写在前面：本文主要是python高级练习部分，介绍了一些高级用法，这些都是零散的小知识，这些可以与函数式编程合在一起使用。
函数式编程[1]：Python中提供的函数式编程主要有：
- map(函数，可迭代式)映射函数
- filter(函数，可迭代式)过滤函数
- reduce(函数，可迭代式)规约函数
- lambda函数
- 列表推导式
- zip()函数
generator 生成
词汇
generator 英[ˈdʒenəreɪtə(r)] 美[ˈdʒɛnəˌretɚ]
n. 发电机，发生器; 电力公司; 生产者，创始者; [乐] 基础低音
My understanding
关于生成器的基本概念，我没有详述，因为有关它的描述，网上一搜，一大把，在我看来，学习最有效的方法就是自己运行案例，通过案例反映生成器的作用。
比如，【点这里】，哈哈这也是总结的。
案例1：元组推导式自动生成 generator
s = (x * x for x in range(5))
print(s)
<generator object <genexpr> at 0x000001343C239F10>
for x in s:
print(x, end=\',\')
0,1,4,9,16,
案例2：Fibonacci sequence
def fib(maximum):
n, a, b = 0, 0, 1
while n < maximum:
yield b
a, b = b, a+b
n += 1
return \'done\'
f = fib(10)
print(\'fib(10)\', f)
fib(10) <generator object fib at 0x000001343C239E08>
for x in f:
print(x, end=\' \')
1 1 2 3 5 8 13 21 34 55
g = fib(5)
while 1:
try:
x = next(g)
print(\'g:\', x)
except StopIteration as e:
print(\'Generator return value:\', e.value)
break
g: 1
g: 1
g: 2
g: 3
g: 5
Generator return value: done
iter 迭代
其实，迭代有可迭代的与迭代器，一个是形容词，一个是名词，所以他们是与区别的。
如果要使用需要导入from collections import Iterable, Iterator
案例1：可迭代的
from collections import Iterable, Iterator
def g():
yield 1
yield 2
yield 3
print(\'Iterable? [1, 2, 3]:\', isinstance([1, 2, 3], Iterable))
print(\'Iterable? \\\'abc\\\':\', isinstance(\'abc\', Iterable))
print(\'Iterable? 123:\', isinstance(123, Iterable))
print(\'Iterable? g():\', isinstance(g(), Iterable))
Iterable? [1, 2, 3]: True
Iterable? \'abc\': True
Iterable? 123: False
Iterable? g(): True
综上：
- 列表是可迭代的
- 字符串是可迭代的
- 数字是不可迭代的的
- 自定义的g()函数是可迭代的
案例2：迭代器
如果我们使用iter()函数作用，可将列表and元组转化为迭代器。
print(\'Iterator? [1, 2, 3]:\', isinstance([1, 2, 3], Iterator))
print(\'Iterator? iter([1, 2, 3]):\', isinstance(iter([1, 2, 3]), Iterator))
print(\'Iterator? \\\'abc\\\':\', isinstance(\'abc\', Iterator))
print(\'Iterator? 123:\', isinstance(123, Iterator))
print(\'Iterator? g():\', isinstance(g(), Iterator))
print(\'Iterator? (1, 2, 3):\', isinstance((1, 2, 3), Iterator))
print(\'Iterator? iter((1, 2, 3)):\', isinstance(iter((1, 2, 3)), Iterator))
Iterator? [1, 2, 3]: False
Iterator? iter([1, 2, 3]): True
Iterator? \'abc\': False
Iterator? 123: False
Iterator? g(): True
Iterator? (1, 2, 3): False
Iterator? iter((1, 2, 3)): True
综上：
- 列表 not is 迭代器
- 采用iter()转化的列表 is 迭代器
- 字符串not is 迭代器
- 数字not is 迭代器
- 自定义的g()函数is 迭代器
- 元组 not is 迭代器
- 采用iter()转化的元组 is 迭代器
案例3：迭代元素
例子1
print(\'for x in [1, 2, 3, 4, 5]:\')
for x in [1, 2, 3, 4, 5]:
print(x)
for x in [1, 2, 3, 4, 5]:
1
2
3
4
5
例子2
print(\'for x in iter([1, 2, 3, 4, 5]):\')
for x in iter([1, 2, 3, 4, 5]):
print(x)
for x in iter([1, 2, 3, 4, 5]):
1
2
3
4
5
例子3
print(\'next():\')
it = iter([1, 2, 3, 4, 5])
print(next(it))
print(next(it))
print(next(it))
print(next(it))
print(next(it))
next():
1
2
3
4
5
案例4：字典迭代
想知道，字典的情况【点我】
构造字典
key = \'a\', \'b\', \'c\'
value = 1, 2, 3
d = dict(zip(key, value))
迭代字典的键
print(\'iter key:\', d)
for k in d.keys():
print(\'key:\', k)
iter key: {\'a\': 1, \'b\': 2, \'c\': 3}
key: a
key: b
key: c
迭代字典的值
