Python cookbook (data structure and algorithm) to find the maximum or minimum N elements implementation method example, pythoncookbook

Python cookbook (data structure and algorithm) to find the maximum or minimum N elements implementation method example, pythoncookbook

This example describes how to find the maximum or minimum N elements in python. We will share this with you for your reference. The details are as follows:

Problem:To find the maximum or minimum N elements in a collection

Solution:In the heapq modulenlargest()Andnsmallest()The two functions are exactly what we need.

>>> import heapq>>> nums=[1,8,2,23,7,-4,18,23,42,37,2]>>> print(heapq.nlargest(3,nums))[42, 37, 23]>>> print(heapq.nsmallest(3,nums))[-4, 1, 2]>>>

These two functions accept a parameter key, allowing it to work on a more complex data structure:

# example.py## Example of using heapq to find the N smallest or largest itemsimport heapqportfolio = [ {'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'])print(cheap)print(expensive)

Python 3.4.0 (v3.4.0:04f714765c13, Mar 16 2014, 19:24:06) [MSC v.1600 32 bit (Intel)] on win32Type "copyright", "credits" or "license()" for more information.>>> ================================ RESTART ================================>>>[{'name': 'YHOO', 'price': 16.35, 'shares': 45}, {'name': 'FB', 'price': 21.09, 'shares': 200}, {'name': 'HPQ', 'price': 31.75, 'shares': 35}][{'name': 'AAPL', 'price': 543.22, 'shares': 50}, {'name': 'ACME', 'price': 115.65, 'shares': 75}, {'name': 'IBM', 'price': 91.1, 'shares': 100}]>>>

If you are looking for a maximum or minimum of N elements, and the number of elements in the set is N hours, the following functions have better performance.

These functions first convert data to a list at the underlying layer, and the elements are arranged in the heap order.

>>> Import heapq >>> nums = [, 7,-,] >>>> heap = list (nums) >>> heap [1, 8, 2, 23, 7,-4, 18, 23, 42, 37, 2] >>> heapq. the heapify (heap) # heapify () parameter must be a list. This function changes the list to a heap for real-time operations. In this way, heap functions can be used in any situation. >>> Heap [-4, 2, 1, 23, 7, 2, 18, 23, 42, 37, 8]> heapq. heappop (heap) # The following is a small 3rd element-4> heapq. heappop (heap) 1 >>> heapq. heappop (heap) 2 >>>

The most important feature of heap is that heap [0] is always the smallest element. Availableheapq.heappop()It is easy to find the minimum value. The complexity of this operation is O (logN), and N represents the heap size.

Summary:

1. When the number of elements to be searched is relatively small, the functionnlargest()Andnsmallest().
2. If you only want to find the minimum and maximum values (N = 1), using min () and max () will be faster.
3. If N is about the same size as the set, the faster method is to sort the set and then slice it (for examplesorted(items)[:N]Orsorted(items)[-N：])
4. heapq. heappush (heap, item): press the item into the heap array heap. If you do not perform this step, the heappop () is invalid;
Heapq. heappop (heap): extracts the smallest value from the heap array and returns it.
Heapq. heapify (list): The parameter must be list. This function changes the list to heap for real-time operations. In this way, heap functions can be used under any circumstances.
Heapq. heappushpop (heap, item): A combination of heappush and heappop. note: heappush (heap, item) is first operated, and heappop (heap) is then operated)
Heapreplace (heap, item): a joint operation between heappop (heap) and heappush (heap, item. Note: The difference between heappushpop (heap, item) and heappushpop is that the order is different. Here we delete the item first and press it into the heap.
Heap, merge (* iterables)

>>> H = [] # define a list >>> from heapq import * # introduce the heapq module >>> h [] >>> heappush (h, 5) # add values to the heap> heappush (h, 2)> heappush (h, 3)> heappush (h, 9)> h # h value [2, 5, 3, 9] >>> heappop (h) # Delete the smallest value from h and return the value 2 >>> h [3, 5, 9] >>> h. append (1) # Note: if it is not pushed into the heap, append a value through append> h # The heap function cannot operate on the added value, or its heap does not exist [3, 5, 9, 1] >>> heappop (h) # The minimum value that can be found from h is 3, instead of 13 >>> heappush (h, 2) # in this case, not only is 2 pushed to the heap, but 1 also enters the heap. >>> H [1, 2, 9, 5] >>> heappop (h) # The operation object already contains 11

>>> H [1, 2, 9, 5] >>> heappop (h) 1 >>> heappushpop (h, 4) # Add 4 to delete the minimum value 2 at the same time and return the minimum value, which is equivalent to the following operations: 2 # heappush (h, 4), heappop (h) >>> h [4, 5, 9]

>>> A = [3, 6, 1] >>> heapify (a) # after converting a into a heap, you can operate on it >>> heappop () 1 >>> B = [, 5] # If B is not a heap, the result is displayed as follows >>> heappop (B) # in sequence, delete the first value and return the result. The minimum value 4 is not selected from the result. >>> heapify (B) # changes to heap, and then the operation >>> heappop (B) 2

>>> A = [] >>> heapreplace (a, 3) # If the list is empty, Traceback (most recent call last): File "<stdin> ", line 1, in <module> IndexError: index out of range >>> heappush (a, 3) >>> a [3] >>> heapreplace (a, 2) # Delete (heappop (a)-> 3), then add (heappush (a, 2) 3 >>> a [2] >>> heappush (, 5) >>> heappush (a, 9) >>> heappush (a, 4) >>> a [2, 4, 9, 5] >>> heapreplace (, 6) # first locate the minimum value from heap a and return it, then add 62 >>> a [4, 5, 9, 6] >>> heapreplace (a, 1) #1 is later added. Before 1 is added, the minimum value of a is 44> a [1, 5, 9, 6].

>>> a=[2,4,6]>>> b=[1,3,5]>>> c=merge(a,b)>>> list(c)[1, 2, 3, 4, 5, 6]