get column names from csv file using pandas [duplicate]

Question

I want to get a list of the column headers from a Pandas DataFrame. The DataFrame will come from user input, so I won't know how many columns there will be or what they will be called.

For example, if I'm given a DataFrame like this:

>>> 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

I would get a list like this:

>>> header_list
['y', 'gdp', 'cap']

Answer 1

You can get the values as a list by doing:

list(my_dataframe.columns.values)

Also you can simply use (as shown in Ed Chum's answer):

list(my_dataframe)

Answer 2

There is a built-in method which is the most performant:

my_dataframe.columns.values.tolist()

.columns returns an Index, .columns.values returns an array and this has a helper function .tolist to return a list.

If performance is not as important to you, Index objects define a .tolist() method that you can call directly:

my_dataframe.columns.tolist()

The difference in performance is obvious:

%timeit df.columns.tolist()
16.7 µs ± 317 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit df.columns.values.tolist()
1.24 µs ± 12.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

---

For those who hate typing, you can just call list on df, as so:

list(df)

Answer 3

Extended Iterable Unpacking (Python 3.5+): [*df] and Friends

Unpacking generalizations (PEP 448) have been introduced with Python 3.5. So, the following operations are all possible.

df = pd.DataFrame('x', columns=['A', 'B', 'C'], index=range(5))
df

   A  B  C
0  x  x  x
1  x  x  x
2  x  x  x
3  x  x  x
4  x  x  x

If you want a list....

[*df]
# ['A', 'B', 'C']

Or, if you want a set,

{*df}
# {'A', 'B', 'C'}

Or, if you want a tuple,

*df,  # Please note the trailing comma
# ('A', 'B', 'C')

Or, if you want to store the result somewhere,

*cols, = df  # A wild comma appears, again
cols
# ['A', 'B', 'C']

... if you're the kind of person who converts coffee to typing sounds, well, this is going consume your coffee more efficiently ;)

P.S.: if performance is important, you will want to ditch the solutions above in favour of

df.columns.to_numpy().tolist()
# ['A', 'B', 'C']

This is similar to Ed Chum's answer, but updated for v0.24 where .to_numpy() is preferred to the use of .values. See this answer (by me) for more information.

Visual Check

Since I've seen this discussed in other answers, you can use iterable unpacking (no need for explicit loops).

print(*df)
A B C

print(*df, sep='\n')
A
B
C

---

Critique of Other Methods

Don't use an explicit for loop for an operation that can be done in a single line (list comprehensions are okay).

Next, using sorted(df) does not preserve the original order of the columns. For that, you should use list(df) instead.

Next, list(df.columns) and list(df.columns.values) are poor suggestions (as of the current version, v0.24). Both Index (returned from df.columns) and NumPy arrays (returned by df.columns.values) define .tolist() method which is faster and more idiomatic.

Lastly, listification i.e., list(df) should only be used as a concise alternative to the aforementioned methods for Python 3.4 or earlier where extended unpacking is not available.

Answer 4

I did some quick tests, and perhaps unsurprisingly the built-in version using dataframe.columns.values.tolist() is the fastest:

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

(I still really like the list(dataframe) though, so thanks EdChum!)

Answer 5

The simplest option would be: list(my_dataframe.columns) or my_dataframe.columns.tolist()

No need for the complex stuff above :)