Help — python sorting


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Python — Sorting Algorithms

Sorting refers to arranging data in a particular format. Sorting algorithm specifies the way to arrange data in a particular order. Most common orders are in numerical or lexicographical order.

The importance of sorting lies in the fact that data searching can be optimized to a very high level, if data is stored in a sorted manner. Sorting is also used to represent data in more readable formats. Below we see five such implementations of sorting in python.

  • Bubble Sort
  • Merge Sort
  • Insertion Sort
  • Shell Sort
  • Selection Sort

Bubble Sort

It is a comparison-based algorithm in which each pair of adjacent elements is compared and the elements are swapped if they are not in order.

When the above code is executed, it produces the following result −

Merge Sort

Merge sort first divides the array into equal halves and then combines them in a sorted manner.

When the above code is executed, it produces the following result −

Insertion Sort

Insertion sort involves finding the right place for a given element in a sorted list. So in beginning we compare the first two elements and sort them by comparing them. Then we pick the third element and find its proper position among the previous two sorted elements. This way we gradually go on adding more elements to the already sorted list by putting them in their proper position.

When the above code is executed, it produces the following result −

Shell Sort

Shell Sort involves sorting elements which are away from ech other. We sort a large sublist of a given list and go on reducing the size of the list until all elements are sorted. The below program finds the gap by equating it to half of the length of the list size and then starts sorting all elements in it. Then we keep resetting the gap until the entire list is sorted.

When the above code is executed, it produces the following result −

Selection Sort

In selection sort we start by finding the minimum value in a given list and move it to a sorted list. Then we repeat the process for each of the remaining elements in the unsorted list. The next element entering the sorted list is compared with the existing elements and placed at its correct position. So at the end all the elements from the unsorted list are sorted.

When the above code is executed, it produces the following result −

Python. Получение справки по Python

Получить справку по питону можно различными способами.

Во-первых, с питоном поставляется .chm-файл с описанием всего. Естественно, на английском. Ну, так и при получении другими способами справка будет не на русском. ��
Что хорошо в этом файле справки — там есть множество небольших примеров. Конечно, можно было сделать и получше — ещё примеров, да с подсветкой… Ну да и так уже неплохо.

Во-вторых, справку можно получить в интерактивном режиме при помощи функции help():

В-третьих, с помощью функции dir можно получить список полей и методов объекта:

В-четвёртых, утилита pydoc даёт возможность просматривать описание модулей:

В-пятых, несложно с помощью pydoc запустить сервис и читать документацию в браузере:

Документация будет доступна на http://127.0.0.1:8088/.

Добавлю только, что мне пришлось подправить модуль pkgutil.py в строке 209 и сделать

Видимо, создатели модулей под виндами не сидят, а уж тем более под NT-виндами. И, соответственно, не в курсе, что в папку System Volume Information проход закрыт.

P.S. Оказывается, пока я обзаводился книжкой по питону, и пока я её неспешно листал вплоть до 30-й страницы… — оказывается, за это время успел выйти релиз третьей версии питона. Сегодня я его скачал и установил. И он сразу же преподнёс мне небольшой сюрприз — перестал работать мой пример. Всё оказалось довольно просто и логично — третья версия более требовательна к синтаксису. Например, она не приемлет print без скобок. В общем, учитывая движение прогресса куда-то в сторону светлого будущего, я хочу сообщить вам, что в последующих заметках я буду использовать синтаксис третьей версии.

3 комментария

А вот я еще 3-й питон не щупал, и пока не собираюсь. Причина — куча библиотек есть под питон 2x, и их никто еще не переписывал под 3-й. Взять хотя бы тот же фреймворк Django — если и перепишут его под 3-й, то нескоро.
Имхо — не такие уж большие изменения в 3-м питоне что бы делать обратную несовместимость. По сути кроме упомянутого оператора print — большие ничего сакцифического нет. А если есть — то доступно через модуль __future__.
Так что я не рекомендовал бы переходить на 3-й питон сейчас

а по докам — как правило хвататет функции dir. Я насколько помню даже не юзал функцию help:) Да и дока на офф. сайте замечательная, особенно после редизайна:)

help полезен, когда не хоца лаза по сайтам ��

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6 примеров сортировки в Python с помощью функции sorted

Общей идиомой в программировании является сортировка списка. Python делает эту задачу очень простой благодаря встроенной функции sorted() которая принимает итерируемый тип и возвращает отсортированный список:

1. Стандартная сортировка


Обратите внимание на то, что функция sorted() возвращает список каждый раз, несмотря на то, какой тип был передан. В случае со словарями, она возвращает отсортированный список словарных ключей.

2. Сортировка сложных структур с использованием ключа

Это нормально работать с вещами, у которых по природе есть определенный порядок, вроде чисел или строк, но что делать с более сложными структурами? Здесь функция sorted() демонстрирует свое великолепие. Функция sorted() принимает ключ в качестве опционально названного параметра. Этот ключ должен быть, сам по себе, функцией, которая принимает один параметр, которая затем используется функцией sorted(), для определения значения в целях дальнейшей сортировки. Давайте взглянем на пример. Скажем, у нас есть класс Person с такими атрибутами как имя и возраст:

(Функция __repr__ является специальной функцией, которая используется для переопределения того, как объект будет представлен в интерпретаторе Python)

Причина, по которой я определил функцию – это выделение порядка сортировки. По умолчанию, представление определенных пользователем объектов выглядит примерно так: “ ”. Если оставить все как есть, то отличать различные экземпляры в будущих примерах будет несколько затруднительно для нас.

