如何使用索引和值迭代 1d NumPy 数组 [英] How to iterate 1d NumPy array with index and value
问题描述
对于python dict
,我可以使用iteritems()
同时循环遍历键和值.但是我找不到 NumPy 数组的这种功能.我必须像这样手动跟踪 idx
:
For python dict
, I could use iteritems()
to loop through key and value at the same time. But I cannot find such functionality for NumPy array. I have to manually track idx
like this:
idx = 0
for j in theta:
some_function(idx,j,theta)
idx += 1
有没有更好的方法来做到这一点?
Is there a better way to do this?
推荐答案
有几个替代方案.下面假设您正在迭代一维 NumPy 数组.
There are a few alternatives. The below assumes you are iterating over a 1d NumPy array.
for j in range(theta.shape[0]): # or range(len(theta))
some_function(j, theta[j], theta)
请注意这是唯一 3 种解决方案中可以与numba
.这一点值得注意,因为显式迭代 NumPy 数组通常只有在与 numba
或其他预编译方法结合使用时才有效.
Note this is the only of the 3 solutions which will work with numba
. This is noteworthy since iterating over a NumPy array explicitly is usually only efficient when combined with numba
or another means of pre-compilation.
for idx, j in enumerate(theta):
some_function(idx, j, theta)
最有效的 3 种一维数组解决方案.请参阅下面的基准测试.
The most efficient of the 3 solutions for 1d arrays. See benchmarking below.
for idx, j in np.ndenumerate(theta):
some_function(idx[0], j, theta)
注意 idx[0]
中的额外索引步骤.这是必要的,因为一维 NumPy 数组的索引(如 shape
)是作为单例元组给出的.对于一维数组,np.ndenumerate
效率低下;它的好处只显示在多维数组中.
Notice the additional indexing step in idx[0]
. This is necessary since the index (like shape
) of a 1d NumPy array is given as a singleton tuple. For a 1d array, np.ndenumerate
is inefficient; its benefits only show for multi-dimensional arrays.
# Python 3.7, NumPy 1.14.3
np.random.seed(0)
arr = np.random.random(10**6)
def enumerater(arr):
for index, value in enumerate(arr):
index, value
pass
def ranger(arr):
for index in range(len(arr)):
index, arr[index]
pass
def ndenumerater(arr):
for index, value in np.ndenumerate(arr):
index[0], value
pass
%timeit enumerater(arr) # 131 ms
%timeit ranger(arr) # 171 ms
%timeit ndenumerater(arr) # 579 ms
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