[英]Counting the number of non-NaN elements in a numpy ndarray in Python
I need to calculate the number of non-NaN elements in a numpy ndarray matrix.我需要计算 numpy ndarray 矩阵中非 NaN 元素的数量。 How would one efficiently do this in Python?
如何在 Python 中有效地做到这一点? Here is my simple code for achieving this:
这是我实现此目的的简单代码:
import numpy as np
def numberOfNonNans(data):
count = 0
for i in data:
if not np.isnan(i):
count += 1
return count
Is there a built-in function for this in numpy? numpy 中是否有内置函数? Efficiency is important because I'm doing Big Data analysis.
效率很重要,因为我正在做大数据分析。
Thnx for any help!感谢您的帮助!
np.count_nonzero(~np.isnan(data))
~
inverts the boolean matrix returned from np.isnan
. ~
反转从np.isnan
返回的布尔矩阵。
np.count_nonzero
counts values that is not 0\false. np.count_nonzero
计算非 0\false 的值。 .sum
should give the same result. .sum
应该给出相同的结果。 But maybe more clearly to use count_nonzero
但也许更清楚地使用
count_nonzero
Testing speed:测试速度:
In [23]: data = np.random.random((10000,10000))
In [24]: data[[np.random.random_integers(0,10000, 100)],:][:, [np.random.random_integers(0,99, 100)]] = np.nan
In [25]: %timeit data.size - np.count_nonzero(np.isnan(data))
1 loops, best of 3: 309 ms per loop
In [26]: %timeit np.count_nonzero(~np.isnan(data))
1 loops, best of 3: 345 ms per loop
In [27]: %timeit data.size - np.isnan(data).sum()
1 loops, best of 3: 339 ms per loop
data.size - np.count_nonzero(np.isnan(data))
seems to barely be the fastest here. data.size - np.count_nonzero(np.isnan(data))
似乎几乎不是这里最快的。 other data might give different relative speed results.其他数据可能会给出不同的相对速度结果。
Even though is not the fastest choice, if performance is not an issue you can use:即使不是最快的选择,如果性能不是问题,您可以使用:
sum(~np.isnan(data))
. sum(~np.isnan(data))
。
In [7]: %timeit data.size - np.count_nonzero(np.isnan(data))
10 loops, best of 3: 67.5 ms per loop
In [8]: %timeit sum(~np.isnan(data))
10 loops, best of 3: 154 ms per loop
In [9]: %timeit np.sum(~np.isnan(data))
10 loops, best of 3: 140 ms per loop
To determine if the array is sparse, it may help to get a proportion of nan values要确定数组是否稀疏,可能有助于获取一定比例的 nan 值
np.isnan(ndarr).sum() / ndarr.size
If that proportion exceeds a threshold, then use a sparse array, eg - https://sparse.pydata.org/en/latest/如果该比例超过阈值,则使用稀疏数组,例如 - https://sparse.pydata.org/en/latest/
An alternative, but a bit slower alternative is to do it over indexing.另一种选择,但有点慢的选择是通过索引来完成。
np.isnan(data)[np.isnan(data) == False].size
In [30]: %timeit np.isnan(data)[np.isnan(data) == False].size
1 loops, best of 3: 498 ms per loop
The double use of np.isnan(data)
and the ==
operator might be a bit overkill and so I posted the answer only for completeness. np.isnan(data)
和==
运算符的双重使用可能有点矫枉过正,所以我发布答案只是为了完整性。
len([i for i in data if np.isnan(i) == True])
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