Python:NumPy .itemsize

The .itemsize attribute of a NumPy ndarray returns the number of bytes used to store each element in the array, as determined by the array’s dtype.

Syntax

ndarray.itemsize

Parameters:

This attribute does not take any parameters.

Return value:

Returns an integer that represents the number of bytes used to store each element in the array, based on its data type (dtype).

Example

In this example, arrays with different data types return different byte sizes per element when accessed through the .itemsize attribute:

import numpy as np

arr_int32 = np.array([1, 2, 3], dtype=np.int32)
arr_float64 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
arr_int8 = np.array([1, 2, 3], dtype=np.int8)

print(arr_int32.itemsize)   # 4 bytes per element
print(arr_float64.itemsize) # 8 bytes per element
print(arr_int8.itemsize)    # 1 byte per element

# Equal to dtype.itemsize
print(arr_int32.itemsize == arr_int32.dtype.itemsize)

Copy to clipboard
Copy to clipboard

This produces output similar to:

4
8
1
True

Copy to clipboard
Copy to clipboard

Codebyte Example

In this example, the .itemsize attribute is used to inspect the memory size of elements for different NumPy dtypes:

Visit us
Visit us
Hide code
Code
Output
Hide output
Loading...
Copy to your clipboard
Copy Codebyte
Run