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 replaceinds = np.where(np.isnan(a))#Place column means in the indices. Align the arrays using takea[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]]
