Reformat/pivot pandas dataframe

For each row, I want that rows’ index to be changed to column_index, and have the whole thing split from x row * y columns to 1 row * x*y columns shape.

import pandas as pd

df = pd.DataFrame(data=[['Jon', 21, 1.77,160],['Jane',44,1.6,130]],columns=['name','age', 'height','weight'])
want = pd.DataFrame(data=[['Jon', 21, 1.77,160,'Jane',44,1.6,130]],columns=['name_0','age_0', 'height_0','weight_0','name_1','age_1', 'height_1','weight_1'])

# original df
   name  age  height  weight
0   Jon   21    1.77     160
1  Jane   44    1.60     130

# desired df - want
  name_0  age_0  height_0  weight_0 name_1  age_1  height_1  weight_1
0    Jon     21      1.77       160   Jane     44       1.6       130

I tried df.unstack().to_frame().T and while it reduces the rows to one, it creates a multiindex, so not ideal:

  name       age     height      weight     
     0     1   0   1      0    1      0    1
0  Jon  Jane  21  44   1.77  1.6    160  130

I don’t think pivot table will work here.

>Solution :

Using stack and flattening the MultiIndex:

out = df.stack().swaplevel().to_frame().T
out.columns = out.columns.map(lambda x: f'{x[0]}_{x[1]}')

Output:

  name_0 age_0 height_0 weight_0 name_1 age_1 height_1 weight_1
0    Jon    21     1.77      160   Jane    44      1.6      130