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Curs

Data Manipulation in Julia

De bazăNivel de competențe

Actualizat 05.2024

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

Începe cursul gratuit

JuliaData Manipulation

4 h

15 videoclipuri

55 Exerciții

4,650 XP

Certificat de realizare

Îndrăgit de cursanți din mii de companii

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Încearcă pentru afaceri

Descrierea cursului

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.

Cerințe prealabile

Introduction to Julia Intermediate Julia

1

Inspecting DataFrames

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.

Diving into DataFrames

Symbols vs. Strings

Describe it to me

Missing anything?

Selecting columns

Column selection

Selecting patterns

Regular penguins

Exploring Data with Visualizations

Flipper distribution

Rating vs. cocoa percentages

Plotting minimum wages over time

Începe capitolul

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.

Dropping and moving columns

Penguin drop

Reordering of wages

Manipulating columns

Using select()

Penguin transformations

Combining chocolates

What shall you use?

Creating new columns

Copy the capitals

Column or row?

Chocolate percentages

Începe capitolul

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 data

Wages multiple ways

Penguin group counts

Unique chocolate beans

Duplicate rows or not?

Grouped summary statistics

Penguin characteristics

Chocolate location vs. rating

Pivoting data

Reshaping wages

Chocolate location pivot

Improving readability with Chain.jl

Chaining chocolates

Penguin plotting in chain

Minimum wage by region

Începe capitolul

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 files

Decimals and delimiters

Loading the 80s

Write it down

Joining data

State joins capitals

Penguin joins

Handling missing values

Dropping missing values

Replacing rating with median

Replacing rating with group median

Efficient workflow

First steps with flights

Missing delays?

Delays on US airports

Începe capitolul

Data Manipulation in Julia

Curs
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