pd.get_dummies() slow on large levels

I'm unsure if this is already the fastest possible method, or if I'm doing this inefficiently.

I want to hot encode a particular categorical column which has 27k+ possible levels. The column has different values in 2 different datasets, so I combined the levels first before using get_dummies()

def hot_encode_column_in_both_datasets(column_name,df,df2,sparse=True):
    col1b = set(df2[column_name].unique())
    col1a = set(df[column_name].unique())
    combined_cats = list(col1a.union(col1b))
    df[column_name] = df[column_name].astype('category', categories=combined_cats)
    df2[column_name] = df2[column_name].astype('category', categories=combined_cats)

    df = pd.get_dummies(df, columns=[column_name],sparse=sparse)
    df2 = pd.get_dummies(df2, columns=[column_name],sparse=sparse)
    try:
        del df[column_name]
        del df2[column_name]
    except:
        pass
    return df,df2

However, Its been running for more than 2 hours and it's still stuck hot encoding.

Could I be doing something wrongly here? Or is it just the nature of running it on large datasets?

Df has 6.8m rows and 27 columns, Df2 has 19990 rows and 27 columns before hot encoding the column that I wanted to.

Advice appreciated, thank you! :)