问题:

Is there a way to avoid using specific values when applying sum and mean in numpy?

I'd like to avoid, for instance, the -999 value when calculating the result.

In [14]: c = np.matrix([[4., 2.],[4., 1.]])

In [15]: d = np.matrix([[3., 2.],[4., -999.]])

In [16]: np.sum([c, d], axis=0)
Out[16]:
array([[   7.,    4.],
       [   8., -998.]])

In [17]: np.mean([c, d], axis=0)
Out[17]:
array([[   3.5,    2. ],
       [   4. , -499. ]])

回答1:

Use a masked array:

>>> c = np.ma.array([[4., 2.], [4., 1.]])
>>> d = np.ma.masked_values([[3., 2.], [4., -999]], -999)

>>> np.ma.array([c, d]).sum(axis=0)
masked_array(data =
 [[7.0 4.0]
 [8.0 1.0]],
             mask =
 [[False False]
 [False False]],
       fill_value = 1e+20)

>>> np.ma.array([c, d]).mean(axis=0)
masked_array(data =
 [[3.5 2.0]
 [4.0 1.0]],
             mask =
 [[False False]
 [False False]],
       fill_value = 1e+20)

回答2:

One option is to replace the specific value with np.nan and then use numpy.nansum and numpy.nanmean as commented by @s.k:

import numpy as np
def nan_if(arr, value):
    return np.where(arr == value, np.nan, arr)

np.nansum([nan_if(c, -999), nan_if(d, -999)], axis=0)
#array([[ 7.,  4.],
#       [ 8.,  1.]])

np.nanmean([nan_if(c, -999), nan_if(d, -999)], axis=0)
#array([[ 3.5,  2. ],
#       [ 4. ,  1. ]])