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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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.
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Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreIn the following Tracks
Julia Fundamentals
Go To Track- 1
Inspecting DataFrames
FreeTake 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.
Diving into DataFrames50 xpSymbols vs. Strings100 xpDescribe it to me100 xpMissing anything?50 xpSelecting columns50 xpColumn selection100 xpSelecting patterns100 xpRegular penguins100 xpExploring Data with Visualizations50 xpFlipper distribution100 xpRating vs. cocoa percentages100 xpPlotting minimum wages over time100 xp - 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.
- 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.
Exploring grouped data50 xpWages multiple ways100 xpPenguin group counts100 xpUnique chocolate beans100 xpDuplicate rows or not?50 xpGrouped summary statistics50 xpPenguin characteristics100 xpChocolate location vs. rating100 xpPivoting data50 xpReshaping wages100 xpChocolate location pivot100 xpImproving readability with Chain.jl50 xpChaining chocolates100 xpPenguin plotting in chain100 xpMinimum wage by region100 xp - 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!
Loading and writing CSV files50 xpDecimals and delimiters100 xpLoading the 80s100 xpWrite it down100 xpJoining data50 xpState joins capitals100 xpPenguin joins100 xpHandling missing values50 xpDropping missing values100 xpReplacing rating with median100 xpReplacing rating with group median100 xpEfficient workflow50 xpFirst steps with flights100 xpMissing delays?100 xpDelays on US airports100 xpWrap-up50 xp
For Business
Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreIn the following Tracks
Julia Fundamentals
Go To TrackCollaborators
Katerina Zahradova
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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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