极客教程查找NumPy数组元素的和与积
在这篇文章中，让我们来讨论如何找到NumPy数组的和与积。
NumPy数组的总和
NumPy数组元素之和可以通过以下方式实现
方法#1：使用 numpy.sum()
语法: numpy.sum(array_name, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
示例:
# importing numpy
import numpy as np
def main():
# initialising array
print('Initialised array')
gfg = np.array([[1, 2, 3], [4, 5, 6]])
print(gfg)
# sum along row
print(np.sum(gfg, axis=1))
# sum along column
print(np.sum(gfg, axis=0))
# sum of entire array
print(np.sum(gfg))
# use of out
# initialise a array with same dimensions
# of expected output to use OUT parameter
b = np.array([0]) # np.int32)#.shape = 1
print(np.sum(gfg, axis=1, out=b))
# the output is stored in b
print(b)
# use of keepdim
print('with axis parameter')
# output array's dimension is same as specified
# by the axis
print(np.sum(gfg, axis=0, keepdims=True))
# output consist of 3 columns
print(np.sum(gfg, axis=1, keepdims=True))
# output consist of 2 rows
print('without axis parameter')
print(np.sum(gfg, keepdims=True))
# we added 100 to the actual result
print('using initial parameter in sum function')
print(np.sum(gfg, initial=100))
# False allowed to skip sum operation on column 1 and 2
# that's why output is 0 for them
print('using where parameter ')
print(np.sum(gfg, axis=0, where=[True, False, False]))
if __name__ == "__main__":
main()
输出:
Initialised array
[[1 2 3]
[4 5 6]]
[ 6 15]
[5 7 9]
21
[21]
[21]
with axis parameter
[[5 7 9]]
[[ 6]
[15]]
without axis parameter
[[21]]
using initial parameter in sum function
121
using where parameter
[5 0 0]
注意：对由非数字（NaNs）元素组成的数组元素使用numpy.sum会产生错误，为了避免这种情况，我们使用numpy.sum()。nansum()参数与前者相似，只是后者不支持where和initial.。
方法二：使用 numpy.cumsum()
返回给定数组中元素的累积和。
语法: numpy.cumsum(array_name, axis=None, dtype=None, out=None)
示例:
# importing numpy
import numpy as np
def main():
# initialising array
print('Initialised array')
gfg = np.array([[1, 2, 3], [4, 5, 6]])
print('original array')
print(gfg)
# cumulative sum of the array
print(np.cumsum(gfg))
# cumulative sum of the array along
# axis 1
print(np.cumsum(gfg, axis=1))
# initialising a 2x3 shape array
b = np.array([[None, None, None], [None, None, None]])
# finding cumsum and storing it in array
np.cumsum(gfg, axis=1, out=b)
# printing resultant array
print(b)
if __name__ == "__main__":
main()
输出:
Initialised array
original array
[[1 2 3]
[4 5 6]]
[ 1 3 6 10 15 21]
[[ 1 3 6]
[ 4 9 15]]
[[1 3 6]
[4 9 15]]
NumPy数组的乘积
NumPy数组的乘积可以通过以下方式实现
方法#1：使用 numpy.prod()
Syntax: numpy. prod (array_name, axis=None, dtype=None, out=None, keepdims=
, initial= , where= )
示例:
# importing numpy
import numpy as np
def main():
# initialising array
print('Initialised array')
gfg = np.array([[1, 2, 3], [4, 5, 6]])
print(gfg)
# product along row
print(np.prod(gfg, axis=1))
# product along column
print(np.prod(gfg, axis=0))
# sum of entire array
print(np.prod(gfg))
# use of out
# initialise a array with same dimensions
# of expected output to use OUT parameter
b = np.array([0]) # np.int32)#.shape = 1
print(np.prod(gfg, axis=1, out=b))
# the output is stored in b
print(b)
# use of keepdim
print('with axis parameter')
# output array's dimension is same as specified
# by the axis
print(np.prod(gfg, axis=0, keepdims=True))
# output consist of 3 columns
print(np.prod(gfg, axis=1, keepdims=True))
# output consist of 2 rows
print('without axis parameter')
print(np.prod(gfg, keepdims=True))
# we initialise product to a factor of 10
# instead of 1
print('using initial parameter in sum function')
print(np.prod(gfg, initial=10))
# False allowed to skip sum operation on column 1 and 2
# that's why output is 1 which is default initial value
print('using where parameter ')
print(np.prod(gfg, axis=0, where=[True, False, False]))
if __name__ == "__main__":
main()
输出:
Initialised array
[[1 2 3]
[4 5 6]]
[ 6 120]
[ 4 10 18]
720
[720]
[720]
with axis parameter
[[ 4 10 18]]
[[ 6]
[120]]
without axis parameter
[[720]]
using initial parameter in sum function
7200
using where parameter
[4 1 1]
方法二：使用 numpy.cumprod()
返回数组的累积乘积。
语法: numpy.cumsum(array_name, axis=None, dtype=None, out=None)axis = [integer,Optional]
# importing numpy
import numpy as np
def main():
# initialising array
print('Initialised array')
gfg = np.array([[1, 2, 3], [4, 5, 6]])
print('original array')
print(gfg)
# cumulative product of the array
print(np.cumprod(gfg))
# cumulative product of the array along
# axis 1
print(np.cumprod(gfg, axis=1))
# initialising a 2x3 shape array
b = np.array([[None, None, None], [None, None, None]])
# finding cumprod and storing it in array
np.cumprod(gfg, axis=1, out=b)
# printing resultant array
print(b)
if __name__ == "__main__":
main()
输出:
Initialised array
original array
[[1 2 3]
[4 5 6]]
[ 1 2 6 24 120 720]
[[ 1 2 6]
[ 4 20 120]]
[[1 2 6]
[4 20 120]]