Suppose I have the following:

df = pd.DataFrame({'a':range(2), 'b':range(2), 'c':range(2), 'd':range(2)})

I'd like to "pop" two columns ('c' and 'd') off the dataframe, into a new dataframe, leaving 'a' and 'b' behind in the original df. The following does not work:

df2 = df.pop(['c', 'd'])

Here's my error:

TypeError: '['c', 'd']' is an invalid key

Does anyone know a quick, classy solution, besides doing the following?

df2 = df[['c', 'd']]
df3 = df[['a', 'b']]

I know the above code is not that tedious to type, but this is why DataFrame.pop was invented--to save us a step when popping one column off a database.

This will have to be a two step process (you cannot get around this, because as rightly mentioned, pop works for a single column and returns a Series).

First, slice df (step 1), and then drop those columns (step 2).

df2 = df[['c', 'd']].copy()
df = df.drop(['c', 'd'], axis=1)

And here's the one-liner version using pd.concat:

df2 = pd.concat([df.pop(x) for x in ['c', 'd']], axis=1)

This is still a two step process, but you're doing it in one line.

df

   a  b
0  0  0
1  1  1

df2

   c  d
0  0  0
1  1  1

With that said, I think there's value in allowing pop to take a list-like of column headers appropriately returning a DataFrame of popped columns. This would make a good feature request for GitHub, assuming one has the time to write one up.