Python – Python时代与机器学习

2018年11月27日2018年11月27日

Python Tip 3: Python lambda表达式

Python中的Lambda表达式很可能让人迷惑，我们还是从一段官方解释文字开始：

Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition.

理解lambda表达式的本质需要回答两个问题：为什么需要lambda？lambda的好处在哪里？

首先为什么需要lambda? 让我们看几个例子

>>> add_one = lambda x: x + 1
>>> add_one(1)
2
>>> add_one(2)
3
>>> add_one(100)
101
>>> def add_one_fun(x):
...     return x + 1
... 
>>> add_one_fun(1)
2
>>> add_one_fun(100)
101
>>> x_sum_y = lambda x, y: x + y
>>> x_sum_y(1, 1)
2
>>> x_sum_y(1, 10)
11
>>> def sum_fun(x, y):
...     return x + y
... 
>>> sum_fun(1, 1)
2
>>> sum_fun(1, 2)
3
>>> sum_fun(1, 10)
11

很遗憾，lambda并非必须，每个lambda都可以用函数替代。那么我们为什么需要lambda表达式？或者lambda表达式有什么优点？其实，官方文档里已经说得很明确了：首先，它是一个小的匿名函数，可以用在任何需要函数对象的地方，它的语法被严格限制在一行表达式（并且不应该太复杂）；从语义上来讲，它们只是正常的函数定义的语法糖。

Python不是一个纯函数式编程语言，但是引入了sorted, map, reduce, filter等函数式编程的一些特性, 可以将函数作为参数传给这些函数，这里，lambda表达式就派上用场了。我们熟悉的大概是sorted函数了：

>>> test_list = [('two', 2), ('three', 3), ('one', 1), ('five', 5), ('four', 4)]
>>> sorted(test_list, key=lambda x: x[1])
[('one', 1), ('two', 2), ('three', 3), ('four', 4), ('five', 5)]
>>> sorted(test_list, key=lambda x: x[0])
[('five', 5), ('four', 4), ('one', 1), ('three', 3), ('two', 2)]

map() 接收一个 function 和一个 iterable 作为参数，该函数会返回一个迭代器，该迭代器将 function 应用于 iterable 的所有元素，并产生结果。下述示例，对列表中所有元素求平方和立方：

>>> test_list = [2, 3, 5, 7, 11, 13, 19, 17]
>>> map_list = list(map(lambda x: x ** 2, test_list))
>>> map_list
[4, 9, 25, 49, 121, 169, 361, 289]
>>> map_list = list(map(lambda x: x ** 3, test_list))
>>> map_list
[8, 27, 125, 343, 1331, 2197, 6859, 4913]

reduce() 函数会对参数序列中元素进行累积。函数将一个数据集合（链表，元组等）中的所有数据进行下列操作：用传给 reduce 中的函数 function（有两个参数）先对集合中的第 1、2 个元素进行操作，得到的结果再与第三个数据用 function 函数运算，最后得到一个结果。注意在python3中reduce已经从全局函数中移除，需要从functools中import，我们来看一个1到100整数求和的例子:

>>> test_list = list(range(1, 101))
>>> test_list
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]
>>> from functools import reduce
>>> reduce(lambda x, y: x + y, test_list)
5050

filter() 提供了一种优雅的方法来过滤 iterable（可迭代对象，例如：列表）中的所有元素，过滤条件是 function 返回 True：

>>> test_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>> filter_list = list(filter(lambda x: x % 3 == 0, test_list))
>>> filter_list
[3, 6, 9, 12, 15]
>>> filter_list = list(filter(lambda x: x % 5 == 0, test_list))
>>> filter_list
[5, 10, 15]

注：原创文章，转载请注明出处及保留链接“Python时代”：http://www.pythonage.com/

本文链接地址：Python Tip 3: Python lambda表达式 http://www.pythonage.com/?p=47

2018年11月22日2018年11月22日

Python Tip 2: Python *args **kwargs 用法

关于Python中 *args 和 **kwargs的用法，网上有很多介绍文章，还是先看一段官方的解释：

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (*name must occur before **name.)

首先需要了解Python函数传递参数的方式有两种：

位置参数（positional argument）
关键词参数（keyword argument）

然后再来看*args（*name)与**kwargs(**name)的区别，两者都是python中的可变参数：

*args表示任何多个无名参数，它本质是一个tuple；

>>> def args_one(narg, *args):
...     print("Normal arg:", narg)
...     for arg in args:
...         print("other args:", arg)
... 
>>> args_one(1, 2, 3, 4, 5)
Normal arg: 1
other args: 2
other args: 3
other args: 4
other args: 5
>>> args_one('a', 'b', 'c', 'd', 'e')
Normal arg: a
other args: b
other args: c
other args: d
other args: e
>>> tuple_args = (1, 2, 3, 4, 5)
>>> args_one(1, tuple_args)
Normal arg: 1
other args: (1, 2, 3, 4, 5)
>>> args_one(1, *tuple_args)
Normal arg: 1
other args: 1
other args: 2
other args: 3
other args: 4
other args: 5

