why is groupby in pandas not displaying-CodePudding

I have a df like:

df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
                              'Parrot', 'Parrot','Elephant','Elephant','Elephant'],
                   'Max Speed': [380, 370, 24, 26,5,7,3]})

I would like to groupby Animal.

if I do in a notebook:

a = df.groupby(['Animal'])
display(a)

I get:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f945bdd7b80>

I expected something like:

What I ultimate want to do is sort the df by number of animal appearances (Elephant 3, falcon 2 etc)

CodePudding user response：

You need check DataFrame.groupby:

Group DataFrame using a mapper or by a Series of columns.

So it is not for remove duplicates values by column, but for aggregation.

If need remove duplicated vales, set to empty string use:

df.loc[df['Animal'].duplicated(), 'Animal'] = ''
print (df)
     Animal  Max Speed
0    Falcon        380
1                  370
2    Parrot         24
3                   26
4  Elephant          5
5                    7
6                    3

If need groupby:

for i, g in df.groupby(['Animal']):
    print (g)
    
     Animal  Max Speed
4  Elephant          5
5  Elephant          7
6  Elephant          3
   Animal  Max Speed
0  Falcon        380
1  Falcon        370
   Animal  Max Speed
2  Parrot         24
3  Parrot         26

CodePudding user response：

The groupby object requires an action, like a max or a min. This will result in two things:

A regular pandas data frame
The grouping key appearing once

You clearly expect both of the Falcon entries to remain so you don't actually want to do a groupby. If you want to see the entries with repeated animal values hidden, you would do that by setting the Animal column as the index. I say that because your input data frame is already in the order you wanted to display.

CodePudding user response：

Use mask:

>>> df.assign(Animal=df['Animal'].mask(df['Animal'].duplicated(), ''))
     Animal  Max Speed
0    Falcon        380
1                  370
2    Parrot         24
3                   26
4  Elephant          5
5                    7
6                    3
>>>

Or as index:

df.assign(Animal=df['Animal'].mask(df['Animal'].duplicated(), '')).set_index('Animal')
          Max Speed
Animal             
Falcon          380
                370
Parrot           24
                 26
Elephant          5
                  7
                  3
>>>