You've managed one way how of dealing with NaN values, but let's think about what we can do with the others. Look at the number of outliers in each column one more time:

In the previous chapter, you received the result:

The simplest way is to delete all rows that contain missing values. For instance, 86 rows with ages are missing, as well as 1 row in the column 'Fare'. Let's figure out how we can delete them. In pandas, you can do so using one simple function, similar to the one in the previous chapter, called .dropna():

Here, you can use inplace=True to save all changes. Let's talk about the function. If a row in the column 'Age' has a NaN value, this function will delete all values in the same row. The whole row will be deleted if at least one value in the row is missing.