Pivot Dataframe With Duplicate Values

consider the below pd.DataFrame temp = pd.DataFrame({'label_0':[1,1,1,2,2,2],'label_1':['a','b','c',np.nan,'c','b'], 'values':[0,2,4,np.nan,8,5]}) print(temp) label_0 labe

Solution 1:

d = {}
for _0, _1, v in zip(*map(temp.get, temp)):
    d.setdefault(_1, {})[_0] = v

pd.DataFrame.from_dict(d, orient='index')

       1    2
a    0.0  NaN
b    2.0  5.0
c    4.0  8.0
NaN  NaN  NaN

OR

pd.DataFrame.from_dict(d, orient='index').rename_axis('label_1').reset_index()

  label_1    1    2
0       a  0.0  NaN
1       b  2.0  5.0
2       c  4.0  8.0
3     NaN  NaN  NaN

Solution 2:

Another way is to use set_index and unstack:

temp.set_index(['label_0','label_1'])['values'].unstack(0)

Output:

label_0    1    2
label_1          
NaN      NaN  NaN
a        0.0  NaN
b        2.0  5.0
c        4.0  8.0

Solution 3:

You can do fillna then pivot

temp.fillna('NaN').pivot(*temp.columns).T
Out[251]: 
label_0    1    2
label_1          
NaN      NaN  NaN
a          0  NaN
b          2    5
c          4    8

Solution 4:

Seems like a straightforward pivot works:

temp.pivot(columns='label_0', index='label_1', values='values')

Output:

label_0     1       2
label_1         
NaN         NaN     NaN
a           0.0     NaN
b           2.0     5.0
c           4.0     8.0