How do you add rows to an existing data frame with default values for certain rows based on condition?

Question

I have a data set that includes information about fund daily performance. In my full data set there are daily metrics between 12/1/15 and 6/29/17 but not all of the funds have information for each one of these days.

What I would like to do is add rows for each day between the min and max dates (12/1/15 and 6/29/17) and have N/A values for adjusted close prices.

#ORIGINAL DATASET (EXAMPLE)
# List of Tuples
records = [ ('Fund 1', 34, '12/1/18') ,
             ('Fund 1', 53, '12/2/18') ,
             ('Fund 1', 42, '12/3/18') ,
             ('Fund 2', 44, '12/3/18') ,
             ('Fund 1', 32, '12/4/18') ,
             ('Fund 2', 31, '12/4/18') ,
             ('Fund 2', 57, '12/5/18')  ]

#Create a DataFrame object
dfObj = pd.DataFrame(records, columns = ['Fund' , '$', 'Date']) 

dfObj

Fund    $   Date
Fund 1  34  12/1/18
Fund 1  53  12/2/18
Fund 1  42  12/3/18
Fund 2  44  12/3/18
Fund 1  32  12/4/18
Fund 2  31  12/4/18
Fund 2  57  12/5/18

#DESIRED DATASET (EXAMPLE)
# List of Tuples
desired_records = [ ('Fund 1', 34, '12/1/18') ,
             ('Fund 1', 53, '12/2/18') ,
             ('Fund 1', 42, '12/3/18') ,
             ('Fund 2', 44, '12/3/18') ,
             ('Fund 1', 32, '12/4/18') ,
             ('Fund 2', 31, '12/4/18'),
            ('Fund 2', 0, '12/1/18') ,
             ('Fund 2', 0, '12/2/18'),
             ('Fund 2', 57, '12/5/18'),
             ('Fund 1', 0, '12/5/18')  ]

#Create a DataFrame object
desired_df = pd.DataFrame(desired_records, columns = ['Fund' , '$', 'Date']) 

desired_df.sort_values(by=['Date'])

Fund    $   Date
Fund 1  34  12/1/18
Fund 2  0   12/1/18
Fund 1  53  12/2/18
Fund 2  0   12/2/18
Fund 1  42  12/3/18
Fund 2  44  12/3/18
Fund 1  32  12/4/18
Fund 2  31  12/4/18
Fund 2  57  12/5/18
Fund 1  0   12/5/18

Is this a simple fix?

Answer 1

In your case using stack and unstack

df=df.set_index(['Date','Fund']).unstack(fill_value=0).stack().reset_index()
Out[138]: 
      Date    Fund   $
0  12/1/18  Fund 1  34
1  12/1/18  Fund 2   0
2  12/2/18  Fund 1  53
3  12/2/18  Fund 2   0
4  12/3/18  Fund 1  42
5  12/3/18  Fund 2  44
6  12/4/18  Fund 1  32
7  12/4/18  Fund 2  31
8  12/5/18  Fund 1   0
9  12/5/18  Fund 2  57