Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB

If with Python for information, you might have most likely skilled the frustration of ready minutes for a Pandas operation to complete.

At first, every little thing appears effective, however as your dataset grows and your workflows develop into extra complicated, your laptop computer all of the sudden feels prefer it’s getting ready for lift-off.

A few months in the past, I labored on a mission analyzing e-commerce transactions with over 3 million rows of knowledge.

It was a reasonably fascinating expertise, however more often than not, I watched easy groupby operations that usually ran in seconds all of the sudden stretch into minutes.

At that time, I noticed Pandas is wonderful, however it isn’t all the time sufficient.

This text explores trendy options to Pandas, together with Polars and DuckDB, and examines how they’ll simplify and enhance the dealing with of huge datasets.

For readability, let me be upfront about a number of issues earlier than we start.

This text will not be a deep dive into Rust reminiscence administration or a proclamation that Pandas is out of date.

As a substitute, it’s a sensible, hands-on information. You will note actual examples, private experiences, and actionable insights into workflows that may prevent time and sanity.

Why Pandas Can Really feel Sluggish

Again once I was on the e-commerce mission, I keep in mind working with CSV recordsdata over two gigabytes, and each filter or aggregation in Pandas usually took a number of minutes to finish.

Throughout that point, I’d stare on the display screen, wishing I may simply seize a espresso or binge a number of episodes of a present whereas the code ran.

The principle ache factors I encountered have been velocity, reminiscence, and workflow complexity.

Everyone knows how giant CSV recordsdata devour huge quantities of RAM, typically greater than what my laptop computer may comfortably deal with. On high of that, chaining a number of transformations additionally made code more durable to take care of and slower to execute.

Polars and DuckDB handle these challenges in several methods.

Polars, in-built Rust, makes use of multi-threaded execution to course of giant datasets effectively.

DuckDB, however, is designed for analytics and executes SQL queries without having you to load every little thing into reminiscence.

Mainly, every of them has its personal superpower. Polars is the speedster, and DuckDB is sort of just like the reminiscence magician.

And the very best half? Each combine seamlessly with Python, permitting you to reinforce your workflows and not using a full rewrite.

Setting Up Your Setting

Earlier than we begin coding, be sure that your setting is prepared. For consistency, I used Pandas 2.2.0, Polars 0.20.0, and DuckDB 1.9.0.

Pinning variations can prevent complications when following tutorials or sharing code.

pip set up pandas==2.2.0 polars==0.20.0 duckdb==1.9.0

In Python, import the libraries:

import pandas as pd
import polars as pl
import duckdb
import warnings
warnings.filterwarnings("ignore")

For instance, I’ll use an e-commerce gross sales dataset with columns resembling order ID, product ID, area, nation, income, and date. You may obtain related datasets from Kaggle or generate artificial information.

Loading Knowledge

Loading information effectively units the tone for the remainder of your workflow. I keep in mind a mission the place the CSV file had almost 5 million rows.

Pandas dealt with it, however the load instances have been lengthy, and the repeated reloads throughout testing have been painful.

It was a type of moments the place you would like your laptop computer had a “quick ahead” button.

Switching to Polars and DuckDB utterly improved every little thing, and all of the sudden, I may entry and manipulate the info nearly immediately, which truthfully made the testing and iteration processes way more satisfying.

With Pandas:

df_pd = pd.read_csv("gross sales.csv")
print(df_pd.head(3))

With Polars:

df_pl = pl.read_csv("gross sales.csv")
print(df_pl.head(3))

With DuckDB:

con = duckdb.join()
df_duck = con.execute("SELECT * FROM 'gross sales.csv'").df()
print(df_duck.head(3))

DuckDB can question CSVs instantly with out loading your complete datasets into reminiscence, making it a lot simpler to work with giant recordsdata.

Filtering Knowledge

The issue right here is that filtering in Pandas may be gradual when coping with thousands and thousands of rows. I as soon as wanted to investigate European transactions in a large gross sales dataset. Pandas took minutes, which slowed down my evaluation.

