What Pandas data type is passed to transform or apply in a groupby

Question

When trying to debug groupby function applications, someone suggested that I use a dummy function to "see what is being passed" into the function for each group. Sure, I'm game:

import numpy as np
import pandas as pd

np.random.seed(0) # so we can all play along at home

categories = list('abc')
categories = categories * 4
data_1 = np.random.randn(len(categories))
data_2 = np.random.randn(len(categories))

df = pd.DataFrame({'category': categories, 'data_1': data_1, 'data_2': data_2})

def f(x):
    print type(x)
    return x

print 'single column transform'
df.groupby(['category'])['data_1'].transform(f)
print '
'

print 'single column (nested) transform'
df.groupby(['category'])[['data_1']].transform(f)
print '
'

print 'multiple column transform'
df.groupby(['category'])[['data_1', 'data_2']].transform(f)

print '
'
print '
'

print 'single column apply'
df.groupby(['category'])['data_1'].apply(f)
print '
'

print 'single column (nested) apply'
df.groupby(['category'])[['data_1']].apply(f)
print '
'

print 'multiple column apply'
df.groupby(['category'])[['data_1', 'data_2']].apply(f)

This puts the following into my standard output:

single column transform





single column (nested) transform







multiple column transform










single column apply





single column (nested) apply






multiple column apply

So it seems like:

Transform
- Single column: 3 Series
- Single column (nested): 2 Series and 3 DataFrame
- Multiple columns: 3 Series and 3 DataFrame
Apply
- Single column: 3 Series
- Single column (nested): 4 DataFrame
- Multiple columns: 4 DataFrame

What's going on here? Can anyone explain why each of these 6 calls is leading to the series of objects described above being passed to the function specified?

What Pandas data type is passed to transform or apply in a groupby

1 Answers