07 3 / 2014

Python has sorted function which sorts iterable in ascending or descending order.

# Sort descending
In [95]: sorted([1, 2, 3, 4], reverse=True)
Out[95]: [4, 3, 2, 1]

# Sort ascending
In [96]: sorted([1, 2, 3, 4], reverse=False)
Out[96]: [1, 2, 3, 4]

sorted(iterable, reverse=True)[:n] will yield first n largest numbers. There is an alternate way.

Python has heapq which implements heap datastructure. heapq has function nlargest and nsmallest which take arguments n number of elements, iterable like list, dict, tuple, generator and optional argument key.

In [85]: heapq.nlargest(10, [1, 2, 3, 4,])
Out[85]: [4, 3, 2, 1]

In [88]: heapq.nlargest(10, xrange(1000))
Out[88]: [999, 998, 997, 996, 995, 994, 993, 992, 991, 990]

In [89]: heapq.nlargest(10, [1000]*10)
Out[89]: [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]

In [99]: heapq.nsmallest(3, [-10, -10.0, 20.34, 0.34, 1])
Out[99]: [-10, -10.0, 0.34]

Let’s say marks is a list of dictionary containing students marks. Now with heapq it is possible to find highest and lowest mark in a subject.

In [113]: marks = [{'name': "Ram", 'chemistry': 23},{'name': 'Kumar', 'chemistry': 50}, {'name': 'Franklin', 'chemistry': 89}]

In [114]: heapq.nlargest(1, marks, key=lambda mark: mark['chemistry'])
Out[114]: [{'chemistry': 89, 'name': 'Franklin'}]

In [115]: heapq.nsmallest(1, marks, key=lambda mark: mark['chemistry'])
Out[115]: [{'chemistry': 23, 'name': 'Ram'}]

heapq can be used for building priority queue.

Note: IPython is used in examples where In [114]: means Input line number 114 and Out[114] means Output line number 114.