Python numpy.sum()

numpy.sum(arr, axis, dtype, out) :该函数返回指定轴上的数组元素之和。

参数 :
arr :输入阵列。
axis :我们想沿着这个轴来计算和值。否则，它将认为Arr是平坦的（在所有轴上工作）。 axis = 0意味着沿列工作， axis = 1意味着沿行工作。
out :不同的数组，我们要把结果放在其中。该数组必须具有与预期输出相同的尺寸。默认为无。
initial :[标量，可选] 和的起始值。

返回 :数组元素的总和（如果没有轴，则为标量值）或沿指定轴的和值的数组。

代码 #1:

# Python Program illustrating 
# numpy.sum() method
import numpy as np 
       
# 1D array 
arr = [20, 2, .2, 10, 4]  
   
print("\nSum of arr : ", np.sum(arr)) 
   
print("Sum of arr(uint8) : ", np.sum(arr, dtype = np.uint8)) 
print("Sum of arr(float32) : ", np.sum(arr, dtype = np.float32))
   
print ("\nIs np.sum(arr).dtype == np.uint : ", 
       np.sum(arr).dtype == np.uint) 
  
print ("Is np.sum(arr).dtype == np.float : ", 
       np.sum(arr).dtype == np.float)

输出:

Sum of arr :  36.2
Sum of arr(uint8) :  36
Sum of arr(float32) :  36.2

Is np.sum(arr).dtype == np.uint :  False
Is np.sum(arr).dtype == np.uint :  True

代码 #2:

# Python Program illustrating 
# numpy.sum() method
import numpy as np 
       
# 2D array 
arr = [[14, 17, 12, 33, 44],   
       [15, 6, 27, 8, 19],  
       [23, 2, 54, 1, 4,]]  
   
print("\nSum of arr : ", np.sum(arr)) 
   
print("Sum of arr(uint8) : ", np.sum(arr, dtype = np.uint8)) 
print("Sum of arr(float32) : ", np.sum(arr, dtype = np.float32))
   
print ("\nIs np.sum(arr).dtype == np.uint : ", 
                 np.sum(arr).dtype == np.uint) 
  
print ("Is np.sum(arr).dtype == np.uint : ", 
              np.sum(arr).dtype == np.float)

输出:

Sum of arr :  279
Sum of arr(uint8) :  23
Sum of arr(float32) :  279.0

Is np.sum(arr).dtype == np.uint :  False
Is np.sum(arr).dtype == np.uint :  False

代码 #3:

# Python Program illustrating 
# numpy.sum() method 
       
import numpy as np 
       
# 2D array  
arr = [[14, 17, 12, 33, 44],   
       [15, 6, 27, 8, 19],  
       [23, 2, 54, 1, 4,]]  
   
print("\nSum of arr : ", np.sum(arr)) 
print("Sum of arr(axis = 0) : ", np.sum(arr, axis = 0)) 
print("Sum of arr(axis = 1) : ", np.sum(arr, axis = 1))
  
print("\nSum of arr (keepdimension is True): \n",
      np.sum(arr, axis = 1, keepdims = True))

输出:

Sum of arr :  279
Sum of arr(axis = 0) :  [52 25 93 42 67]
Sum of arr(axis = 1) :  [120  75  84]

Sum of arr (keepdimension is True): 
 [[120]
 [ 75]
 [ 84]]