Pandas vs Polars: Which Data Processor Runs Faster

Main Takeaways

Polars (Rust-based) often outperforms Pandas (Python-based) by 3-10x on large ETL workloads, though gains depend on dataset size and operations.

On smaller data (<1M rows), Pandas may remain competitive and offers better ecosystem integration.

Pandas is still great for familiarity and ecosystem integration, but if your workflows are hitting performance walls, Polars is the better choice for speed and scalability.

Deploying Polars pipelines on Shuttle makes ETL workloads production-ready with minimal overhead.

When your data science workflows start hitting performance walls, the choice between Python's Pandas and Rust's Polars becomes critical. I recently discovered this firsthand while processing millions of rows of data that pushed my Python scripts to their breaking point.

The problem many data engineering teams face today isn't just about handling large datasets; it's about doing it efficiently without burning through compute resources or waiting hours for ETL processes to complete. Traditional Python approaches with Pandas, while familiar and feature-rich, often become bottlenecks as data volumes grow.

This article will walk you through a comprehensive performance comparison between Pandas and Polars using real-world data processing tasks. You'll see exact code implementations, actual benchmark results, and learn how to deploy high-performance data pipelines using Shuttle. The results will change how you approach data processing in production.

Benchmark Dataset: NYC Taxi Trip Data for Real-World ETL

For this comparison, I used the NYC Yellow Taxi dataset from January 2015—12.7 million trip records stored in CSV files totalling about 2.1 GB. This dataset serves as an excellent proxy for real-world ETL challenges that data science teams encounter daily.

The dataset characteristics make it representative of typical production scenarios:

Scale: 12.7 million rows with 19 columns across multiple data types
Data quality issues: Missing values, invalid coordinates, and outlier detection requirements
Mixed operations: Requires loading data, cleaning, aggregations, and complex filtering
Real-world complexity: Timestamps, geospatial coordinates, and categorical data sources

The ETL pipeline covers five core operations that appear in most data processing workflows:

Load: Reading CSV data from storage into memory or lazy frames
Clean: Handling missing data, filtering invalid values, and data type conversions
Aggregate: Grouping operations across temporal and categorical dimensions
Filter: Complex multi-condition filtering and sorting operations
Export: Writing processed results back to storage systems

This represents typical data pipeline tasks in production environments where teams process transaction logs, sensor data, or user behaviour analytics regularly.

Dataset sample showing column structure and data types from NYC taxi data

Performance Bottlenecks in Python ETL with Pandas

Before diving into solutions, let's examine the specific performance bottlenecks that make Pandas challenging for large-scale data processing operations. These limitations become apparent when working with datasets that exceed available system memory or require complex data transformations.

Eager Loading and Memory Bloat

Pandas uses eager evaluation, meaning every operation executes immediately and creates intermediate results in memory. When you load CSV files, Pandas reads the entire dataset into RAM regardless of whether you'll use all columns or rows:

import pandas as pd
import time

# Pandas immediately loads entire file into memory
start = time.time()
df = pd.read_csv("yellow_tripdata_2015-01.csv")  # 2.1 GB file
load_time = time.time() - start

print(f"Loaded {len(df):,} rows in {load_time:.2f}s")
print(f"Memory usage: ~4.6 GB peak")

This eager approach creates memory pressure as each transformation step generates new DataFrames, leading to memory usage that can exceed 2-3x the original dataset size during processing operations.

The Global Interpreter Lock Problem

Python's GIL prevents true multi-threaded execution for CPU-intensive operations, meaning Pandas can only utilize one CPU core at a time for most data processing tasks:

# This aggregation uses only one CPU core despite having 8+ cores available
daily_stats = df.groupby(df['tpep_pickup_datetime'].dt.date).agg({
    'trip_distance': ['count', 'mean', 'sum'],
    'total_amount': ['mean', 'sum', 'std'],
    'passenger_count': ['sum', 'mean']
})

Modern systems with 8, 16, or more CPU cores remain underutilized, creating a significant performance bottleneck for data-intensive operations.

Handling Missing Values and Data Types

Pandas processes missing values through multiple passes over the data, with each operation requiring a full scan of all rows and columns:

# Each operation scans the entire dataset separately
df_cleaned = df.dropna(subset=['pickup_longitude', 'pickup_latitude'])
df_filled = df_cleaned.fillna({'passenger_count': 1})
df_typed = df_filled.astype({'passenger_count': 'int32'})

These sequential operations become increasingly expensive as datasets grow, particularly when dealing with wide schemas containing many columns with different data types.

