Lecture III - Packages and Data Management

Applied Optimization with Julia

Author

Affiliation

Dr. Tobias Vlćek

University of Hamburg

Recap of Last Week

Goals for Today

After this lecture, you will:

Have refreshed the basics from last week’s tutorials
Know what packages are and why we use them
Have seen your first DataFrame
Be ready to start this week’s tutorials

Variables and Data Types

Variables are used to store values
Assign a value to a variable using the = operator
You can use different data types for variables
You can change the value of a variable

You can use the typeof function to check the type of a variable.

Vectors and Matrices

Vectors and matrices are used to store multiple values
You can create them using the [ and ] operators
Access their elements using square brackets

You can use the push! function to add elements to a vector or the pop! function to remove elements from a vector.

Comparisons and Logic

Comparisons are used to compare values
== checks if two values are equal
!= checks if two values are not equal
< and > check if one value is smaller or greater than the other
<= and >= also allow the two values to be equal
&& is true if both conditions are true
|| is true if at least one of two conditions is true

Loops

Loops are used to repeat code
for loop repeats code for a fixed number of times
while loop repeats code as long as a condition is true

for product in ["apple", "banana", "cherry"]
    println("We sell: $product")
end

We sell: apple
We sell: banana
We sell: cherry

Conditionals

Conditionals run code only under certain conditions
if executes code if a condition is true
elseif checks a further condition if the previous one was false
else executes code if none of the conditions were true

Conditionals are not loops: they decide whether code runs, while loops decide how often it runs.

Dictionaries

Dictionaries store key-value pairs, like a lookup table
Access a value by its key using square brackets
keys and values return all keys and values

student_ids = Dict("Elio" => 1001, "Bob" => 1002)
println("Elio's ID: ", student_ids["Elio"])

Elio's ID: 1001

Later in the course, we will use dictionaries to store and look up the data of our optimization models.

Solutions from last Week

The solutions to the tutorials are made available at the end of each week
You can access them in the project folder on GitHub
Click on the GitHub icon (the Octocat) at the bottom right of the page

You can ask questions anytime in class or via email!

Packages & Data

Why Packages?

Question: Do we have to write all code ourselves?

Luckily not! We can use packages written by others
Packages are collections of reusable code, e.g. DataFrames.jl for tables or Plots.jl for charts
Julia’s package manager Pkg installs them for us:

using Pkg
Pkg.add("DataFrames")

Data Management with DataFrames

DataFrames.jl stores tabular data, like a spreadsheet in code

using DataFrames
sales = DataFrame(
    product = ["apple", "banana", "cherry"],
    price = [0.50, 0.30, 2.00],
    sold = [120, 90, 30]
)

3×3 DataFrame

Row	product	price	sold
	String	Float64	Int64
1	apple	0.5	120
2	banana	0.3	90
3	cherry	2.0	30

This week’s tutorials show how to read, write, and plot such data

Your Turn

Five Tutorials

Topics of the Tutorials

Functions: Learn how to define and use functions
Packages: Learn how to install and use packages
DataFrames: Learn how to work with tabular data in Julia
IO: Learn how to read and write data in Julia
Plots: Learn how to create plots in Julia

Get Started with the Tutorials

Download this week’s tutorials and start with the first one
Remember, you can ask questions anytime!

And that’s it for this lecture!

The remaining time we will already start working on this week’s tutorials.

Literature

Lauwens, B., & Downey, A. B. (2019). Think Julia: How to think like a computer scientist (First edition). O’Reilly®. Link to the free book website.
Julia Documentation

For more interesting literature to learn more about Julia, take a look at the literature list of this course.