40個(gè)你可能不知道的Python技巧附代碼
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、使用拆箱進(jìn)行變量交換
>>> a, b = 1, 2 >>> a, b = b, a >>> a, b (2, 1)
3、擴(kuò)展的拆箱(Python 3支持)
>>> a, *b, c = [1, 2, 3, 4, 5] >>> a 1 >>> b [2, 3, 4] >>> c 5
4、負(fù)數(shù)索引
>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> a[-1] 10 >>> a[-3] 8
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]
6、負(fù)數(shù)索引的列表切片
>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> a[-4:-2] [7, 8]
7、帶步數(shù)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]
8、負(fù)數(shù)步數(shù)的列表切片
>>> 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]
9、列表切片賦值
>>> 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、切片命名(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]
11、遍歷列表索引和值(enumerate)
>>> a = ["Hello", "world", "!"] >>> for i, x in enumerate(a): ... print "{}: {}".format(i, x) ... 0: Hello 1: world 2: !
12、遍歷字典的KEY和VALUE(dict.iteritems)
>>> m = {"a": 1, "b": 2, "c": 3, "d": 4} >>> for k, v in m.iteritems(): ... print "{}: {}".format(k, v) ... a: 1 c: 3 b: 2 d: 4 # 注意:Python 3中要使用dict.items
13、壓縮 & 解壓列表和可遍歷對(duì)象
>>> 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")]
14、使用zip分組相鄰列表項(xiàng)
>>> a = [1, 2, 3, 4, 5, 6] >>> # Using iterators >>> 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,)] >>> # Using slices >>> from itertools import islice >>> group_adjacent = lambda a, k: zip(*(islice(a, i, None, 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,)]
15、使用zip & iterators實(shí)現(xiàn)推拉窗(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)]
16、使用zip反相字典對(duì)象
>>> 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"}
17、合并列表
>>> 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] Note: according to Python"s documentation on sum, itertools.chain.from_iterable is the preferred method for this.
18、生成器
>>> 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
19、字典解析
>>> 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"}
20、使用字典解析反相字典對(duì)象
>>> 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"}
21、命名的tuples(collections.namedtuple)
>>> Point = collections.namedtuple("Point", ["x", "y"]) >>> p = Point(x=4.0, y=2.0) >>> p Point(x=4.0, y=2.0) >>> p.x 4.0 >>> p.y 2.0
22、繼承命名tuples
>>> 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=4.0, y=2.0) >>> q = Point(x=2.0, y=3.0) >>> p + q Point(x=6.0, y=5.0)
23、Set & Set運(yùn)算
>>> 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
24、Multisets運(yùn)算(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})
25、列表中出現(xiàn)最多的元素(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)]
26、雙向隊(duì)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])
27、限制長(zhǎng)度的雙向隊(duì)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
28、排序字典(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
29、默認(rèn)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]"
30、使用defaultdict代表tree
>>> 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" } } # 查看更多:https://gist.github.com/hrldcpr/2012250
31、映射對(duì)象到唯一的計(jì)數(shù)數(shù)字(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
32、最大 & 最小元素(heapq.nlargest and 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]
33、笛卡爾積(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
34、組合(itertools.combinations and 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
35、排列(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
36、鏈接可遍歷對(duì)象(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)
37、根據(jù)給定的KEY分組(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 " ".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=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
38、在任意目錄啟動(dòng)HTTP服務(wù)
python -m SimpleHTTPServer 5000
Serving HTTP on 0.0.0.0 port 5000 ...
39、Python之禪
>>> import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren"t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you"re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it"s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let"s do more of those!
40、使用C風(fēng)格的大括號(hào)代替Python縮進(jìn)來(lái)表示作用域
>>> from __future__ import braces
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