For an array with shape (3, 4), size is 3 times 4, or 12 — it counts every individual element across all dimensions, unlike len(arr), which only returns the length of the first, outermost, dimension. This distinction matters for multi-dimensional arrays: len() on a 2D array gives the number of rows, while .size gives the total number of individual values across the whole array.

1Understanding ndarray.size

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Don't confuse len(arr) with arr.size for multi-dimensional arrays — len() only measures the first axis, e.g. the number of rows, while .size is the total element count across every axis combined.

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import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.size)
print(len(arr))

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2Practical Example

Here is a real-world application of ndarray.size showing how it is used in production NumPy code.

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import numpy as np

cube = np.zeros((2, 3, 4))
print(cube.size)

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3Best Practices

Follow these guidelines when working with ndarray.size:

1. Use .size when you need the total element count across all dimensions, and len() only when you specifically want the length of just the first axis

2. Use .size, rather than manually multiplying .shape's values together, to get the total element count directly

3. Combine .size with .itemsize, or use .nbytes directly, to estimate an array's memory footprint before allocating something large

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Tip: Don't confuse len(arr) with arr.size for multi-dimensional arrays — len() only measures the first axis, e.g. the number of rows, while .size is the total element count across every axis combined.

editor.html

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.size)
print(len(arr))

localhost:3000

Examples

Example 01Basic Usage

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.size)
print(len(arr))

Example 02Advanced Example

import numpy as np

cube = np.zeros((2, 3, 4))
print(cube.size)

Best Practices

Use .size when you need the total element count across all dimensions, and len() only when you specifically want the length of just the first axis
Use .size, rather than manually multiplying .shape's values together, to get the total element count directly
Combine .size with .itemsize, or use .nbytes directly, to estimate an array's memory footprint before allocating something large

Interview Question

For a 2D array with shape (3, 4), why does len(arr) return 3 while arr.size returns 12?▼

Hint: Think about what each one is actually measuring.

len(arr) reports the length of the array's first axis only — for a shape of (3, 4), that's 3 rows. arr.size instead reports the total number of individual scalar elements across every dimension combined, which for a (3, 4) array is 3 times 4, or 12. This is exactly the same distinction as calling len() on a list of lists, which counts the outer list's length, versus manually counting every value nested inside every sublist.

Exercises

MediumPractice using ndarray.size in a real scenario.

View Solution

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.size)
print(len(arr))

Frequently Asked Questions

For a 2D array with shape (3, 4), why does len(arr) return 3 while arr.size returns 12?▼

Related Functions

ndarray-shapendarray-ndimlen()