added nbytes.md

content/numpy/concepts/ndarray/terms/nbytes/nbytes.md

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+---
+Title: '.nbytes'
+Description: 'Returns the total number of bytes consumed by the elements of the array.'
+Subjects:
+  - 'Code Foundations'
+  - 'Computer Science'
+Tags:
+  - 'Arrays'
+  - 'Attributes'
+  - 'Memory'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+The **`.nbytes`** attribute returns the total number of bytes consumed by the elements of a [NumPy array](https://www.codecademy.com/resources/docs/numpy/ndarray). 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
+
+```pseudo
+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,
+
+$12 \text{ elements} \times 8 \text{ bytes}/\text{element} = 96 \text{ bytes}$:
+
+```py
+# 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}")
+```
+
+The result will be similar to the following (the value of `arr.itemsize` might vary based on system architecture):
+
+```shell
+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
+```
+
+## 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:
+
+```codebyte/python
+import numpy as np
+
+# Create a 2x3 array of type float32 (4 bytes per element)
+arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32)
+
+print(f"Array shape: {arr.shape}")
+print(f"Array data type: {arr.dtype}")
+print(f"Bytes per element (.itemsize): {arr.itemsize}")
+print(f"Bytes consumed by elements (.nbytes): {arr.nbytes}")
+```