In this chapter, you'll learn how to create a DataFrame from a CSV, how to inspect a DataFrame, how to select subsets of rows and columns and how to sort and summarize a DataFrame.

Introduction to the Polars DataFrame

Load a CSV into a DataFrame

Inspect a DataFrame

Subsetting a DataFrame

Creating a Series from a DataFrame

Subsetting a DataFrame with bracket notation

Select a subset of columns with .select()

Sorting and summarizing a DataFrame

Sorting the EV data

Summarizing a DataFrame

Extreme values in a DataFrame

Creating DataFrames and Selecting Data

Next up, you'll learn how to transform data with expressions, how to add or update columns in a DataFrame, how to work with multiple columns and get an introduction to lazy mode and query optimization.

Transforming columns

Transforming Data with an Expression

Aggregating a column

Transforming Data

Adding columns

Adding a new column

Using a Dtype-specific Expression

Renaming and dropping columns

Working with multiple columns

Transforming Multiple Columns

Creating new columns with a suffix

Combining selectors

Introducing lazy mode

Creating a Lazy Query

What drives EV prices?

Counting occurences

Transforming Data with Expressions

In the final chapter, you'll learn how to filter a DataFrame to get a subset of rows, how to handle missing or duplicated values, grouping by one or more columns and converting a DataFrame between long and wide formats.

Filtering rows

Filtering with a single condition

Combining multiple filter conditions

Negating a predicate

Handling missing and duplicate values

Finding and dropping missing values

Replacing missing values

Dropping duplicate values

Grouped aggregations

Counting values

Group-by aggregation

Multiple Aggregations on a Column

Pivoting and unpivoting

Pivoting a DataFrame

Unpivoting Data from Wide to Long Format

Wrapping up

Analyzing Data

Electric Vehicles

Music Streams

Discover how to manipulate data and extract insights with Polars, a powerful, general-purpose package for working with tabular data in Python. Designed for speed and efficiency, Polars is a great choice for everything from quick data exploration to detailed analytics. In this course, you'll learn the key techniques for transforming, cleaning, analyzing, and visualizing tabular data.

<h2>Discover Efficient Data Manipulation with Polars</h2>Polars is a powerful, general-purpose package for working with tabular data in Python. Designed for speed and efficiency, Polars is a great choice for everything from quick data exploration to detailed analytics. In this course, you'll learn the fundamentals of using Polars to work with your data.<br><br><h2>Load, Explore, and Clean Your Data</h2>You'll start by learning how to import CSV files into Polars DataFrames, summarize their contents, and select the data that matters most. Next, you’ll discover how to clean your dataset by finding and removing missing or duplicated data.<br><br><h2>Analyze and Visualize Your Data Efficiently</h2>Then you'll tackle more detailed data analysis as you split your data into groups and calculate statistics for each group. You’ll also practice transforming columns with Polars expressions, and see how Polars makes it easy to transform multiple columns at once. Visualization is crucial for getting insight from your data and communicating these insights to others. By the end of the course you'll be able to create clear visualizations to present insights.<br><br><h2>Optimize with Polars Lazy Execution</h2>A powerful feature of Polars is that it can optimize your code to boost performance. You'll learn how to enable optimization and understand how these optimizations work. With your experience from this course, you’ll be ready to use Polars for a wide range of real-world data tasks and uncover valuable insights.

Intermediate Python

Learn how to efficiently transform, clean, and analyze data using Polars, a high-performance Python library for fast and flexible data manipulation.

Introduction to Polars | Learn data manipulation with Polars

Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.

Introduction to Polars

Subsetting a DataFrame with bracket notation

Introduction to Polars

Hands-on interactive exercise