Python 如果 numpy 数组元素高于特定阈值，则将它们设置为零
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Set numpy array elements to zero if they are above a specific threshold
提问by bluevoxel
Say, I have a numpy array consists of 10elements, for example:
说，我有一个由10元素组成的 numpy 数组，例如：
a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
Now I want to efficiently set all avalues higher than 10to 0, so I'll get:
现在我想有效地将所有a值设置为高于10to 0，所以我会得到：
[2, 0, 0, 7, 9, 0, 0, 0, 5, 3]
[2, 0, 0, 7, 9, 0, 0, 0, 5, 3]
Because I currently use a forloop, which is very slow:
因为我目前使用的是一个for循环，速度很慢：
# Zero values below "threshold value".
def flat_values(sig, tv):
"""
:param sig: signal.
:param tv: threshold value.
:return:
"""
for i in np.arange(np.size(sig)):
if sig[i] < tv:
sig[i] = 0
return sig
How can I achieve that in the most efficient way, having in mind big arrays of, say, 10^6elements?
我怎样才能以最有效的方式实现这一点，考虑到10^6元素的大数组？
采纳答案by Marcus Müller
Generally, list comprehensions are faster than forloops in python (because python knows that it doesn't need to care for a lot of things that might happen in a regular forloop):
通常，列表推导式比forpython 中的循环更快（因为 python 知道它不需要关心在常规for循环中可能发生的很多事情）：
a = [0 if a_ > thresh else a_ for a_ in a]
but, as @unutbu correctly pointed out, numpy allows list indexing, and element-wise comparison giving you index lists, so:
但是，正如@unutbu 正确指出的那样，numpy 允许列表索引和元素比较为您提供索引列表，因此：
super_threshold_indices = a > thresh
a[super_threshold_indices] = 0
would be even faster.
会更快。
Generally, when applying methods on vectors of data, have a look at numpy.ufuncs, which often perform much better than python functions that you map using any native mechanism.
通常，在对数据向量应用方法时，请查看numpy.ufuncs，它的性能通常比使用任何本机机制映射的 Python 函数要好得多。
回答by unutbu
In [7]: a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
In [8]: a[a > 10] = 0
In [9]: a
Out[9]: array([2, 0, 0, 7, 9, 0, 0, 0, 5, 3])
回答by yellow01
If you don't want to change your original array
如果您不想更改原始数组
In [1]: import numpy as np
In [2]: a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
In [3]: b = a * (a <= 10)
In [4]: a
Out[4]: array([ 2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
In [5]: b
Out[5]: array([2, 0, 0, 7, 9, 0, 0, 0, 5, 3])
