Dropping rows

When you know that a specific column will be critical to your analysis, and only a small fraction of rows are missing a value in that column, it often makes sense to remove those rows from the dataset.

During this course, the driver_gender column will be critical to many of your analyses. Because only a small fraction of rows are missing driver_gender, we'll drop those rows from the dataset.

Diese Übung ist Teil des Kurses

Analyzing Police Activity with pandas

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Anleitung zur Übung

Count the number of missing values in each column.
Drop all rows that are missing driver_gender by passing the column name to the subset parameter of .dropna().
Count the number of missing values in each column again, to verify that none of the remaining rows are missing driver_gender.
Examine the DataFrame's .shape to see how many rows and columns remain.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Count the number of missing values in each column
print(ri.isnull().____)

# Drop all rows that are missing 'driver_gender'
ri.____(subset=[____], inplace=True)

# Count the number of missing values in each column (again)
print(ri.____.____)

# Examine the shape of the DataFrame
print(____)

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