Combine duplicated rows by unique values in column to fill blank columns in another value

Imagine I have the following data (slice as example):

ID      Salary Component 1    Value1      Salary Component 2       Value2
10000   Basic Salary          22000     
10000                                     Housing Allowance        13200

How can I combine rows per ID in a way that I have only one row per ID and filling blank information of columns using other rows column values when filled? It would result in this data:

ID      Salary Component 1    Value1      Salary Component 2       Value2
10000   Basic Salary          22000       Housing Allowance        13200

Thank you for the help!

>Solution :

If need first non missing value per groups is possible use:

df = df.replace('', np.nan).groupby('ID', as_index=False).first()

If possible multiple values and need aggreagte e.g. by types:

f = lambda x: x.mean() if np.issubdtype(x.dtype, np.number) else ','.join(x.unique())

df = df.replace('', np.nan).groupby('ID', as_index=False).agg(f)