Pandas pivot_table replaces nan with 0 aggfunc='sum'

Question

I am using a multi-value pivot_table of this form:

pivot = df.pivot_table(index=[indices], columns=['column'], values=['start_value','end_value','delta','name','unit'], aggfunc='sum')

The dataframe df contains columns ['start_value','end_value','delta','name','unit'] all of dtype object. This is because 'name' & 'unit' are actually string columns, 'start_value', 'end_value' and 'delta' float columns. Object dtype is an attempt to make the pivot_table work, even though dtypes are different (content-wise).

When one of the values is non-nan, any nan value is converted to a 0, instead of a nan.

df:

indices, column, 'start_value','end_value','delta','name','unit'
A,       '1nan',  nan,          1000,      nan,    'test', 'USD'
A,       'other', nan,          nan,       nan,    'test2', 'USD'

Results in pivot:

indices, ('1nan', 'start_value'), ('1nan', 'end_value'), ('1nan', 'delta'),('1nan', 'name'), ('1nan', 'unit'), ('other', 'start_value'), ('other', 'end_value'), ('other', 'delta'), ('other', 'name'), ('other', 'unit')
A, 0 [should be nan], 1000, 0 [should be nan], 'test','USD', nan, nan, nan, 'test2', 'USD'

Any suggestion on how to get nans instead of 0s?

Answer 1

Alternative solution is use GroupBy.sum with parameter min_count=1, but there are removed non numeric columns:

df = (df.groupby(['indices', 'column'])
                ['start_value','end_value','delta','name','unit']
                 .sum(min_count=1) 
                  .unstack()
                    )
print (df)
        start_value         end_value          delta        
column       '1nan' 'other'    '1nan' 'other' '1nan' 'other'
indices                                                     
A               NaN     NaN    1000.0     NaN    NaN     NaN

because with pivot_table are removed NaNs columns:

df = df.pivot_table(index=['indices'], 
                    columns=['column'], 
                    values=['start_value','end_value','delta','name','unit'], 
                    aggfunc=lambda x: x.sum(min_count=1)
                    )
print (df)
        end_value    name            unit        
column     '1nan'  '1nan'  'other' '1nan' 'other'
indices                                          
A          1000.0  'test'  'test2'  'USD'   'USD'