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  • 30个有关Python的小技巧

    原地址：http://blog.jobbole.com/63320/

    本文由 伯乐在线 - Kevin Sun 翻译自 sahandsaba。欢迎加入技术翻译小组。转载请参见文章末尾处的要求。

    从我开始学习python的时候，我就开始自己总结一个python小技巧的集合。后来当我什么时候在Stack Overflow
    或者在某个开源软件里看到一段很酷代码的时候，我就很惊讶：原来还能这么做！，当时我会努力的自己尝试一下这段代码，直到我懂了它的整体思路以后，我就把这段代码加到我的集合里。这篇博客其实就是这个集合整理后一部分的公开亮相。如果你已经是个python大牛，那么基本上你应该知道这里面的大多数用法了，但我想你应该也能发现一些你不知道的新技巧。而如果你之前是一个c,c++,java的程序员，同时在学习python，或者干脆就是一个刚刚学习编程的新手，那么你应该会看到很多特别有用能让你感到惊奇的实用技巧，就像我当初一样。

    每一个技巧和语言用法都会在一个个实例中展示给大家，也不需要有其他的说明。我已经尽力把每个例子弄的通俗易懂，但是因为读者对python的熟悉程度不同，仍然可能难免有一些晦涩的地方。所以如果这些例子本身无法让你读懂，至少这个例子的标题在你后面去google搜索的时候会帮到你。

    整个集合大概是按照难易程度排序，简单常见的在前面，比较少见的在最后。

    1.1 拆箱

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    >>> a, b, c = 1, 2, 3
    >>> a, b, c
    (1, 2, 3)
    >>> a, b, c = [1, 2, 3]
    >>> a, b, c
    (1, 2, 3)
    >>> a, b, c = (2 * i + 1 for i in range(3))
    >>> a, b, c
    (1, 3, 5)
    >>> a, (b, c), d = [1, (2, 3), 4]
    >>> a
    1
    >>> b
    2
    >>> c
    3
    >>> d
    4

    1.2 拆箱变量交换

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    >>> a, b = 1, 2
    >>> a, b = b, a
    >>> a, b
    (2, 1)

    1.3 扩展拆箱（只兼容python3）

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    >>> a, *b, c = [1, 2, 3, 4, 5]
    >>> a
    1
    >>> b
    [2, 3, 4]
    >>> c
    5

    1.4 负数索引

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    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> a[-1]
    10
    >>> a[-3]
    8

    1.5 切割列表

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    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> a[2:8]
    [2, 3, 4, 5, 6, 7]

    1.6 负数索引切割列表

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    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> a[-4:-2]
    [7, 8]

    1.7指定步长切割列表

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    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> a[::2]
    [0, 2, 4, 6, 8, 10]
    >>> a[::3]
    [0, 3, 6, 9]
    >>> a[2:8:2]
    [2, 4, 6]

    1.8 负数步长切割列表

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    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> a[::-1]
    [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
    >>> a[::-2]
    [10, 8, 6, 4, 2, 0]

    1.9 列表切割赋值

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    >>> a = [1, 2, 3, 4, 5]
    >>> a[2:3] = [0, 0]
    >>> a
    [1, 2, 0, 0, 4, 5]
    >>> a[1:1] = [8, 9]
    >>> a
    [1, 8, 9, 2, 0, 0, 4, 5]
    >>> a[1:-1] = []
    >>> a
    [1, 5]

    1.10 命名列表切割方式

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    >>> a = [0, 1, 2, 3, 4, 5]
    >>> LASTTHREE = slice(-3, None)
    >>> LASTTHREE
    slice(-3, None, None)
    >>> a[LASTTHREE]
    [3, 4, 5]

    1.11 列表以及迭代器的压缩和解压缩

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    >>> a = [1, 2, 3]
    >>> b = ['a', 'b', 'c']
    >>> z = zip(a, b)
    >>> z
    [(1, 'a'), (2, 'b'), (3, 'c')]
    >>> zip(*z)
    [(1, 2, 3), ('a', 'b', 'c')]

