Looking to preserve NaN values when changing the shape of the dataframe.

These two questions may be related:

• How to preserve NaN instead of filling with zeros in pivot table?
• How to make two NaN as NaN after the operation instead of making it zero?

but not been able to use the answers provided - can I set a min count for np.sum somehow?

import pandas as pd
import numpy as np
df = pd.DataFrame([['Y1', np.nan], ['Y2', np.nan], ['Y1', 6], ['Y2',8]], columns=['A', 'B'], index=['1988-01-01','1988-01-01', '1988-01-04', '1988-01-04'])
df.index.name = 'Date'
dfpivot_df = pd.pivot_table(df, values='B', index=['Date'], columns=['A'],aggfunc=np.sum)
pivot_df

The output is:

A   Y1  Y2
Date        
1988-01-01  0.0 0.0
1988-01-04  6.0 8.0

and the desired output is:

A   Y1  Y2
Date        
1988-01-01  NaN NaN
1988-01-04  6.0 8.0

From the helpful comments the following solution meets my requirements:

pivot_df_2 = pd.pivot_table(df, values='B', index=['Date'], columns=['A'],aggfunc=min, dropna=False)
pivot_df_2

Values are supposed to be unique per slot so replacing the sum function with a min function shouldn't make a difference (in my case)