CODEWITHSUNDEEP
pythonpandasgroup-bypivot
Siraj S.
Siraj S.

Reputation: 3751

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 label_1 values
    0   1           a   0.0
    1   1           b   2.0
    2   1           c   4.0
    3   2          NaN  NaN
    4   2           c   8.0
    5   2           b   5.0

my desired output is 

     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

I have tried pd.pivot and wrangling around with pd.gropuby but cannot get to the desired output due to duplicate entries. any help most appreciated.

Upvotes: 1

Views: 69

Answers (4)

Quang Hoang
Quang Hoang

Reputation: 150785

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

Upvotes: 0

piRSquared
piRSquared

Reputation: 294488

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

Upvotes: 3

Scott Boston
Scott Boston

Reputation: 153500

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

Upvotes: 3

BENY
BENY

Reputation: 323326

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

Upvotes: 2

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