print(\'iter value:\', d)
for v in d.values():
print(\'value:\', v)
iter value: {\'a\': 1, \'b\': 2, \'c\': 3}
value: 1
value: 2
value: 3
迭代字典的键值对
print(\'iter item:\', d)
for k, v in d.items():
print(\'item:\', k, v)
iter item: {\'a\': 1, \'b\': 2, \'c\': 3}
item: a 1
item: b 2
item: c 3
列表迭代与推导
列表操作原理，【点我】
列表迭代
for i, value in enumerate([\'A\', \'B\', \'C\']):
print(i, value)
0 A
1 B
2 C
for x, y in [(1, 1), (2, 4), (3, 9)]:
print(x, y)
1 1
2 4
3 9
列表推导式
print([x*x for x in range(1, 11)])
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
print([x*x for x in range(1, 11) if x%2 == 0])
[4, 16, 36, 64, 100]
print([m + n for m in \'ABC\' for n in \'XYZ\'])
[\'AX\', \'AY\', \'AZ\', \'BX\', \'BY\', \'BZ\', \'CX\', \'CY\', \'CZ\']
d = {\'x\':\'A\', \'y\':\'B\', \'z\':\'C\'}
print([k + \'=\' + v for k, v in d.items()])
[\'x=A\', \'y=B\', \'z=C\']
L = [\'Hello\', \'World\', \'Apple\', \'IBM\']
print([s.lower() for s in L])
[\'hello\', \'world\', \'apple\', \'ibm\']
切片
L = [\'Michael\', \'Sarah\', \'Tracy\', \'Bob\', \'Jack\']
for i, value in enumerate(L):
print("L列表中元素的序：{}-->{}-->{}".format(i, value, i-len(L)))
L列表中元素的序：0-->Michael-->-5
L列表中元素的序：1-->Sarah-->-4
L列表中元素的序：2-->Tracy-->-3
L列表中元素的序：3-->Bob-->-2
L列表中元素的序：4-->Jack-->-1
print(\'L[0:3] =\', L[0:3])
print(\'L[:3] =\', L[:3])
print(\'L[1:3] =\', L[1:3])
print(\'L[-2:] =\', L[-2:])
L[0:3] = [\'Michael\', \'Sarah\', \'Tracy\']
L[:3] = [\'Michael\', \'Sarah\', \'Tracy\']
L[1:3] = [\'Sarah\', \'Tracy\']
L[-2:] = [\'Bob\', \'Jack\']
R = list(range(100))
print(\'R[:10] =\', R[:10])
print(\'R[-10:] =\', R[-10:])
print(\'R[10:20] =\', R[10:20])
print(\'R[:10:2] =\', R[:10:2])
print(\'R[::5] =\', R[::5])
R[:10] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
R[-10:] = [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]
R[10:20] = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
R[:10:2] = [0, 2, 4, 6, 8]
R[::5] = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95]
关于yeild的例子
完整的代码
def each_ascii(s):
for ch in s:
yield ord(ch)
return \'%s chars\' % len(s)
def yield_from(s):
r = yield from each_ascii(s)
print(r)
def main():
for x in each_ascii(\'abc\'):
print(x) # => \'a\', \'b\', \'c\'
it = each_ascii(\'xyz\')
try:
while True:
print(next(it)) # => \'x\', \'y\', \'z\'
except StopIteration as s:
print(s.value) # => \'3 chars\'
# using yield from in main() will change main() from function to generator:
# r = yield from each_ascii(\'hello\')
for ch in yield_from(\'hello\'):
pass
main()
代码分析
def each_ascii(s):
for ch in s:
yield ord(ch)
return \'%s chars\' % len(s)
def yield_from(s):
r = yield from each_ascii(s)
print(r)
for x in each_ascii(\'abc\'):
print(x)
97
98
99
it = each_ascii(\'xyz\')
try:
while True:
print(next(it))
except StopIteration as s:
print(s.value)
120
121
122
3 chars
for ch in yield_from(\'hello\'):
pass
5 chars
总结
有时候，对编程不感冒，但是把一个复杂的知识点多运行几遍，太复杂了，把代码分成小代码运行，最后综合理解，并整理成文。当然，这涉及到调试代码部分，现在我还是个小菜鸟，调试用的不太熟，当然代码量很大的时候可能要用到调试部分。
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