Introduction
Missing values
2. NaN values
Duplicate rows
Outliers
Joining datasets
Summary

Instruction

Let's get started! A very common situation is when some values are missing from our dataset. Let's see what we can do about that.

For this lesson, you will have to load a dataset that contains some basic statistics related to IT desk employees. Each employee is shown along with their experience (number of months they have worked in that position), and the number of tickets they resolved last month.

Exercise

Read the ticket_statistics.csv file as a pandas DataFrame into a variable named ticket_statistics and show its contents.

Take a look at the tickets column. You can see some NaN values in there. We'll talk about them in a second.

Stuck? Here's a hint!

Use the pandas.read_csv function to load ticket_statistics.csv. Finally, show the contents by providing the name of the variable.