join - Splitting multiple columns into rows in pandas dataframe

Question

I have a pandas dataframe as follows:

ticker    account      value         date
aa       assets       100,200       20121231, 20131231
bb       liabilities  50, 150       20141231, 20131231

I would like to split df['value'] and df['date'] so that the dataframe looks like this:

ticker    account      value         date
aa       assets       100           20121231
aa       assets       200           20131231 
bb       liabilities  50            20141231
bb       liabilities  150           20131231

Would greatly appreciate any help.

Answer 1

You can first split columns, create Series by stack and remove whitespaces by strip:

s1 = df.value.str.split(',', expand=True).stack().str.strip().reset_index(level=1, drop=True)
s2 = df.date.str.split(',', expand=True).stack().str.strip().reset_index(level=1, drop=True)

Then concat both Series to df1:

df1 = pd.concat([s1,s2], axis=1, keys=['value','date'])

Remove old columns value and date and join:

print (df.drop(['value','date'], axis=1).join(df1).reset_index(drop=True))
  ticker      account value      date
0     aa       assets   100  20121231
1     aa       assets   200  20131231
2     bb  liabilities    50  20141231
3     bb  liabilities   150  20131231