System monitor showing single-core CPU usage during Pandas operations

How Polars Uses Rust for Fast, Multi-Threaded Data Processing

Polars takes a fundamentally different approach to data processing by leveraging Rust's performance characteristics and implementing lazy evaluation throughout the system. This architecture enables significant performance improvements for ETL operations on large datasets. To understand why polars works so well, let's look at the key features of polars:

Lazy Evaluation and Query Planning

Instead of executing operations immediately, Polars builds a query plan that gets optimized before any actual data processing begins:

use polars::prelude::*;

// Create lazy frame - no data loading yet
let df = LazyFrame::scan_csv("yellow_tripdata_2015-01.csv", ScanArgsCSV::default())?
    .filter(col("trip_distance").gt(0))
    .select([col("pickup_datetime"), col("trip_distance"), col("total_amount")])
    .group_by([col("pickup_datetime").dt().date()])
    .agg([col("trip_distance").mean(), col("total_amount").sum()]);

// Only execute when explicitly requested
let result = df.collect()?;

This lazy approach allows Polars to analyze the entire pipeline and apply optimizations like predicate pushdown, column pruning, and operation fusion before touching any data.

Query Optimization Techniques

Polars automatically applies several query optimization techniques that reduce I/O operations and memory usage:

Predicate Pushdown: Filters get moved closer to data sources, reducing the amount of data that needs to be loaded:

// Polars pushes this filter down to the CSV reader level
let filtered_data = LazyFrame::scan_csv("data.csv", ScanArgsCSV::default())?
    .filter(col("passenger_count").gt(0))  // Applied during file reading
    .select([col("trip_distance"), col("total_amount")]);

Column Pruning: Only required columns get loaded from storage, reducing memory usage and I/O:

// Only loads pickup_datetime and trip_distance columns
let df = LazyFrame::scan_csv("data.csv", ScanArgsCSV::default())?
    .select([col("pickup_datetime"), col("trip_distance")])
    .collect()?;

Multi-Core Processing and Memory Efficiency

Rust's native threading capabilities allow Polars to utilize all available CPU cores automatically. Operations like aggregations, joins, and sorting distribute work across threads without the GIL limitations that constrain Python:

// Automatically uses all CPU cores for groupby operations
let daily_stats = LazyFrame::scan_csv("data.csv", ScanArgsCSV::default())?
    .group_by([col("pickup_datetime").dt().date()])
    .agg([
        col("trip_distance").count().alias("trip_count"),
        col("trip_distance").mean().alias("avg_distance"),
        col("total_amount").sum().alias("total_revenue")
    ])
    .collect()?;  // Parallel execution across all cores

Memory efficiency comes from Rust's ownership system and Polars' streaming capabilities, which process data in chunks rather than loading entire datasets into memory. As the graph belows shows, Polars also maximizes CPU utilization, distributing work across all cores for consistently fast execution.

CPU usage graph showing all cores utilized during Polars operations

Pandas vs Polars ETL Pipeline Examples

Let's examine side-by-side implementations of the same ETL pipeline using both libraries. These examples show identical data processing logic implemented with each tool's best practices.

Pandas Implementation: Traditional ETL Approach

import pandas as pd
import numpy as np
from datetime import datetime
import time
import json

class PandasETL:
    def __init__(self, file_path):
        self.file_path = file_path
        self.df = None
        self.metrics = {}

    def load_and_clean_data(self):
        """Load CSV data and perform cleaning operations"""
        print("Loading and cleaning data...")
        start_time = time.time()

        # Load entire CSV into memory
        self.df = pd.read_csv(self.file_path)

        # Clean invalid coordinates and trip data
        self.df = self.df[
            (self.df['pickup_longitude'] != 0) &
            (self.df['pickup_latitude'] != 0) &
            (self.df['trip_distance'] > 0) &
            (self.df['trip_distance'] < 100) &
            (self.df['passenger_count'] > 0) &
            (self.df['passenger_count'] <= 6)
        ]

        # Convert datetime columns
        self.df['tpep_pickup_datetime'] = pd.to_datetime(
            self.df['tpep_pickup_datetime']
        )
        self.df['tpep_dropoff_datetime'] = pd.to_datetime(
            self.df['tpep_dropoff_datetime']
        )

        # Calculate trip duration
        self.df['trip_duration_minutes'] = (
            self.df['tpep_dropoff_datetime'] - self.df['tpep_pickup_datetime']
        ).dt.total_seconds() / 60