Давайте сделаем список людей:

Сама по себе функция sorted() не знает, что делать со списком людей:

Однако, мы можем указать функции sorted(), какой атрибут сортировать, указав используемый ключ. Давайте определим это в следующем примере:

Функция ключа должна принять один аргумент и выдать значение, на котором базируется сортировка. Функция sorted() должна вызвать функцию key в каждом элементе используемой итерируемой, и использовать значение выдачи при сортировке списка.

Обратите внимание на то, что мы передаем ссылку на саму функцию, не вызывая ее и передаем ссылку к её возвращаемому значению. Это очень важный момент. Помните, sorted() будет использовать функцию key, вызывая её в каждом элементе итерируемой.

Давайте взглянем на еще один код, на этот раз определяем возраст как значение для сортировки:

3. Обратная сортировка

Функция sorted() намного упрощает сортировку в обратном порядке. Функция принимает опциональный параметр под названием reverse, который действует по строгой логике.

4. Сортировка с использованием функции attrgetter

В этот раз, возвращаемый список отсортирован по возрасту, как мы и ожидали. Фактически, сортировка по определенному атрибуту объекта это простая задача Python, которую может выполнить стандартная библиотека, благодаря функции, которая может генерировать функции ключей для вас:

Результат вызова attrgetter() – это функция, схожая с предыдущими двумя, которые мы только что рассмотрели. Мы определяем имя атрибута для выборки, после чего attrgetter генерирует функцию, которая принимает объект и возвращает определенный атрибут из этого объекта.

Таким образом, attrgetter(name) возвращает функцию, которая ведет себя также как и определенная раннее нашей функцией byName_key():

Функция attrgetter(age) возвращает функцию, которая ведет себя также как и определенная раннее нашей функцией byAge_key():

5. Предварительное использование key в функции сортировки

До сих пор нашими ключевыми функциями были простые считыватели атрибутов, но они также могут вычислять значения для сортировки. Давайте взглянем на еще один пример. На этот раз мы определим класс Snake:

У нашей змеи есть имя, toxicity (токсичность, мерило того, насколько токсичен её яд) и agression (представленная в виде числа от 0 до 1, которое указывает на вероятность того, что змея нападет).

Надежный сайт по продвижению doctorsmm предлагает купить подписчиков на свой Телеграмм канал по очень выгодным и притягательным ценам от 51 рубля за сотню аккаунтов. Кроме того, Вы сможете подобрать наиболее оптимальную для Вашего сообщества скорость поступления, которая доходит до 1000 единиц в сутки.

Теперь предположим, что мы можем подсчитать, насколько опасная змея, основываясь на показателях токсичности и агрессивности, и можем отсортировать список змей по степени их опасности:

Змеи отсортированы в ожидаемом нами порядке (несмотря на то, что гремучая змея (rattlesnake) более ядовита, чем кобра (kingCobra), уровень агрессивности кобры делает её более опасной).

6. Случайная сортировка

Ключи не обязаны иметь какую-либо связь с сортируемыми элементами (однако, это не самый продуктивный способ сортировать что-либо). Мы можем создать случайный порядок со следующим ключом:

Функция random() – это часть стандартной библиотеки random, которая выдает числа в случайном порядке от 0 до 1. Сортировка с использованием данного ключа выдает, кто бы мог подумать, случайный порядок:

В данной статье мы рассмотрели то, как Python создает отсортированные списки (и другие итерируемые) и то, насколько это просто. По умолчанию, функция sorted() возвращает список, содержимое которого упорядоченно в естественном порядке (что, в общем, именно то что мы ожидаем от чисел и строк). Желающие углубиться в то, как работает функция sorted() могут обратиться к документации Python.

fosshelp

Friday, April 2, 2010

Sorting in Python

Sorting in Python

To sort a list in Python, use the “sort” method. For example:

Note that sort is a method, and the list is changed in place.

Suppose you have a matrix, and you want to sort by second column. Example Solution:

li=[[2,6],[1,3],[5,4]]
li.sort(lambda x, y: cmp(x[1],y[1]))
print li; # prints [[1, 3], [5, 4], [2, 6]]

The argument to sort is a function of two arguments, that returns -1, 0, 1. This function is a decision function that tells sort() how to decide the order of any two elements in your list. If the first argument is “less” then second argument, the function should return -1. If equal, then 0. Else, 1.

Here’s a more complex example. Suppose you have a list of strings.

