Collapsing rows in a Pandas dataframe if all rows have only one value in their columns

Question

I have following DF

         col1  |  col2   | col3   | col4   | col5  | col6
    0    -     |   15.0  |  -     |  -     |   -   |  -
    1    -     |   -            


        

Answer 1

You can use max, but you need to convert the null values in the string-valued columsn (which is a bit ugly unfortunately)

>>> df = pd.DataFrame({'col1':[np.nan, "ABC1"], 'col2':[15.0, np.nan]})

>>> df.apply(lambda c: c.fillna('') if c.dtype is np.dtype('O') else c).max()
col1    ABC1
col2      15
dtype: object

You could also you a combination of backfill and forwardfill to fill in the gaps, this could be useful if only want to apply this to some of your columns:

>>> df.apply(lambda c: c.fillna(method='bfill').fillna(method='ffill'))