Consider the following dataframe example:

maturity_date   simulation  simulated_price realized_price
30/06/2010      1           0.539333333     0.611
30/06/2010      2           0.544           0.611
30/06/2010      3           0.789666667     0.611
30/06/2010      4           0.190333333     0.611
30/06/2010      5           0.413666667     0.611

Apart from setting aside the value of the last column and concatenating, is there any other way to adjust the dataframe such that the last column becomes row?

Here is the desired output:

maturity_date   simulation      simulated_price
30/06/2010      1               0.539333333     
30/06/2010      2               0.544           
30/06/2010      3               0.789666667     
30/06/2010      4               0.190333333     
30/06/2010      5               0.413666667     
30/06/2010      realized_price  0.611           

Consider the following dataframe example:

maturity_date   simulation  simulated_price realized_price
30/06/2010      1           0.539333333     0.611
30/06/2010      2           0.544           0.611
30/06/2010      3           0.789666667     0.611
30/06/2010      4           0.190333333     0.611
30/06/2010      5           0.413666667     0.611

Apart from setting aside the value of the last column and concatenating, is there any other way to adjust the dataframe such that the last column becomes row?

Here is the desired output:

maturity_date   simulation      simulated_price
30/06/2010      1               0.539333333     
30/06/2010      2               0.544           
30/06/2010      3               0.789666667     
30/06/2010      4               0.190333333     
30/06/2010      5               0.413666667     
30/06/2010      realized_price  0.611           

• python
• pandas

1 Answer 1

Maybe easier is processing dictionary from last row, DataFrame.pop trick is for remove original column realized_price:

d = df.iloc[-1].to_dict()
d['simulated_price'] = d.pop('realized_price')
d['simulation'] = 'realized_price'
df.loc[len(df.pop('realized_price'))] = d

Alternative:

last = df.columns[-1]
d = df.iloc[-1].to_dict()
d['simulated_price'] = d.pop(last)
d['simulation'] = last
df.loc[len(df.pop(last))] = d

print (df)

  maturity_date      simulation  simulated_price
0    30/06/2010               1         0.539333
1    30/06/2010               2         0.544000
2    30/06/2010               3         0.789667
3    30/06/2010               4         0.190333
4    30/06/2010               5         0.413667
5    30/06/2010  realized_price         0.611000

Another idea is use DataFrame.loc for set new row with default index of DataFrame by select last row in DataFrame.iloc, rename and reappend simulation with new value realized_price in Series.reindex:

s = (df.iloc[-1].drop(['simulated_price','simulation'])
                .rename({'realized_price':'simulated_price'})
                .reindex(df.columns[:-1], fill_value='realized_price'))

df.loc[len(df.pop('realized_price'))] = s

print (df)

  maturity_date      simulation  simulated_price
0    30/06/2010               1         0.539333
1    30/06/2010               2         0.544000
2    30/06/2010               3         0.789667
3    30/06/2010               4         0.190333
4    30/06/2010               5         0.413667
5    30/06/2010  realized_price         0.611000

Alternative is first reassign column simulation, then get last row and processing Series:

s = (df.assign(simulation='realized_price')
       .iloc[-1]
       .drop(['simulated_price'])
       .rename({'realized_price':'simulated_price'}))

df.loc[len(df.pop('realized_price'))] = s

print (df)


  maturity_date      simulation  simulated_price
0    30/06/2010               1         0.539333
1    30/06/2010               2         0.544000
2    30/06/2010               3         0.789667
3    30/06/2010               4         0.190333
4    30/06/2010               5         0.413667
5    30/06/2010  realized_price         0.611000

Another idea with concat:

out = (pd.concat([df, 
                  df.iloc[[-1]]
                    .assign(simulation='realized_price',
                            simulated_price=df['realized_price'].iat[0])],
                 ignore_index=True)
        .drop('realized_price', axis=1))
print (out)
  maturity_date      simulation  simulated_price
0    30/06/2010               1         0.539333
1    30/06/2010               2         0.544000
2    30/06/2010               3         0.789667
3    30/06/2010               4         0.190333
4    30/06/2010               5         0.413667
5    30/06/2010  realized_price         0.611000