Numpy maskearray . cum sum()函数| Python
原文:https://www . geeksforgeeks . org/numpy-masked array-cum sum-function-python/
numpy.MaskedArray.cumsum() 返回给定轴上被屏蔽的数组元素的累积和。计算期间，屏蔽值在内部设置为 0。但是，它们的位置被保存，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: 轴:【int，可选】计算累计和的轴。默认值(无)是计算展平数组的总和。 数据类型:【数据类型，可选】返回数组的类型，以及元素相乘的累加器的类型。 out:【n 数组，可选】存储结果的位置。 - >如果提供，它必须具有输入广播到的形状。 - >如果未提供或无，则返回新分配的阵列。
Return:【cum sum _ along _ axis，ndarray】除非指定 out，否则将返回一个保存结果的新数组，在这种情况下，将返回对 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)
Output:
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)
Output:
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]
