Reformat/pivot pandas dataframe

Question

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.

Asked By: noblerthanoedipus

||

Source

Answer 1

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

Answered By: mozway

Answer 2

If you want a more manual method using a new dataframe, here is an alternate solution:

df = pd.DataFrame(data=[['Jon', 21, 1.77,160],['Jane',44,1.6,130]],columns=['name','age', 'height','weight'])
new_df = pd.DataFrame()

for i in range(len(df)):
    new_df = pd.concat([new_df, pd.DataFrame([[df.name.iloc[i], df.age.iloc[i], df.height.iloc[i], df.weight.iloc[i]]], columns = ['name_'+str(i), 'age_'+str(i),'height_'+str(i),'weight_'+str(i)])], axis=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

Hope that helps 🙂

Answered By: SavvY

Answer 3

Mostly like @mozway’s answer:

s = df.stack()
pd.DataFrame(data=s.values.reshape(1, -1), 
             columns=s.index.map(lambda x: f"{x[1]}_{x[0]}"))

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

Answered By: SomeDude

Reformat/pivot pandas dataframe

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