Apply multiple functions to multiple groupby columns

Question

The docs show how to apply multiple functions on a groupby object at a time using a dict with the output column names as the keys:

In [563]: grouped[\'D\'].a         


        

Answer 1

This is a twist on 'exans' answer that uses Named Aggregations. It's the same but with argument unpacking which allows you to still pass in a dictionary to the agg function.

The named aggs are a nice feature, but at first glance might seem hard to write programmatically since they use keywords, but it's actually simple with argument/keyword unpacking.

animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
                         'height': [9.1, 6.0, 9.5, 34.0],
                         'weight': [7.9, 7.5, 9.9, 198.0]})
 
agg_dict = {
    "min_height": pd.NamedAgg(column='height', aggfunc='min'),
    "max_height": pd.NamedAgg(column='height', aggfunc='max'),
    "average_weight": pd.NamedAgg(column='weight', aggfunc=np.mean)
}

animals.groupby("kind").agg(**agg_dict)

The Result

      min_height  max_height  average_weight
kind                                        
cat          9.1         9.5            8.90
dog          6.0        34.0          102.75