Feature Engineering Made Easy

Imputing categorical features

Now that we have an understanding of the data we are working with, let's take a look at our missing values:

• To do this, we can use the isnull method available to us in pandas for DataFrames. This method returns a boolean same-sized object indicating if the values are null.
• We will then sum these to see which columns have missing data:

X.isnull().sum()>>>>boolean                1
city                   1
ordinal_column         0
quantitative_column    1
dtype: int64

Here, we can see that three of our columns are missing values. Our course of action will be to impute these missing values.

If you recall, we implemented scikit-learn's Imputer class in a previous chapter to fill in numerical data. Imputer does have a categorical option, most_frequent, however ...