    1.12 列表相邻元素压缩器

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    >>> a = [1, 2, 3, 4, 5, 6]
    >>> zip(*([iter(a)] * 2))
    [(1, 2), (3, 4), (5, 6)]
     
    >>> group_adjacent = lambda a, k: zip(*([iter(a)] * k))
    >>> group_adjacent(a, 3)
    [(1, 2, 3), (4, 5, 6)]
    >>> group_adjacent(a, 2)
    [(1, 2), (3, 4), (5, 6)]
    >>> group_adjacent(a, 1)
    [(1,), (2,), (3,), (4,), (5,), (6,)]
     
    >>> zip(a[::2], a[1::2])
    [(1, 2), (3, 4), (5, 6)]
     
    >>> zip(a[::3], a[1::3], a[2::3])
    [(1, 2, 3), (4, 5, 6)]
     
    >>> group_adjacent = lambda a, k: zip(*(a[i::k] for i in range(k)))
    >>> group_adjacent(a, 3)
    [(1, 2, 3), (4, 5, 6)]
    >>> group_adjacent(a, 2)
    [(1, 2), (3, 4), (5, 6)]
    >>> group_adjacent(a, 1)
    [(1,), (2,), (3,), (4,), (5,), (6,)]

    1.13 在列表中用压缩器和迭代器滑动取值窗口

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    >>> def n_grams(a, n):
    ...     z = [iter(a[i:]) for i in range(n)]
    ...     return zip(*z)
    ...
    >>> a = [1, 2, 3, 4, 5, 6]
    >>> n_grams(a, 3)
    [(1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6)]
    >>> n_grams(a, 2)
    [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]
    >>> n_grams(a, 4)
    [(1, 2, 3, 4), (2, 3, 4, 5), (3, 4, 5, 6)]

    1.14 用压缩器反转字典

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    >>> m = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
    >>> m.items()
    [('a', 1), ('c', 3), ('b', 2), ('d', 4)]
    >>> zip(m.values(), m.keys())
    [(1, 'a'), (3, 'c'), (2, 'b'), (4, 'd')]
    >>> mi = dict(zip(m.values(), m.keys()))
    >>> mi
    {1: 'a', 2: 'b', 3: 'c', 4: 'd'}

    1.15 列表展开

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    >>> a = [[1, 2], [3, 4], [5, 6]]
    >>> list(itertools.chain.from_iterable(a))
    [1, 2, 3, 4, 5, 6]
     
    >>> sum(a, [])
    [1, 2, 3, 4, 5, 6]
     
    >>> [x for l in a for x in l]
    [1, 2, 3, 4, 5, 6]
     
    >>> a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
    >>> [x for l1 in a for l2 in l1 for x in l2]
    [1, 2, 3, 4, 5, 6, 7, 8]
     
    >>> a = [1, 2, [3, 4], [[5, 6], [7, 8]]]
    >>> flatten = lambda x: [y for l in x for y in flatten(l)] if type(x) is list else [x]
    >>> flatten(a)
    [1, 2, 3, 4, 5, 6, 7, 8]

    1.16 生成器表达式

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    >>> g = (x ** 2 for x in xrange(10))
    >>> next(g)
    0
    >>> next(g)
    1
    >>> next(g)
    4
    >>> next(g)
    9
    >>> sum(x ** 3 for x in xrange(10))
    2025
    >>> sum(x ** 3 for x in xrange(10) if x % 3 == 1)
    408

    1.17 字典推导

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    >>> m = {x: x ** 2 for x in range(5)}
    >>> m
    {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
     
    >>> m = {x: 'A' + str(x) for x in range(10)}
    >>> m
    {0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4', 5: 'A5', 6: 'A6', 7: 'A7', 8: 'A8', 9: 'A9'}

    1.18 用字典推导反转字典

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    >>> m = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
    >>> m
    {'d': 4, 'a': 1, 'b': 2, 'c': 3}
    >>> {v: k for k, v in m.items()}
    {1: 'a', 2: 'b', 3: 'c', 4: 'd'}

