Convert a pandas Series of lists into a numpy array

Question

I want to convert a pandas Series of strings of list of numbers into a numpy array. What I have is something like:

ds = pd.Series(['[1 -2 0 1.2 4.34]', '[3.3 4 0 -1 9.1]'])

My desired output:

arr = np.array([[1, -2, 0, 1.2, 4.34], [3.3, 4, 0, -1, 9.1]])

What I have done so far is to convert the pandas Series to a Series of a list of numbers as:

ds1 = ds.apply(lambda x: [float(number) for number in x.strip('[]').split(' ')])

but I don't know how to go from ds1 to arr .

Answer 1

Use Series.str.strip + Series.str.split and create a new np.array with dtype=float :

arr = np.array(ds.str.strip('[]').str.split().tolist(), dtype='float')

Result:

print(arr)

array([[ 1.  , -2.  ,  0.  ,  1.2 ,  4.34],
       [ 3.3 ,  4.  ,  0.  , -1.  ,  9.1 ]])

Answer 2

You can try to remove the "[]" from the Series object first, then things will become easier, https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.split.html .

ds1 = ds.str.strip("[]")
# split and exapand the data, conver to numpy array
arr = ds1.str.split(" ", expand=True).to_numpy(dtype=float)

Then arr will be the right format you want,

array([[ 1.  , -2.  ,  0.  ,  1.2 ,  4.34],
       [ 3.3 ,  4.  ,  0.  , -1.  ,  9.1 ]])

Then I did a little profiling in comparison with Shubham's colution.

# Shubham's way
%timeit arr = np.array(ds.str.strip('[]').str.split().tolist(), dtype='float')
332 µs ± 5.72 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

# my way
%timeit ds.str.strip("[]").str.split(" ", expand=True).to_numpy(dtype=float)
741 µs ± 4.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Obviously, his solution is much faster! Cheers!