        # Remove trips with invalid duration
        self.df = self.df[
            (self.df['trip_duration_minutes'] > 0) &
            (self.df['trip_duration_minutes'] < 480)
        ]

        load_clean_time = time.time() - start_time
        self.metrics['load_clean_time'] = load_clean_time

        print(f"✅ Loaded and cleaned {len(self.df):,} rows in {load_clean_time:.2f}s")
        return self

    def aggregate_data(self):
        """Perform aggregation operations"""
        print("Performing aggregations...")
        start_time = time.time()

        # Add date columns for grouping
        self.df['date'] = self.df['tpep_pickup_datetime'].dt.date
        self.df['hour'] = self.df['tpep_pickup_datetime'].dt.hour

        # Daily statistics
        daily_stats = self.df.groupby('date').agg({
            'trip_distance': ['count', 'mean', 'sum'],
            'trip_duration_minutes': 'mean',
            'passenger_count': 'sum',
            'total_amount': ['mean', 'sum']
        })

        # Hourly patterns
        hourly_stats = self.df.groupby('hour').agg({
            'trip_distance': ['count', 'mean'],
            'total_amount': 'mean'
        })

        aggregate_time = time.time() - start_time
        self.metrics['aggregate_time'] = aggregate_time

        print(f"✅ Aggregations completed in {aggregate_time:.2f}s")
        return self

Polars Implementation: Lazy ETL Pipeline

use polars::prelude::*;
use std::collections::HashMap;
use std::time::Instant;

pub struct PolarsETL {
    metrics: HashMap<String, f64>,
}

impl PolarsETL {
    pub fn new() -> Self {
        Self { metrics: HashMap::new() }
    }

    pub fn run_etl_pipeline(&mut self, file_path: &str) -> PolarsResult<DataFrame> {
        println!("🚀 Starting Polars ETL pipeline...");
        let total_start = Instant::now();

        // Build lazy query plan
        let lazy_df = LazyFrame::scan_csv(file_path, ScanArgsCSV::default())?
            .select([
                col("pickup_longitude"),
                col("pickup_latitude"),
                col("trip_distance"),
                col("passenger_count"),
                col("tpep_pickup_datetime"),
                col("tpep_dropoff_datetime"),
                col("total_amount")
            ])
            // Apply filters (pushed down to scan level)
            .filter(
                col("pickup_longitude").neq(lit(0.0))
                    .and(col("pickup_latitude").neq(lit(0.0)))
                    .and(col("trip_distance").gt(lit(0.0)))
                    .and(col("trip_distance").lt(lit(100.0)))
                    .and(col("passenger_count").gt(lit(0)))
                    .and(col("passenger_count").lt_eq(lit(6)))
            )
            // Parse datetime columns
            .with_columns([
                col("tpep_pickup_datetime").str().strptime(
                    DataType::Datetime(TimeUnit::Microseconds, None),
                    StrptimeOptions::default(),
                    lit("coerce")
                ),
                col("tpep_dropoff_datetime").str().strptime(
                    DataType::Datetime(TimeUnit::Microseconds, None),
                    StrptimeOptions::default(),
                    lit("coerce")
                )
            ])
            // Calculate trip duration
            .with_columns([
                (col("tpep_dropoff_datetime") - col("tpep_pickup_datetime"))
                    .dt().total_minutes()
                    .alias("trip_duration_minutes")
            ])
            // Filter by trip duration
            .filter(
                col("trip_duration_minutes").gt(lit(0.0))
                    .and(col("trip_duration_minutes").lt(lit(480.0)))
            );

        // Execute aggregations
        let daily_stats = lazy_df
            .clone()
            .with_columns([col("tpep_pickup_datetime").dt().date().alias("date")])
            .group_by([col("date")])
            .agg([
                col("trip_distance").count().alias("trip_count"),
                col("trip_distance").mean().alias("avg_trip_distance"),
                col("trip_distance").sum().alias("total_trip_distance"),
                col("trip_duration_minutes").mean().alias("avg_duration"),
                col("passenger_count").sum().alias("total_passengers"),
                col("total_amount").mean().alias("avg_fare"),
                col("total_amount").sum().alias("total_revenue")
            ])
            .collect()?;

        let total_time = total_start.elapsed().as_secs_f64();
        self.metrics.insert("total_time".into(), total_time);

        println!("✅ ETL pipeline completed in {:.2f}s", total_time);
        Ok(daily_stats)
    }
}

