Merge two DataFrame on the index, but if one DFs is missing an index I want it to create Null (Nan) values if one of the DFs is missing that index

I want to merge two DataFrames on the index. But if one of those DataFrames is missing an index value I want it to put null (‘Nan’) values in the place of the new DataFrame for whatever Dataframe is missing that index.

import pandas as pd

dict1 = {
    'Short Name': ['SOO','BS', 'SOC'],
    'File': ['r1','r2','r3'],
    'acc1': ['321','321','321']
}

dict2 = {
    'Short Name': ['S00','SOC'],
    'File': ['r1','r2'],
    'acc2': ['123','123']
}

df1 = pd.DataFrame(dict1)
df1.set_index('Short Name', inplace=True)
df1

df2 = pd.DataFrame(dict2)
df2.set_index('Short Name', inplace=True)
df2

new_df = pd.merge(df1,df2, on='Short Name')

The output that I’m trying to achieve is something that looks like this:

             File_x  acc1  File_y  acc2
Short Name
       SOO    r1     321    r1      123
        BS    r2     321    Nan     Nan
       SOC    r3     321    r2      123

[DataFrame of dict1][1]
[DataFrame of dict2][2]
[1]: https://i.stack.imgur.com/u5g0y.png
[2]: https://i.stack.imgur.com/AwenX.png

>Solution :

Try join

out = df1.join(df2,lsuffix='_x',rsuffix='_y',how='left')
Out[934]: 
           File_x acc1 File_y acc2
Short Name                        
SOO            r1  321    NaN  NaN
BS             r2  321    NaN  NaN
SOC            r3  321     r2  123