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Course

Data Transformation with Polars

IntermediateSkill Level

4.9+

Updated 03/2026

Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.

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PythonData Manipulation

4 hr

14 videos

54 Exercises

4,700 XP

Statement of Accomplishment

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Course Description

Take your Polars skills further with intermediate techniques for data transformation and analysis. Learn to clean and search text, work with time series using rolling and window functions, and combine DataFrames through joins and concatenation. You'll also create reusable custom expressions and apply advanced analytics, such as quantiles, histograms, and correlations. By the end, you'll be equipped to handle complex data workflows with confidence.

Prerequisites

Introduction to Polars Intermediate Python for Developers

1

Transforming Data with Expressions

In this chapter, you'll learn how to clean and search text data, extract patterns from strings, and use conditional expressions to transform columns.

Transforming Data with Expressions

Converting column data types

Removing whitespace

Concatenating text columns

Searching and extracting text

Searching text

Extracting strings from text

Replacing multiple patterns

Creating conditional expressions

Categorizing customers with conditions

Chaining multiple conditions

Filling missing values

2

Working with Time Series and Window Functions

Learn how to parse and manipulate dates, filter and aggregate data by time periods, and calculate rolling and cumulative statistics.

Parsing and manipulating dates

Parsing datetime strings

Extracting the hour component

Calculating rental period end times

Filtering and aggregating by time period

Filtering for a specific datetime

Filtering for a time range

Calculating weekly rental totals

Rolling and cumulative aggregations

Calculating a rolling sum

Calculating cumulative rentals

Rolling aggregations on multiple columns

Transforming data by group

Calculating the hourly total with window functions

Calculating the difference from hourly mean

Grouping by multiple columns with window functions

3

Combining and Joining DataFrames

This chapter covers combining DataFrames via concatenation, joining on single or multiple columns, and matching rows based on conditions or nearest values.

Concatenating DataFrames

Stacking regional listings

Adding flag columns

Adding new rows efficiently

Joining DataFrames

Adding host information

Matching on unique combinations

Preserving all listings

Finding gaps in both DataFrames

Joining DataFrames by condition or nearest match

Matching listings to budget requests

Filtering with multiple conditions

Matching to the nearest price update

4

Custom Transformations and Advanced Analytics

Learn how to build reusable custom expressions, categorize data using quantiles and fixed thresholds, and analyze rankings and correlations.

Applying custom transformations

Converting streams to billions

Creating reusable expressions

Adding a custom expression

Calculating quantiles and histograms

Calculating popularity quantiles

Binning streams into tiers

Building a popularity histogram

Calculating ranks and correlations

Ranking albums with tie handling

Building a correlation matrix

Custom summaries with describe

Wrapping up

Data Transformation with Polars

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Abdelrahman

7 days ago

Carlo

last week

Very good overview of the typical operations usually present in data analysis workflow

Frank

last week

Zhiyuan Shawn

last week

Andrew

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Paula Angelica

last week

Abdelrahman

"Very good overview of the typical operations usually present in data analysis workflow"

Carlo

Frank

FAQs

What will I learn in this course?

You will learn intermediate Polars techniques, including text transformations, time series operations, combining DataFrames through joins, and advanced analytics like quantiles and correlations.

Who is this course intended for?

Data professionals who have completed Introduction to Polars and Intermediate Python for Developers and want to handle more complex data workflows.

How will this course help me in my career?

You will gain practical skills in data transformation and analysis using Polars, a fast-growing library valued for its speed and efficiency in data engineering roles.

Join over 19 million learners and start Data Transformation with Polars today!

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