numpy.sum() in Python (original) (raw)

Last Updated : 30 Jan, 2026

numpy.sum() is a NumPy function used to calculate the sum of array elements. It can sum values across the entire array or along a specific axis. It also allows controlling the output data type, initial value and shape of the result.

Python `

import numpy as np arr = np.array([5, 10, 15]) print(np.sum(arr))

`

**Explanation: np.sum(arr) adds all elements (5 + 10 + 15) and returns the total.

Syntax:

numpy.sum(arr, axis=None, dtype=None, out=None, initial=0, keepdims=False)

**Parameters:

• **arr: Input array whose elements are to be summed.
• **axis: Axis along which the sum is computed. 0 -> column-wise, 1 -> row-wise and None -> entire array
• **dtype: Data type of the returned sum.
• **out: Output array to store the result.
• **initial: Starting value of the sum.
• **keepdims: Keeps the reduced axis as dimension in result.

**Example 1: This example shows how numpy.sum() works on a 1D array and how changing the dtype affects the result.

Python `

import numpy as np

arr = np.array([20, 2, 0.2, 10, 4])

print(np.sum(arr)) print(np.sum(arr, dtype=np.uint8)) print(np.sum(arr, dtype=np.float32))

`

**Explanation:

• np.sum(arr) computes the sum using NumPy’s default data type, preserving decimal values.
• np.sum(arr, dtype=np.uint8) forces the result into an 8-bit unsigned integer, which supports values only from 0 to 255.
• Decimal values are discarded, and sums exceeding this range overflow and wrap around modulo 256.
• np.sum(arr, dtype=np.float32) performs the sum using floating-point arithmetic, preserving precision.

**Note: Using small integer data types such as uint8 may produce unexpected results due to overflow, not calculation errors.

**Example 2: This example calculates the sum of a 2D array and shows how using different data types changes the output.

Python `

import numpy as np

arr = np.array([ [14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4] ])

print(np.sum(arr)) print(np.sum(arr, dtype=np.uint8)) print(np.sum(arr, dtype=np.float32))

`

**Example 3: This example demonstrates summing a 2D array along rows, columns, and using keepdims=True.

Python `

import numpy as np

arr = np.array([ [14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4] ])

print(np.sum(arr)) print(np.sum(arr, axis=0)) print(np.sum(arr, axis=1)) print(np.sum(arr, axis=1, keepdims=True))

`

Output

279 [52 25 93 42 67] [120 75 84] [[120] [ 75] [ 84]]

**Explanation:

• np.sum(arr, axis=0) column-wise sum.
• np.sum(arr, axis=1) row-wise sum.
• np.sum(arr, axis=1, keepdims=True) preserves the reduced dimension, returning a column-shaped result.