    1.19 命名元组

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    >>> Point = collections.namedtuple('Point', ['x', 'y'])
    >>> p = Point(x=1.0, y=2.0)
    >>> p
    Point(x=1.0, y=2.0)
    >>> p.x
    1.0
    >>> p.y
    2.0

    1.20 继承命名元组

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    >>> class Point(collections.namedtuple('PointBase', ['x', 'y'])):
    ...     __slots__ = ()
    ...     def __add__(self, other):
    ...             return Point(x=self.x + other.x, y=self.y + other.y)
    ...
    >>> p = Point(x=1.0, y=2.0)
    >>> q = Point(x=2.0, y=3.0)
    >>> p + q
    Point(x=3.0, y=5.0)

    1.21 操作集合

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    >>> A = {1, 2, 3, 3}
    >>> A
    set([1, 2, 3])
    >>> B = {3, 4, 5, 6, 7}
    >>> B
    set([3, 4, 5, 6, 7])
    >>> A | B
    set([1, 2, 3, 4, 5, 6, 7])
    >>> A & B
    set([3])
    >>> A - B
    set([1, 2])
    >>> B - A
    set([4, 5, 6, 7])
    >>> A ^ B
    set([1, 2, 4, 5, 6, 7])
    >>> (A ^ B) == ((A - B) | (B - A))
    True

    1.22 操作多重集合

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    >>> A = collections.Counter([1, 2, 2])
    >>> B = collections.Counter([2, 2, 3])
    >>> A
    Counter({2: 2, 1: 1})
    >>> B
    Counter({2: 2, 3: 1})
    >>> A | B
    Counter({2: 2, 1: 1, 3: 1})
    >>> A & B
    Counter({2: 2})
    >>> A + B
    Counter({2: 4, 1: 1, 3: 1})
    >>> A - B
    Counter({1: 1})
    >>> B - A
    Counter({3: 1})

    1.23 统计在可迭代器中最常出现的元素

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    >>> A = collections.Counter([1, 1, 2, 2, 3, 3, 3, 3, 4, 5, 6, 7])
    >>> A
    Counter({3: 4, 1: 2, 2: 2, 4: 1, 5: 1, 6: 1, 7: 1})
    >>> A.most_common(1)
    [(3, 4)]
    >>> A.most_common(3)
    [(3, 4), (1, 2), (2, 2)]

    1.24 两端都可操作的队列

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    >>> Q = collections.deque()
    >>> Q.append(1)
    >>> Q.appendleft(2)
    >>> Q.extend([3, 4])
    >>> Q.extendleft([5, 6])
    >>> Q
    deque([6, 5, 2, 1, 3, 4])
    >>> Q.pop()
    4
    >>> Q.popleft()
    6
    >>> Q
    deque([5, 2, 1, 3])
    >>> Q.rotate(3)
    >>> Q
    deque([2, 1, 3, 5])
    >>> Q.rotate(-3)
    >>> Q
    deque([5, 2, 1, 3])

    1.25 有最大长度的双端队列

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    >>> last_three = collections.deque(maxlen=3)
    >>> for i in xrange(10):
    ...     last_three.append(i)
    ...     print ', '.join(str(x) for x in last_three)
    ...
    0
    0, 1
    0, 1, 2
    1, 2, 3
    2, 3, 4
    3, 4, 5
    4, 5, 6
    5, 6, 7
    6, 7, 8
    7, 8, 9

    1.26 可排序词典

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    >>> m = dict((str(x), x) for x in range(10))
    >>> print ', '.join(m.keys())
    1, 0, 3, 2, 5, 4, 7, 6, 9, 8
    >>> m = collections.OrderedDict((str(x), x) for x in range(10))
    >>> print ', '.join(m.keys())
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9
    >>> m = collections.OrderedDict((str(x), x) for x in range(10, 0, -1))
    >>> print ', '.join(m.keys())
    10, 9, 8, 7, 6, 5, 4, 3, 2, 1

