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Data Manipulation in Julia

Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.

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4 Hours15 Videos55 Exercises

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Course Description

Julia is a new and exciting programming language designed for machine learning, scientific computing, and data mining. This course will provide you with the knowledge necessary for starting your own data manipulation journey in Julia. We'll build on your knowledge of DataFrames from the Introduction to Julia and Intermediate Julia courses. By the end of the course, you'll be equipped with core skills for inspecting, transforming, grouping, visualizing DataFrames, and many more.
  1. 1

    Inspecting DataFrames

    Free

    Take your first steps toward complex data manipulation in Julia! Learn how to personalize your experience with the DataFrames package and how to create data visualizations using the Plots package.

    Play Chapter Now
    Diving into DataFrames
    50 xp
    Symbols vs. Strings
    100 xp
    Describe it to me
    100 xp
    Missing anything?
    50 xp
    Selecting columns
    50 xp
    Column selection
    100 xp
    Selecting patterns
    100 xp
    Regular penguins
    100 xp
    Exploring Data with Visualizations
    50 xp
    Flipper distribution
    100 xp
    Rating vs. cocoa percentages
    100 xp
    Plotting minimum wages over time
    100 xp
  2. 2

    Working with columns

    Columns are the basic building blocks of DataFrames. Knowing how to handle columns is essential for your data manipulation journey. You'll learn how to reorder and drop columns, as well as how to apply functions to individual rows and whole columns.

    Play Chapter Now
    Dropping and moving columns
    50 xp
    Penguin drop
    100 xp
    Reordering of wages
    100 xp
    Manipulating columns
    50 xp
    Using select()
    100 xp
    Penguin transformations
    100 xp
    Combining chocolates
    100 xp
    What shall you use?
    100 xp
    Creating new columns
    50 xp
    Copy the capitals
    100 xp
    Column or row?
    100 xp
    Chocolate percentages
    100 xp
  3. 3

    Aggregating DataFrames

    In this chapter, you'll learn how to group data and calculate grouped summary statistics, as well as how to create pivot tables. You'll also learn how to improve the readability of your code with the Chain.jl package.

    Play Chapter Now
    Exploring grouped data
    50 xp
    Wages multiple ways
    100 xp
    Penguin group counts
    100 xp
    Unique chocolate beans
    100 xp
    Duplicate rows or not?
    50 xp
    Grouped summary statistics
    50 xp
    Penguin characteristics
    100 xp
    Chocolate location vs. rating
    100 xp
    Pivoting data
    50 xp
    Reshaping wages
    100 xp
    Chocolate location pivot
    100 xp
    Improving readability with Chain.jl
    50 xp
    Chaining chocolates
    100 xp
    Penguin plotting in chain
    100 xp
    Minimum wage by region
    100 xp
  4. 4

    Improving Your Workflow

    Learn how to load and join datasets, as well as how to handle missing values. Then it's time to fit everything together!

    Play Chapter Now
    Loading and writing CSV files
    50 xp
    Decimals and delimiters
    100 xp
    Loading the 80s
    100 xp
    Write it down
    100 xp
    Joining data
    50 xp
    State joins capitals
    100 xp
    Penguin joins
    100 xp
    Handling missing values
    50 xp
    Dropping missing values
    100 xp
    Replacing rating with median
    100 xp
    Replacing rating with group median
    100 xp
    Efficient workflow
    50 xp
    First steps with flights
    100 xp
    Missing delays?
    100 xp
    Delays on US airports
    100 xp
    Wrap-up
    50 xp

In the following tracks

Julia Fundamentals

Datasets

FlightsChocolatesUS_minimum_wagesAirportsAirlines

Collaborators

Collaborator's avatar
Katerina Zahradova
Collaborator's avatar
Izzy Weber
Collaborator's avatar
Arne Warnke

Prerequisites

Introduction to JuliaIntermediate Julia
Katerina Zahradova HeadshotKaterina Zahradova

Content Developer

Kat is working as a Content Developer for DataCamp. She holds a Ph.D. in Mathematics with years of experience teaching mathematics, programming (Python and Julia), and designing educational material.
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