En este artículo, analicemos cómo encontrar la suma y el producto de arrays NumPy.
Suma de la array NumPy
La suma de los elementos de la array NumPy se puede lograr de las siguientes maneras
Método #1: Usar numpy.sum()
Sintaxis: numpy.sum(array_name, axis=Ninguno, dtype=Ninguno, out=Ninguno, keepdims=<sin valor>, initial=<sin valor>, where=<sin valor>)
Ejemplo:
Python3
# 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()
Producción:
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]
Nota: el uso de numpy.sum en elementos de array que consisten en elementos Not a Number (NaNs) da un error. Para evitar esto, usamos numpy. nansum() los parámetros son similares a los primeros, excepto que este último no es compatible con where e initial.
Método #2: Usar numpy.cumsum()
Devuelve la suma acumulada de los elementos de la array dada.
Sintaxis: numpy.cumsum(array_name, axis=Ninguno, dtype=Ninguno, out=Ninguno)
Ejemplo:
Python3
# 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()
Producción:
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]]
Producto de la array NumPy
El producto de arrays NumPy se puede lograr de las siguientes maneras
Método #1: Usar numpy.prod()
Sintaxis: numpy. prod (array_name, axis=Ninguno, dtype=Ninguno, out=Ninguno, keepdims=<sin valor>, initial=<sin valor>, where=<sin valor>)
Ejemplo:
Python3
# 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()
Producción:
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]
Método #2: Usar numpy.cumprod()
Devuelve un producto acumulativo de la array.
Sintaxis: numpy.cumsum(array_name, axis=Ninguno, dtype=Ninguno, out=Ninguno)axis = [entero, Opcional]
Python3
# 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()
Producción:
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]]
Publicación traducida automáticamente
Artículo escrito por technikue20 y traducido por Barcelona Geeks. The original can be accessed here. Licence: CCBY-SA