~list tuple dict set
1、collections.Counter
collections.Counter 属于dict，计算出现几次

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c.update({'a':3}) 更新

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2、defaultdict 支持自定义方法提供默认值的dict

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6、import array
array.array('c',"sdfdsfsfsdfs")
array.array('i',range(4))

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7、heapq 二叉树堆
heapq内置模块位于./Anaconda3/Lib/heapq.py，提供基于堆的优先排序算法
堆的逻辑结构就是完全二叉树，并且二叉树中父节点的值小于等于该节点的所有子节点的值。这种实现可以使用 heap[k] <= heap[2k+1] 并且 heap[k] <= heap[2k+2] （其中 k 为索引，从 0 开始计数）的形式体现，对于堆来说，最小元素即为根元素 heap[0]。
1.初始化
可以通过 list 对 heap 进行初始化，或者通过 api 中的 heapify 将已知的 list 转化为 heap 对象。

2. heapq.py中提供的函数方法
   heapq.heappush(heap, item)
   heapq.heappop(heap)：返回 root 节点，即 heap 中最小的元素。
   heapq.heapreplace(heap,item): python3中heappushpop的更高效版。
   heapq.heappushpop(heap, item)：向 heap 中加入 item 元素，并返回 heap 中最小元素。
   heapq.heapify(x)：Transform list into a heap, in-place, in O(len(x)) time
   heapq.merge(*iterables, key=None, reverse=False)
   heapq.nlargest(n, iterable, key=None)：返回可枚举对象中的 n 个最大值，并返回一个结果集 list，key 为对该结果集的操作。
   heapq.nsmallest(n, iterable, key=None)：同上相反
   heapq._heappop_max(heap): Maxheap version of a heappop
   heapq._heapreplace_max(heap,item)：Maxheap version of a heappop followed by a heappush.
   heapq._heapify_max(x)：Transform list into a maxheap, in-place, in O(len(x)) time
   heapq._siftdown(heap,startpos,pos)： Follow the path to the root, moving parents down until finding a place
   heapq._siftup(heap,pos)：Bubble up the smaller child until hitting a leaf
   heapq._siftdown_max(heap,startpos,pos)：Maxheap variant of _siftdown
   heapq._siftup_max(heap,pos)：Maxheap variant of _siftup

import heapq
def heapsort(iterable):
h = []
for i in iterable:
heapq.heappush(h, i)
return [heapq.heappop(h) for i in range(len(h))]

method 1: sort to list

s = [3, 5, 1, 2, 4, 6, 0, 1]
print(heapsort(s))
'''
[0, 1, 1, 2, 3, 4, 5, 6]
'''

method 2: use key to find price_min

portfolio = [{'name': 'IBM', 'shares': 100, 'price': 91.1},
{'name': 'AAPL', 'shares': 50, 'price': 543.22},
{'name': 'FB', 'shares': 200, 'price': 21.09},
{'name': 'HPQ', 'shares': 35, 'price': 31.75},
{'name': 'YHOO', 'shares': 45, 'price': 16.35},
{'name': 'ACME', 'shares': 75, 'price': 115.65}]
cheap = heapq.nsmallest(1, portfolio, key=lambda s:s['price'])
print(cheap)
'''
[{'name': 'YHOO', 'shares': 45, 'price': 16.35}]
'''

method 3: use while to push min element

def heapilize_list(x):
n = len(x)
# 获取存在子节点的节点 index 列表，并对每个节点单元进行最小堆处理
for i in reversed(range(n // 2)):
raiseup_node(x, i)

def put_down_node(heap, startpos, pos):
current_item = heap[pos]
# 判断单元中最小子节点与父节点的大小
while pos > startpos:
parent_pos = (pos - 1) >> 1
parent_item = heap[parent_pos]
if current_item < parent_item:
heap[pos] = parent_item
pos = parent_pos
continue
break
heap[pos] = current_item

def raiseup_node(heap, pos):
heap_len = len(heap)
start_pos = pos
current_item = heap[pos]
left_child_pos = pos * 2 + 1
while left_child_pos < heap_len:
right_child_pos = left_child_pos + 1
# 将这个单元中的最小子节点元素与父节点元素进行位置调换
if right_child_pos < heap_len and not heap[left_child_pos] < heap[right_child_pos]:
left_child_pos = right_child_pos
heap[pos] = heap[left_child_pos]
pos = left_child_pos
left_child_pos = pos * 2 + 1
heap[pos] = current_item
put_down_node(heap, start_pos, pos)

p = [4, 6, 2, 10, 1]
heapilize_list(p)
print(p)
'''
[1, 4, 2, 10, 6]
'''

8、 bisect有序列表
bisect.bisect(list,5)
bisect.insort()

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-- coding: cp936 --

python 27

xiaodeng

python之模块pprint之常见用法

import pprint
data = [(1,{'a':'A','b':'B','c':'C','d':'D'}),(2,{'e':'E','f':'F','g':'G','h':'H','i':'I','j':'J','k':'K','l':'L'}),]
print '--'*30

1、打印效果

pprint.pprint (data)
'''

[(1, {'a': 'A', 'b': 'B', 'c': 'C', 'd': 'D'}),
(2,
{'e': 'E',
'f': 'F',
'g': 'G',
'h': 'H',
'i': 'I',
'j': 'J',
'k': 'K',
'l': 'L'})]
'''

2、格式化

data = [(1,{'a':'A','b':'B','c':'C','d':'D'}),(2,{'e':'E','f':'F','g':'G','h':'H','i':'I','j':'J','k':'K','l':'L'}),]
result=pprint.pformat(data)
for key in result.splitlines():
print key

'''
[(1, {'a': 'A', 'b': 'B', 'c': 'C', 'd': 'D'}),
(2,
{'e': 'E',
'f': 'F',
'g': 'G',
'h': 'H',
'i': 'I',
'j': 'J',
'k': 'K',
'l': 'L'})]
'''