pandas group by function applied to a column

Question

In the Groupby documentation, I only see examples of grouping by functions applied to the index of axis 0 or to the labels of the columns. I see no examples discussing how to group by a label derived from applying a function to a column. I would think this would be done using apply. Is the example below the best way to do this?

df = pd.DataFrame({'name' : np.random.choice(['a','b','c','d','e'], 20), 
               'num1': np.random.randint(low = 30, high=100, size=20),
               'num2': np.random.randint(low = -3, high=9, size=20)})

df.head()

  name  num1 num2
0   d   34  7
1   b   49  6
2   a   51  -1
3   d   79  8
4   e   72  5

def num1_greater_than_60(number_num1):
    if number_num1 >= 60:
        return 'greater'
    else:
        return 'less'

df.groupby(df['num1'].apply(num1_greater_than_60))

Answer 1

from DataFrame.groupby() docs:

by : mapping, function, str, or iterable
    Used to determine the groups for the groupby.
    If ``by`` is a function, it's called on each value of the object's
    index. If a dict or Series is passed, the Series or dict VALUES
    will be used to determine the groups (the Series' values are first
    aligned; see ``.align()`` method). If an ndarray is passed, the
    values are used as-is determine the groups. A str or list of strs
    may be passed to group by the columns in ``self``

so we can do it this way:

In [35]: df.set_index('num1').groupby(num1_greater_than_60)[['name']].count()
Out[35]:
         name
greater    15
less        5