This function returns the sum of array elements over the specified axis.
Syntax: numpy.sum(arr, axis, dtype, out):
Parameters:
- arr: Input array.
- axis: The axis along which we want to calculate the sum value. Otherwise, it will consider arr to be flattened(works on all the axes). axis = 0 means along the column and axis = 1 means working along the row.
- out: Different array in which we want to place the result. The array must have the same dimensions as the expected output. The default is None.
- initial : [scalar, optional] Starting value of the sum.
Return: Sum of the array elements (a scalar value if axis is none) or array with sum values along the specified axis.
Example 1:
This Python program uses numpy.sum() to calculate the sum of a 1D array. It demonstrates summing with different data types (uint8 and float32) and checks if the result's data type matches np.uint and np.float. The output shows how the sum can vary with different types.
Python
# 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)
Output:
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.float : True
Example 2:
This Python program uses NumPy to compute the sum of a 2D array arr with different data types. It demonstrates the use of np.sum() to calculate the sum of elements in arr and outputs results for different data types (uint8 and float32). It also checks if the sum's data type matches np.uint or np.float.
Python
# 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.float : ",
np.sum(arr).dtype == np.float)
Output:
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.float : False
Example 3:
This Python program uses numpy.sum() to compute the sum of elements in a 2D array. It calculates the total sum, sums along rows (axis=0), sums along columns (axis=1), and sums along columns while keeping the dimensions (keepdims=True).
Python
# 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))
Output:
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]]
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