numpy 数组:用列的平均值替换 nan 值 [英] numpy array: replace nan values with average of columns
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问题描述
我有一个主要用实数填充的 numpy 数组,但其中也有一些 nan
值.
I've got a numpy array filled mostly with real numbers, but there is a few nan
values in it as well.
如何将 nan
替换为它们所在列的平均值?
How can I replace the nan
s with averages of columns where they are?
推荐答案
无需循环:
print(a)
[[ 0.93230948 nan 0.47773439 0.76998063]
[ 0.94460779 0.87882456 0.79615838 0.56282885]
[ 0.94272934 0.48615268 0.06196785 nan]
[ 0.64940216 0.74414127 nan nan]]
#Obtain mean of columns as you need, nanmean is convenient.
col_mean = np.nanmean(a, axis=0)
print(col_mean)
[ 0.86726219 0.7030395 0.44528687 0.66640474]
#Find indices that you need to replace
inds = np.where(np.isnan(a))
#Place column means in the indices. Align the arrays using take
a[inds] = np.take(col_mean, inds[1])
print(a)
[[ 0.93230948 0.7030395 0.47773439 0.76998063]
[ 0.94460779 0.87882456 0.79615838 0.56282885]
[ 0.94272934 0.48615268 0.06196785 0.66640474]
[ 0.64940216 0.74414127 0.44528687 0.66640474]]
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