How do you pop multiple columns off a Pandas dataframe, into a new dataframe?

If you don't want to copy your original pd.DataFrame, using list comprehension has nice code

list_to_pop = ['a', 'b']
[df.pop(col) for col in list_to_pop]

Here's an alternative, but I'm not sure if it's more classy than your original solution:

df2 = pd.DataFrame([df.pop(x) for x in ['c', 'd']]).T
df3 = pd.DataFrame([df.pop(x) for x in ['a', 'b']]).T

Output:

print(df2)
#   c  d
#0  0  0
#1  1  1

print(df3)
#   a  b
#0  0  0
#1  1  1

new_df = old_df.loc[:,pop_columns]

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

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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.