Environment Setup for Reproducible Results

To run these benchmarks consistently, I used the following setup:

# Python environment
python3 -m venv pandas_env
source pandas_env/bin/activate
pip install pandas==2.1.0 numpy==1.24.0 psutil

# Rust environment
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
cargo --version
rustc --version

# System specifications for reproducibility
# CPU: 8-core Intel i7 (16 threads)
# RAM: 32GB DDR4
# Storage: NVMe SSD
# OS: Ubuntu 22.04 LTS

Code Comparison Showing Pandas vs Polars side by side

Code comparison showing Pandas vs Polars side by side

These side-by-side implementations highlight the key design differences between Pandas and Polars. Pandas follows an eager, memory-intensive approach, while Polars builds an optimized lazy query plan that executes more efficiently. With both pipelines producing the same analytical outputs, the real distinction emerges in how they perform at scale.

Polars vs Pandas Performance on ETL Tasks

After running identical ETL operations on the 12.7 million row NYC taxi dataset, the performance differences are substantial. Here are the detailed benchmark results across all major operations:

Execution Time Comparison

ETL Operation	Pandas (seconds)	Polars (seconds)	Speedup Factor	Notes
Load + Clean	43.60	Deferred	-	Polars defers load/clean until execution phase
Aggregations	9.21	13.80	0.67x *	Polars executes load + clean + aggregation together
Filter + Sort	9.42	5.25	1.8x	Polars benefits from predicate pushdown & parallelism
Export Results	0.14	0.05	2.8x	Polars writes faster due to streaming
Total Pipeline	62.37s	19.10s	3.3x

*Includes deferred load/clean phase in Polars

Memory Usage Analysis

These results are environment-specific. In practice, Polars often uses 30-60% less memory on large CSV workloads due to column pruning and streaming, though actual savings depend on schema and operations.

Pandas Memory Profile

Peak usage: 4,658 MB during processing operations
Memory pattern: Immediate spike during CSV loading
Garbage collection: Frequent pauses for cleanup
Intermediate objects: Multiple DataFrame copies in memory

Polars Memory Profile

Peak usage: ~2,100 MB during aggregation execution
Memory pattern: Steady increase only during actual processing
No garbage collection: Rust's ownership system manages memory
Streaming operations: Data processed in manageable chunks

CPU Utilization Patterns

Polars automatically parallelizes across available cores. Pandas relies on single-threaded execution for most operations unless explicitly offloaded (e.g., via Dask, Modin).

Pandas CPU Usage

Single-thread utilization: ~12.5% of 8-core system (1 core)
GIL limitations: Other threads blocked during computation
Load balancing: Uneven system resource usage

Polars CPU Usage

Multi-thread utilization: ~85% of 8-core system (all cores)
Parallel operations: Concurrent processing across cores
Efficient scheduling: Even load distribution across threads

Why Polars is Faster

The dramatic performance differences stem from fundamental architectural choices. Understanding these differences helps explain when and why you might choose one approach over the other for your data processing systems.

Eager vs Lazy Execution Models

Pandas Eager Execution:

# Each operation executes immediately
df = pd.read_csv("data.csv")           # Load: 32.5s
df_clean = df[df['distance'] > 0]      # Filter: 8.2s
df_agg = df_clean.groupby('date').sum() # Aggregate: 9.1s
# Total: 49.8s across separate operations

Polars Lazy Execution:

// Build query plan without execution
let df = LazyFrame::scan_csv("data.csv", ScanArgsCSV::default())?
    .filter(col("distance").gt(0))     // Added to plan: ~0s
    .group_by([col("date")])           // Added to plan: ~0s
    .sum()                             // Added to plan: ~0s
    .collect()?;                       // Execute all: 13.8s

The lazy approach allows Polars to optimize the entire pipeline as a single operation, eliminating intermediate steps and reducing data movement.

Efficient Memory Access Patterns

Polars leverages several memory optimization techniques:

Columnar Data Layout: Data stored column-wise enables better cache locality and vectorized operations.

SIMD Instructions: Single Instruction, Multiple Data processing accelerates numerical computations.

Zero-Copy Operations: Data transformations avoid unnecessary memory allocation when possible.

Streaming Execution: Large datasets are processed in chunks that fit in the CPU cache.

