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 拆箱
- >>> 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
2 拆箱变量交换
- >>> a, b = 1, 2
- >>> a, b = b, a
- >>> a, b
- (2, 1)
3 扩展拆箱(只兼容python3)
- >>> a, *b, c = [1, 2, 3, 4, 5]
- >>> a
- 1
- >>> b
- [2, 3, 4]
- >>> c
- 5
4 负数索引
- >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- >>> a[-1]
- 10
- >>> a[-3]
- 8
5 切割列表
- >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- >>> a[2:8]
- [2, 3, 4, 5, 6, 7]
6 负数索引切割列表
- >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- >>> a[-4:-2]
- [7, 8]
7 指定步长切割列表
- >>> 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]
8 负数步长切割列表
- >>> 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]
9 列表切割赋值
- >>> 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]
10 命名列表切割方式
- >>> a = [0, 1, 2, 3, 4, 5]
- >>> LASTTHREE = slice(-3, None)
- >>> LASTTHREE
- slice(-3, None, None)
- >>> a[LASTTHREE]
- [3, 4, 5]
11 列表以及迭代器的压缩和解压缩
- >>> 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')]
12 列表相邻元素压缩器
- >>> 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,)]
13 在列表中用压缩器和迭代器滑动取值窗口
- >>> 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)]
14 用压缩器反转字典
- >>> 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'}
15 列表展开
- >>> 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]
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
17 字典推导
- >>> 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'}
18 用字典推导反转字典
- >>> 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'}
19 命名元组
- >>> 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
20 继承命名元组
- >>> 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)
21 操作集合
- >>> 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
22 操作多重集合
- >>> 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})
23 统计在可迭代器中最常出现的元素
- >>> 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)]
24 两端都可操作的队列
- >>> 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])
25 有最大长度的双端队列
- >>> 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
26 可排序词典
- >>> 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
27 默认词典
- >>> 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]'
28 默认字典的简单树状表达
- >>> 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"
- }
- }
29 对象到唯一计数的映射
- >>> 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
30 最大和最小的几个列表元素
- >>> 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]
31 两个列表的笛卡尔积
- >>> 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
32 列表组合和列表元素替代组合
- >>> 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
33 列表元素排列组合
- >>> 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
34 可链接迭代器
- >>> 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)
35 根据文件指定列类聚
- >>> 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
原文链接: sahandsaba 翻译: 伯乐在线 - Kevin Sun
译文链接: http://blog.jobbole.com/63320/