BUG: groupby.first/last with nans

[jreback]

This is incorrect, as this is applied column by column (as they are different dtypes)
so _first_compat should first compute the mask then use it.

from SO

In [18]: df = pd.DataFrame([{'id':"a",'val':np.nan, 'val2':-1},{'id':"a",'val':'TREE','val2':15}])

In [19]: df
Out[19]: 
  id   val  val2
0  a   NaN    -1
1  a  TREE    15

In [20]: df.groupby('id').first()
Out[20]: 
     val  val2
id            
a   TREE    -1

[behzadnouri]

this is designed behavior, and does not depend on types:

>>> df
   jim  joe  jolie
0    0    1    NaN
1    0  NaN      2
>>> df.dtypes
jim      float64
joe      float64
jolie    float64
dtype: object
>>> df._data.nblocks
1
>>> df.groupby('jim').first()
     joe  jolie
jim            
0      1      2

[jreback]

I think it's incorrect for the frame case (series case ok)

[jorisvandenbossche]

Related: #6732

[jreback]

I think this is confusing at the least. So you have multiple values on the first row, some of which are NaN. I guess .head(1) is the 'correct' answer here. And .first() does its thing. I think maybe this should NOT be the default behavior.