numpy的数组：平均列替换NaN值 [英] numpy array: replace nan values with average of columns

问题描述

我有实数大多是填补了numpy的阵列，但在这几个男值，以及

I've got a numpy array filled mostly with real numbers, but there is a few nan values in it as well.

我如何与列的平均值替换男就是他们在哪里？

How can I replace the nans with averages of columns where they are?

推荐答案

没有需要循环：

import  scipy.stats as stats
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 just convenient.
col_mean = stats.nanmean(a,axis=0)
print col_mean
[ 0.86726219  0.7030395   0.44528687  0.66640474]

#Find indicies 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]]

这篇关于numpy的数组：平均列替换NaN值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！