Convert columns into multiple rows in pandas dataframe

You can use melt method.

df = pd.melt(d, id_vars=["Deal", "Year", "Financial_Data"], 
             value_name="Quarter").drop(['variable'],axis=1).sort_values('Quarter')

Output

   Deal  Year  Financial_Data  Quarter
0     1  1991             120        1
3     1  1991             120        2
6     1  1991             120        3
1     2  1992              80        4
4     2  1992              80        5
7     2  1992              80        6
2     3  1993             100        7
5     3  1993             100        8
8     3  1993             100        9

If you have many columns, you can use df.columns.tolist() method in order to achieve your requirement.

column_list = df.columns.tolist()
id_vars_list = column_list[:2] + column_list[-1:]

The statement will become

df = pd.melt(d, id_vars=id_vars_list, 
             value_name="Quarter").drop(['variable'],axis=1).sort_values('Quarter')

This is done using melt:

pd.melt(df, id_vars=['Deal','Year','Financial_Data'], value_vars=['Quarter_1','Quarter_2','Quarter_3'])
   Deal  Year  Financial_Data   variable  value
0     1  1991             120  Quarter_1      1
1     2  1992              80  Quarter_1      4
2     3  1993             100  Quarter_1      7
3     1  1991             120  Quarter_2      2
4     2  1992              80  Quarter_2      5
5     3  1993             100  Quarter_2      8
6     1  1991             120  Quarter_3      3
7     2  1992              80  Quarter_3      6
8     3  1993             100  Quarter_3      9

Cleaning it up a little:

>>> pd.melt(df, id_vars=['Deal','Year','Financial_Data'], value_vars=['Quarter_1','Quarter_2','Quarter_3']).drop('variable',axis=1).sort_values('value')
   Deal  Year  Financial_Data  value
0     1  1991             120      1
3     1  1991             120      2
6     1  1991             120      3
1     2  1992              80      4
4     2  1992              80      5
7     2  1992              80      6
2     3  1993             100      7
5     3  1993             100      8
8     3  1993             100      9