Subsetting index from Pandas DataFrame

Question

I have a DataFrame with columns [A, B, C, D, E, F, G, H].

An index has been made with columns [D, G, H]:

>>> print(dgh_columns)
Index(['D', 'G', 'H'], dtype='object')

How can I retrieve the original DataFrame without the columns D, G, H ?

Is there an index subset operation?

Ideally, this would be:

df[df.index - dgh_columns]

But this doesn't seem to work

Answer 1

I think you can use Index.difference:

df[df.columns.difference(dgh_columns)]

Sample:

df = pd.DataFrame({'A':[1,2,3],
                   'B':[4,5,6],
                   'C':[7,8,9],
                   'D':[1,3,5],
                   'E':[7,8,9],
                   'F':[1,3,5],
                   'G':[5,3,6],
                   'H':[7,4,3]})

print (df)
   A  B  C  D  E  F  G  H
0  1  4  7  1  7  1  5  7
1  2  5  8  3  8  3  3  4
2  3  6  9  5  9  5  6  3

dgh_columns = pd.Index(['D', 'G', 'H'])
print (df[df.columns.difference(dgh_columns)])
   A  B  C  E  F
0  1  4  7  7  1
1  2  5  8  8  3
2  3  6  9  9  5

Numpy solution with numpy.setxor1d or numpy.setdiff1d:

dgh_columns = pd.Index(['D', 'G', 'H'])
print (df[np.setxor1d(df.columns, dgh_columns)])
   A  B  C  E  F
0  1  4  7  7  1
1  2  5  8  8  3
2  3  6  9  9  5

---

dgh_columns = pd.Index(['D', 'G', 'H'])
print (df[np.setdiff1d(df.columns, dgh_columns)])
   A  B  C  E  F
0  1  4  7  7  1
1  2  5  8  8  3
2  3  6  9  9  5