Exercise

Missing values

It is very rare to find a dataset that doesn't contain any missing values. Missing values are represented as NaN in pandas. You can use the isnull() pandas function to check for missing values. pd.isnull(df['column']) will return True if the value is missing, or False if there are no missing values.

Compared to R, missing values behave a little differently in Python. For example, the .mean() method automatically ignores missing values in Python. You can also recode missing values with the .fillna() method. This will replace all missing values in the column with the provided value.

In this exercise, we've modified the tips dataset such that it contains some missing values.

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Print the rows in tips where 'total_bill' is missing.