Missing data

In this exercise, let's examine missing values. When working with data, you will inevitably come across missing values. These can occur for a number of reasons - they could be intentionally missing, or they could have been left out accidentally. Either way, identifying missing values is critical to understand before making any changes or drawing any insight from your data.

In this example you will first show the missing values in each column of the data, and you will then drop missing values from a column. Sample data has been loaded for you into the DataFrame sales_df.

This exercise is part of the course

Intermediate Julia

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Describe the DataFrame to find columns with missing values
println(____(____))

# Count the number of rows in the DataFrame
println(____(____))

Edit and Run Code

This exercise is part of the course

Intermediate Julia

BeginnerSkill Level

4.9+

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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

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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

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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

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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 Exercise 8: R functions in Julia Exercise 9: Cleaning Data Exercise 10: Renaming columns Exercise 11: Missing data

Current Exercise

Exercise 12: Advanced missing data Exercise 13: Congratulations!