Hướng dẫn dùng numeric order python
Hàm List sort() trong Python sắp xếp các đối tượng của list. Nó sắp xếp các mục theo thứ tự giảm dần và tăng dần. Nó nhận một tham số tùy chọn 'reverse' để sắp xếp danh sách theo thứ tự giảm dần. Theo mặc định, các phần tử của list được sắp xếp theo thứ tự tăng dần. Show Cú phápCú pháp của sort() trong Python: Ví dụ sau minh họa cách sử dụng của sort() để sắp xếp các phần tử của đối tượng list theo thứ tự tăng dần. list1 = ['java', 'python', 'c++', 'php', 'sql'] list2 = [4, 2, 8, 10, 6] list1.sort() list2.sort() print ("List1 duoc sap xep: ", list1) print ("List2 duoc sap xep: ", list2) Chạy chương trình Python trên sẽ cho kết quả: List1 duoc sap xep: ['c++', 'java', 'php', 'python', 'sql'] List2 duoc sap xep: [2, 4, 6, 8, 10] Ví dụ 2: hàm List sort() trong PythonVí dụ sau minh họa cách sử dụng của sort() để sắp xếp các phần tử của đối tượng list theo thứ tự giảm dần. list1 = ['java', 'python', 'c++', 'php', 'sql'] list2 = [4, 2, 8, 10, 6] list1.sort(reverse=True) list2.sort(reverse=True) print ("List1 duoc sap xep: ", list1) print ("List2 duoc sap xep: ", list2) Chạy chương trình Python trên sẽ cho kết quả: List1 duoc sap xep: ['sql', 'python', 'php', 'java', 'c++'] List2 duoc sap xep: [10, 8, 6, 4, 2] Ví dụ 3: hàm List sort() trong PythonVí dụ sau minh họa cách sử dụng của sort() để sắp xếp các phần tử char của đối tượng list theo thứ tự giảm dần. list1 = ['a', 'p', 'p', 'l', 'e'] list1.sort(reverse=True) print ("List1 duoc sap xep: ", list1) Chạy chương trình Python trên sẽ cho kết quả: List1 duoc sap xep: ['p', 'p', 'l', 'e', 'a'] Andrew Dalke and Raymond Hettinger Nội dung chính 0.1 Python lists have a built-in In this document, we explore the various techniques for sorting data using Python. Sorting Basics¶A simple ascending sort is very easy: just call the >>> sorted([5, 2, 3, 1, 4]) [1, 2, 3, 4, 5] You can also use the >>> a = [5, 2, 3, 1, 4] >>> a.sort() >>> a [1, 2, 3, 4, 5] Another difference is that the
>>> sorted({1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'}) [1, 2, 3, 4, 5] Key Functions¶Both For example, here’s a case-insensitive string comparison: >>> sorted("This is a test string from Andrew".split(), key=str.lower) ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This'] The value of the key parameter should be a function (or other callable) that takes a single argument and returns a key to use for sorting purposes. This technique is fast because the key function is called exactly once for each input record. A common pattern is to sort complex objects using some of the object’s indices as keys. For example: >>> student_tuples = [ ... ('john', 'A', 15), ... ('jane', 'B', 12), ... ('dave', 'B', 10), ... ] >>> sorted(student_tuples, key=lambda student: student[2]) # sort by age [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] The same technique works for objects with named attributes. For example: >>> class Student: ... def __init__(self, name, grade, age): ... self.name = name ... self.grade = grade ... self.age = age ... def __repr__(self): ... return repr((self.name, self.grade, self.age)) >>> student_objects = [ ... Student('john', 'A', 15), ... Student('jane', 'B', 12), ... Student('dave', 'B', 10), ... ] >>> sorted(student_objects, key=lambda student: student.age) # sort by age [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] Operator Module Functions¶The key-function patterns
shown above are very common, so Python provides convenience functions to make accessor functions easier and faster. The Using those functions, the above examples become simpler and faster: >>> from operator import itemgetter, attrgetter >>> sorted(student_tuples, key=itemgetter(2)) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] >>> sorted(student_objects, key=attrgetter('age')) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age: >>> sorted(student_tuples, key=itemgetter(1,2)) [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)] >>> sorted(student_objects, key=attrgetter('grade', 'age')) [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)] Ascending and Descending¶Both >>> sorted(student_tuples, key=itemgetter(2), reverse=True) [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] >>> sorted(student_objects, key=attrgetter('age'), reverse=True) [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] Sort Stability and Complex Sorts¶Sorts are guaranteed to be stable. That means that when multiple records have the same key, their original order is preserved. >>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)] >>> sorted(data, key=itemgetter(0)) [('blue', 1), ('blue', 2), ('red', 1), ('red', 2)] Notice how the two records for blue retain their original order so
