你可能不知道的30个Python语言的特点技巧
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
你可能不知道的30个Python语言的特点技巧的更多相关文章
- 你可能不知道的 30 个 Python 语言的特点技巧
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)> ...
- 30 个 Python 语言的特点技巧
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 ...
- 如何玩转Python? 一文总结30种Python的窍门和技巧
Python作为2019年必备语言之一,展现了不可替代作用.对于所有的数据科学工作者,如何提高使用Python的效率,这里,总结了30种Python的最佳实践.技巧和窍门.希望这些可以帮助大家在202 ...
- 你可能不知道的java、python、JavaScript以及jquary循环语句的区别
一.概述 java循环语句分为四种形式,分别是 while, do/while, for, foreach: python中循环语句有两种,while,for: JavaScript中循环语句有四种, ...
- 大部分人都不知道的8个python神操作
01 print 打印带有颜色的信息 大家知道 Python 中的信息打印函数 Print,一般我们会使用它打印一些东西,作为一个简单调试. 但是你知道么,这个 Print 打印出来的字体颜色是可以设 ...
- Python之几个技巧特点
今天偶然看到一篇文章<你可能不知道的30个Python语言的提点技巧>,虽然做python有几年了,但中间还是好多不知道或没想到,特在这里做下摘抄. 原文地址: http://soft.c ...
- 学写PEP,参与Python语言的设计
如果你为Python写了一篇PEP,这篇PEP成功的被Python指导委员会接受了,那么以后你在吹牛皮的时候你就可以说我主导了Python语言某个特性的设计工作. -- 跬蟒 我就问你主导Python ...
- 关于Python你不得不知道的Python语言特点
首先什么是语言?什么是编程? 准确来说是:定义计算机程序的语言,用来向计算机发送指令 个人理解: 语言:是一种交流的工具或者方式.比如我们的汉语普通话.各地的方言.外语中的英语.俄语.日语等.我们 ...
- [转]Python程序员必须知道的30条编程技巧
30 tips & tricks for Python Programming 1 直接交换两个数字位置 x, y = 10, 20 print(x, y) x, y = y, x prin ...
随机推荐
- ASP.NET26个性能优化方法
1.数据库访问性能优化 (1)数据库的连接和关闭 访问数据库资源需要创建连接.打开连接和关闭连接几个操作.这些过程需要多次与数据库交换信息以通过身份验证,比较耗费服务器资源. ASP.NET中提供了连 ...
- Java 全角字符转半角字符
1.java代码里有时候会遇到代码注入的安全问题,为了防止这种问题,增加了一个过滤功能.主要是过滤全角字符,把url不能识别的全角字符转换成半角字符 public class Test { publi ...
- poj 1742 Coins （多重背包）
http://poj.org/problem?id=1742 n个硬币,面值分别是A1...An,对应的数量分别是C1....Cn.用这些硬币组合起来能得到多少种面值不超过m的方案. 多重背包,不过这 ...
- leetcode：Power of Two
Given an integer, write a function to determine if it is a power of two. 分析:这道题让我们判断一个数是否为2的次方数(而且要求 ...
- Hadoop学习过程知识积累
1. Hadoop 64位centos 下编译 hadoop 2.6.0 源码 2. 学习问题汇总 hadoop多次格式化后,导致datanode启动不了 3. HDFS Hadoop中NameNod ...
- Debian 如何更改hdmi接口输出的分辨率
1. 先删除无用的文件: a) Boot/boot.src b) Boot/script.bin 2. 打开boot/script.fex,找到disp_init节点 a) 将screenX_outp ...
- Spring MVC的UrlBasedViewResolver和InternalResourceViewResolver
Spring MVC使用ViewResolver来根据controller中返回的view名关联到具体的View对象.使用View对象来渲染返回值以生成最终的视图,如html,json或pdf等. S ...
- 【第二篇】.NET用NPOI读取Excel表格并在页面预览
博主用的是npoi2.0.1,支持.xls也支持.xlsx 直接上代码吧. <table class="table table-bordered table-striped" ...
- BZOJ 4552 排序
省选TM都能有BC原题? ... #include<iostream> #include<cstdio> #include<cstring> #include< ...
- myisam 与innodb的区别
myisam 与innodb的区别 frm结构 fri索引 frd数据 innodb:一个表一个文件:frm文件 所有的innodb表,都使用表空间储存, 数据和索引的保存文件不同,myisam 分开 ...