>>> my_dataframe
    y  gdp  cap
0   1    2    5
1   2    3    9
2   8    7    2
3   3    4    7
4   6    7    7
5   4    8    3
6   8    2    8
7   9    9   10
8   6    6    4
9  10   10    7

>>> header_list
[y, gdp, cap]

list(my_dataframe.columns.values)

list(my_dataframe)

my_dataframe.columns.values.tolist()

list(df)

In [1]: %timeit [column for column in df]
1000 loops, best of 3: 81.6 µs per loop

In [2]: %timeit df.columns.values.tolist()
10000 loops, best of 3: 16.1 µs per loop

In [3]: %timeit list(df)
10000 loops, best of 3: 44.9 µs per loop

In [4]: % timeit list(df.columns.values)
10000 loops, best of 3: 38.4 µs per loop

df.columns.tolist()

>>> list(my_dataframe)
['y', 'gdp', 'cap']

>>> [c for c in my_dataframe]
['y', 'gdp', 'cap']

In [97]: %timeit df.columns.values.tolist()
100000 loops, best of 3: 2.97 µs per loop

In [98]: %timeit df.columns.tolist()
10000 loops, best of 3: 9.67 µs per loop

[column for column in my_dataframe]

sorted(df)

df.columns

list(my_dataframe.columns)

n = []
for i in my_dataframe.columns:
    n.append(i)
print n

df = pd.DataFrame({'col1' : np.random.randn(3), 'col2' : np.random.randn(3)},
                 index=['a', 'b', 'c'])