join or merge values calculated on grouped pandas dataframe

Question

I have a DataFrame containing density values. I'd like to group by the 'hour' value, bin the densities, and add a new column to my original df, containing the bin number. This is failing, however:

df = pd.DataFrame({
    'hours': np.random.randint(0, 24, 10000),
    'density' : np.random.sample(10000)})

def func(df):
    """"calculates equal intervals of a series or array"""
    intervals = pysal.esda.mapclassify.Equal_Interval(df.density, 5)
    # yb is an ndarray containing the bin indices, 0 - 4 in this case 
    return intervals.yb

df['bins'] = df.groupby(df.hours).transform(func)

Gives AssertionError: length of join_axes must not be equal to 0

If I just group the object and apply the interval function, it looks like this:

grp = df.groupby(df.hours).apply(func)
grp

Out[106]:
hours
0        [2, 4, 3, 4, 0, 4, 2, 2, 0, 1, 0, 0, 2, 2, 0, ...
1        [4, 1, 0, 4, 0, 2, 2, 3, 2, 3, 0, 3, 4, 3, 2, ...
2        [4, 1, 0, 2, 3, 4, 1, 1, 0, 3, 4, 4, 2, 4, 0, ...
3        [3, 0, 0, 4, 0, 0, 0, 1, 2, 2, 0, 2, 2, 2, 1, ...
4        [0, 1, 1, 2, 1, 3, 1, 3, 2, 2, 1, 4, 0, 4, 2, ...
5        [2, 0, 2, 1, 3, 1, 1, 0, 4, 4, 2, 1, 4, 1, 2, ...
6        [1, 2, 3, 3, 3, 2, 4, 1, 2, 1, 2, 0, 3, 2, 0, ...
7        [3, 0, 3, 1, 3, 1, 2, 1, 4, 2, 1, 2, 1, 1, 1, ...
8        [0, 1, 4, 3, 0, 1, 0, 0, 1, 0, 2, 1, 0, 1, 1, ...
9        [4, 2, 0, 4, 1, 3, 2, 3, 4, 1, 1, 4, 4, 4, 4, ...
10       [4, 4, 3, 3, 1, 2, 3, 0, 2, 4, 2, 4, 0, 2, 2, ...
11       [0, 1, 3, 0, 1, 1, 1, 1, 2, 1, 2, 0, 3, 3, 4, ...
12       [3, 1, 1, 0, 4, 4, 3, 0, 1, 2, 1, 1, 4, 2, 0, ...
13       [1, 1, 0, 2, 0, 1, 4, 1, 2, 2, 3, 1, 2, 0, 3, ...
14       [2, 4, 0, 2, 1, 2, 0, 4, 4, 2, 3, 4, 2, 1, 1, ...
15       [2, 4, 3, 4, 1, 0, 3, 1, 2, 0, 3, 4, 2, 2, 3, ...
16       [0, 4, 2, 3, 3, 4, 0, 3, 2, 0, 1, 0, 0, 2, 0, ...
17       [3, 1, 4, 4, 0, 4, 1, 0, 4, 3, 3, 2, 3, 1, 4, ...
18       [4, 3, 0, 2, 4, 2, 2, 0, 2, 2, 1, 2, 1, 0, 1, ...
19       [3, 0, 3, 1, 1, 0, 1, 1, 3, 3, 2, 3, 4, 0, 0, ...
20       [3, 0, 1, 4, 0, 0, 4, 2, 4, 2, 2, 0, 4, 0, 0, ...
21       [4, 2, 3, 3, 1, 2, 0, 4, 2, 0, 2, 2, 1, 2, 2, ...
22       [0, 4, 1, 1, 3, 1, 4, 1, 3, 4, 4, 0, 4, 4, 4, ...
23       [4, 1, 2, 0, 2, 0, 0, 0, 2, 3, 1, 1, 3, 0, 1, ...
dtype: object

Is there a standard way to join or merge values calculated from a grouped object, or should I be using transform differently?

Answer 1

Try to transform on column like this -

df['bins'] = df.groupby(df.hours).density.transform(func)

Note: func needs to be changed to receive Series as arg