reshape a pandas dataframe

suppose a dataframe like this one:

df = pd.DataFrame([[1,2,3,4],[5,6,7,8],[9,10,11,12]], columns = ['A', 'B', 'A1', 'B1'])

I would like to have a dataframe which looks like:

what does not work:

new_rows = int(df.shape[1]/2) * df.shape[0]
new_cols = 2
df.values.reshape(new_rows, new_cols, order='F')

of course I could loop over the data and make a new list of list but there must be a better way. Any ideas ?

The pd.wide_to_long function is built almost exactly for this situation, where you have many of the same variable prefixes that end in a different digit suffix. The only difference here is that your first set of variables don't have a suffix, so you will need to rename your columns first.

The only issue with pd.wide_to_long is that it must have an identification variable, i, unlike melt. reset_index is used to create a this uniquely identifying column, which is dropped later. I think this might get corrected in the future.

df1 = df.rename(columns={'A':'A1', 'B':'B1', 'A1':'A2', 'B1':'B2'}).reset_index()
pd.wide_to_long(df1, stubnames=['A', 'B'], i='index', j='id')\
  .reset_index()[['A', 'B', 'id']]

    A   B id
0   1   2  1
1   5   6  1
2   9  10  1
3   3   4  2
4   7   8  2
5  11  12  2