Dropping missing values
Dropping missing values is the simplest way of handling them. While it sometimes makes sense to replace them, other times it is better to drop them altogether. In this exercise, you'll be working with the wages
dataset, which contains missing values in all columns. So let's drop them all! Or not…
The wages
dataset and the DataFrames
package have been loaded for you.
This exercise is part of the course
Data Manipulation in Julia
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
println(size(wages))
# Drop all missing values
____
# Print describe and size functions
println(____)
println(____)