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Introduction to Polars

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3 hr
3,600 XP
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Course Description

Discover Efficient Data Manipulation with Polars

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.

Load, Explore, and Clean Your Data

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.

Analyze and Visualize Your Data Efficiently

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.

Optimize with Polars Lazy Execution

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.
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  1. 1

    Creating DataFrames and Selecting Data

    Free

    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.

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    Introduction to the Polars DataFrame
    50 xp
    Load a CSV into a DataFrame
    100 xp
    Inspect a DataFrame
    100 xp
    Subsetting a DataFrame
    50 xp
    Creating a Series from a DataFrame
    100 xp
    Subsetting a DataFrame with bracket notation
    100 xp
    Select a subset of columns with .select()
    100 xp
    Sorting and summarizing a DataFrame
    50 xp
    Sorting the EV data
    100 xp
    Summarizing a DataFrame
    100 xp
    Extreme values in a DataFrame
    100 xp
  2. 2

    Transforming Data with Expressions

    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.

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    Transforming columns
    50 xp
    Transforming Data with an Expression
    100 xp
    Aggregating a column
    100 xp
    Transforming Data
    100 xp
    Adding columns
    50 xp
    Adding a new column
    100 xp
    Using a Dtype-specific Expression
    100 xp
    Renaming and dropping columns
    100 xp
    Working with multiple columns
    50 xp
    Transforming Multiple Columns
    100 xp
    Creating new columns with a suffix
    100 xp
    Combining selectors
    100 xp
    Introducing lazy mode
    50 xp
    Creating a Lazy Query
    100 xp
    What drives EV prices?
    100 xp
  3. 3

    Analyzing Data

    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.

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    Filtering rows
    50 xp
    Filtering with a single condition
    100 xp
    Combining multiple filter conditions
    100 xp
    Negating a predicate
    100 xp
    Handling missing and duplicate values
    50 xp
    Finding and dropping missing values
    100 xp
    Replacing missing values
    100 xp
    Dropping duplicate values
    100 xp
    Grouped aggregations
    50 xp
    Counting values
    100 xp
    Group-by aggregation
    100 xp
    Multiple Aggregations on a Column
    100 xp
    Pivoting and unpivoting
    50 xp
    Pivoting a DataFrame
    100 xp
    Unpivoting Data from Wide to Long Format
    100 xp
    Wrapping up
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

datasets

Electric VehiclesMusic Streams

collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf

prerequisites

Intermediate Python
Liam Brannigan HeadshotLiam Brannigan

Senior Data Scientist, Polars Contributor

Liam is a dedicated data enthusiast who has led numerous projects to production as a senior data scientist. Liam is also Polars contributor who tries to ensure the project is accessible to new users.
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