如果我只有numpy.array的字符串表示形式：
>>> import numpy as np
>>> arr = np.random.randint(0, 10, (10, 10))
>>> print(arr) # this one!
[[9 4 7 3]
[1 6 4 2]
[6 7 6 0]
[0 5 6 7]]
如何将其转换回numpy数组？实际插入,并不复杂，但我正在寻找一种程序化的方法。
使用,替换空格的简单正则表达式实际上适用于单位数整数：
>>> import re
>>> sub = re.sub('\s+', ',', """[[8 6 2 4 0 2]
... [3 5 8 4 5 6]
... [4 6 3 3 0 3]]
... """)
>>> sub
'[[8,6,2,4,0,2],[3,5,8,4,5,6],[4,6,3,3,0,3]],' # the trailing "," is a bit annoying
它可以被转换为几乎（dtype可能丢失，但没关系）相同的数组：
>>> import ast
>>> np.array(ast.literal_eval(sub)[0])
array([[8, 6, 2, 4, 0, 2],
[3, 5, 8, 4, 5, 6],
[4, 6, 3, 3, 0, 3]])
但它无法用于多位数整数和浮点数：
>>> re.sub('\s+', ',', """[[ 0. 1. 6. 9. 1. 4.]
... [ 4. 8. 2. 3. 6. 1.]]
... """)
'[[,0.,1.,6.,9.,1.,4.],[,4.,8.,2.,3.,6.,1.]],'
因为它们在开头有一个额外的,。
解决方案不一定需要基于正则表达式，任何其他适用于 unabriged 的方法（不会缩短为...）bool / int / float / complex数组为1 -4维度可以。
答案 0 :(得分：3)
这是一个非常手动的解决方案：
import re
import numpy
def parse_array_str(array_string):
tokens = re.findall(r''' # Find all...
\[ | # opening brackets,
\] | # closing brackets, or
[^\[\]\s]+ # sequences of other non-whitespace characters''',
array_string,
flags = re.VERBOSE)
tokens = iter(tokens)
# Chomp first [, handle case where it's not a [
first_token = next(tokens)
if first_token != '[':
# Input must represent a scalar
if next(tokens, None) is not None:
raise ValueError("Can't parse input.")
return float(first_token) # or int(token), but not bool(token) for bools
list_form = []
stack = [list_form]
for token in tokens:
if token == '[':
# enter a new list
stack.append([])
stack[-2].append(stack[-1])
elif token == ']':
# close a list
stack.pop()
else:
stack[-1].append(float(token)) # or int(token), but not bool(token) for bools
if stack:
raise ValueError("Can't parse input - it might be missing text at the end.")
return numpy.array(list_form)
或者基于检测插入逗号的位置的手动解决方案：
import re
import numpy
pattern = r'''# Match (mandatory) whitespace between...
(?<=\]) # ] and
\s+
(?= \[) # [, or
|
(?<=[^\[\]\s])
\s+
(?= [^\[\]\s]) # two non-bracket non-whitespace characters
'''
# Replace such whitespace with a comma
fixed_string = re.sub(pattern, ',', array_string, flags=re.VERBOSE)
output_array = numpy.array(ast.literal_eval(fixed_string))
答案 1 :(得分：1)
<强>更新
np.array(ast.literal_eval(re.sub(r'\]\s*\[',
r'],[',
re.sub(r'(\d+)\s+(\d+)',
r'\1,\2',
a.replace('\n','')))))
<强>测试
In [345]: a = np.random.rand(3,3,20).__str__()
In [346]: np.array(ast.literal_eval(re.sub(r'\]\s*\[',
...: r'],[',
...: re.sub(r'(\d+)\s+(\d+)',
...: r'\1,\2',
...: a.replace('\n','')))))
Out[346]:
array([[[ 1.61804506e-01, 8.12734833e-01, 6.35872020e-01, 7.45560321e-01, 7.60322379e-01, 1.50271532e-01, 7.43559134e-01, 5.21169923e-
01, 4.10560219e-01, 1.77891635e-01,
8.77997042e-01, 5.52165694e-02, 4.40322089e-01, 8.82732323e-01, 3.12101843e-01, 9.49019544e-01, 1.69709407e-01, 5.35675968e-
01, 3.53186538e-01, 2.39804555e-01],
[ 2.59834852e-01, 7.13464074e-01, 4.24374709e-01, 7.45214854e-01, 2.54193920e-01, 9.43753568e-01, 3.19657128e-02, 6.04311934e-
01, 4.58913230e-01, 9.21777675e-01,
7.60741980e-02, 8.25952339e-01, 1.37270639e-01, 7.42065132e-01, 9.05089275e-01, 9.90206513e-02, 2.00671342e-01, 9.29283429e-
