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
Exercise instructions
- Print out the number of missing values in the
length_of_time
,state
, andtype
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 newufo_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(____)