Is pandas.DataFrame.columns.values.tolist() the same as pandas.DataFrame.columns.tolist()

Question

We have both code popping up in our codebase

pandas.DataFrame.columns.values.tolist()
pandas.DataFrame.columns.tolist()

Are these always identical? I'm not sure why the values variant pops up in the places it does, seems like the direct columns.tolist() is all that's needed to get the column names. I'm looking to clean up the code a bit if this is the case.

Introspecting a bit seems to suggest values is just some implementation detail being a numpy.ndarray

>>> import pandas
>>> d = pandas.DataFrame( { 'a' : [1,2,3], 'b' : [0,1,3]} )
>>> d
   a  b
0  1  0
1  2  1
2  3  3
>>> type(d.columns)
<class 'pandas.core.indexes.base.Index'>
>>> type(d.columns.values)
<class 'numpy.ndarray'>
>>> type(d.columns.tolist())
<class 'list'>
>>> type(d.columns.values.tolist())
<class 'list'>
>>> d.columns.values
array(['a', 'b'], dtype=object)
>>> d.columns.values.tolist()
['a', 'b']
>>> d.columns
Index(['a', 'b'], dtype='object')
>>> d.columns.tolist()
['a', 'b']

Answer 1

Output is same, but if really big df timings are different:

np.random.seed(23)
df = pd.DataFrame(np.random.randint(3, size=(5,10000)))
df.columns = df.columns.astype(str)
print (df)

In [90]: %timeit df.columns.values.tolist()
10000 loops, best of 3: 79.5 µs per loop

In [91]: %timeit df.columns.tolist()
10000 loops, best of 3: 173 µs per loop

Also uses different functions:

Index.values with numpy.ndarray.tolist

Index.tolist

Thanks Mitch for another solution:

In [93]: %timeit list(df.columns.values)
1000 loops, best of 3: 169 µs per loop

Answer 2

d = pandas.DataFrame( { 'a' : [1,2,3], 'b' : [0,1,3]} )

or you can simply do

list(d)# it is same with d.columns.tolist()
Out[327]: ['a', 'b']

#  Time 
% timeit list(df) # after run the time , this is the slowest on my side . 
10000 loops, best of 3: 135 µs per loop