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Updated on 2025-03-01

Implementation example of Python built-in function memoryview

Pythonmemoryview()A function is a built-in function that allows you to manipulate different slices of the same array without copying its contents. This can improve performance when processing large datasets or arrays.

Function definition

memoryview()The basic syntax of a function is as follows:

memoryview(obj)

obj: An object that supports buffer interfaces, such as a byte string or byte array.

The function returns amemoryviewObject.

Basic usage

Create memoryview

byte_array = bytearray('ABC', 'utf-8')
mv = memoryview(byte_array)

print(mv[0])  # Output: 65

Slice memoryview

print(mv[1:3])  # Output: <memory at 0x...>print(bytes(mv[1:3]))  # Output: b'BC'

Modify memoryview

mv[1] = 90
print(byte_array)  # Output: bytearray(b'AZC')

Advanced Usage

Multidimensional array

memoryviewCan be used to manipulate multi-dimensional arrays, which is very useful when processing images or scientifically computed data.

import array
import numpy as np

arr = ('i', [1, 2, 3, 4, 5])
mv = memoryview(arr)

# Convert to a 2D array using numpynp_arr = (mv).reshape((1, 5))
print(np_arr)  # Output: [[1 2 3 4 5]]

Used in conjunction with NumPy

memoryviewCan be used in conjunction with NumPy arrays for efficient data processing.

import numpy as np

np_arr = ([1, 2, 3, 4, 5])
mv = memoryview(np_arr)
print(())  # Output: [1, 2, 3, 4, 5]

Things to note

  • memoryviewObjects do not own the memory they refer to, and their behavior is undefined when the original objects are deleted.
  • memoryviewOnly used for objects that support the buffer protocol.

in conclusion

memoryview()is a very useful built-in function in Python, especially when dealing with large data sets. It provides an efficient way to access and modify data without copying. Through the above routine, we can seememoryview()Applications in real programming and how to use it effectively to optimize performance.

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