3.熟悉Python

实习第3天：

遇到一个Python题目，是要求找出一个数组中最小的n个数，那么我们可以用堆排序或者快排，那么用优先级队列是最快的了，那么在Python中，优先级队列的数据结构是怎么样的呢？

可以查看自己类文件中的Queue.py就可以看到它的类定义和对应的实现

在我的Pycharm中，打出import Que后就会提示出这个类的具体路径，在我的mac电脑下是在

/system/libray/Frameworks/Python.framework/version/2.7/lib/python2.7/Queue.py

整个文件的源代码如下：

"""A multi-producer, multi-consumer queue."""from time import time as _timetry:    import threading as _threadingexcept ImportError:    import dummy_threading as _threadingfrom collections import dequeimport heapq__all__ = ['Empty', 'Full', 'Queue', 'PriorityQueue', 'LifoQueue']class Empty(Exception):    "Exception raised by Queue.get(block=0)/get_nowait()."    passclass Full(Exception):    "Exception raised by Queue.put(block=0)/put_nowait()."    passclass Queue:    """Create a queue object with a given maximum size.    If maxsize is <= 0, the queue size is infinite.    """    def __init__(self, maxsize=0):        self.maxsize = maxsize        self._init(maxsize)        # mutex must be held whenever the queue is mutating.  All methods        # that acquire mutex must release it before returning.  mutex        # is shared between the three conditions, so acquiring and        # releasing the conditions also acquires and releases mutex.        self.mutex = _threading.Lock()        # Notify not_empty whenever an item is added to the queue; a        # thread waiting to get is notified then.        self.not_empty = _threading.Condition(self.mutex)        # Notify not_full whenever an item is removed from the queue;        # a thread waiting to put is notified then.        self.not_full = _threading.Condition(self.mutex)        # Notify all_tasks_done whenever the number of unfinished tasks        # drops to zero; thread waiting to join() is notified to resume        self.all_tasks_done = _threading.Condition(self.mutex)        self.unfinished_tasks = 0    def task_done(self):        """Indicate that a formerly enqueued task is complete.        Used by Queue consumer threads.  For each get() used to fetch a task,        a subsequent call to task_done() tells the queue that the processing        on the task is complete.        If a join() is currently blocking, it will resume when all items        have been processed (meaning that a task_done() call was received        for every item that had been put() into the queue).        Raises a ValueError if called more times than there were items        placed in the queue.        """        self.all_tasks_done.acquire()        try:            unfinished = self.unfinished_tasks - 1            if unfinished <= 0:                if unfinished < 0:                    raise ValueError('task_done() called too many times')                self.all_tasks_done.notify_all()            self.unfinished_tasks = unfinished        finally:            self.all_tasks_done.release()    def join(self):        """Blocks until all items in the Queue have been gotten and processed.        The count of unfinished tasks goes up whenever an item is added to the        queue. The count goes down whenever a consumer thread calls task_done()        to indicate the item was retrieved and all work on it is complete.        When the count of unfinished tasks drops to zero, join() unblocks.        """        self.all_tasks_done.acquire()        try:            while self.unfinished_tasks:                self.all_tasks_done.wait()        finally:            self.all_tasks_done.release()    def qsize(self):        """Return the approximate size of the queue (not reliable!)."""        self.mutex.acquire()        n = self._qsize()        self.mutex.release()        return n    def empty(self):        """Return True if the queue is empty, False otherwise (not reliable!)."""        self.mutex.acquire()        n = not self._qsize()        self.mutex.release()        return n    def full(self):        """Return True if the queue is full, False otherwise (not reliable!)."""        self.mutex.acquire()        n = 0 < self.maxsize == self._qsize()        self.mutex.release()        return n    def put(self, item, block=True, timeout=None):        """Put an item into the queue.        If optional args 'block' is true and 'timeout' is None (the default),        block if necessary until a free slot is available. If 'timeout' is        a non-negative number, it blocks at most 'timeout' seconds and raises        the Full exception if no free slot was available within that time.        