Python en R importeren

Voordat we Python- en R-functies in onze Julia-programma’s gebruiken, moeten we de juiste pakketten importeren en instellen. Voor Python gebruiken we het pakket PythonCall, en voor R gebruiken we het pakket RCall.

Beide pakketten bevatten functies waarmee we andere pakketten kunnen aanroepen. In PythonCall gebruik je pyimport om een pakket te importeren, en in RCall gebruik je @rimport.

Stel in deze oefeningen je omgeving zo in dat zowel Python- als R-imports werken. Let op: de pakketten zijn al voor je geïnstalleerd in de lokale omgeving.

Deze oefening maakt deel uit van de cursus

Julia voor gevorderden

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

# Import PythonCall
using ____

# Import math using PythonCall
____ ____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Julia voor gevorderden

SkillTag.level.beginnerSkillTag.label

4.7+

Begin gratis met de cursus

Loops are one of the core concepts that underpin Julia. In this chapter, you'll learn about for loops and while loops, and how to use them to iterate over data structures that you are already familiar with. You will also cover ranges, a useful tool for generating sequences of data.

Exercise 1: For loops Exercise 2: For loops question Exercise 3: Loop over a vector Exercise 4: Loops and enumerate Exercise 5: Looping over nested structures Exercise 6: While loops Exercise 7: Writing a while loop Exercise 8: While loops for iteration Exercise 9: Ranges Exercise 10: Ranges - iteration Exercise 11: Defining ranges Exercise 12: Looping over ranges Exercise 13: Splat unpacking

This chapter focuses on expanding your knowledge of the data structures available in Julia. Learn how to use tuples, dictionaries, multi-dimensional arrays, and structures to store and traverse data quickly and efficiently.

Exercise 1: Tuples Exercise 2: When should you use a tuple?Exercise 3: Create, index, and slice a tuple Exercise 4: Create a NamedTuple for a person Exercise 5: Dictionaries Exercise 6: Create untyped dict, iterate Exercise 7: Create typed dict, iterate Exercise 8: Modify keys/values in dict, use get()Exercise 9: Multi-dimensional arrays Exercise 10: Create 1D and 2D arrays Exercise 11: Index and slice a 2D array Exercise 12: Array merging Exercise 13: Structs Exercise 14: Define a structure Exercise 15: Mutable and typed structs

In this chapter, you’ll extend your understanding of functions, exploring positional, keyword, and default function arguments. You will also cover code execution timing, getting a strong understanding of how to measure the time your code takes to run. This chapter will round off with a capstone on writing your own functions to solve real-world problems.

Exercise 1: Execution time and measurement Exercise 2: Built-in time macro Exercise 3: Timing a function Exercise 4: Positional and Default Arguments, Type Declarations Exercise 5: Positional arguments recap Exercise 6: Default arguments Exercise 7: Type declarations Exercise 8: Keyword Arguments Exercise 9: Keyword arguments Exercise 10: Variable number of arguments Exercise 11: Writing Your Own Function Exercise 12: Writing your own functions Exercise 13: Writing your own functions - structs

This final chapter will introduce anonymous functions and will recap one of the powerful features of Julia; multiple dispatch. You will learn how to use functions from Python and R packages within Julia and discover how to clean and modify data within dataframes.

Exercise 1: Anonieme functies en multiple dispatch Exercise 2: Multiple dispatch Exercise 3: Anonieme functies Exercise 4: DataFrames filteren Exercise 5: Functies importeren uit Python en R Exercise 6: Python en R importeren

Huidige oefening

Exercise 7: Python-functies in Julia Exercise 8: R-functies in Julia Exercise 9: Gegevens opschonen Exercise 10: Kolommen hernoemen Exercise 11: Ontbrekende data Exercise 12: Geavanceerde ontbrekende data Exercise 13: Gefeliciteerd!