When to Use Polars vs DuckDB for Data Analysis

Mar 9, 2026

I needed to choose between Polars and DuckDB for a new data analysis project. Both tools promised better performance than pandas, but they work differently. I spent time testing both and understanding their strengths.

The Core Difference

Polars is a DataFrame library first. You use Python code to transform data. DuckDB is a SQL database that runs in your Python process. You write SQL queries to analyze data.

I found this distinction matters more than raw performance numbers. Let me show you what I learned.

When I Chose Polars

I started with a project that needed complex data transformation pipelines. The workflow looked like this:

import polars as pl

# Lazy evaluation - doesn't execute until collect()
result = (
    pl.scan_csv("sales_data.csv")
    .filter(pl.col("amount") > 1000)
    .group_by("category")
    .agg([
        pl.col("amount").sum().alias("total"),
        pl.col("amount").mean().alias("average"),
        pl.col("amount").std().alias("std_dev")
    ])
    .sort("total", descending=True)
    .collect()
)

What impressed me was how Polars optimized this query automatically. It reorganized operations, eliminated redundant computations, and selected the best algorithms. I didn’t need to think about optimization - the lazy API handled it.

Polars shined when I built multi-step transformations:

import polars as pl

# Complex pipeline with conditional logic
pipeline = (
    pl.scan_parquet("orders.parquet")
    .with_columns([
        (pl.col("price") * pl.col("quantity")).alias("total"),
        pl.when(pl.col("region") == "US")
        .then(pl.col("total") * 0.9)  # US discount
        .otherwise(pl.col("total"))
        .alias("discounted_total")
    ])
    .filter(pl.col("discounted_total") > 100)
    .group_by("customer_id")
    .agg([
        pl.col("discounted_total").sum().alias("lifetime_value"),
        pl.col("order_id").count().alias("order_count")
    ])
)

This felt natural as a Python developer. I could build transformations step by step, debug intermediate results, and use familiar programming constructs.

When I Switched to DuckDB

Then I worked on a project replicating an OLAP platform locally. The data team already had SQL queries from their production system. Using DuckDB made sense:

import duckdb

con = duckdb.connect()

# Direct SQL on CSV files
result = con.execute("""
    SELECT
        category,
        SUM(amount) as total,
        AVG(amount) as average,
        STDDEV(amount) as std_dev
    FROM read_csv('sales_data.csv')
    WHERE amount > 1000
    GROUP BY category
    ORDER BY total DESC
""").df()

What I liked about DuckDB:

No setup required - just connect and query
Full SQL support including window functions, CTEs, and subqueries
Familiar syntax if you know SQL
Great for analytical queries on local datasets

The zero-configuration approach appealed to me. I didn’t need to think about lazy vs eager evaluation or query optimization plans. DuckDB handled everything.

Trying Both Together

I discovered they work well together. I could register a Polars DataFrame in DuckDB and query it with SQL:

import polars as pl
import duckdb

# Create data with Polars
df = pl.DataFrame({
    "category": ["A", "B", "A", "B", "C"],
    "value": [10, 20, 30, 40, 50]
})

# Register in DuckDB
con = duckdb.connect()
con.register("polars_data", df)

# Query with SQL
result = con.execute("""
    SELECT category, SUM(value) as total, AVG(value) as average
    FROM polars_data
    GROUP BY category
    ORDER BY total DESC
""").df()

This worked with zero data copying - a real performance advantage when working with large datasets.

Polars SQL Interface

I learned that Polars also has SQL capabilities. This surprised me:

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "category": ["A", "B", "A", "B"],
    "value": [10, 20, 30, 40]
})

# Create SQL context
ctx = pl.SQLContext(data=df)

# Execute SQL
result = ctx.execute("""
    SELECT
        category,
        SUM(value) as total,
        AVG(value) as average
    FROM data
    GROUP BY category
    ORDER BY total DESC
""").collect()

This helped when I had SQL queries but wanted to stay in the Polars ecosystem.

Performance Considerations

I tested both with a 10 million row dataset. Results showed similar performance for analytical queries, but differences emerged based on workflow:

Polars advantages:

Lazy evaluation optimizes before execution
Better for complex multi-step transformations
Zero-copy between operations
Multi-threading and SIMD support

DuckDB advantages:

SQL optimization is mature and battle-tested
Excellent for aggregations and joins
Vectorized execution engine
Direct SQL on files without loading into memory

The performance gap wasn’t as important as workflow fit. Both tools were significantly faster than pandas for most operations.

Multi-Language Support

I worked on a project using Python for analysis but needing the same transformations in a Node.js service. Polars made this easy:

const pl = require("nodejs-polars");

// Same API as Python
const result = pl
  .scanCSV("data.csv")
  .filter(pl.col("value").gt(20))
  .groupBy("category")
  .agg([
    pl.col("value").sum().alias("total"),
    pl.col("value").mean().alias("average")
  ])
  .sort("total", { descending: true })
  .collect();

DuckDB also supports multiple languages, but the API differs. Polars offered more consistency across languages.

Ibis for Unified Execution

I discovered Ibis, which provides a unified API across multiple backends including Polars and DuckDB:

import ibis

# Use Polars backend
con = ibis.polars.connect({"data": "sales.csv"})

# Switch to DuckDB backend
con = ibis.duckdb.connect("sales.db")

# Same Ibis expressions work with both backends
t = con.table("data")
result = t.filter(t.amount > 1000).group_by("category").aggregate(
    total=t.amount.sum(),
    average=t.amount.mean()
)

This helped when I needed to support multiple execution engines.

My Decision Framework

After extensive testing, here’s how I decide:

Choose Polars when:

You prefer Python syntax and want to build transformation pipelines
You need lazy evaluation with automatic optimization
Your project uses multiple languages
You want zero-copy between operations
You’re comfortable with the DataFrame mental model

Choose DuckDB when:

You’re comfortable with SQL and need full OLAP features
You’re replicating a production OLAP platform locally
You have existing SQL queries to port
You want zero-configuration database functionality
Your team has strong SQL backgrounds

Start with what matches your current workflow:

If you use pandas today and like the DataFrame API, try Polars first. The learning curve is smaller and you get SQL capabilities when needed.
If you come from a SQL background or work with existing SQL queries, start with DuckDB. The Python integration makes it easy to work with DataFrames.

Both tools are excellent choices. The key is matching the tool to your workflow, not chasing benchmark numbers. They’re also designed to work together, so you can use both in the same project when it makes sense.

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:

👨‍💻 Polars Documentation
👨‍💻 DuckDB Documentation
👨‍💻 Ibis Framework
👨‍💻 Reddit Discussion

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