Otherwise ('block' is false), put an item on the queue if a free slot        is immediately available, else raise the Full exception ('timeout'        is ignored in that case).        """        self.not_full.acquire()        try:            if self.maxsize > 0:                if not block:                    if self._qsize() == self.maxsize:                        raise Full                elif timeout is None:                    while self._qsize() == self.maxsize:                        self.not_full.wait()                elif timeout < 0:                    raise ValueError("'timeout' must be a non-negative number")                else:                    endtime = _time() + timeout                    while self._qsize() == self.maxsize:                        remaining = endtime - _time()                        if remaining <= 0.0:                            raise Full                        self.not_full.wait(remaining)            self._put(item)            self.unfinished_tasks += 1            self.not_empty.notify()        finally:            self.not_full.release()    def put_nowait(self, item):        """Put an item into the queue without blocking.        Only enqueue the item if a free slot is immediately available.        Otherwise raise the Full exception.        """        return self.put(item, False)    def get(self, block=True, timeout=None):        """Remove and return an item from the queue.        If optional args 'block' is true and 'timeout' is None (the default),        block if necessary until an item is available. If 'timeout' is        a non-negative number, it blocks at most 'timeout' seconds and raises        the Empty exception if no item was available within that time.        Otherwise ('block' is false), return an item if one is immediately        available, else raise the Empty exception ('timeout' is ignored        in that case).        """        self.not_empty.acquire()        try:            if not block:                if not self._qsize():                    raise Empty            elif timeout is None:                while not self._qsize():                    self.not_empty.wait()            elif timeout < 0:                raise ValueError("'timeout' must be a non-negative number")            else:                endtime = _time() + timeout                while not self._qsize():                    remaining = endtime - _time()                    if remaining <= 0.0:                        raise Empty                    self.not_empty.wait(remaining)            item = self._get()            self.not_full.notify()            return item        finally:            self.not_empty.release()    def get_nowait(self):        """Remove and return an item from the queue without blocking.        Only get an item if one is immediately available. Otherwise        raise the Empty exception.        """        return self.get(False)    # Override these methods to implement other queue organizations    # (e.g. stack or priority queue).    # These will only be called with appropriate locks held    # Initialize the queue representation    def _init(self, maxsize):        self.queue = deque()    def _qsize(self, len=len):        return len(self.queue)    # Put a new item in the queue    def _put(self, item):        self.queue.append(item)    # Get an item from the queue    def _get(self):        return self.queue.popleft()class PriorityQueue(Queue):    '''Variant of Queue that retrieves open entries in priority order (lowest first).    Entries are typically tuples of the form:  (priority number, data).    '''    def _init(self, maxsize):        self.queue = []    def _qsize(self, len=len):        return len(self.queue)    def _put(self, item, heappush=heapq.heappush):        heappush(self.queue, item)    def _get(self, heappop=heapq.heappop):        return heappop(self.queue)class LifoQueue(Queue):    '''Variant of Queue that retrieves most recently added entries first.'''    def _init(self, maxsize):        self.queue = []    def _qsize(self, len=len):        return len(self.queue)    def _put(self, item):        self.queue.append(item)    def _get(self):        return self.queue.pop()

如何定义是小优先还是大优先呢：

如何定义最大优先尚未解决，反正默认是最小优先的

再次遇到一个Pythont题目，出处是：

https://www.nowcoder.com/practice/8fecd3f8ba334add803bf2a06af1b993?tpId=13&tqId=11185&tPage=2&rp=2&ru=/ta/coding-interviews&qru=/ta/coding-interviews/question-ranking

准备用list存储数据，然后重载比较运算符进行排序，是否是可以像C++一样使用list.sort(cmp)呢？

这是可以的，也看了list.sort([func])可选参数重是有函数，后来我发现老是没有排序成功的一个原因是，不像C++中直接return a + b < b + a，而是要分别判断<,>,=，分别返回-1，1，0才可以成功排序，以后要注意这个区别了