    1.27 默认词典

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    >>> m = dict()
    >>> m['a']
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    KeyError: 'a'
    >>>
    >>> m = collections.defaultdict(int)
    >>> m['a']
    0
    >>> m['b']
    0
    >>> m = collections.defaultdict(str)
    >>> m['a']
    ''
    >>> m['b'] += 'a'
    >>> m['b']
    'a'
    >>> m = collections.defaultdict(lambda: '[default value]')
    >>> m['a']
    '[default value]'
    >>> m['b']
    '[default value]'

    1.28 默认字典的简单树状表达

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    >>> import json
    >>> tree = lambda: collections.defaultdict(tree)
    >>> root = tree()
    >>> root['menu']['id'] = 'file'
    >>> root['menu']['value'] = 'File'
    >>> root['menu']['menuitems']['new']['value'] = 'New'
    >>> root['menu']['menuitems']['new']['onclick'] = 'new();'
    >>> root['menu']['menuitems']['open']['value'] = 'Open'
    >>> root['menu']['menuitems']['open']['onclick'] = 'open();'
    >>> root['menu']['menuitems']['close']['value'] = 'Close'
    >>> root['menu']['menuitems']['close']['onclick'] = 'close();'
    >>> print json.dumps(root, sort_keys=True, indent=4, separators=(',', ': '))
    {
        "menu": {
            "id": "file",
            "menuitems": {
                "close": {
                    "onclick": "close();",
                    "value": "Close"
                },
                "new": {
                    "onclick": "new();",
                    "value": "New"
                },
                "open": {
                    "onclick": "open();",
                    "value": "Open"
                }
            },
            "value": "File"
        }
    }

    1.29 对象到唯一计数的映射

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    >>> import itertools, collections
    >>> value_to_numeric_map = collections.defaultdict(itertools.count().next)
    >>> value_to_numeric_map['a']
    0
    >>> value_to_numeric_map['b']
    1
    >>> value_to_numeric_map['c']
    2
    >>> value_to_numeric_map['a']
    0
    >>> value_to_numeric_map['b']
    1

    1.30 最大和最小的几个列表元素

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    >>> a = [random.randint(0, 100) for __ in xrange(100)]
    >>> heapq.nsmallest(5, a)
    [3, 3, 5, 6, 8]
    >>> heapq.nlargest(5, a)
    [100, 100, 99, 98, 98]

    1.31 两个列表的笛卡尔积

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    >>> for p in itertools.product([1, 2, 3], [4, 5]):
    (1, 4)
    (1, 5)
    (2, 4)
    (2, 5)
    (3, 4)
    (3, 5)
    >>> for p in itertools.product([0, 1], repeat=4):
    ...     print ''.join(str(x) for x in p)
    ...
    0000
    0001
    0010
    0011
    0100
    0101
    0110
    0111
    1000
    1001
    1010
    1011
    1100
    1101
    1110
    1111

    1.32 列表组合和列表元素替代组合

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    >>> for c in itertools.combinations([1, 2, 3, 4, 5], 3):
    ...     print ''.join(str(x) for x in c)
    ...
    123
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    134
    135
    145
    234
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    245
    345
    >>> for c in itertools.combinations_with_replacement([1, 2, 3], 2):
    ...     print ''.join(str(x) for x in c)
    ...
    11
    12
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    22
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    33

    1.33 列表元素排列组合

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    >>> for p in itertools.permutations([1, 2, 3, 4]):
    ...     print ''.join(str(x) for x in p)
    ...
    1234
    1243
    1324
    1342
    1423
    1432
    2134
    2143
    2314
    2341
    2413
    2431
    3124
    3142
    3214
    3241
    3412
    3421
    4123
    4132
    4213
    4231
    4312
    4321

