How is pandas groupby method actually working?

So I was trying to understand pandas.dataFrame.groupby() function and I came across this example on the documentation:

    In [1]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ...:                           'foo', 'bar', 'foo', 'foo'],
   ...:                    'B' : ['one', 'one', 'two', 'three',
   ...:                           'two', 'two', 'one', 'three'],
   ...:                    'C' : np.random.randn(8),
   ...:                    'D' : np.random.randn(8)})
   ...: 

In [2]: df
Out[2]: 
     A      B         C         D
0  foo    one  0.469112 -0.861849
1  bar    one -0.282863 -2.104569
2  foo    two -1.509059 -0.494929
3  bar  three -1.135632  1.071804
4  foo    two  1.212112  0.721555
5  bar    two -0.173215 -0.706771
6  foo    one  0.119209 -1.039575
7  foo  three -1.044236  0.271860

Not to further explore I did this:

print(df.groupby('B').head())

it outputs the same dataFrame but when I do this:

print(df.groupby('B'))

it gives me this:

<pandas.core.groupby.DataFrameGroupBy object at 0x7f65a585b390>

What does this mean? In a normal dataFrame printing .head() simply outputs the first 5 rows what’s happening here?

And also why does printing .head() gives the same output as the dataframe? Shouldn’t it be grouped by the elements of the column 'B'?

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Answers:

Method 1

Answers:

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Method 1

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

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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