pandas return columns in dataframe that are not in other dataframe

Question

I have two dataframes that look like this:

df_1 = pd.DataFrame({
'A' : [1.0, 2.0, 3.0, 4.0],
'B' : [100, 200, 300, 400],
'C' : [2, 3, 4, 5] 
                   })

df_2 = pd.DataFrame({
'B' : [1.0, 2.0, 3.0, 4.0],
'C' : [100, 200, 300, 400],
'D' : [2, 3, 4, 5] 
                  })

Now if I utilize pandas .isin function I can do something nifty like this

>>> print df_2.columns.isin(df_1.columns)
array([ True,  True, False], dtype=bool)

Columns B and C from df_2 exist in df_1 while D doesn't

My question is: does anyone know of a way to return the columns' labels for columns that exist in df_2 but not in df_1

something like this

array([u'D'], dtype=string)

Thank you in advance!

Answer 1

Pandas index object have set-like properties, so you can directly do:

df_2.columns.difference(df_1.columns)
Index([u'D'], dtype='object')

You can also use operators like &|^ to compute intersection, union and symmetric difference:

df_1.columns & df_2.columns
Index([u'B', u'C'], dtype='object')

df_1.columns | df_2.columns
Index([u'A', u'B', u'C', u'D'], dtype='object')

df_1.columns ^ df_2.columns
Index([u'A', u'D'], dtype='object')

There use to be the -operator for difference, now deprecated:

df_2.columns - df_1.columns
FutureWarning: using '-' to provide set differences with Indexes is deprecated, use .difference()
Index([u'D'], dtype='object')