Python:NumPy .nbytes

The .nbytes attribute returns the total number of bytes consumed by the elements of a NumPy array. This value is calculated as the product of the number of elements in the array (given by .size) and the number of bytes per element (given by .itemsize).

Syntax

ndarray.nbytes

Parameters:

The .nbytes attribute takes no parameters.

Return value:

Returns an integer representing the total number of bytes consumed by the array elements.

Example

The following example creates a one-dimensional NumPy array arr with 12 elements. The .nbytes attribute reports the total bytes used by all array elements. On a 64-bit system where the default integer type (int64) uses 8 bytes per element, $12 \text{ elements} \times 8 \text{ bytes}/\text{element} = 96 \text{ bytes}$:

# Import NumPy
import numpy as np

# Create a NumPy array with 12 elements (default type is usually int64, or 8 bytes per item)
arr = np.arange(12)

# Use the '.nbytes' attribute
total_bytes_nbytes = arr.nbytes

print(f"Array: {arr}")
print(f"Bytes per element (.itemsize): {arr.itemsize}")
print(f"Total number of elements (.size): {arr.size}")
print(f"Total bytes consumed (.nbytes): {total_bytes_nbytes}")

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The result will be similar to the following (the value of arr.itemsize might vary based on system architecture):

Array: [ 0  1  2  3  4  5  6  7  8  9 10 11]
Bytes per element (.itemsize): 8
Total number of elements (.size): 12
Total bytes consumed (.nbytes): 96

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Codebyte Example

The example below demonstrates a two-dimensional NumPy array arr with a specified data type (float32). Since float32 uses 4 bytes per element and the array contains $2 \times 3 = 6$ elements, the total memory consumed is $6 \times 4 = 24$ bytes:

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