---
title: "DuckDB vs Pandas vs Polars: Performance Comparison for Python | MotherDuck"
description: "Compare DuckDB, Pandas, and Polars side by side with benchmarks and code examples. Learn which Python data library is fastest for your analytics workload."
canonical: "https://motherduck.com/videos/duckdb-vs-pandas-vs-polars-for-python-devs/"
---

[BACK TO VIDEOS](/videos/)

[YouTube](/videos/?category=YouTube#videos-and-webinars-library)[Python](/videos/?category=Python#videos-and-webinars-library)[Tutorial](/videos/?category=Tutorial#videos-and-webinars-library)

# DuckDB vs Pandas vs Polars: Performance Comparison for Python

2023/06/01Featuring: [Mehdi Ouazza](/authors/mehdi-ouazza/)

> **TL;DR:** DuckDB, Pandas, and Polars aren't enemies—they're complementary tools that integrate seamlessly via Apache Arrow. In benchmarks on 33M rows, DuckDB was fastest, Polars came second (with lazy evaluation), and Pandas ran out of memory.

## The Three Frameworks Compared

| Feature | DuckDB | Pandas | Polars |
| --- | --- | --- | --- |
| Type | In-process OLAP database | Data frame library | Data frame library |
| Backend | C++ | NumPy → Arrow (2.0+) | Rust |
| Query style | SQL (+ relational API) | DataFrame methods | DataFrame + SQL |
| Lazy evaluation | Via SQL optimization | No | Yes |
| Larger-than-memory | Yes | No | Yes (lazy mode) |

## Why DuckDB in a Python Workflow?

"But why do I need a database in my Python environment?"

DuckDB is an **in-process database**—it runs in the same process as your Python app. Install with pip and you're ready to go.

Key advantages:

- **Blazingly fast**: Vectorized columnar query execution

- **Built-in extensions**: JSON, Parquet, S3, spatial data—no extra pip packages

- **Single-file format**: ACID-compliant database file

- **Arrow integration**: Zero-copy data sharing with Pandas/Polars

## Installation Size Matters

Comparing site-packages folder sizes:

- **DuckDB**: Smallest footprint, extensions loaded on-demand

- **Polars**: Lightweight compared to Pandas

- **Pandas**: Largest, most dependencies

> "Less dependencies, less code, less problems."

## Syntax Comparison

The same operation (extract domain, group by, count) can be written in:

- **DuckDB SQL**: Using regexp_extract and GROUP BY

- **DuckDB Relational API**: Chained method calls in Python

- **Polars**: DataFrame method chaining with str.extract

Each has its own syntax style, but all achieve the same result.

## Benchmark: 33M Rows of Hacker News Data

**Task**: Read Parquet, extract domains, group by, count, write to S3

| Framework | Result |
| --- | --- |
| DuckDB | Fastest |
| Polars (lazy) | Second |
| Pandas | Out of memory |

**Important**: Polars required lazy evaluation (`LazyFrame`) to avoid memory blowup. You need to know the framework's optimization features to use it correctly.

## The Apache Arrow Advantage

All three frameworks support Arrow, enabling:

- **Zero-copy conversion** between DuckDB, Pandas, and Polars

- **Query Pandas with SQL** via DuckDB

- **Mix and match** the best tool for each step

DuckDB provides methods to convert results directly to Pandas DataFrames or Polars DataFrames, and can query existing DataFrames with SQL.

## Versatility

- **DuckDB**: CLI, Python, Rust, Java, Swift (mobile!)

- **Polars**: Python, Rust, new CLI

- **Pandas**: Python only, but massive visualization ecosystem (Seaborn, Plotly, etc.)

## Should You Use DuckDB?

> "It depends on your use case... but DuckDB can easily be installed with just `pip install`. It adds little overhead to your development. You should do it."

**TL;DR**: Leverage the best of all worlds—they work together.

TABLE OF CONTENTS

- The Three Frameworks Compared
- Why DuckDB in a Python Workflow?
- Installation Size Matters
- Syntax Comparison
- Benchmark: 33M Rows of Hacker News Data
- The Apache Arrow Advantage
- Versatility
- Should You Use DuckDB?

Start using MotherDuck now!

[Try 7 Days Free](https://auth.motherduck.com/authorize?app_source=web&response_type=code&client_id=bza3KWQpxRAFlTlRFXUo29AOg9xD7zcp&redirect_uri=https%3A%2F%2Fapp.motherduck.com%2F&state=STATE&auth_flow=signup&screen_hint=signup&ext-ph_distinct_id=ae89563e-88a2-42d4-9841-ef09013677ed)

## Related Videos

[45:32](/videos/motherduck-flights-agent-native-data-pipelines/)[2026-06-17](/videos/motherduck-flights-agent-native-data-pipelines/)

### [Introducing Flights: Agent-Native Data Pipelines in MotherDuck](/videos/motherduck-flights-agent-native-data-pipelines/)

See how MotherDuck Flights lets you build and schedule Python data pipelines with an AI agent — from a single prompt to a running ingest pipeline and live dashboard.

Webinar

Data Pipelines

AI, ML and LLMs

MotherDuck Features

Python

[1:02:00](/videos/ai-for-sales-marketing-teams/)[2026-05-07](/videos/ai-for-sales-marketing-teams/)

### [AI Data Analysis for Sales & Marketing Teams: Real Demos, No SQL Required](/videos/ai-for-sales-marketing-teams/)

Watch how an AE and a marketer use AI to analyze their own business data without writing SQL or waiting on a data team.

Webinar

AI, ML and LLMs

MotherDuck Features

Tutorial

[1:00:14](/videos/building-ai-chatbot-saas-mcp/)[2026-03-11](/videos/building-ai-chatbot-saas-mcp/)

### [Building an Analytics Chatbot for your SaaS app in 1 day](/videos/building-ai-chatbot-saas-mcp/)

Learn how to build a conversational AI chatbot for your SaaS product using the MotherDuck MCP server, with scoped data access and streaming responses.

Webinar

AI, ML and LLMs

Tutorial

MotherDuck Features

[View all](/videos/)