Dropping missing data

In this exercise, you'll remove some of the rows where certain columns have missing values. You're going to look at the length_of_time column, the state column, and the type column. You'll drop any row that contains a missing value in at least one of these three columns.

This exercise is part of the course

Preprocessing for Machine Learning in Python

View Course

Exercise instructions

Print out the number of missing values in the length_of_time, state, and type columns, in that order, using .isna() and .sum().
Drop rows that have missing values in at least one of these columns.
Print out the shape of the new ufo_no_missing dataset.

Hands-on interactive exercise

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

# Count the missing values in the length_of_time, state, and type columns, in that order
print(ufo[[____, ____, ____]].____.____)

# Drop rows where length_of_time, state, or type are missing
ufo_no_missing = ____

# Print out the shape of the new dataset
print(____)

Edit and Run Code