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.

Acest exercițiu face parte din cursul

Intermediate Julia

Exercițiu interactiv practic

Încearcă acest exercițiu completând acest cod de exemplu.

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

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

Editează și rulează codul

Acest exercițiu face parte din cursul

Intermediate Julia

SkillTag.level.beginnerSkillTag.label

4.8+

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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

Exercițiul curent

Exercise 12: Advanced missing data Exercise 13: Congratulations!