How to unmelt a completely melted table

Question

I have this dataframe df which I have melted and then using pd.pivot_table I am able to get the table structure back at least looking at the rows it seems so - but the indexes become MultiIndex type - is there a way to change to back to RangeIndex as it was in the original df

Here are the steps

>>> import pandas as pd
>>> df = pd.DataFrame({'Name': ['Bob', 'John', 'Foo', 'Bar', 'Alex', 'Tom'], 
                   'Math': ['A+', 'B', 'A', 'F', 'D', 'C'], 
                   'Mental Math': ['A', 'A', 'B', 'C', 'E', 'B'], 
                   'English': ['C', 'B', 'B', 'A+', 'F', 'A'],
                   'Bengali': ['C', 'C', 'A', 'B', 'A+', 'B'],
                   'Age': [13, 16, 16, 15, 15, 13]})
>>> print(df)
    Name Math Mental Math English Bengali  Age
0   Bob   A+           A       C       C   13
1  John    B           A       B       C   16
2   Foo    A           B       B       A   16
3   Bar    F           C      A+       B   15
4  Alex    D           E       F      A+   15
5   Tom    C           B       A       B   13

>>> d = df.melt(var_name='Column', value_name='Value')
>>> print(d)

          Column Value
0          Name   Bob
1          Name  John
2          Name   Foo
3          Name   Bar
4          Name  Alex
5          Name   Tom
6          Math    A+
7          Math     B
8          Math     A
9          Math     F
10         Math     D
11         Math     C
12  Mental Math     A
13  Mental Math     A
14  Mental Math     B
15  Mental Math     C
16  Mental Math     E
17  Mental Math     B
18      English     C
19      English     B
20      English     B
21      English    A+
22      English     F
23      English     A
24      Bengali     C
25      Bengali     C
26      Bengali     A
27      Bengali     B
28      Bengali    A+
29      Bengali     B
30          Age    13
31          Age    16
32          Age    16
33          Age    15
34          Age    15
35          Age    13

Is it possible to get back to the original table as in the original indexes Using pandas.pivot_table tried to return to the original dataframe but if you see the index now has become a MultiIndex type

>>> df_back = pd.pivot_table(data = d, index=['Name','Age'], columns=['Column'], aggfunc=''.join, margins=False)

>>> print(df_back)

             Value                         
Column   Bengali English Math Mental Math
Name Age                                 
Alex 15       A+       F    D           E
Bar  15        B      A+    F           C
Bob  13        C       C   A+           A
Foo  16        A       B    A           B
John 16        C       B    B           A
Tom  13        B       A    C           B

>>> print(df_back.index)

MultiIndex([('Alex', 15),
            ( 'Bar', 15),
            ( 'Bob', 13),
            ( 'Foo', 16),
            ('John', 16),
            ( 'Tom', 13)],
           names=['Name', 'Age'])

>>> print(df.index)

RangeIndex(start=0, stop=6, step=1)

Answer 1

As @mozway said your code does not work, some steps must be missing

But to go from d to df you can use unstack. You need a unique index for this to work hence this is where 'count' column comes in

(d.assign(count = np.arange(len(d))%6)
    .set_index(['count','Column'])
    .unstack(level=1)
    .reset_index(drop = True)
    .droplevel(level = 0, axis=1)
)

output

      Age  Bengali    English    Math    Mental Math    Name
--  -----  ---------  ---------  ------  -------------  ------
 0     13  C          C          A+      A              Bob
 1     16  C          B          B       A              John
 2     16  A          B          A       B              Foo
 3     15  B          A+         F       C              Bar
 4     15  A+         F          D       E              Alex
 5     13  B          A          C       B              Tom