Accessing every 1st element of Pandas DataFrame column containing lists

Question

I have a Pandas DataFrame with a column containing lists objects

      A
0   [1,2]
1   [3,4]
2   [8,9] 
3   [2,6]

How can I access the first element of each list and save it into a new column of the DataFrame? To get a result like this:

      A     new_col
0   [1,2]      1
1   [3,4]      3
2   [8,9]      8
3   [2,6]      2

I know this could be done via iterating over each row, but is there any "pythonic" way?

Answer 1

As always, remember that storing non-scalar objects in frames is generally disfavoured, and should really only be used as a temporary intermediate step.

That said, you can use the .str accessor even though it's not a column of strings:

>>> df = pd.DataFrame({"A": [[1,2],[3,4],[8,9],[2,6]]})
>>> df["new_col"] = df["A"].str[0]
>>> df
        A  new_col
0  [1, 2]        1
1  [3, 4]        3
2  [8, 9]        8
3  [2, 6]        2
>>> df["new_col"]
0    1
1    3
2    8
3    2
Name: new_col, dtype: int64

Answer 2

You can use map and a lambda function

df.loc[:, 'new_col'] = df.A.map(lambda x: x[0])

---

Answer 3

Use apply with x[0]:

df['new_col'] = df.A.apply(lambda x: x[0])
print df
        A  new_col
0  [1, 2]        1
1  [3, 4]        3
2  [8, 9]        8
3  [2, 6]        2

Answer 4

You can use the method str.get (same as str[]):

df['A'].str.get(0)

It behaves similarly to the Python dictionary's get method, returning NaN if the desired index is missing. For example:

s = pd.Series([[1, 2], []])
s.str.get(0) # s.str[0]

Output:

0    1.0
1    NaN
dtype: float64

Answer 5

You can just use a conditional list comprehension which takes the first value of any iterable or else uses None for that item. List comprehensions are very Pythonic.

df['new_col'] = [val[0] if hasattr(val, '__iter__') else None for val in df["A"]]

>>> df
        A  new_col
0  [1, 2]        1
1  [3, 4]        3
2  [8, 9]        8
3  [2, 6]        2

Timings

df = pd.concat([df] * 10000)

%timeit df['new_col'] = [val[0] if hasattr(val, '__iter__') else None for val in df["A"]]
100 loops, best of 3: 13.2 ms per loop

%timeit df["new_col"] = df["A"].str[0]
100 loops, best of 3: 15.3 ms per loop

%timeit df['new_col'] = df.A.apply(lambda x: x[0])
100 loops, best of 3: 12.1 ms per loop

%timeit df.A.map(lambda x: x[0])
100 loops, best of 3: 11.1 ms per loop

Removing the safety check ensuring an interable.

%timeit df['new_col'] = [val[0] for val in df["A"]]
100 loops, best of 3: 7.38 ms per loop