With Pandas:

filtered_pd = df_pd[df_pd.region == "Europe"]

Polars is quicker and may course of a number of filters effectively:

filtered_pl = df_pl.filter(pl.col("area") == "Europe")

DuckDB makes use of SQL syntax:

filtered_duck = con.execute("""
    SELECT *
    FROM 'gross sales.csv'
    WHERE area = 'Europe'
""").df()

Now you may filter via giant datasets in seconds as an alternative of minutes, leaving you extra time to concentrate on the insights that actually matter.

Aggregating Giant Datasets Shortly

Aggregation is commonly the place Pandas begins to really feel gradual. Think about calculating whole income per nation for a advertising and marketing report.

In Pandas:

agg_pd = df_pd.groupby("nation")["revenue"].sum().reset_index()

In Polars:

agg_pl = df_pl.groupby("nation").agg(pl.col("income").sum())

In DuckDB:

agg_duck = con.execute("""
    SELECT nation, SUM(income) AS total_revenue
    FROM 'gross sales.csv'
    GROUP BY nation
""").df()

I keep in mind operating this aggregation on a ten million-row dataset. In Pandas, it took almost half an hour. Polars accomplished the identical operation in underneath a minute.

The sense of aid was nearly like ending a marathon and realizing your legs nonetheless work.

Becoming a member of Datasets at Scale

Becoming a member of datasets is a type of issues that sounds easy till you might be truly knee-deep within the information.

In actual tasks, your information normally lives in a number of sources, so you need to mix them utilizing shared columns like buyer IDs.

I realized this the onerous method whereas engaged on a mission that required combining thousands and thousands of buyer orders with an equally giant demographic dataset.

Every file was large enough by itself, however merging them felt like attempting to power two puzzle items collectively whereas your laptop computer begged for mercy.

Pandas took so lengthy that I started timing the joins the identical method individuals time how lengthy it takes their microwave popcorn to complete.

Spoiler: the popcorn gained each time.

Polars and DuckDB gave me a method out.

With Pandas:

merged_pd = df_pd.merge(pop_df_pd, on="nation", how="left")

Polars:

merged_pl = df_pl.be part of(pop_df_pl, on="nation", how="left")

DuckDB:

merged_duck = con.execute("""
    SELECT *
    FROM 'gross sales.csv' s
    LEFT JOIN 'pop.csv' p
    USING (nation)
""").df()

Joins on giant datasets that used to freeze your workflow now run easily and effectively.

Lazy Analysis in Polars

One factor I didn’t admire early in my information science journey was how a lot time will get wasted whereas operating transformations line by line.

Polars approaches this in another way.

It makes use of a way referred to as lazy analysis, which primarily waits till you might have accomplished defining your transformations earlier than executing any operations.

It examines your complete pipeline, determines probably the most environment friendly path, and executes every little thing concurrently.

It’s like having a pal who listens to your total order earlier than strolling to the kitchen, as an alternative of 1 who takes every instruction individually and retains going backwards and forwards.

This TDS article indepthly explains lazy analysis.

Right here’s what the circulation appears like:

Pandas:

df = df[df["amount"] > 100]
df = df.groupby("section").agg({"quantity": "imply"})
df = df.sort_values("quantity")

Polars Lazy Mode:

import polars as pl

df_lazy = (
    pl.scan_csv("gross sales.csv")
      .filter(pl.col("quantity") > 100)
      .groupby("section")
      .agg(pl.col("quantity").imply())
      .type("quantity")
)

end result = df_lazy.acquire()

The primary time I used lazy mode, it felt unusual not seeing instantaneous outcomes. However as soon as I ran the ultimate .acquire(), the velocity distinction was apparent.

Lazy analysis gained’t magically clear up each efficiency subject, but it surely brings a stage of effectivity that Pandas wasn’t designed for.

Conclusion and takeaways

Working with giant datasets doesn’t must really feel like wrestling along with your instruments.

Utilizing Polars and DuckDB confirmed me that the issue wasn’t all the time the info. Generally, it was the device I used to be utilizing to deal with it.

If there’s one factor you’re taking away from this tutorial, let or not it’s this: you don’t must abandon Pandas, however you may attain for one thing higher when your datasets begin pushing their limits.

Polars provides you velocity in addition to smarter execution, then DuckDB permits you to question large recordsdata like they’re tiny. Collectively, they make working with giant information really feel extra manageable and fewer tiring.

If you wish to go deeper into the concepts explored on this tutorial, the official documentation of Polars and DuckDB are good locations to begin.