. Real-World Example: When Each Library Shines

5.1 Pandas: Quick Prototype

I needed to analyze a 200MB CSV of user signups. Pandas was perfect:

pandas_prototype.py

import pandas as pd

# Quick prototype: 200MB file
df = pd.read_csv('user_signups.csv')

# Fast iteration for analysis
df['signup_date'] = pd.to_datetime(df['created_at'])
df['month'] = df['signup_date'].dt.to_period('M')
monthly = df.groupby('month').size()

print(monthly)
# Total time: 2 seconds

5.2 Polars: Production Pipeline

I built a data pipeline for a client processing 30GB of daily transaction logs:

polars_pipeline.py

import polars as pl

# Production pipeline: 30GB daily files
def process_daily_logs(filepath: str) -> pl.DataFrame:
    """Process daily transaction logs efficiently."""
    return (
        pl.scan_csv(filepath)
        .filter(pl.col('status') == 'completed')
        .with_columns([
            (pl.col('amount') * 1.1).alias('amount_with_tax'),
            pl.col('timestamp').str.to_datetime('%Y-%m-%d %H:%M:%S')
        ])
        .groupby(['merchant_id', pl.col('timestamp').dt.hour()])
        .agg([
            pl.col('amount').sum().alias('total_amount'),
            pl.col('transaction_id').n_unique().alias('transaction_count')
        ])
        .collect()
    )

result = process_daily_logs('transactions_20260505.csv')
print(result)
# Total time: 4 minutes on a 16GB laptop

5.3 PySpark: Enterprise Scale

A data engineering team I consulted for processes 500GB+ of clickstream data across a 10-node cluster:

pyspark_enterprise.py

from pyspark.sql import SparkSession
from pyspark.sql import functions as F

# Enterprise scale: 500GB+ across 10 nodes
spark = SparkSession.builder \
    .appName('ClickstreamAnalysis') \
    .config('spark.executor.memory', '32g') \
    .config('spark.executor.cores', '4') \
    .getOrCreate()

# Reads from distributed storage (S3, HDFS, etc.)
df = spark.read.parquet('s3://company-data/clickstream/2026/05/')

result = (
    df.filter(F.col('event_type') == 'page_view')
    .groupBy('user_id', 'page_category')
    .agg(
        F.count('*').alias('page_views'),
        F.sum('time_on_page').alias('total_time')
    )
    .withColumn('avg_time_per_page', F.col('total_time') / F.col('page_views'))
)

result.write.parquet('s3://company-data/analysis/results/')

spark.stop()
# Total time: 45 minutes across 10 nodes (would take days on single machine)

6. Common Mistakes I Made (So You Don’t Have To)

6.1 Mistake 1: Using Pandas for Everything

I used Pandas for a 20GB file and waited 30 minutes for a simple groupby operation. The solution? Switch to Polars:

comparison.py

# BEFORE (Pandas - slow)
import pandas as pd
df = pd.read_csv('large_file.csv')  # OOM error or swapping
result = df.groupby('category')['value'].sum()

# AFTER (Polars - fast)
import polars as pl
result = (
    pl.scan_csv('large_file.csv')
    .groupby('category')
    .agg(pl.col('value').sum())
    .collect()
)

6.2 Mistake 2: Jumping to PySpark Too Early

I spent a day setting up a Spark cluster for a 5GB file. Pandas could handle it in 30 seconds. Polars in 5 seconds.

6.3 Mistake 3: Ignoring Lazy Evaluation

I didn’t use lazy evaluation in Polars initially, loading data eagerly:

polars_eager_vs_lazy.py

# WRONG: Eager loading (like Pandas)
df = pl.read_csv('large_file.csv')  # Loads everything immediately

# RIGHT: Lazy evaluation
result = (
    pl.scan_csv('large_file.csv')  # Just creates a plan
    .filter(pl.col('value') > 100)  # Adds to plan
    .select(['category', 'value'])  # Adds to plan
    .collect()  # Executes optimized plan
)

Lazy evaluation lets Polars optimize the query and only read necessary columns.

7. When to Use Each: My Recommendation

Based on my experience across different projects:

1. Start with Pandas for exploration and prototyping (files < 1GB)
2. Switch to Polars when files exceed 1GB or you need faster processing
3. Consider PySpark only when:
   • Data exceeds 100GB
   • You need distributed processing
   • Data is already in a cluster environment (S3, HDFS)

For most modern data engineering tasks on a single machine, Polars is the sweet spot.

8. Summary

In this post, I shared my journey of choosing between Pandas, Polars, and PySpark. The key takeaway:

• Pandas for small data and prototyping (<1GB)
• Polars for medium data and speed (1GB-100GB)
• PySpark for big data and distributed processing (>100GB)

Don’t make my mistakes—match the library to your data size and infrastructure. Start with Polars if you’re unsure; it’s production-ready and handles most modern data processing tasks efficiently on a single machine.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 Pandas Documentation
• 👨‍💻 Polars User Guide
• 👨‍💻 PySpark Documentation
• 👨‍💻 Reddit Discussion: Pandas vs Polars vs PySpark

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

Comments

Tags:

• python
• data-processing
• pandas
• polars
• pyspark

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