**kwargs表示关键字参数，它本质上是一个dict:

>>> def args_two(narg, **kwargs):
...     print("Normal arg:", narg)
...     for key, value in kwargs.items():
...         print("other keyword arg: %s: %s" % (key, value))
... 
>>> args_two(narg=1, karg1="one", karg2="two")
Normal arg: 1
other keyword arg: karg1: one
other keyword arg: karg2: two
>>> key_args = {"a": "A", "b": "B", "c": "C"}
# 以下两个是错误的调用示范
>>> args_two('a', key_args)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: args_two() takes 1 positional argument but 2 were given
>>> args_two('a', *key_args)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: args_two() takes 1 positional argument but 4 were given
# 正确做法
>>> args_two('a', **key_args)
Normal arg: a
other keyword arg: a: A
other keyword arg: b: B
other keyword arg: c: C

同时使用*args和**kwargs时，必须*args参数列要在**kwargs前，让我们最后来看一个综合例子：

>>> def args_fun(normal, *args, **kwargs):
...     print("narmal=", normal)
...     print("*args=", args)
...     print("**args=", kwargs)
... 
>>> args_fun(1)
narmal= 1
*args= ()
**args= {}
>>> args_fun(1, 2)
narmal= 1
*args= (2,)
**args= {}
>>> args_fun(1, 2, 3)
narmal= 1
*args= (2, 3)
**args= {}
>>> args_fun(1, a=1, b=2, c=3)
narmal= 1
*args= ()
**args= {'a': 1, 'b': 2, 'c': 3}
>>> args_fun(1, 2, 3, a=1, b=2, c=3)
narmal= 1
*args= (2, 3)
**args= {'a': 1, 'b': 2, 'c': 3}
>>> args_fun(1, a=1, b=2, c=3, 2, 3)
  File "<stdin>", line 1
SyntaxError: positional argument follows keyword argument

注：原创文章，转载请注明出处及保留链接“Python时代”：http://www.pythonage.com/

本文链接地址：Python Tip 2: python *args **kwargs 用法 http://www.pythonage.com/?p=15

2018年10月30日2018年10月30日

Python Tip 1：Python循环语句中的else语法(for else, while else)

在Python的学习过程中，for...else..., while...else...语法可能是比较令人困惑的一个Python知识点。先来看一段Python官方的说明：

"Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the list (with for) or when the condition becomes false (with while), but not when the loop is terminated by a break statement."

Python循环语句(for, while)有可能带一个else分支，当一个for循环正常执行完毕时或者当一个while循环正常执行完毕（循环条件变为false）时它被触发执行，但是如果这个循环被break语句非正常中止时，则这个else分支不执行。让我们来看几个例子：

1）for循环正常执行：

>>> for i in range(7):
...     print(i)
... else:
...     print("No abnormal interruption")
... 
0
1
2
3
4
5
6
No abnormal interruption

2) for循环“非正常”break中断:

>>> for i in range(7):
...     print(i)
...     if i == 3:
...         break
... else:
...     print("No abnormal interruption")
... 
0
1
2
3

3) while循环正常执行：

>>> i = 0
>>> while i < 7:
...     print(i)
...     i += 1
... else:
...     print("No abnormal interruption")
... 
0
1
2
3
4
5
6
No abnormal interruption

4) while循环“非正常”break中断：

>>> i = 0
>>> while i < 7:
...     print(i)
...     i += 1
...     if i == 3:
...         break
... else:
...     print("No abnormal interruption")
... 
0
1
2

总结起来就是：当循环中没有break时，else被执行，当循环中有break并且被触发时，else不被执行，类似这个case，即使循环语句中有break，但是如果没有触发break正常执行完毕，也会执行else:

>>> for i in range(7):
...     print(i)
...     if i > 7:
...         break
... else:
...     print("No abnormal interruption")
... 
0
1
2
3
4
5
6
No abnormal interruption

那么，这个有点绕口的Python语法糖有什么好处呢？让我们再看一个Case，这个Case参考自Python官方的一个寻找素数的例子：

>>> for n in range(2, 20):
...     for x in range(2, n):
...         if n % x == 0:
...            print(n, 'equals', x, '*', n//x)
...            break
...     else:
...         print(n, 'is a prime number')
... 
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
10 equals 2 * 5
11 is a prime number
12 equals 2 * 6
13 is a prime number
14 equals 2 * 7
15 equals 3 * 5
16 equals 2 * 8
17 is a prime number
18 equals 2 * 9
19 is a prime number

想一想，如果没有else语句，如何实现？我是这样实现的，加一个flag标志来判断：

>>> for n in range(2, 20):
...     flag = 0
...     for x in range(2, n):
...         if n % x == 0:
...             print(n, 'equals', x, '*', n//x)
...             flag = 1
...             break
...     if flag == 0:
...         print(n, 'is a prime number')
... 
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
10 equals 2 * 5
11 is a prime number
12 equals 2 * 6
13 is a prime number
14 equals 2 * 7
15 equals 3 * 5
16 equals 2 * 8
17 is a prime number
18 equals 2 * 9
19 is a prime number

这样看来，是不是觉得前者更 pythonic 一些？

注：原创文章，转载请注明出处及保留链接“Python时代”：http://www.pythonage.com/

本文链接地址：Python Tip 1：Python循环语句中的else语法(for else, while else)

2018年9月24日2018年9月24日

The Zen of Python

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!