    1.34 可链接迭代器

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    >>> a = [1, 2, 3, 4]
    >>> for p in itertools.chain(itertools.combinations(a, 2), itertools.combinations(a, 3)):
    ...     print p
    ...
    (1, 2)
    (1, 3)
    (1, 4)
    (2, 3)
    (2, 4)
    (3, 4)
    (1, 2, 3)
    (1, 2, 4)
    (1, 3, 4)
    (2, 3, 4)
    >>> for subset in itertools.chain.from_iterable(itertools.combinations(a, n) for n in range(len(a) + 1))
    ...     print subset
    ...
    ()
    (1,)
    (2,)
    (3,)
    (4,)
    (1, 2)
    (1, 3)
    (1, 4)
    (2, 3)
    (2, 4)
    (3, 4)
    (1, 2, 3)
    (1, 2, 4)
    (1, 3, 4)
    (2, 3, 4)
    (1, 2, 3, 4)

    1.35 根据文件指定列类聚

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    >>> import itertools
    >>> with open('contactlenses.csv', 'r') as infile:
    ...     data = [line.strip().split(',') for line in infile]
    ...
    >>> data = data[1:]
    >>> def print_data(rows):
    ...     print ' '.join(' '.join('{: <16}'.format(s) for s in row) for row in rows)
    ...
     
    >>> print_data(data)
    young               myope                   no                      reduced                 none
    young               myope                   no                      normal                  soft
    young               myope                   yes                     reduced                 none
    young               myope                   yes                     normal                  hard
    young               hypermetrope            no                      reduced                 none
    young               hypermetrope            no                      normal                  soft
    young               hypermetrope            yes                     reduced                 none
    young               hypermetrope            yes                     normal                  hard
    pre-presbyopic      myope                   no                      reduced                 none
    pre-presbyopic      myope                   no                      normal                  soft
    pre-presbyopic      myope                   yes                     reduced                 none
    pre-presbyopic      myope                   yes                     normal                  hard
    pre-presbyopic      hypermetrope            no                      reduced                 none
    pre-presbyopic      hypermetrope            no                      normal                  soft
    pre-presbyopic      hypermetrope            yes                     reduced                 none
    pre-presbyopic      hypermetrope            yes                     normal                  none
    presbyopic          myope                   no                      reduced                 none
    presbyopic          myope                   no                      normal                  none
    presbyopic          myope                   yes                     reduced                 none
    presbyopic          myope                   yes                     normal                  hard
    presbyopic          hypermetrope            no                      reduced                 none
    presbyopic          hypermetrope            no                      normal                  soft
    presbyopic          hypermetrope            yes                     reduced                 none
    presbyopic          hypermetrope            yes                     normal                  none
     
    >>> data.sort(key=lambda r: r[-1])
    >>> for value, group in itertools.groupby(data, lambda r: r[-1]):
    ...     print '-----------'
    ...     print 'Group: ' + value
    ...     print_data(group)
    ...
    -----------
    Group: hard
    young               myope                   yes                     normal                  hard
    young               hypermetrope            yes                     normal                  hard
    pre-presbyopic      myope                   yes                     normal                  hard
    presbyopic          myope                   yes                     normal                  hard
    -----------
    Group: none
    young               myope                   no                      reduced                 none
    young               myope                   yes                     reduced                 none
    young               hypermetrope            no                      reduced                 none
    young               hypermetrope            yes                     reduced                 none
    pre-presbyopic      myope                   no                      reduced                 none
    pre-presbyopic      myope                   yes                     reduced                 none
    pre-presbyopic      hypermetrope            no                      reduced                 none
    pre-presbyopic      hypermetrope            yes                     reduced                 none
    pre-presbyopic      hypermetrope            yes                     normal                  none
    presbyopic          myope                   no                      reduced                 none
    presbyopic          myope                   no                      normal                  none
    presbyopic          myope                   yes                     reduced                 none
    presbyopic          hypermetrope            no                      reduced                 none
    presbyopic          hypermetrope            yes                     reduced                 none
    presbyopic          hypermetrope            yes                     normal                  none
    -----------
    Group: soft
    young               myope                   no                      normal                  soft
    young               hypermetrope            no                      normal                  soft
    pre-presbyopic      myope                   no                      normal                  soft
    pre-presbyopic      hypermetrope            no                      normal                  soft
    presbyopic          hypermetrope            no                      normal       
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  • 原文地址：https://www.cnblogs.com/lanye/p/3798776.html
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