[Python] Advanced features

Slicing

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L[:10:2] # [0, 2, 4, 6, 8] L[::5] # 所有数，每5个取一个 # [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95] L[:] # copy L

Iterating

1 2	for x, y in [(1, 1), (2, 4), (3, 9)]: print(x, y)

List Comprehension

A list comprehension allows you to easily create a list based on some processing or selection criteria.

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myList = [x * x for x in range(1, 11) if x % 2 != 0] [ch.upper() for ch in 'comprehension' if ch not in 'aeiou'] combinations = [m + n for m in 'ABC' for n in 'XYZ'] # ['AX', 'AY', 'AZ', 'BX', 'BY', 'BZ', 'CX', 'CY', 'CZ']

Generator

Referennce: https://www.liaoxuefeng.com/wiki/1016959663602400/1017318207388128

Create a generator:

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L = [x * x for x in range(10)] L [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] g = (x * x for x in range(10)) g <generator object <genexpr> at 0x1022ef630> next(g) 0 >>> for n in g: print(n)

Create a generator for fibbonacci:

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def (k): # print first k fibbonacci number n, a, b = 0, 0, 1 while n < k: print(b) a, b = b, a + b n = n + 1 return 'done' def (max): n, a, b = 0, 0, 1 while n < max: yield b # Change print to yield, and fib would be a generator a, b = b, a + b n = n + 1 return 'done' >>> f = fib(6) >>> f <generator object fib at 0x104feaaa0>

generator和函数的执行流程不一样。函数是顺序执行，遇到return 大专栏  [Python] Advanced features语句或者最后一行函数语句就返回。而变成generator的函数，在每次调用next()的时候执行，遇到yield语句返回，再次执行时从上次返回的yield语句处继续执行。

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def odd(): print('step 1') yield 1 print('step 2') yield(3) print('step 3') yield(5) >>> o = odd() >>> next(o) step 1 1 >>> next(o) step 2 3 >>> next(o) step 3 5 >>> next(o) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration

118. Pascal’s Triangle

Leetcode: https://leetcode.com/problems/pascals-triangle/

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def generate(self, numRows): """ :type numRows: int :rtype: List[List[int]] """ def row(num): n, prev, cur = 1, [1], [1, 1] while n <= num: yield prev prev = cur temp = [0] + prev + [0] cur = [temp[i] + temp[i - 1] for i in range(1, len(temp))] n += 1 return [r for r in row(numRows)]

Iterator

可以直接作用于for循环的对象统称为可迭代对象：Iterable. list, set, dict, str, tuple.

而生成器不但可以作用于for循环，还可以被next()函数不断调用并返回下一个值，直到最后抛出StopIteration错误表示无法继续返回下一个值了。可以被next()函数调用并不断返回下一个值的对象称为迭代器：Iterator

All generators are Interator, not all Iterable are Iterator.(list, set, dict, str, tuple)

But we can use iter() to transform iterables into interator.

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>>> isinstance(iter([]), Iterator) True >>> isinstance(iter('abc'), Iterator) True

Python的Iterator对象表示的是一个数据流，Iterator对象可以被next()函数调用并不断返回下一个数据，直到没有数据时抛出StopIteration错误。可以把这个数据流看做是一个有序序列，但我们却不能提前知道序列的长度，只能不断通过next()函数实现按需计算下一个数据，所以Iterator的计算是惰性的，只有在需要返回下一个数据时它才会计算。

Iterator甚至可以表示一个无限大的数据流，例如全体自然数。而使用list是永远不可能存储全体自然数的。