Another option, without dropping or filtering in a loop:
import numpy as np
import pandas as pd
# Create a dataframe with columns A,B,C and D
df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
# include the columns you want
df[df.columns[df.columns.isin(['A', 'B'])]]
# or more simply include columns:
df[['A', 'B']]
# exclude columns you don't want
df[df.columns[~df.columns.isin(['C','D'])]]
# or even simpler since 0.24
df[df.columns.difference(['C', 'D'])]