You want to sort them by the number embedded in them. What you have to do, is to provide sort() method a order-deciding function, that takes two strings, and compares the integer inside the string. Here’s the solution:


def myComp (x,y):
import re
def getNum(str): return float(re.findall(r’\d+’,str)[0])
return cmp(getNum(x),getNum(y))

li.sort(myComp)
print li # returns [‘web7-s.jpg’, ‘my23i.jpg’, ‘fris88large.jpg’, ‘my283.jpg’]

Here, we defined a function myComp to tell sort about the ordering. Normally, one would use the “lambda” construct, but Python’s lambda construct cannot be used to define any function that takes more than one Python-line to express.

Python’s “ sort ” method’s optional parameters: “ key ” and “ reverse ”

Most of the time, sorting is done for a list of atomic element such as [3,2,4]. This is simply done by myList.sort() without any argument. Other than simple list, sort is frequently used on matrixes (e.g. [[2,6],[1,3],[5,4]]). For matrixes, almost always a particular column is used for the basis of ordering. For example, if we want to sort by second column, we do: “li.sort(lambda x, y: cmp(x[1],y[1]))”. Since this is frequently used, Python provides a somewhat shorter syntax for it, by specifying the column used as the ordering “key”. For example:

li=[[2,6],[1,3],[5,4]]
li.sort(key=lambda x:x[1] ) # is equivalent to the following
#li.sort(lambda x, y: cmp(x[1],y[1]))
print li; # prints [[1, 3], [5, 4], [2, 6]]

Цукерберг рекомендует:  Создание списков с оглавлением на jQuery

Because the Python compiler is not very refined, this specialized syntax is algorithmically a order of magnitude faster than the general form “lambda x, y: cmp(x[1],y[1])”.

That is to say, the programer must now learn 2 syntaxes for the ordering function, one general and one specialized, and he must choose the right one otherwise he will be penalized by a magnitude of slow down. This also means that, in order to make the right choice of the syntax, the programer must known in advance of the nature of the list to be sorted.

Another >
The following are equivalent:

Sorting in Python

Sorting is a common activity in the life of software developers . In a standard Python installation, developers have two options to sort their data, either by using the function sorted() or the method sort().

On the surface they seem to be quite similar but upon closer inspection there are some stark differences in terms of the data types they can be used on, the return value they produce, and whether the sorted data is permanently modified or not. Additionally, for the uninitiated, they may produce some unexpected results.

Let’s take a look at sorted() through a series of code examples using numerical data first.

Sorted() is a built-in function. It provides an ordered list as a return value. The sorting is in ascending order by default, and the sorted data is not saved meaning the sorting is not in-place.

Numbers_tuple_sorted and numbers_set_sorted are of type list because sorted() returns an ordered list.

The returned list object can be cast to another data type.

Sorted() with strings

Sorted() treats a string like a list. It iterates over each element, sorts each character, including spaces.

Lists with non-comparable data types can’t be sorted.

A combination of integers and strings that are all numbers can be cast to comparable data types by using a list comprehension.

Evaluations are implicitly converted by Python to boolean data type.

When sorting strings, case matters. Sorted() can be used on a list of strings to sort the values in ascending order, which appears alphabetically by default.

Using the built_in function ord(), we can get the integer value of the Unicode code point of a character. If the first letter is the same, then sorted() will use the second character to determine the order all the way to the end of the string.

Using sorted() with reverse=True changes the default sorting to descending order.

What makes sorted() so versatile and flexible is the keyword parameter called key

Key expects a function to be passed to it as an argument. The result of the function will ultimately determine the sort order of the object that sorted() is used on.

As mentioned earlier, Python uses a Unicode code points value, meaning every character is mapped to a numeric value (i.e. ‘A’ =65, ‘B’=66, ‘a’=97, ‘b’=98, and ‘ ’ = 32). Notice that capital letters have a lower value than lower case letters. To sort a mixed case strings in alphabetical order, we apply the built-in method str.lower (we could have also used str.upper).

The argument key in sorted() is extremely powerful because almost any function, built-in or user-defined, can be used to manipulate the output order. In the employee_salary_tuple below, we have a list of tuples containing employee names and their corresponding salaries. We used a lambda function to sort them by salary.

Suppose we need to sort strings in reverse order. We can accomplish this by creating a function that reverses the word and then use this function as an argument for key in sorted() as shown below.

Instead of writing a standalone function, we can also use a lambda function.

Now, let’s investigate sort()

Sort() is a method of the list class and can only be used on lists.

There is no ordered output in sort(), so the assignment to a new variable only passes a None type.

Sort() does the sorting in-place. If we print values_to_sort, we see that it has changed and its original order was not maintained.

The same key and reverse arguments in sorted() are also available in sort().

In summary, sort() works only on a list data type, modifies the list it is called on in-place, and has no return value. Sorted() works on a list, tuple, or set. It returns a list that can be assigned to a variable, and does not modify the original list in-place.

Python help()

The syntax of help() is:

help() Parameters


The help() method takes maximum of one parameter.

  • object (optional) — you want to generate the help of the given object

How help() works in Python?

The help() method is used for interactive use. It’s recommenced to try it in your interpreter when you need help to write Python program and use Python modules.

object is passed to help() (not a string)

Try these on Python shell.

