np.add(a, b) is the underlying universal function, or ufunc, that Python's + operator dispatches to when either operand is a NumPy array, applying addition to every corresponding pair of elements at C speed rather than looping in Python. When the two arrays don't have identical shapes, NumPy applies its broadcasting rules to try to align them automatically — for example, adding a scalar to an array applies that scalar to every element, and adding a 1D array to a compatible 2D array applies it to every row.

1Understanding np.add()

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Prefer the + operator over calling np.add() directly for everyday code — they're functionally identical, but + is far more common and readable; np.add() itself is more useful when you need to pass an explicit output array via its out= parameter to avoid allocating a new one.

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import numpy as np

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])
print(np.add(a, b))

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2Practical Example

Here is a real-world application of np.add() showing how it is used in production NumPy code.

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import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6]])
row = np.array([10, 20, 30])
print(matrix + row)

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3Best Practices

Follow these guidelines when working with np.add():

1. Use the + operator for everyday addition; reach for np.add() explicitly mainly when you need its out= parameter to write into a pre-allocated array

2. Understand NumPy's broadcasting rules before relying on adding arrays of different shapes, so unintended broadcasts don't silently produce a wrong result

3. Check that both operands' dtypes are what you expect after an addition, since NumPy may silently upcast to a more general type

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Tip: Prefer the + operator over calling np.add() directly for everyday code — they're functionally identical, but + is far more common and readable; np.add() itself is more useful when you need to pass an explicit output array via its out= parameter to avoid allocating a new one.

editor.html

import numpy as np

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])
print(np.add(a, b))

localhost:3000

Examples

Example 01Basic Usage

import numpy as np

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])
print(np.add(a, b))

Example 02Advanced Example

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6]])
row = np.array([10, 20, 30])
print(matrix + row)

Best Practices

Use the + operator for everyday addition; reach for np.add() explicitly mainly when you need its out= parameter to write into a pre-allocated array
Understand NumPy's broadcasting rules before relying on adding arrays of different shapes, so unintended broadcasts don't silently produce a wrong result
Check that both operands' dtypes are what you expect after an addition, since NumPy may silently upcast to a more general type

Interview Question

What determines whether NumPy can add two arrays of different shapes together, and what happens if it can't?▼

Hint: Think about NumPy's broadcasting rules.

NumPy compares the two shapes dimension by dimension, starting from the trailing, rightmost, dimension, and considers them compatible if, at every position, the sizes are equal or one of them is 1 — a size-1 dimension gets virtually stretched to match the other. If the shapes are compatible under those rules, NumPy broadcasts the smaller array across the larger one without actually copying data. If they're not compatible at any dimension, NumPy raises a ValueError about operands not being broadcastable together.

Exercises

MediumPractice using np.add() in a real scenario.

View Solution

import numpy as np

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])
print(np.add(a, b))

Frequently Asked Questions

What determines whether NumPy can add two arrays of different shapes together, and what happens if it can't?▼

Related Functions

np-subtractnp-multiplyndarray-shape