Filling missing data (imputation) by group

Many statistical and machine learning packages cannot determine the best action to take when missing data entries are encountered. Dealing with missing data is natural in pandas (both in using the default behavior and in defining a custom behavior). In Chapter 1, you practiced using the .dropna() method to drop missing values. Now, you will practice imputing missing values. You can use .groupby() and .transform() to fill missing data appropriately for each group.

Your job is to fill in missing 'age' values for passengers on the Titanic with the median age from their 'gender' and 'pclass'. To do this, you'll group by the 'sex' and 'pclass' columns and transform each group with a custom function to call .fillna() and impute the median value.

The DataFrame has been pre-loaded as titanic. Explore it in the IPython Shell by printing the output of titanic.tail(10). Notice in particular the NaNs in the 'age' column.

Instructions

Group titanic by 'sex' and 'pclass'. Save the result as by_sex_class.
Write a function called impute_median() that fills missing values with the median of a series. This has been done for you.
Call .transform() with impute_median on the 'age' column of by_sex_class.
Print the output of titanic.tail(10). This has been done for you - hit 'Submit Answer' to see how the missing values have now been imputed.

Take Hint (-30xp)