If string is passed as an argument, name of a module, function, class, method, keyword, or documentation topic, and a help page is printed.

string is passed as an argument to help()

If string is passed as an argument, the given string is looked up as the name of a module, function, class, method, keyword, or documentation topic, and a help page is printed.

Try these on Python shell.

no argument is passed to help()

If no argument is passed, Python’s help utility (interactive help system) starts on the console.

Then, you can enter the name of the topic to get help on writing Python programs and using Python modules. For example:

To quit the help utility and return to the interpreter, you need to type quit and press enter.

natsort 6.2.0

pip install natsort Copy PIP instructions

Last released: Nov 13, 2020

Simple yet flexible natural sorting in Python.

Statistics

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License: MIT License (MIT)

Requires: Python >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*

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    • 5 — Production/Stable
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    • OSI Approved :: MIT License
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    • OS Independent
  • Programming Language
    • Python
    • Python :: 2
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    • Python :: 3.8


  • Topic
    • Scientific/Engineering :: Information Analysis
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Project description

Simple yet flexible natural sorting in Python.

NOTE: Please see the Deprecation Schedule section for changes in natsort version 6.0.0 and in the upcoming version 7.0.0.

Quick Description

When you try to sort a list of strings that contain numbers, the normal python sort algorithm sorts lexicographically, so you might not get the results that you expect:

Notice that it has the order (‘1’, ‘10’, ‘2’) — this is because the list is being sorted in lexicographical order, which sorts numbers like you would letters (i.e. ‘b’, ‘ba’, ‘c’).

natsort provides a function natsorted that helps sort lists “naturally” (“naturally” is rather ill-defined, but in general it means sorting based on meaning and not computer code point). Using natsorted is simple:

natsorted identifies numbers anywhere in a string and sorts them naturally. Below are some other things you can do with natsort (also see the examples for a quick start guide, or the api for complete details).

Note: natsorted is designed to be a drop-in replacement for the built-in sorted function. Like sorted, natsorted does not sort in-place . To sort a list and assign the output to the same variable, you must explicitly assign the output to a variable:

Please see Generating a Reusable Sorting Key and Sorting In-Place for an alternate way to sort in-place naturally.

Quick Examples

Sorting Versions

natsort does not actually comprehend version numbers. It just so happens that the most common versioning schemes are designed to work with standard natural sorting techniques; these schemes include MAJOR.MINOR, MAJOR.MINOR.PATCH, YEAR.MONTH.DAY. If your data conforms to a scheme like this, then it will work out-of-the-box with natsorted (as of natsort version >= 4.0.0):

If you need to versions that use a more complicated scheme, please see these examples.

Sorting by Real Numbers (i.e. Signed Floats)

This is useful in scientific data analysis (and was the default behavior of natsorted for natsort version bytes type on Python 3, but convenience functions are provided that help you decode to str first:

Generating a Reusable Sorting Key and Sorting In-Place

Under the hood, natsorted works by generating a custom sorting key using natsort_keygen and then passes that to the built-in sorted. You can use the natsort_keygen function yourself to generate a custom sorting key to sort in-place using the list.sort method.

All of the algorithm customizations mentioned in the Further Customizing Natsort section can also be applied to natsort_keygen through the alg keyword option.

Other Useful Things

The best way to debug natsorted() is to generate a key using natsort_keygen() with the same options being passed to natsorted. One can take a look at exactly what is being done with their input using this key — it is highly recommended to look at this issue describing how to debug for how to debug, and also to review the How Does Natsort Work? page for why natsort is doing that to your data.

If you are trying to sort custom classes and running into trouble, please take a look at https://github.com/SethMMorton/natsort/issues/60. In short, custom classes are not likely to be sorted correctly if one relies on the behavior of __lt__ and the other rich comparison operators in their custom class — it is better to use a key function with natsort, or use the natsort key as part of your rich comparison operator definition.

natsort gave me results I didn’t expect, and it’s a terrible library! Did you try to debug using the above advice? If so, and you still cannot figure out the error, then please file an issue. How does natsort work?

If you don’t want to read How Does Natsort Work?, here is a quick primer.

natsort provides a key function that can be passed to list.sort() or sorted() in order to modify the default sorting behavior. This key is generated on-demand with the key generator natsort.natsort_keygen(). natsort.natsorted() is essentially a wrapper for the following code:

Users can further customize natsort sorting behavior with the key and/or alg options (see details in the Further Customizing Natsort section).

The key generated by natsort_keygen always returns a tuple. It does so in the following way (some details omitted for clarity):

  1. Assume the input is a string, and attempt to split it into numbers and non-numbers using regular expressions. Numbers are then converted into either int or float.
  2. If the above fails because the input is not a string, assume the input is some other sequence (e.g. list or tuple), and recursively apply the key to each element of the sequence.
  3. If the above fails because the input is not iterable, assume the input is an int or float, and just return the input in a tuple.

Because a tuple is always returned, a TypeError should not be common unless one tries to do something odd like sort an int against a list.

Shell script

natsort comes with a shell script called natsort, or can also be called from the command line with python -m natsort.

