Python functions in Julia

Now that you have imported the math package from Python in the previous exercise using PythonCall, you can begin to call functions from the package.

You have imported math using the alias pymath, so when calling functions from math, prefix the function with pymath. This makes it clear that you are using an imported Python version of the package.

Este ejercicio forma parte del curso

Intermediate Julia

Instrucciones del ejercicio

Define a vector x containing the values from negative three to three in increments of one, including zero, as you normally would using Julia's square bracket syntax.
Use the Python function fabs in the math package to get the absolute value of the second value in x.
Use the Python function pow in the math package to raise the sixth value of x to the seventh value of x.

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using PythonCall
pymath = pyimport("math")

# Define a vector x from -3 to 3
x = ____

# Print the absolute value of the second value in x using pymath
println(____.____(____))

# Print the sixth value of x raised to the power of the 7th value of x
println(____.____(x[6], ____))

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Este ejercicio forma parte del curso

Intermediate Julia

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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: Anonymous Functions and Multiple Dispatch Exercise 2: Multiple dispatch Exercise 3: Anonymous functions Exercise 4: Filtering DataFrames Exercise 5: Importing Functions from Python and R Exercise 6: Importing Python and R Exercise 7: Python functions in Julia

Ejercicio actual

Exercise 8: R functions in Julia Exercise 9: Cleaning Data Exercise 10: Renaming columns Exercise 11: Missing data Exercise 12: Advanced missing data Exercise 13: Congratulations!