第一章：数据结构和算法
1.1 查找最大或者最小的n个元素
heapq 模块的两个函数 nlargest() nsmallest()
import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2] #列表或者元组都适用
print(heapq.nlargest(3, nums)) # Prints [42, 37, 23]
print(heapq.nsmallest(3, nums)) # Prints [-4, 1, 2]复杂情况
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(3, portfolio, key=lambda s: s['price'])
expensive = heapq.nlargest(3, portfolio, key=lambda s: s['price'])
以 price 的值进行比较
1.2 实现一个按优先级排序的队列 (利用 heapq)并且在这个队列上面每次pop操作总是返回优先级最高的那个元素
优先队列
import heapq
class PriorityQueue:
def __init__(self):
self._queue = []
self._index = 0
def push(self, item, priority):
# 插队列的时候，按照 -priority 从小到大排列 存入队列
heapq.heappush(self._queue, (-priority, self._index, item))
self._index += 1
def pop(self):
return heapq.heappop(self._queue)[-1]队列的使用：
class Item:
def __init__(self, name):
self.name = name
def __repr__(self):
return 'Item({!r})'.format(self.name)
>>> q = PriorityQueue()
>>> q.push(Item('foo'), 1)
>>> q.push(Item('bar'), 5)
>>> q.push(Item('spam'), 4)
>>> q.push(Item('grok'), 1)
[(-5, 1, Item('bar')), (-1, 0, Item('foo')), (-4, 2, Item('spam')), (-1, 3, Item('grok'))]
>>> q.pop()
Item('bar')
>>> q.pop()
Item('spam')
>>> q.pop()
Item('foo')
>>> q.pop()
Item('grok')
>>> 1.3 字典运算
prices = {'ACME': 45.23,'AAPL': 612.78, 'IBM': 205.55,'HPQ': 37.20, 'FB': 10.75}
对字典的值进行操作计算通常用zip()
min_price = min(zip(prices.values(), prices.keys()))
# min_price is (10.75, 'FB')
max_price = max(zip(prices.values(), prices.keys()))
# max_price is (612.78, 'AAPL')还可以使用 zip()和 sorted() 函数来排列字典数据
prices_sorted = sorted(zip(prices.values(), prices.keys()))
# prices_sorted is [(10.75, 'FB'), (37.2, 'HPQ'),
# (45.23, 'ACME'), (205.55, 'IBM'), (612.78, 'AAPL')]
1.3.1 查找两个字典的相同点
a = {'x':1,'y':2,'z':3} b = {'w':10,'x':11,'y':2}
a.keys() & b.keys() # { 'x', 'y' }
# Find keys in a that are not in b
a.keys() - b.keys() # { 'z' }
# Find (key,value) pairs in common
a.items() & b.items() # { ('y', 2) }