Lecture III - Packages and Data Management

Applied Optimization with Julia

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
  • Have seen Git again, nice and slow
  • 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.

Git, One More Time

Last week’s Git part was a lot at once. Here is the short version:

  • Git takes snapshots (commits) of your project, so you can always go back
  • The everyday workflow is just two steps in Zed’s Git panel:
    1. Stage your changed files
    2. Write a short message and commit

Commit after each tutorial exercise, that is a good rhythm. History and undoing can wait.

No Git setup yet? No problem: do it this week, and ask me if anything blocks you.

Git and Your Group

  • You hand in the assignments as a group of up to three. So why not share one repository?
  • One person creates a repository on GitHub and invites the others
  • Everyone works on their own copy and shares changes via push and pull
  • You always see who changed what, and when

This is optional: a shared cloud folder works too. But a shared repository is exactly how teams share code in practice.

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

Data Management with DataFrames

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

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 in class or via email!

And that’s it for this lecture!

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

Literature

Literature

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