NumPy sum()

 import numpy as np array1 = np.array([1, 2, 3, 4, 5]) 
# use sum() to calculate sum of array1 elements result = np.sum(array1)
 print(result) # Output : 15

sum() Syntax

The syntax of sum() is:

 numpy.sum(array, axis=None, dtype=None, out=None, keepdims=<no value>)

sum() Arguments

The sum() function takes following arguments:

• array - the input array
• axis (optional) - the axis along which the sum is calculated
• dtype (optional) - the data type of the returned sum
• out (optional) - the output array where the result will be stored
• keepdims (optional) - whether to preserve the input array's dimension (bool)

sum() Return Value

The sum() function returns the sum of array elements

Example 1: sum() With 2-D Array

The axis argument defines how we can find the sum of elements in a 2-D array.

• If axis = None, the array is flattened and the sum of the flattened array is returned.
• If axis = 0, the sum is calculated column-wise.
• If axis = 1, the sum is calculated row-wise.

 import numpy as np array = np.array([[10, 17, 25], [15, 11, 22]]) 
# return the sum of elements of the flattened array result1 = np.sum(array)
 print('The sum of flattened array: ', result1) 
# return the column-wise sum result2 = np.sum(array, axis = 0)
 print('Column-wise sum (axis 0): ', result2) 
# return the row-wise sum result2 = np.sum(array, axis = 1)
 print('Row-wise sum (axis 1): ', result2)

Output

 The sum of flattened array: 100 Column-wise sum (axis 0): [25 28 47] Row-wise sum (axis 1): [52 48]

Example 2: Use out to Store the Result in Desired Location

 import numpy as np array1 = np.array([[10, 17, 25], [15, 11, 22], [11, 19, 20]]) # create an empty array array2= np.array([0, 0, 0]) 
# pass the 'out' argument to store the result in array2 np.sum(array1, axis = 0, out = array2)
 print(array2)

Output

 [36 47 67]

Here, after specifying out=array2, the result of sum of array1 along axis=0 is stored in the array2 array.

Example 3: sum() With keepdims

When keepdims = True, the dimensions of the resulting array matches the dimension of an input array.

 import numpy as np array1 = np.array([[10, 17, 25], [15, 11, 22]]) print('Dimensions of original array: ', array1.ndim) 
result = np.sum(array1, axis = 1)
 print('\n Without keepdims: \n', result) print('Dimensions of array: ', result.ndim) 
# set keepdims to True to retain the dimension of the input array result = np.sum(array1, axis = 1, keepdims = True)
 print('\n With keepdims: \n', result) print('Dimensions of array: ', result.ndim)

Output

 Dimensions of original array: 2 Without keepdims: [52 48] Dimensions of array: 1 With keepdims: [[52] [48]] Dimensions of array: 2

Without keepdims, the result is simply a one-dimensional array of indices.

With keepdims, the resulting array has the same number of dimensions as the input array.