Transposing a column in a pandas dataframe while keeping other column intact with duplicates

Question

My data frame is as follows

selection_id  last_traded_price
430494        1.46
430494        1.48
430494        1.56
430494        1.57
430495        2.45
43         


        

Answer 1

You can use cumcount for Counter for new columns names created by set_index + unstack or pandas.pivot:

g = df.groupby('selection_id').cumcount()
df = df.set_index(['selection_id',g])['last_traded_price'].unstack()
print (df)
                 0     1     2     3
selection_id                        
430494        1.46  1.48  1.56  1.57
430495        2.45  2.67  2.72  2.87

Similar solution with pivot:

df = pd.pivot(index=df['selection_id'], 
              columns=df.groupby('selection_id').cumcount(), 
              values=df['last_traded_price'])
print (df)
                 0     1     2     3
selection_id                        
430494        1.46  1.48  1.56  1.57
430495        2.45  2.67  2.72  2.87

Answer 2

Option 1
groupby + apply

v = df.groupby('selection_id').last_traded_price.apply(list)
pd.DataFrame(v.tolist(), index=v.index)

                 0     1     2     3
selection_id                        
430494        1.46  1.48  1.56  1.57
430495        2.45  2.67  2.72  2.87

---

Option 2
You can do this with pivot, as long as you have another column of counts to pass for the pivoting (it needs to be pivoted along something, that's why).

df['Count'] = df.groupby('selection_id').cumcount()
df.pivot('selection_id', 'Count', 'last_traded_price')

Count            0     1     2     3
selection_id                        
430494        1.46  1.48  1.56  1.57
430495        2.45  2.67  2.72  2.87