Requirements

natsort requires Python version 2.7 or Python 3.5 or greater. Python 3.4 is unofficially supported, meaning that support has not been removed, but it is no longer tested.

Optional Dependencies


fastnumbers

The most efficient sorting can occur if you install the fastnumbers package (version >=2.0.0); it helps with the string to number conversions. natsort will still run (efficiently) without the package, but if you need to squeeze out that extra juice it is recommended you include this as a dependency. natsort will not require (or check) that fastnumbers is installed at installation.

PyICU

It is recommended that you install PyICU if you wish to sort in a locale-dependent manner, see https://natsort.readthedocs.io/en/master/locale_issues.html for an explanation why.

Installation

If you want to install the Optional Dependencies, you can use the “extras” notation at installation time to install those dependencies as well — use fast for fastnumbers and icu for PyICU.

How to Run Tests

Please note that natsort is NOT set-up to support python setup.py test.

The recommended way to run tests is with tox. After installing tox, running tests is as simple as executing the following in the natsort directory:

tox will create virtual a virtual environment for your tests and install all the needed testing requirements for you. You can specify a particular python version with the -e flag, e.g. tox -e py36. Static analysis is done with tox -e flake8. You can see all available testing environments with tox --listenvs .

If you do not wish to use tox, you can install the testing dependencies with the dev/requirements.txt file and then run the tests manually using pytest.

Note that above I invoked python -m pytest instead of just pytest — this is because the former puts the CWD on sys.path.

How to Build Documentation

If you want to build the documentation for natsort, it is recommended to use tox:

This will place the documentation in build/sphinx/html. If you do not which to use tox, you can do the following:

Deprecation Schedule

Dropping Python 2.7 Support

natsort version 7.0.0 will drop support for Python 2.7.

The version 6.X branch will remain as a “long term support” branch where bug fixes are applied so that users who cannot update from Python 2.7 will not be forced to use a buggy natsort version. Once version 7.0.0 is released, new features will not be added to version 6.X, only bug fixes.

Dropped Deprecated APIs

In natsort version 6.0.0, the following APIs and functions were removed

  • number_type keyword argument (deprecated since 3.4.0)
  • signed keyword argument (deprecated since 3.4.0)
  • exp keyword argument (deprecated since 3.4.0)
  • as_path keyword argument (deprecated since 3.4.0)
  • py3_safe keyword argument (deprecated since 3.4.0)
  • ns.TYPESAFE (deprecated since version 5.0.0)
  • ns.DIGIT (deprecated since version 5.0.0)
  • ns.VERSION (deprecated since version 5.0.0)
  • versorted() (discouraged since version 4.0.0, officially deprecated since version 5.5.0)
  • index_versorted() (discouraged since version 4.0.0, officially deprecated since version 5.5.0)

In general, if you want to determine if you are using deprecated APIs you can run your code with the following flag

By default DeprecationWarnings are not shown, but this will cause them to be shown. Alternatively, you can just set the environment variable PYTHONWARNINGS to “default::DeprecationWarning” and then run your code.

Dropped Pipenv for Development

natsort version 6.0.0 no longer uses Pipenv to install development dependencies.

Dropped Python 2.6 and 3.3 Support

natsort version 6.0.0 dropped support for Python 2.6 and Python 3.3.

How to Use sorted() and sort() in Python

Table of Contents

All programmers will have to write code to sort items or data at some point. Sorting can be critical to the user experience in your application, whether it’s ordering a user’s most recent activity by timestamp, or putting a list of email recipients in alphabetical order by last name. Python sorting functionality offers robust features to do basic sorting or customize ordering at a granular level.

In this guide, you’ll learn how to sort various types of data in different data structures, customize the order, and work with two different methods of sorting in Python.

By the end of this tutorial, you’ll know how to:

  • Implement basic Python sorting and ordering on data structures
  • Differentiate between sorted() and .sort()
  • Customize a complex sort order in your code based on unique requirements

For this tutorial, you’ll need a basic understanding of lists and tuples as well as sets. Those data structures will be used in this tutorial, and some basic operations will be performed on them. Also, this tutorial uses Python 3, so example output in this tutorial might vary slightly if you’re using Python 2.

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Ordering Values With sorted()


To get started with Python sorting, you’re first going to see how to sort both numeric data and string data.

Sorting Numbers

You can use Python to sort a list by using sorted() . In this example, a list of integers is defined, and then sorted() is called with the numbers variable as the argument:

The output from this code is a new, sorted list. When the original variable is printed, the initial values are unchanged.

This example shows four important characteristics of sorted() :

  1. The function sorted() did not have to be defined. It’s a built-in function that is available in a standard installation of Python.
  2. sorted() , with no additional arguments or parameters, is ordering the values in numbers in an ascending order, meaning smallest to largest.
  3. The original numbers variable is unchanged because sorted() provides sorted output and does not change the original value in place.
  4. When sorted() is called, it provides an ordered list as a return value.

This last point means that sorted() can be used on a list, and the output can immediately be assigned to a variable:

In this example, there is now a new variable numbers_sorted that stored the output of sorted() .

