How to group by a pandas.Dataframe's columns based on the indexes and values of another pandas.Series?

I'm trying to group by a dataframe's columns together based on another pandas.Series' values and indexes. The Series' indexes refer to the DataFrame's columns but there could be more elements to it. What is the best pythonic way to do this?

For further clarity, here's the unit test I'm trying to resolve (using pytest):

    def test_sum_weights_by_classification_labels_default_arguments():
    portfolio_weights = pd.DataFrame([[0.1, 0.3, 0.4, 0.2],
                                      [0.25, 0.3, 0.25, 0.2],
                                      [0.2, 0.3, 0.1, 0.4]],
                                     index=['2001-01-02', '2001-01-03', '2001-01-04'],
                                     columns=['ABC', 'DEF', 'UVW', 'XYZ'])

    security_classification = pd.Series(['Consumer', 'Energy', 'Consumer', 'Materials', 'Financials', 'Energy'],
                                        index=['ABC', 'DEF', 'GHI', 'RST', 'UVW', 'XYZ'],
                                        name='Classification')

    result_sector_weights = pd.DataFrame([[0.1, 0.5, 0.4],
                                          [0.25, 0.5, 0.25],
                                          [0.2, 0.7, 0.1]],
                                         index=['2001-01-02', '2001-01-03', '2001-01-04'],
                                         columns=['Consumer', 'Energy', 'Financials'])

    pd.testing.assert_frame_equal(clb.sum_weights_by_classification_labels(portfolio_weights, security_classification),
                                  result_sector_weights)

Many thanks in advance!

After further research, I have found a solution. Here's what I came up with using pandas.Series.map on the DataFrame's columns:

def sum_weights_by_classification_labels(security_weights, security_classification):

    classification_weights = security_weights.copy()
    classification_weights.columns = classification_weights.columns.map(security_classification)
    classification_weights = classification_weights.groupby(classification_weights.columns, axis=1).sum()

    return classification_weights

Alternatively using pandas.DataFrame.merge:

def sum_weights_by_classification_labels(security_weights, security_classification):
    
    security_weights_transposed = security_weights.transpose()
    merged_data = security_weights_transposed.merge(security_classification, how='left', left_index=True, 
                                                    right_index=True)
    classification_weights = merged_data.groupby(security_classification.name).sum().transpose()

    return classification_weights

And for the second solution need to add this line to the unit test because cannot merge a Series without a name (the added column needs to have one):

result_sector_weights.columns.name = security_classification.name

I'm keeping this post hoping it might help someone in the future.

This is the way...