Query Rewriting and Optimization

Polars automatically rewrites queries for better performance:

// Original query
let result = LazyFrame::scan_csv("data.csv", ScanArgsCSV::default())?
    .select([col("*")])                    // Select all columns
    .filter(col("amount").gt(100))         // Filter expensive trips
    .select([col("date"), col("amount")])  // Select subset
    .collect()?;

// Polars optimization rewrites this to:
// 1. Scan only date and amount columns (column pruning)
// 2. Apply filter during CSV reading (predicate pushdown)
// 3. Skip unnecessary intermediate selections

These optimizations occur automatically, without requiring code changes, making Polars faster while maintaining simplicity. But performance alone isn't the full story. Once you've squeezed every ounce of speed from your ETL pipeline, the next challenge emerges: how do you take that optimized workflow and actually run it in production at scale, reliably, and without DevOps headaches?

Benchmarking on your laptop is one thing; managing deployments, scaling, SSL certificates, and infrastructure is another. That's where Shuttle comes in. With Shuttle, you can deploy your Polars ETL pipeline as a production-ready API in just a few commands, no containers, no load balancers, no endless YAML files.

Deploying a Rust ETL Pipeline with Shuttle

Of course, benchmarks are only half the story. The real challenge is turning a fast local pipeline into something production-ready. That's where Shuttle helps: it lets you deploy Polars pipelines as APIs without wrestling with infra. Traditional Rust deployment can be complex, but Shuttle abstracts away the infrastructure management.

Building a Production ETL API

Here's how to wrap our Polars ETL pipeline in a web API suitable for production use:

use axum::{routing::get, Router, Json};
use serde::{Serialize, Deserialize};
use shuttle_runtime::main;
use tower_http::cors::CorsLayer;

#[derive(Serialize)]
struct ETLResults {
    processing_time_seconds: f64,
    rows_processed: u64,
    daily_statistics: Vec<DailyStats>,
    performance_summary: String,
}

#[derive(Serialize)]
struct DailyStats {
    date: String,
    trip_count: u64,
    avg_distance: f64,
    total_revenue: f64,
}

async fn run_etl_benchmark() -> Json<ETLResults> {
    let mut etl = PolarsETL::new();

    // In production, you'd load from your data warehouse
    // For demo purposes, we return representative results
    let results = ETLResults {
        processing_time_seconds: 19.1,
        rows_processed: 12_748_986,
        daily_statistics: create_sample_stats(),
        performance_summary: "Processed 12.7M taxi records in 19.1 seconds using Polars".to_string(),
    };

    Json(results)
}

async fn health_check() -> Json<serde_json::Value> {
    Json(serde_json::json!({
        "status": "healthy",
        "service": "Polars ETL Pipeline",
        "capabilities": ["high_throughput_processing", "multi_core_execution", "memory_efficient"]
    }))
}

#[main]
async fn main() -> shuttle_axum::ShuttleAxum {
    let router = Router::new()
        .route("/", get(health_check))
        .route("/etl/benchmark", get(run_etl_benchmark))
        .route("/health", get(health_check))
        .layer(CorsLayer::permissive());

    Ok(router.into())
}

Simple Shuttle Deployment Process

Deploying this ETL pipeline to production requires minimal configuration:

1. Deploy with three commands:

# Install Shuttle CLI
cargo install cargo-shuttle

# Login to Shuttle
shuttle login

# Deploy to production
shuttle deploy

Screenshot showing cargo shuttle deployed

Shuttle handles all the complex infrastructure concerns:

Container orchestration and scaling
Load balancing and networking
SSL certificate management
Monitoring and logging systems
Automatic deployments from Git

Integration with Data Systems

For production use, you can connect this pipeline to various data sources and destinations:

// Example: Reading from cloud storage
let df = LazyFrame::scan_csv("s3://data-bucket/taxi-data/*.csv", ScanArgsCSV::default())?;

// Example: Writing to data warehouse
let result = df.collect()?;
result.write_parquet("s3://output-bucket/processed-data.parquet", ParquetWriteOptions::default())?;

// Example: Streaming processing
let streaming_df = LazyFrame::scan_csv("data/*.csv", ScanArgsCSV::default())?
    .with_streaming(true)  // Process in chunks
    .collect()?;

Migrating from Pandas to Polars: A Practical Guide

The decision to migrate from Pandas to Polars shouldn't be all-or-nothing. Here's a practical approach for teams considering the transition while minimizing risk and disruption.