You can confirm all of these observations by calling help() on sorted() . The optional arguments key and reverse will be covered later in the tutorial:

Technical Detail: If you’re transitioning from Python 2 and are familiar with its function of the same name, you should be aware of a couple important changes in Python 3:

  1. Python 3’s sorted() does not have a cmp parameter. Instead, only key is used to introduce custom sorting logic.
  2. key and reverse must be passed as keyword arguments, unlike in Python 2, where they could be passed as positional arguments.

If you need to convert a Python 2 cmp function to a key function, then check out functools.cmp_to_key() . This tutorial will not cover any examples using Python 2.

sorted() can be used on tuples and sets very similarly:

Notice how even though the input was a set and a tuple, the output is a list because sorted() returns a new list by definition. The returned object can be cast to a new type if it needs to match the input type. Be careful if attempting to cast the resulting list back to a set, as a set by definition is unordered:

The numbers_set_sorted value when cast to a set is not ordered, as expected. The other variable, numbers_tuple_sorted , retained the sorted order.

Sorting Strings

str types sort similarly to other iterables, like list and tuple. The example below shows how sorted() iterates through each character in the value passed to it and orders them in the output:

sorted() will treat a str like a list and iterate through each element. In a str , each element means each character in the str . sorted() will not treat a sentence differently, and it will sort each character, including spaces.

.split() can change this behavior and clean up the output, and .join() can put it all back together. We will cover the specific order of the output and why it is that way shortly:

The original sentence in this example is converted into a list of words instead of leaving it as a str . That list is then sorted and combined to form a str again instead of a list.

Limitations and Gotchas With Python Sorting

It is worth noting some limitations and odd behavior that can arise when you’re using Python to sort values besides integers.

Lists With Non-Comparable Data Types Can’t Be sorted()

There are data types that can’t be compared to each other using just sorted() because they are too different. Python will return an error if you attempt to use sorted() on a list containing non-comparable data. In this example, a None and an int in the same list can’t be sorted because of their incompatibility:

This error shows why Python can’t sort the values given to it. It’s trying to put the values in order by using the less than operator ( ) to determine which value is lower in sorting order. You can replicate this error by manually comparing the two values:

The same TypeError is thrown when you try to compare two non-comparable values without using sorted() .

If the values within the list can be compared and will not throw a TypeError , then the list can be sorted. This prevents sorting iterables with intrinsically unorderable values and producing output that may not make sense.

For example, should the number 1 come before the word apple ? However, if a iterable contains a combination of integers and strings that are all numbers, they can be cast to comparable data types by using a list comprehension:

Each element in mixed_numbers has int() called on it to convert any str values to int values. sorted() is then called and can successfully compare each element and provide a sorted output.

Python can also implicitly convert a value to another type. In the example below, the evaluation of 1 is a false statement, so the output of the evaluation will be False . The number 1 can be converted to True as a bool type, while 0 converts to False .

Even though the elements in the list look different, they can all be converted to Booleans ( True or False ) and compared to each other using sorted() :

‘A’ == ‘B’ and 1 are converted to False and returned in the ordered output.

This example illustrates an important aspect of sorting: sort stability. In Python, when you sort equal values, they will retain their original order in the output. Even though the 1 moved, all the other values are equal so they retain their original order relative to each other. In the example below, all the values are considered equal and will retain their original positions:

If you inspect the original order and the sorted output, you will see that 1 == 2 is converted to False , and all sorted output is in the original order.

When You’re Sorting Strings, Case Matters

sorted() can be used on a list of strings to sort the values in ascending order, which appears to be alphabetically by default:

However, Python is using the Unicode Code Point of the first letter in each string to determine ascending sort order. This means that sorted() will not treat the names Al and al the same. This example uses ord() to return the Unicode Code Point of the first letter in each string:


name[0] is returning the first character in each element of sorted(names_with_case) , and ord() is providing the Unicode Code Point. Even though a comes before M in the alphabet, the code point for M comes before a , so the sorted output has M first.

If the first letter is the same, then sorted() will use the second character to determine order, and the third character if that is the same, and so on, all the way to the end of the string:

Each value of very_similar_strs is identical except for the last character. sorted() will compare the strings, and because the first five characters are the same, the output will be based on the sixth character.

Strings that contain identical values will end up sorted shortest to longest due to the shorter strings not having elements to compare to with the longer strings:

The shortest string, h , is ordered first with the longest, hhhhh , ordered last.

Using sorted() With a reverse Argument

As shown in the help() documentation for sorted() , there is an optional keyword argument called reverse , which will change the sorting behavior based on the Boolean assigned to it. If reverse is assigned True , then the sorting will be in descending order:

The sorting logic remains the same, meaning that the names are still being sorted by their first letter. But the output has been reversed with the reverse keyword set to True .

When False is assigned, the ordering will remain ascending. Any of the previous examples can be used to see the behavior of reverse using both True or False :

sorted() With a key Argument

One of the most powerful components of sorted() is the keyword argument called key . This argument expects a function to be passed to it, and that function will be used on each value in the list being sorted to determine the resulting order.

