Python Numpy MaskedArray.cumsum()函数
numpy.MaskedArray.cumsum() 返回在给定轴上被屏蔽的数组元素的累积和。在计算过程中,被屏蔽的值在内部被设置为0。然而,他们的位置被保存下来,结果将在相同的位置被屏蔽。
语法: numpy.ma.cumsum(axis=None, dtype=None, out=None)
参数:
axis : [ int, optional] 计算累积和的轴。默认情况下(无)是在扁平化的数组上计算总和。
dtype : [dtype, optional] 返回数组的类型,以及与元素相乘的累积器的类型。
out : [ndarray, optional] 一个储存结果的位置。
-> 如果提供,它必须有一个输入广播到的形状。
-> 如果没有提供或没有,将返回一个新分配的数组。
返回 : [cumsum_along_axis, ndarray] 返回一个保存结果的新数组,除非指定out,在这种情况下,返回对out的引用。
代码#1:
# Python program explaining
# numpy.MaskedArray.cumsum() 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.cumsum
# methods to masked array
out_arr = mask_arr.cumsum()
print ("cumulative sum of masked array along default axis : ", out_arr)
输出:
Input array : [[ 1 2]
[ 3 -1]
[ 5 -3]]
Masked array : [[-- 2]
[-- -1]
[5 -3]]
cumulative sum of masked array along default axis : [-- 2 -- 1 6 3]
代码#2:
# Python program explaining
# numpy.MaskedArray.cumsum() 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.cumsum methods
# to masked array
out_arr1 = mask_arr.cumsum(axis = 0)
print ("cumulative sum of masked array along 0 axis : ", out_arr1)
out_arr2 = mask_arr.cumsum(axis = 1)
print ("cumulative sum of masked array along 1 axis : ", out_arr2)
输出:
Input array : [[1 0 3]
[4 1 6]]
Masked array : [[1 0 3]
[4 1 --]]
cumulative sum of masked array along 0 axis : [[1 0 3]
[5 1 --]]
cumulative sum of masked array along 1 axis : [[1 1 4]