Numpy MaskedArray.mean()函数| Python
numpy.MaskedArray.mean()
函数用于返回沿给定轴的屏蔽数组元素的平均值。这里屏蔽的条目被忽略,非有限的结果元素将被屏蔽。
Syntax : numpy.ma.mean(axis=None, dtype=None, out=None)
Parameters:
axis :[ int, optional] Axis along which the mean is computed. The default (None) is to compute the mean over the flattened array.
dtype : [dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
Return : [mean_along_axis, ndarray] A new array holding the result is returned unless out is specified, in which case a reference to out is returned.
代码#1:
# Python program explaining
# numpy.MaskedArray.mean() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input array
in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("Input array : ", in_arr)
# Now we are creating a masked array.
# by making entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]])
print ("Masked array : ", mask_arr)
# applying MaskedArray.mean
# methods to masked array
out_arr = mask_arr.mean()
print ("mean of masked array along default axis : ", out_arr)
Input array : [[ 1 2]
[ 3 -1]
[ 5 -3]]
Masked array : [[-- 2]
[-- -1]
[5 -3]]
mean of masked array along default axis : 0.75
代码#2:
# Python program explaining
# numpy.MaskedArray.mean() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input array
in_arr = geek.array([[1, 0, 3], [ 4, 1, 6]])
print ("Input array : ", in_arr)
# Now we are creating a masked array.
# by making one entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]])
print ("Masked array : ", mask_arr)
# applying MaskedArray.mean methods
# to masked array
out_arr1 = mask_arr.mean(axis = 0)
print ("mean of masked array along 0 axis : ", out_arr1)
out_arr2 = mask_arr.mean(axis = 1)
print ("mean of masked array along 1 axis : ", out_arr2)
Input array : [[1 0 3]
[4 1 6]]
Masked array : [[1 0 3]
[4 1 --]]
mean of masked array along 0 axis : [2.5 0.5 3.0]
mean of masked array along 1 axis : [1.3333333333333333 2.5]