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.

Diese Übung ist Teil des Kurses

Preprocessing for Machine Learning in Python

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Anleitung zur Übung

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.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# 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(____)

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