When to Switch and When to Stick with Pandas

Consider Polars when:

Processing datasets larger than available RAM
ETL operations take more than a few minutes to complete
Memory usage becomes a limiting factor in your systems
CPU cores remain underutilized during data processing
You need predictable performance characteristics

Stick with Pandas when:

Working with datasets under 1GB consistently
Heavy use of domain-specific libraries that integrate with Pandas
Rapid prototyping, where development speed matters more than execution speed
Team lacks Rust experience, and the timeline is tight
Complex data science workflows with many specialized functions

Hybrid Workflows: Wrapping Heavy Steps in Polars

You don't need to rewrite entire systems. Start by identifying performance bottlenecks and replacing them with Polars operations:

import pandas as pd
import polars as pl

def hybrid_etl_pipeline(data_path):
    # Use Polars for heavy data loading and cleaning
    polars_df = pl.scan_csv(data_path)\
        .filter(pl.col("amount") > 0)\
        .with_columns([
            pl.col("timestamp").str.strptime(pl.Date),
            (pl.col("end_time") - pl.col("start_time")).alias("duration")
        ])\
        .collect()

    # Convert to Pandas for specialized analysis
    pandas_df = polars_df.to_pandas()

    # Use existing Pandas-based analysis code
    result = perform_statistical_analysis(pandas_df)

    # Convert back to Polars for final aggregation
    final_result = pl.from_pandas(result)\
        .group_by("category")\
        .agg([pl.col("value").sum(), pl.col("count").count()])\
        .collect()

    return final_result

def perform_statistical_analysis(df):
    # Existing Pandas code remains unchanged
    return df.apply(lambda x: complex_statistical_function(x))

Testing Polars Without Rewriting Your Pipeline

Start with a proof-of-concept approach that validates performance improvements:

# 1. Benchmark existing Pandas operations
import time

def benchmark_pandas_operation():
    start = time.time()
    df = pd.read_csv("large_dataset.csv")
    result = df.groupby("category").agg({
        "amount": ["sum", "mean", "count"],
        "duration": "mean"
    })
    pandas_time = time.time() - start
    return result, pandas_time

# 2. Implement equivalent Polars version
def benchmark_polars_operation():
    start = time.time()
    result = pl.scan_csv("large_dataset.csv")\
        .group_by("category")\
        .agg([
            pl.col("amount").sum().alias("amount_sum"),
            pl.col("amount").mean().alias("amount_mean"),
            pl.col("amount").count().alias("amount_count"),
            pl.col("duration").mean().alias("duration_mean")
        ])\
        .collect()
    polars_time = time.time() - start
    return result, polars_time

# 3. Compare results and performance
pandas_result, pandas_time = benchmark_pandas_operation()
polars_result, polars_time = benchmark_polars_operation()

print(f"Pandas: {pandas_time:.2f}s")
print(f"Polars: {polars_time:.2f}s")
print(f"Speedup: {pandas_time/polars_time:.1f}x")

Gradual Migration Strategy

Phase 1: Identify bottlenecks

Profile existing code to find slowest operations
Measure current memory usage and processing time
Document data types and transformations used

Phase 2: Proof of concept

Implement one critical operation in Polars
Validate identical results between implementations
Measure performance improvements

Phase 3: Expand coverage

Replace additional heavy operations
Build team familiarity with Polars syntax
Update deployment processes to handle Rust code

Phase 4: Full migration

Convert remaining operations where beneficial
Optimize query patterns for maximum performance
Update monitoring and alerting systems

Final Takeaways

Our benchmarks showed Polars delivering a 3.3x speedup over Pandas for this ETL workload, with significantly lower memory usage and full CPU utilization. This performance comes from its modern architecture: lazy evaluation, query optimization, and native multi-threading powered by Rust.

However, performance isn't everything. Pandas remains the pragmatic choice for smaller datasets (<1 GB), rapid prototyping, and tasks deeply integrated with the broader Python data science ecosystem. In practice, many teams adopt a hybrid strategy: using Polars for heavy data preparation and falling back to Pandas for specialized analysis and ML model integration.

Ultimately, if your current pipelines are hitting performance walls, Polars offers a clear path to faster, more scalable processing. But turning that local speed into a production-ready system presents the next hurdle. This is where Shuttle completes the picture. It abstracts away the complexity of containers and infrastructure, allowing you to deploy a high-performance Polars pipeline as a scalable API in minutes, not days. It turns benchmarks into real-world applications without the DevOps overhead.

Ready to see the difference yourself? Deploy the complete Polars ETL benchmark and run your own comparisons with real data.

View the complete benchmark code on GitHub →