Pandas Melt with Multiple Value Vars

Question

I have a data set which is in wide format like this

   Index Country     Variable 2000 2001 2002 2003 2004 2005
   0     Argentina   var1     12   15   18            


        

Answer 1

Option 1

Using melt then unstack for var1, var2, etc...

(df1.melt(id_vars=['Country','Variable'],var_name='Year')
    .set_index(['Country','Year','Variable'])
    .squeeze()
    .unstack()
    .reset_index())

Output:

Variable    Country  Year  var1  var2
0         Argentina  2000    12     1
1         Argentina  2001    15     3
2         Argentina  2002    18     2
3         Argentina  2003    17     5
4         Argentina  2004    23     7
5         Argentina  2005    29     5
6            Brazil  2000    20     0
7            Brazil  2001    23     1
8            Brazil  2002    25     2
9            Brazil  2003    29     2
10           Brazil  2004    31     3
11           Brazil  2005    32     3

Option 2

Using pivot then stack:

(df1.pivot(index='Country',columns='Variable')
   .stack(0)
   .rename_axis(['Country','Year'])
   .reset_index())

Output:

Variable    Country  Year  var1  var2
0         Argentina  2000    12     1
1         Argentina  2001    15     3
2         Argentina  2002    18     2
3         Argentina  2003    17     5
4         Argentina  2004    23     7
5         Argentina  2005    29     5
6            Brazil  2000    20     0
7            Brazil  2001    23     1
8            Brazil  2002    25     2
9            Brazil  2003    29     2
10           Brazil  2004    31     3
11           Brazil  2005    32     3

Option 3 (ayhan's solution)

Using set_index, stack, and unstack:

(df.set_index(['Country', 'Variable'])
   .rename_axis(['Year'], axis=1)
   .stack()
   .unstack('Variable')
   .reset_index())

Output:

Variable    Country  Year  var1  var2
0         Argentina  2000    12     1
1         Argentina  2001    15     3
2         Argentina  2002    18     2
3         Argentina  2003    17     5
4         Argentina  2004    23     7
5         Argentina  2005    29     5
6            Brazil  2000    20     0
7            Brazil  2001    23     1
8            Brazil  2002    25     2
9            Brazil  2003    29     2
10           Brazil  2004    31     3
11           Brazil  2005    32     3