For the categorical data, we cannot calculate simple descriptive statistics. Thus, we are going to calculate frequencies for each distinct value in each categorical column. Here's a code snippet that will achieve this:

rdd_cat = (
    census_subset
    .select(cols_cat + ['label'])
    .rdd
    .map(lambda row: [e for e in row])
)

results_cat = {}

for i, col in enumerate(cols_cat + ['label']):
    results_cat[col] = (
        rdd_cat
        .groupBy(lambda row: row[i])
        .map(lambda el: (el[0], len(el[1])))
        .collect()
    )

First, we repeat what we have just done for the numerical columns but for the categorical ones: we subset census_subset to only the categorical columns and the label, access the underlying RDD, and transform each row into a list. We're going to store the results in the results_cat dictionary. We loop through all the categorical columns and aggregate the data using the .groupBy(...) transformation. Finally, we create a list of tuples where the first element is the value (el[0]) and the second element is the frequency (len(el[1])).

Now that we have our data aggregated, let's see what we deal with:

As you can see, we are dealing with an imbalanced sample: it is heavily skewed toward males and mostly white people. Also, in 1994 there were not many people earning more than $50,000, only about a quarter.