To demonstrate a basic example, let’s assume the requirement for ordering a specific list is the length of the strings in the list, shortest to longest. The function to return the length of a string, len() , will be used with the key argument:

The resulting order is a list with a string order of shortest to longest. The length of each element in the list is determined by len() and then returned in ascending order.

Let’s return to the earlier example of sorting by first letter when the case is different. key can be used to solve that problem by converting the entire string to lowercase:

The output values have not been converted to lowercase because key does not manipulate the data in the original list. During sorting, the function passed to key is being called on each element to determine sort order, but the original values will be in the output.

There are two main limitations when you’re using functions with the key argument.

First, the number of required arguments in the function passed to key must be one.

The example below shows the definition of an addition function that takes two arguments. When that function is used in key on a list of numbers, it fails because it is missing a second argument. Each time add() is called during the sort, it is only receiving one element from the list at a time:

The second limitation is that the function used with key must be able to handle all the values in the iterable. For example, you have a list of numbers represented as strings to be used in sorted() , and key is going to attempt to convert them to numbers using int . If a value in the iterable can’t be cast to an integer, then the function will fail:

Each numeric value as a str can be converted to int , but four can’t. This causes a ValueError to be raised and explain that four can’t be converted to int because it is invalid.

The key functionality is extremely powerful because almost any function, built-in or user-defined, can be used to manipulate the output order.

If the ordering requirement is to order an iterable by the last letter in each string (and if the letter is the same, then to use the next letter), then a function can be defined and then used in the sorting. The example below defines a function that reverses the string passed to it, and then that function is used as the argument for key :

The word[::-1] slice syntax is used to reverse a string. Each element will have reverse_word() applied to it, and the sorting order will be based on the characters in the backwards word.

Instead of writing a standalone function, you can use a lambda function defined in the key argument.

A lambda is an anonymous function that:

  1. Must be defined inline
  2. Doesn’t have a name
  3. Can’t contain statements
  4. Will execute just like a function

In the example below, the key is defined as a lambda with no name, the argument taken by the lambda is x , and x[::-1] is the operation that will be performed on the argument:

x[::-1] is called on each element and reverses the word. That reversed output is then used for sorting, but the original words are still returned.

If the requirement changes, and the order should be reversed as well, then the reverse keyword can be used alongside the key argument:

lambda functions are also useful when you need to sort class objects based on a property. If you have a group of students and need to sort them by their final grade, highest to lowest, then a lambda can be used to get the grade property from the class :

This example uses namedtuple to produce classes with name and grade attributes. The lambda calls getattr() on each element and returns the value for grade .

reverse is set to True to make the ascending output flipped to be descending so that the highest grades are ordered first.

The possibilities are endless for how ordering can be done when you leverage both the key and reverse keyword arguments on sorted() . Code can be kept clean and short when you use a basic lambda for a small function, or you can write a whole new function, import it, and use it in the key argument.

Ordering Values With .sort()

The very similarly named .sort() differs quite a bit from the sorted() built-in. They accomplish more or less the same thing, but the help() documentation for list.sort() highlights two of the most critical differences between .sort() and sorted() :

First, sort is a method of the list class and can only be used with lists. It is not a built-in with an iterable passed to it.

Second, .sort() returns None and modifies the values in place. Let’s take a look at the impacts of both of these differences in code:

There are some pretty dramatic differences in how .sort() operates compared to sorted() in this code example:


  1. There is no ordered output of .sort() , so the assignment to a new variable only passes a None type.
  2. The values_to_sort list has been changed in place, and the original order is not maintained in any way.

These differences in behavior make .sort() and sorted() absolutely not interchangeable in code, and they can produce wildly unexpected outcomes if one is used in the wrong way.

.sort() has the same key and reverse optional keyword arguments that produce the same robust functionality as sorted() . Here, you can sort a list of phrases by the second letter of the third word and return the list in reverse:

In this sample, a lambda is used to do the following:

  1. Split each phrase into a list of words
  2. Find the third element, or word in this case
  3. Find the second letter in that word

When to Use sorted() and When to Use .sort()

You’ve seen the differences between sorted() and .sort() , but when do you use which?

Let’s say there is a 5k race coming up: The First Annual Python 5k. The data from the race needs to be captured and sorted. The data that needs to be captured is the runner’s bib number and the number of seconds it took to finish the race:

As the runners cross the finish line, each Runner will be added to a list called runners . In 5k races, not all runners cross the starting line at the same time, so the first person to cross the finish line might not actually be the fastest person:

Each time a runner crosses the finish line, their bib number and their total duration in seconds is added to runners .

Now, the dutiful programmer in charge of handling the outcome data sees this list, knows that the top 5 fastest participants are the winners that get prizes, and the remaining runners are going to be sorted by fastest time.

There are no requirements for multiple types of sorting by various attributes. The list is a reasonable size. There is no mention of storing the list somewhere. Just sort by duration and grab the five participants with the lowest duration:

The programmer chooses to use a lambda in the key argument to get the duration attribute from each runner and sort runners in place using .sort() . After runners is sorted, the first 5 elements are stored in top_five_runners .

