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.
Questo esercizio fa parte del corso
Preprocessing for Machine Learning in Python
Istruzioni dell'esercizio
- Print out the number of missing values in the
length_of_time,state, andtypecolumns, in that order, using.isna()and.sum(). - Drop rows that have missing values in at least one of these columns.
- Print out the
shapeof the newufo_no_missingdataset.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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(____)