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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

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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(____)
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