我有一个numpy 3d数组，我想在其中找到零值出现的概率。
因此，首先需要计算轴= 0中存在多少零。
与arr.sum（axis = 0）类似，有任何方法可以返回我的3d数组中具有零点数的2D数组。
>>> print arr
[[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 7.43459761e-02 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
...,
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 4.58999968e+00
1.50299997e+01 2.30100002e+01]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 1.86000001e+00
5.51999998e+00 1.77899990e+01]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
...,
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 9.39900055e+01
1.11450005e+02 1.15800003e+02]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 8.20799942e+01
9.74399948e+01 1.06649994e+02]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
[[ 0.00000000e+00 3.74535918e-02 0.00000000e+00 ..., 3.89999986e-01
9.89999950e-01 9.30000007e-01]
[ 9.29514784e-03 5.75268008e-02 0.00000000e+00 ..., 7.50000000e-01
9.89999950e-01 1.28999996e+00]
[ 0.00000000e+00 7.26988986e-02 5.94767854e-02 ..., 1.71000004e+00
1.43999994e+00 7.19999969e-01]
...,
[ 4.54575920e+00 4.91925001e+00 1.09031944e+01 ..., 1.12470001e+02
9.32400055e+01 6.66599884e+01]
[ 0.00000000e+00 6.33960581e+00 1.05395260e+01 ..., 1.37279984e+02
1.22159996e+02 7.25400009e+01]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
...,
[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
8.99999961e-02 0.00000000e+00]
...,
[ 2.09804267e-01 1.32204843e+00 6.83585852e-02 ..., 7.19999969e-01
1.49999991e-01 0.00000000e+00]
[ 3.02928180e-01 6.30806535e-02 2.42170334e+00 ..., 4.86000013e+00
3.98999977e+00 5.48999977e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
...,
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 5.39999962e-01
5.99999987e-02 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 1.50000000e+00
1.19999997e-01 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
...,
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
答案 0 :(得分：1)
only_z = numpy.copy(arr)
only_z[only_z==0]=1
only_z[only_z!=1]=0
only_z_sum = only_z.sum(axis=0)
prob_of_z = only_z_sum/31
这是我现在能找到的最简单的方法，我有所有出现零的概率。
>>> print prob_of_z
[[ 0.96774194 0.80645161 0.90322581 0.90322581 0.87096774 0.90322581
0.87096774 0.90322581 0.90322581 0.83870968 0.83870968 0.83870968
0.87096774 0.93548387 0.90322581 0.93548387 0.90322581 0.96774194]
[ 0.93548387 0.77419355 0.90322581 0.90322581 0.90322581 0.90322581
0.87096774 0.87096774 0.90322581 0.80645161 0.77419355 0.80645161
0.90322581 0.93548387 0.93548387 0.93548387 0.90322581 0.93548387]
[ 0.80645161 0.80645161 0.83870968 0.87096774 0.87096774 0.83870968
0.87096774 0.83870968 0.90322581 0.83870968 0.87096774 0.90322581
0.87096774 0.90322581 0.87096774 0.90322581 0.90322581 0.87096774]
[ 0.83870968 0.74193548 0.80645161 0.87096774 0.83870968 0.80645161
0.83870968 0.83870968 0.87096774 0.83870968 0.83870968 0.77419355
0.77419355 0.77419355 0.77419355 0.83870968 0.80645161 0.80645161]
[ 0.80645161 0.80645161 0.77419355 0.83870968 0.83870968 0.83870968
0.83870968 0.83870968 0.80645161 0.77419355 0.77419355 0.74193548
0.74193548 0.77419355 0.70967742 0.83870968 0.77419355 0.77419355]
[ 0.77419355 0.77419355 0.74193548 0.77419355 0.80645161 0.77419355
0.74193548 0.67741935 0.64516129 0.67741935 0.70967742 0.77419355
0.70967742 0.70967742 0.80645161 0.80645161 0.70967742 0.67741935]
[ 0.70967742 0.77419355 0.70967742 0.70967742 0.67741935 0.70967742
0.74193548 0.58064516 0.5483871 0.61290323 0.74193548 0.64516129
0.67741935 0.74193548 0.74193548 0.70967742 0.74193548 0.74193548]
[ 0.67741935 0.67741935 0.64516129 0.64516129 0.64516129 0.67741935
0.61290323 0.58064516 0.58064516 0.58064516 0.64516129 0.64516129
0.67741935 0.67741935 0.67741935 0.74193548 0.67741935 0.70967742]
[ 0.61290323 0.64516129 0.64516129 0.67741935 0.64516129 0.61290323
0.51612903 0.48387097 0.5483871 0.61290323 0.70967742 0.64516129
0.58064516 0.58064516 0.67741935 0.67741935 0.64516129 0.58064516]
[ 0.58064516 0.64516129 0.64516129 0.58064516 0.61290323 0.48387097
0.48387097 0.48387097 0.61290323 0.61290323 0.67741935 0.61290323
0.58064516 0.61290323 0.64516129 0.67741935 0.74193548 0.64516129]
[ 0.67741935 0.61290323 0.5483871 0.51612903 0.5483871 0.58064516
0.51612903 0.58064516 0.58064516 0.61290323 0.58064516 0.5483871
0.58064516 0.64516129 0.70967742 0.67741935 0.70967742 0.67741935]
[ 0.74193548 0.70967742 0.48387097 0.48387097 0.48387097 0.51612903
0.51612903 0.5483871 0.48387097 0.5483871 0.51612903 0.58064516
0.58064516 0.61290323 0.70967742 0.64516129 0.67741935 0.61290323]
[ 0.51612903 0.77419355 0.48387097 0.48387097 0.41935484 0.48387097
0.48387097 0.51612903 0.48387097 0.41935484 0.41935484 0.51612903
0.5483871 0.5483871 0.64516129 0.58064516 0.64516129 0.61290323]
[ 0.67741935 0.74193548 0.74193548 0.61290323 0.5483871 0.48387097
0.48387097 0.38709677 0.38709677 0.41935484 0.4516129 0.51612903
0.51612903 0.58064516 0.5483871 0.64516129 0.58064516 0.58064516]
[ 0.70967742 0.70967742 0.70967742 0.67741935 0.41935484 0.41935484
0.48387097 0.48387097 0.48387097 0.58064516 0.58064516 0.61290323
0.58064516 0.58064516 0.67741935 0.58064516 0.61290323 0.64516129]
[ 0.74193548 0.74193548 0.64516129 0.61290323 0.58064516 0.32258065
0.41935484 0.35483871 0.41935484 0.5483871 0.64516129 0.61290323
0.61290323 0.51612903 0.51612903 0.5483871 0.51612903 0.64516129]
[ 0.77419355 0.74193548 0.74193548 0.70967742 0.64516129 0.58064516
0.35483871 0.38709677 0.48387097 0.5483871 0.61290323 0.58064516
0.5483871 0.48387097 0.5483871 0.4516129 0.58064516 0.58064516]
[ 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. ]]
>>>