01, 8.87469279e-01, 2.78824797e-01],
[ 5.49303597e-01, 1.68139999e-01, 9.52643331e-01, 8.97801805e-01, 8.34317042e-01, 3.61338265e-01, 1.97822206e-01, 1.44672484e-
01, 4.62311800e-01, 6.45563044e-01,
3.96650080e-01, 9.66557989e-01, 5.55279111e-01, 6.95327885e-01, 8.77989215e-01, 3.09452892e-01, 4.34898544e-02, 6.18538982e-
01, 6.11605477e-03, 5.30348496e-03]],
[[ 4.67741090e-01, 4.18749234e-01, 4.92742479e-01, 3.12952835e-01, 1.66866007e-01, 1.81524074e-01, 3.48737055e-01, 3.96121943e-
01, 7.56894807e-01, 4.99569007e-02,
9.48425036e-01, 1.30331685e-01, 3.60872691e-01, 4.98930072e-01, 7.14775531e-01, 5.50048525e-01, 6.12293600e-01, 6.24329775e-
01, 3.74200599e-01, 6.77087300e-01],
[ 3.64029724e-01, 5.12225561e-01, 6.52844356e-01, 1.36063860e-01, 5.95311924e-01, 7.31286536e-01, 3.85353941e-01, 1.17983007e-
01, 3.78948410e-01, 3.66223737e-01,
4.78195933e-01, 3.46903190e-01, 7.59476546e-01, 4.38877386e-01, 7.33342832e-01, 9.38044045e-01, 6.80193266e-01, 1.76191976e-
01, 2.84027688e-01, 8.85565762e-01],
[ 1.25801396e-01, 7.62014084e-01, 7.57817614e-01, 5.44511396e-01, 2.77615151e-01, 6.94968328e-01, 9.64537639e-01, 7.79804895e-
01, 8.45911428e-01, 1.59562236e-01,
7.14207030e-01, 9.26019437e-01, 1.84258959e-01, 8.37627772e-01, 9.72586483e-01, 3.87408269e-01, 1.95596555e-01, 3.51684372e-
02, 7.14297398e-02, 2.70039164e-01]],
[[ 3.03855673e-01, 7.72762928e-01, 5.63591643e-01, 7.58142274e-01, 4.71340149e-01, 1.50447988e-01, 4.24416607e-01, 3.53647908e-
01, 4.83022443e-01, 7.72650844e-01,
4.05579568e-01, 4.64825394e-01, 3.74864150e-01, 8.04635163e-01, 3.29960889e-01, 8.82488417e-01, 6.05332753e-01, 1.84514406e-
01, 4.47145930e-01, 6.96907260e-01],
[ 1.54041028e-01, 2.33380875e-01, 7.34935729e-01, 8.13397766e-01, 6.26194271e-02, 9.40103450e-01, 6.24356287e-01, 2.26074683e-
01, 5.43054373e-01, 7.03495296e-02,
4.68091539e-02, 7.30366454e-01, 5.27159134e-01, 1.33293015e-01, 4.68391358e-01, 8.25307079e-01, 9.74953928e-01, 2.20242983e-
01, 3.42050900e-01, 7.86851567e-01],
[ 4.49176834e-01, 2.77129577e-01, 1.18051369e-01, 4.99016389e-01, 4.54702611e-04, 2.17932718e-01, 8.83065335e-01, 9.58966789e-
02, 1.52448380e-01, 7.18588641e-01,
3.73546613e-01, 1.66186769e-01, 5.80381932e-01, 3.42510041e-01, 6.75739930e-01, 1.85382205e-01, 3.26533424e-01, 7.35004900e-
01, 9.22527439e-01, 9.96079190e-01]]])
旧回答：
我们可以尝试使用Pandas：
import io
import pandas as pd
In [294]: pd.read_csv(io.StringIO(a.replace('\n', '').replace(']', '\n').replace('[','')),
delim_whitespace=True, header=None).values
Out[294]:
array([[ 0.96725219, 0.01808783, 0.63087793, 0.45407222, 0.30586779, 0.04848813, 0.01797095],
[ 0.87762897, 0.07705762, 0.33049588, 0.91429797, 0.5776607 , 0.18207652, 0.2355932 ],
[ 0.68803166, 0.31540537, 0.92606902, 0.83542726, 0.43457601, 0.44952604, 0.35121332],
[ 0.14366487, 0.23486924, 0.16421432, 0.27709387, 0.19646975, 0.8243488 , 0.37708642],
[ 0.07594925, 0.36608386, 0.02087877, 0.07507932, 0.40005067, 0.84625563, 0.62827931],
[ 0.63662663, 0.41408688, 0.43447501, 0.22135816, 0.58944708, 0.66456168, 0.5871466 ],
[ 0.16807584, 0.70981667, 0.18597074, 0.02034372, 0.94706437, 0.61333699, 0.8444439 ]])
注意：它可能仅适用于没有...（省略号）