convert string representation of array to numpy array in python


I can convert a string representation of a list to a list with ast.literal_eval. Is there an equivalent for a numpy array?

x = arange(4)
xs = str(x)
'[0 1 2 3]'
# how do I convert xs back to an array

Using ast.literal_eval(xs) raises a SyntaxError. I can do the string parsing if I need to, but I thought there might be a better solution.

Asked By: jdmcbr



For 1D arrays, Numpy has a function called fromstring, so it can be done very efficiently without extra libraries.

Briefly you can parse your string like this:

s = '[0 1 2 3]'
a = np.fromstring(s[1:-1],, sep=' ')
print(a) # [0 1 2 3]

For nD arrays, one can use .replace() to remove the brackets and .reshape() to reshape to desired shape, or use Merlin’s solution.

Answered By: Da Tong

Try this:

xs = '[0 1 2 3]'

import re, ast
ls = re.sub('s+', ',', xs)
a = np.array(ast.literal_eval(ls))
a  # -> array([0, 1, 2, 3])    
Answered By: Merlin

if elements of lists are 2D float. ast.literal_eval() cannot handle a lot very complex list of list of nested list.

Therefore, it is better to parse list of list as dict and dump the string.

while loading a saved dump, ast.literal_eval() handles dict as strings in a better way. convert the string to dict and then dict to list of list

k = np.array([[[0.09898942, 0.22804536],[0.06109612, 0.19022354],[0.93369348, 0.53521671],[0.64630094, 0.28553219]],[[0.94503154, 0.82639528],[0.07503319, 0.80149062],[0.1234832 , 0.44657691],[0.7781163 , 0.63538195]]])

d = dict(enumerate(k.flatten(), 1))
d = str(d) ## dump as string  (pickle and other packages parse the dump as bytes)

m = ast.literal_eval(d) ### convert the dict as str to  dict

m = np.fromiter(m.values(), dtype=float) ## convert m to nparray

Answered By: Soum

Use np.matrix to convert a string to a numpy matrix. Then, use np.asarray to convert the matrix to a numpy array with the same shape.

>>> s = "[1,2]; [3,4]"
>>> a = np.asarray(np.matrix(s)) 
>>> a
array([[1, 2],
       [3, 4]])

In contrast to the accepted answer, this works also for two dimensional arrays.

Answered By: Erel Segal-Halevi
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