NumPy sum()

The sum() function is used to calculate the sum of array elements along a specified axis or across all axes.

Example

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.

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