I have the following DataFrame (df):
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
I liked Shoresh's answer to use set functionality to remove columns when you don't know the location, however this didn't work for my purpose as I need to keep the original column order (which has arbitrary column labels).
I got this to work though by using IndexedSet from the boltons package.
I also needed to re-add multiple column labels, so for a more general case I used the following code:
from boltons.setutils import IndexedSet
cols = list(IndexedSet(df.columns.tolist()) - set(['mean', 'std']))
cols[0:0] =['mean', 'std']
df = df[cols]
Hope this is useful to anyone searching this thread for a general solution.