填写熊猫数据框中的缺失数据

Question

I have a Pandas dataframe with two indexes

                              Column1
indexA   indexB                        
1001     aaa                        1
         bbb                        1
         ccc                        1
1002     ddd                        1
         eee                        1

and would like indexB to have the same values for each value of indexA :

                              Column1
indexA   indexB                        
1001     aaa                        1
         bbb                        1
         ccc                        1
         ddd                        0
         eee                        0
1002     aaa                        0
         bbb                        0
         ccc                        0
         ddd                        1
         eee                        1

My first thought was to unstack, fillna with 0 and then stack it, but this seems like overkill. Is there an easier method?

EDIT: Alexander's answer below works though it takes a long time (my original dataframe has 350k rows). I changed that solution slightly:

df =  pd.read_sql(sql=sql, con=db_eng, index_col=index)
idx = pd.MultiIndex.from_product([df.index.levels[0], df.index.levels[1]], names=df.index.names)
df.reindex(idx).fillna(value=0)

Also found these two questions after posting this:

• Insert 0-values for missing dates within MultiIndex
• How to fill the missing record of Pandas dataframe in pythonic way?

Answer 1

There is probably a better way to do this. I created a new MultiIndex using pd.MultiIndex.from_product . I then created a new dataframe with a dummy value, joined the existing dtaframe, and deleted the dummy column.

df = pd.DataFrame({'index_0': ['a', 'a', 'b', 'b', 'b'], 
                   'index_1': ['A', 'B', 'A', 'B', 'C'], 
                   'vals': [1, 2, 3, 4, 5]}).set_index(['index_0', 'index_1'])

>>> df 
                 vals
index_0 index_1      
a       A           1
        B           2
b       A           3
        B           4
        C           5

idx = pd.MultiIndex.from_product([df.index.levels[0], df.index.levels[1]], 
                                 names=df.index.names)
new_df = pd.DataFrame({'_dummy_': [1] * len(idx)}, index=idx).join(df)
del new_df['_dummy_']

>>> new_df
                 vals
index_0 index_1      
a       A           1
        B           2
        C         NaN
b       A           3
        B           4
        C           5