num py masked array . cumber()函数| Python
原文:https://www . geeksforgeeks . org/numpy-masked array-cum prod-function-python/
numpy.MaskedArray.cumprod() 返回给定轴上被屏蔽的数组元素的累积乘积。计算期间，屏蔽值在内部设置为 1。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.cumprod(axis=None, dtype=None, out=None)参数:
轴:【int，可选】计算累计乘积的轴。缺省值(无)是计算展平数组上的积。 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。如果未指定 dtype，则默认为 arr 的 dtype，除非 arr 具有精度小于默认平台整数的整数 dtype。在这种情况下，将使用默认的平台整数。 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:【cumprod _ 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.cumprod() 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.cumprod
# methods to masked array
out_arr = mask_arr.cumprod()
print ("cumulative product 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 -- -2 -10 30]
代码#2 :
# Python program explaining
# numpy.MaskedArray.cumprod() 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.cumprod methods
# to masked array
out_arr1 = mask_arr.cumprod(axis = 0)
print ("cumulative product of masked array along 0 axis : ", out_arr1)
out_arr2 = mask_arr.cumprod(axis = 1)
print ("cumulative product 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 product of masked array along 0 axis : [[1 0 3]
[4 0 --]]
cumulative product of masked array along 1 axis : [[1 0 0]
[4 4 --]]
