How to print a groupby object

I want to print the result of grouping with Pandas.

I have a dataframe:

import pandas as pd
df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})
print(df)

       A  B
0    one  0
1    one  1
2    two  2
3  three  3
4  three  4
5    one  5

When printing after grouping by 'A' I have the following:

print(df.groupby('A'))

<pandas.core.groupby.DataFrameGroupBy object at 0x05416E90>

How can I print the dataframe grouped?

If I do:

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

I obtain the dataframe as if it was not grouped:

             A  B
A                
one   0    one  0
      1    one  1
two   2    two  2
three 3  three  3
      4  three  4
one   5    one  5

I was expecting something like:

             A  B
A                
one   0    one  0
      1    one  1
      5    one  5
two   2    two  2
three 3  three  3
      4  three  4

Simply do:

grouped_df = df.groupby('A')

for key, item in grouped_df:
    print(grouped_df.get_group(key), "\n\n")

Deprecation Notice: ix was deprecated in 0.20.0

This also works,

grouped_df = df.groupby('A')    
gb = grouped_df.groups

for key, values in gb.iteritems():
    print(df.ix[values], "\n\n")

For selective key grouping: Insert the keys you want inside the key_list_from_gb, in following, using gb.keys(): For Example,

gb = grouped_df.groups
gb.keys()

key_list_from_gb = [key1, key2, key3]

for key, values in gb.items():
    if key in key_list_from_gb:
        print(df.ix[values], "\n")

If you're simply looking for a way to display it, you could use describe():

grp = df.groupby['colName']
grp.describe()

This gives you a neat table.

In addition to previous answers:

Taking your example,

df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})

Then simple 1 line code

df.groupby('A').apply(print)

In Jupyter Notebook, if you do the following, it prints a nice grouped version of the object. The apply method helps in creation of a multiindex dataframe.

by = 'A'  # groupby 'by' argument
df.groupby(by).apply(lambda a: a[:])

Output:

             A  B
A                
one   0    one  0
      1    one  1
      5    one  5
three 3  three  3
      4  three  4
two   2    two  2

If you want the by column(s) to not appear in the output, just drop the column(s), like so.

df.groupby(by).apply(lambda a: a.drop(by, axis=1)[:])

Output:

         B
A         
one   0  0
      1  1
      5  5
three 3  3
      4  4
two   2  2

Here, I am not sure as to why .iloc[:] does not work instead of [:] at the end. So, if there are some issues in future due to updates (or at present), .iloc[:len(a)] also works.