How is pandas groupby method actually working?

When you use just

df.groupby('A')

You get a GroupBy object. You haven’t applied any function to it at that point. Under the hood, while this definition might not be perfect, you can think of a groupby object as:

• An iterator of (group, DataFrame) pairs, for DataFrames, or
• An iterator of (group, Series) pairs, for Series.

To illustrate:

df = DataFrame({'A' : [1, 1, 2, 2], 'B' : [1, 2, 3, 4]})
grouped = df.groupby('A')

# each `i` is a tuple of (group, DataFrame)
# so your output here will be a little messy
for i in grouped:
    print(i)
(1,    A  B
0  1  1
1  1  2)
(2,    A  B
2  2  3
3  2  4)

# this version uses multiple counters
# in a single loop.  each `group` is a group, each
# `df` is its corresponding DataFrame
for group, df in grouped:
    print('group of A:', group, '\n')
    print(df, '\n')
group of A: 1 

   A  B
0  1  1
1  1  2 

group of A: 2 

   A  B
2  2  3
3  2  4 

# and if you just wanted to visualize the groups,
# your second counter is a "throwaway"
for group, _ in grouped:
    print('group of A:', group, '\n')
group of A: 1 

group of A: 2 

Now as for .head. Just have a look at the docs for that method:

Essentially equivalent to .apply(lambda x: x.head(n))

So here you’re actually applying a function to each group of the groupby object. Keep in mind .head(5) is applied to each group (each DataFrame), so because you have less than or equal to 5 rows per group, you get your original DataFrame.

Consider this with the example above. If you use .head(1), you get only the first 1 row of each group:

print(df.groupby('A').head(1))
   A  B
0  1  1
2  2  3