跳转至

Chapter 1 The Python Data Model

概要: Python中的数据模型概览

创建时间: 2022.11.27 18:26:09

更新时间: 2022.11.27 20:09:54

字符串格式化

优选 f-string 形式

随机选取可迭代容器的元素

不要自己造轮子，使用标准库的 random.choice

更Pythonic的数据模型

  1. 使你自己定义的数据类的行为更加符合标准数据模型的风格
  2. 为自定义数据类增加 特殊方法（special method），如 __getitem__, __len__ 等（见下示例1-1）
Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
import collections

Card = collections.namedtuple('Card', ['rank', 'suit'])

class FrenchDeck:
    ranks = [str(n) for n in range(2, 11)] + list('JQKA')
    suits = 'spades diamonds clubs hearts'.split()

    def __init__(self):
        self._cards = [Card(rank, suit) for suit in self.suits
                                        for rank in self.ranks]

    def __len__(self):
        return len(self._cards)

    def __getitem__(self, position):
        return self._cards[position]

操作符(operator)重载

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
"""
vector2d.py: a simplistic class demonstrating some special methods

It is simplistic for didactic reasons. It lacks proper error handling,
especially in the ``__add__`` and ``__mul__`` methods.

This example is greatly expanded later in the book.

Addition::

    >>> v1 = Vector(2, 4)
    >>> v2 = Vector(2, 1)
    >>> v1 + v2
    Vector(4, 5)

Absolute value::

    >>> v = Vector(3, 4)
    >>> abs(v)
    5.0

Scalar multiplication::

    >>> v * 3
    Vector(9, 12)
    >>> abs(v * 3)
    15.0

"""


import math

class Vector:

    def __init__(self, x=0, y=0):
        self.x = x
        self.y = y

    def __repr__(self):
        return f'Vector({self.x!r}, {self.y!r})'

    def __abs__(self):
        return math.hypot(self.x, self.y)

    def __bool__(self):
        return bool(abs(self))

    def __add__(self, other):
        x = self.x + other.x
        y = self.y + other.y
        return Vector(x, y)

    def __mul__(self, scalar):
        return Vector(self.x * scalar, self.y * scalar)

抽象基类ABC (abstract base class)

对标准库的 collections.abc ，其实现的接口范式可用下图概括
image.png
从上图可以看出以下两方面内容
Python中所有的 Collection ABC 必须实现

  1. 可迭代：支持 for 迭代和 unpacking 解包操作
  2. 可求大小：支持使用内建的 len 获取数据长度
  3. 容器化：支持 in 操作符获取容器内元素

Collection中三大数据类型

  1. 序列 Sequence ，比如 list 和 str
  2. 映射 Mapping ，比如 dict 和 collections.defaultdict 等
  3. 集合 Set ，比如 set 和 frozenset

特殊方法(Special method)

关于每个方法的具体说明，参考 3. Data model — Python 3.11.0 documentation

非操作符类型

类别 方法名
String/bytes representation __repr__ __str__ __format__ __bytes__ __fspath__
Conversion to number __bool__ __complex__ __int__ __float__ __hash__ __index__
Emulating collections __len__ __getitem__ __setitem__ __delitem__ __contains__
Iteration __iter__ __aiter__ __next__ __anext__ __reversed__
Callable or coroutine execution __call__ __await__
Context management __enter__ __exit__ __aexit__ __aenter__
Instance creation and destruction __new__ __init__ __del__
Attribute management __getattr__ __getattribute__ __setattr__ __delattr__ __dir__
Attribute descriptors __get__ __set__ __delete__ __set_name__
Abstract base classes __instancecheck__ __subclasscheck__
Class metaprogramming __prepare__ __init_subclass__ __class_getitem__ __mro_entries__

操作符类型

操作符类别 操作符 方法名
Unary numeric - + abs() __neg__ __pos__ __abs__
Rich comparison < <= == != > >= __lt__ __le__ __eq__ __ne__ __gt__ __ge__
Arithmetic
 + - * / // % @ divmod() round() ** pow() __add__ __sub__ __mul__ __truediv__ __floordiv__ __mod__ __matmul__ __divmod__ __round__ __pow__
Reversed arithmetic (arithmetic operators with swapped operands) __radd__ __rsub__ __rmul__ __rtruediv__ __rfloordiv__ __rmod__ __rmatmul__ __rdivmod__ __rpow__
Augmented assignment arithmetic += -= *= /= //= %= @= **= __iadd__ __isub__ __imul__ __itruediv__ __ifloordiv__ __imod__ __imatmul__ __ipow__
Bitwise & | ^ << >> ~ __and__ __or__ __xor__ __lshift__ __rshift__ __invert__
Reversed bitwise (bitwise operators with swapped operands) __rand__ __ror__ __rxor__ __rlshift__ __rrshift__
Augmented assignment bitwise &= |= ^= <<= >>= __iand__ __ior__ __ixor__ __ilshift__ __irshift__

为什么len(obj)速度比obj.length()更快？

No method is called for the built-in objects of CPython: the length is simply read from a field in a C struct.

即对内建对象而言，使用 len() 时直接读取的C结构的数据，因此速度要远快于自定义的 obj.lenth()

参考