that This wonderful property lets you build complex sorts in a series of sorting steps. For example, to sort the student data by descending grade and then ascending age, do the age sort first and then sort again using grade: >>> s = sorted(student_objects, key=attrgetter('age')) # sort on secondary key >>> sorted(s, key=attrgetter('grade'), reverse=True) # now sort on primary key, descending [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] This can be abstracted out into a wrapper function that can take a list and tuples of field and order to sort them on multiple passes. >>> def multisort(xs, specs): ... for key, reverse in reversed(specs): ... xs.sort(key=attrgetter(key), reverse=reverse) ... return xs >>> multisort(list(student_objects), (('grade', True), ('age', False))) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] The Timsort algorithm used in Python does multiple sorts efficiently because it can take advantage of any ordering already present in a dataset. The Old Way Using Decorate-Sort-Undecorate¶This idiom is called Decorate-Sort-Undecorate after its three steps:
For example, to sort the student data by grade using the DSU approach: >>> decorated = [(student.grade, i, student) for i, student in enumerate(student_objects)] >>> decorated.sort() >>> [student for grade, i, student in decorated] # undecorate [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] This idiom works because tuples are compared lexicographically; the first items are compared; if they are the same then the second items are compared, and so on. It is not strictly necessary in all cases to include the index i in the decorated list, but including it gives two benefits:
Another name for this idiom is Schwartzian transform, after Randal L. Schwartz, who popularized it among Perl programmers. Now that Python sorting provides key-functions, this technique is not often needed. The Old Way Using the cmp Parameter¶Many constructs given in this HOWTO assume Python 2.4 or later. Before that, there was no In Py3.0, the cmp parameter was removed entirely (as part of a
larger effort to simplify and unify the language, eliminating the conflict between rich comparisons and the In Py2.x, sort allowed an optional function which can be called for doing the comparisons. That function should take two arguments to be compared and then return a negative value for less-than, return zero if they are equal, or return a positive value for greater-than. For example, we can do: >>> def numeric_compare(x, y): ... return x - y >>> sorted([5, 2, 4, 1, 3], cmp=numeric_compare) [1, 2, 3, 4, 5] Or you can reverse the order of comparison with: >>> def reverse_numeric(x, y): ... return y - x >>> sorted([5, 2, 4, 1, 3], cmp=reverse_numeric) [5, 4, 3, 2, 1] When porting code from Python 2.x to 3.x, the situation can arise when you have the user supplying a comparison function and you need to convert that to a key function. The following wrapper makes that easy to do: def cmp_to_key(mycmp): 'Convert a cmp= function into a key= function' class K: def __init__(self, obj, *args): self.obj = obj def __lt__(self, other): return mycmp(self.obj, other.obj) < 0 def __gt__(self, other): return mycmp(self.obj, other.obj) > 0 def __eq__(self, other): return mycmp(self.obj, other.obj) == 0 def __le__(self, other): return mycmp(self.obj, other.obj) <= 0 def __ge__(self, other): return mycmp(self.obj, other.obj) >= 0 def __ne__(self, other): return mycmp(self.obj, other.obj) != 0 return K To convert to a key function, just wrap the old comparison function: >>> sorted([5, 2, 4, 1, 3], key=cmp_to_key(reverse_numeric)) [5, 4, 3, 2, 1] In Python 3.2, the Odd and Ends¶
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