你可能不知道的30个Python语言的特点技巧

jopen 9年前

1 介绍

从我开始学习Python时我就决定维护一个经常使用的“窍门”列表。不论何时当我看到一段让我觉得“酷,这样也行!”的代码时(在一个例子中、在StackOverflow、在开源码软件中,等等),我会尝试它直到理解它,然后把它添加到列表中。这篇文章是清理过列表的一部分。如果你是一个有经验的Python程序员,尽管你可能已经知道一些,但你仍能发现一些你不知道的。如果你是一个正在学习Python的C、C++或Java程序员,或者刚开始学习编程,那么你会像我一样发现它们中的很多非常有用。

 

每个窍门或语言特性只能通过实例来验证,无需过多解释。虽然我已尽力使例子清晰,但它们中的一些仍会看起来有些复杂,这取决于你的熟悉程度。所以如果看过例子后还不清楚的话,标题能够提供足够的信息让你通过Google获取详细的内容。

列表按难度排序,常用的语言特征和技巧放在前面。

1.1   分拆

>>> 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   交换变量分拆

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

1.3   拓展分拆 (Python 3下适用)

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

1.4   负索引

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

1.5   列表切片 (a[start:end])

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

1.6   使用负索引的列表切片

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

1.7   带步进值的列表切片 (a[start:end:step])

>>> 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   负步进值得列表切片

>>> 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   列表切片赋值

>>> 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   命名切片 (slice(start, end, step))

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

1.11   zip打包解包列表和倍数

>>> 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   使用zip合并相邻的列表项

>>> 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  使用zip和iterators生成滑动窗口 (n -grams) 

>>> from itertools import islice  >>> def n_grams(a, n):  ...     z = (islice(a, i, None) 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   使用zip反转字典

>>> 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   摊平列表:

>>> 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]    注意: 根据Python的文档,itertools.chain.from_iterable是首选。

1.16   生成器表达式

>>> 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   迭代字典

>>> 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   通过迭代字典反转字典

>>> 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   命名序列 (collections.namedtuple)

>>> 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   命名列表的继承:

>>> 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   集合及集合操作

>>> 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   多重集及其操作 (collections.Counter)

>>> 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   迭代中最常见的元素 (collections.Counter)

>>> 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   双端队列 (collections.deque)

>>> 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 (collections.deque)

>>> 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   字典排序 (collections.OrderedDict)

>>> 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   缺省字典 (collections.defaultdict)

>>> 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   用缺省字典表示简单的树

>>> 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"      }  }    (到https://gist.github.com/hrldcpr/2012250查看详情)

1.29   映射对象到唯一的序列数 (collections.defaultdict)

>>> 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   最大最小元素 (heapq.nlargest和heapq.nsmallest)

>>> 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   笛卡尔乘积 (itertools.product)

>>> 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   组合的组合和置换 (itertools.combinations 和 itertools.combinations_with_replacement)

>>> 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   排序 (itertools.permutations)

>>> 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   链接的迭代 (itertools.chain)

>>> 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   按给定值分组行 (itertools.groupby)

>>> from operator import itemgetter  >>> 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=itemgetter(-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

原文地址:http://sahandsaba.com/thirty-python-language-features-and-tricks-you-may-not-know.html