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    35个Python编程小技巧

    2016-06-06 11:30:22原创530
    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

    代码如下:


    >>> 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

    代码如下:

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


    1.3 扩展拆箱(只兼容python3)

    代码如下:

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


    1.4 负数索引

    代码如下:

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


    1.5 切割列表

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> m = dict()
    >>> m['a']
    Traceback (most recent call last):
    File "", line 1, in
    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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> for c in itertools.combinations([1, 2, 3, 4, 5], 3):
    ... print ''.join(str(x) for x in c)
    ...
    123
    124
    125
    134
    135
    145
    234
    235
    245
    345
    >>> for c in itertools.combinations_with_replacement([1, 2, 3], 2):
    ... print ''.join(str(x) for x in c)
    ...
    11
    12
    13
    22
    23
    33


    1.33 列表元素排列组合

    代码如下:

    >>> 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

    代码如下:

    >>> 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

    代码如下:

    >>> 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 '\n'.join('\t'.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 soft

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