Transpose Columns into 1 Column in Pandas

Question

Sample Data

I have question about transposing (melting or stacking?) and joining data sets.

I have 3 dataframes (above) and I want to transform and join them to make the outcome that I have on the right.

I've been reading about stack, melt, transpose, and merge and am a bit confused.

Advice on getting the desired outcome?

Thanks!

Answer 1

Starting with:

df1 = df1.set_index('Country')

         2012  2013  2014
Country                  
AFG         1     4     7
AGO         2     5     8
ALF         3     6     9

df2 = df1.mul(2)
df3 = df1.mul(3)

You can .stack() and pd.concat() :

df = pd.concat([df1.stack(), df2.stack(), df3.stack()], axis=1)
df.columns = ['A', 'B', 'C']
df.reset_index().rename(columns={'level_1': 'Year'})


  Country  Year  A   B   C
0     AFG  2012  1   2   3
1     AFG  2013  4   8  12
2     AFG  2014  7  14  21
3     AGO  2012  2   4   6
4     AGO  2013  5  10  15
5     AGO  2014  8  16  24
6     ALF  2012  3   6   9
7     ALF  2013  6  12  18
8     ALF  2014  9  18  27