NumPy sum() – Sum of Array Elements

NumPy sum()

The numpy.sum() function computes the sum of array elements over a specified axis. It can sum all elements or perform summation along a particular axis.

Syntax

numpy.sum(a, axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Parameters

Parameter	Type	Description
a	array_like	Input array containing elements to sum.
axis	None, int, or tuple of ints, optional	Specifies the axis along which summation is performed. Default (None) sums all elements.
dtype	data-type, optional	Data type of the returned array and accumulator.
out	ndarray, optional	Alternative output array where the result is stored. Must have the same shape as expected output.
keepdims	bool, optional	If True, retains the reduced dimensions with size one for broadcasting.
initial	scalar, optional	Starting value for the sum.
where	array_like of bool, optional	Specifies which elements to include in the sum.

Return Value

Returns the sum of array elements. If axis=None, a scalar is returned; otherwise, an array with the specified axis removed is returned.

---

Examples

1. Summing All Elements in an Array

Computing the sum of all elements in a 1D NumPy array.

import numpy as np

# Define a 1D array
arr = np.array([1, 2, 3, 4, 5])

# Compute sum of all elements
total_sum = np.sum(arr)

# Print the result
print("Sum of all elements:", total_sum)

Output:

Sum of all elements: 15

2. Summing Along an Axis

Computing the sum along different axes of a 2D array.

import numpy as np

# Define a 2D array
arr = np.array([[1, 2, 3],
                [4, 5, 6]])

# Sum along axis 0 (columns)
sum_axis0 = np.sum(arr, axis=0)

# Sum along axis 1 (rows)
sum_axis1 = np.sum(arr, axis=1)

# Print the results
print("Sum along axis 0 (column-wise sum):", sum_axis0)
print("Sum along axis 1 (row-wise sum):", sum_axis1)

Output:

Sum along axis 0 (column-wise sum): [5 7 9]
Sum along axis 1 (row-wise sum): [ 6 15]

3. Using keepdims=True to Preserve Dimensions

Preserving reduced dimensions for broadcasting.

import numpy as np

# Define a 2D array
arr = np.array([[1, 2, 3],
                [4, 5, 6]])

# Sum along axis 1 while keeping dimensions
sum_keepdims = np.sum(arr, axis=1, keepdims=True)

# Print the result
print("Sum along axis 1 with keepdims=True:", sum_keepdims)

Output:

Sum along axis 1 with keepdims=True: [[ 6]
 [15]]

4. Specifying an Initial Value

Using an initial value to start the summation from a specific number.

import numpy as np

# Define an array
arr = np.array([1, 2, 3, 4, 5])

# Compute sum with an initial value of 10
sum_with_initial = np.sum(arr, initial=10)

# Print the result
print("Sum with initial value 10:", sum_with_initial)

Output:

Sum with initial value 10: 25

5. Using the where Parameter

Summing only selected elements using a condition.

import numpy as np

# Define an array
arr = np.array([1, 2, 3, 4, 5])

# Define a mask to sum only even numbers
mask = (arr % 2 == 0)

# Compute sum where mask is True
sum_with_condition = np.sum(arr, where=mask)

# Print the result
print("Sum of even numbers:", sum_with_condition)

Output:

Sum of even numbers: 6