Pandas DataFrame.add () - ignore missing columns

Question

I have the following two DataFrames:

>>> history
              above below
asn   country
12345 US          5     4
      MX          6     3
54321 MX          4     5
>>> current
              above below
asn   country
12345 MX          1     0
54321 MX          0     1
      US          1     0

I save the counter "above" and "below" in the historyDataFrame like this:

>>> history = history.add(current, fill_value=0)
>>> history
               above  below
asn   country              
12345 MX         7.0    3.0
      US         5.0    4.0
54321 MX         4.0    6.0
      US         1.0    0.0

This works as long as currentthere are no extra columns in the DataFrame. However, when I add an extra column:

>>> current
              above below cruft
asn   country
12345 MX          1     0   999
54321 MX          0     1   999
      US          1     0   999

I get the following:

>>> history = history.add(current, fill_value=0)
>>> history
               above  below cruft
asn   country              
12345 MX         7.0    3.0 999.0
      US         5.0    4.0   NaN
54321 MX         4.0    6.0 999.0
      US         1.0    0.0 999.0

I want this extra column to be ignored, as it is not present in both DataFrames. Desired Result:

>>> history
               above  below
asn   country              
12345 MX         7.0    3.0
      US         5.0    4.0
54321 MX         4.0    6.0
      US         1.0    0.0

Answer 1

In [27]: history.add(current, fill_value=0)[history.columns]
Out[27]:
               above  below
asn   country
12345 MX         7.0    3.0
      US         5.0    4.0
54321 MX         4.0    6.0
      US         1.0    0.0

Answer 2

Ummm, a new way

pd.concat([df1,df2],join ='inner',axis=0).sum(level=[0,1])

Answer 3

, , :

cols_to_return = ["above", "below"]
history = history[cols_to_return].add(current[cols_to_return], fill_value=0)

, , .