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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

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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(____)
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