Mission accomplished! The race director comes over and informs the programmer that since the current release of Python is 3.7, they have decided that every thirty-seventh person that crossed the finish line is going to get a free gym bag.

At this point, the programmer starts to sweat because the list of runners has been irreversibly changed. There is no way to recover the original list of runners in the order they finished and find every thirty-seventh person.

If you’re working with important data, and there is even a remote possibility that the original data will need to be recovered, then .sort() is not the best option. If the data is a copy, of if it is unimportant working data, of if no one will mind losing it because it can be retrieved, then .sort() can be a fine option.

Alternatively, the runners could have been sorted using sorted() and using the same lambda :

In this scenario with sorted() , the original list of runners is still intact and has not been overwritten. The impromptu requirement of finding every thirty-seventh person to cross the finish line can be accomplished by interacting with the original values:

every_thirtyseventh_runners is created by using a stride in the list slice syntax on runners , which still contains the original order in which the runners crossed the finish line.

How to Sort in Python: Conclusion

.sort() and sorted() can provide exactly the sort order you need if you use them properly with both the reverse and key optional keyword arguments.

Both have very different characteristics when it comes to output and in-place modifications, so make sure you think through any application functionality or program that will be using .sort() as it can irrevocably overwrite data.

For the avid Pythonistas looking for a challenge with sorting, try using more complex data types in sorting: nested iterables. Also, feel free to dive into the open source Python code implementations for the built-ins and read about the sort algorithm used in Python called Timsort.

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Помощь алгоритма сортировки в python

Я играл с программой, которая будет принимать информацию из двух файлов, а затем записывать информацию в один файл в отсортированном порядке.

Так что я сделал, это хранить каждую строку файла как элемент в списке. Я создаю еще одну функцию, которая разбивает каждый элемент на 2d-массив, где я могу легко получить доступ к переменным имени. Оттуда я хочу создать вложенный цикл цикла, который, когда он выполняет итерацию, проверяет максимальное значение в массиве, удаляет значение из списка и добавляет его в новый список до тех пор, пока не будет отсортированный список.

Я думаю, что мне нравится 90% пути, но у меня возникают проблемы, обворачивая логику алгоритмов сортировки. Похоже, проблема просто усложняется, и я продолжаю использовать указатели. Если бы кто-то мог помочь осветить какой-то свет по этому вопросу, я бы очень признателен ему.

У меня возникли проблемы с тем, что когда-то было самое высокое значение, пытающееся добавить это значение в список сортировки, удалив значение из списка разделов и повторно запустив код.

Использование sorted метода намного проще и уже встроено, по одному предложению Джорана. Я отредактировал ваш метод чтения, чтобы он создавал два списка кортежей, представляя строку и длину строки. Сортированный метод возвращает список, отсортированный в соответствии с ключом (длина строки) и порядком убывания ( reverse=True )

EDIT: Я добавил маркеры в кортежи, чтобы вы могли отслеживать, какие строки пришли из какого файла. Может быть, это немного смущает без них

Python Справки

Python Справки позволяют по мере необходимости получать информацию по многим аспектам языка программирования Python, причём прямо в окне программирования.

Привожу информацию о том, как можно получать Python Справки:

Получение справки

При работе с интерпретатором Python у Вас всегда под рукой будет несколько источников справочной информации.

Во-первых, при работе в интерактивном режиме с помощью команды help() можно получить информацию о любом встроенном модуле и о других аспектах языка Python.


Можно просто ввести команду help() и получить общую справочную информацию или команду help(‘имя_модуля’), чтобы получить информацию о конкретном модуле.

Если команде help() передать имя определенной функции, она может вернуть информацию об этой функции.

Большинство функций в языке Python снабжены строками документирования, описывающими порядок их использования. Чтобы вывести строку документирования, достаточно просто вывести содержимое атрибута __doc__.

Например:
>>> print (issubclass.__doc__)
issubclass(C, B) -> bool
Return whether class C is a subclass (i.e., a derived class) of class B.
When using a tuple as the second argument issubclass(X, (A, B, …)),
is a shortcut for issubclass(X, A) or issubclass(X, B) or … (etc.).

(Перевод
Проверяет, является ли класс C подклассом (то есть наследует) класса B.
Когда во втором аргументе передаётся кортеж – issubclass(X, (A, B, …)),
такой вызов является сокращённым вариантом issubclass(X, A) or issubclass(X, B)
or … (и т. д.).
)
>>>

И последнее, но также важное замечание – большинство инсталляций Python включает также команду pydoc, которая может использоваться для получения документации о модулях Python.

Для этого достаточно просто ввести команду topic в системной командной строке.

Команду pydoc в Python 3.6 запустить не удалось:

>>> pydoc winsound
SyntaxError: invalid syntax
>>> help (pydoc)
Traceback (most recent call last):
File «

», line 1, in
help (pydoc)
NameError: name ‘pydoc’ is not defined
>>> pydoc (winsound)
Traceback (most recent call last):
File «

», line 1, in
pydoc (winsound)
NameError: name ‘pydoc’ is not defined

Например, команда help (‘editor’) выдаёт многострочный голубой Help on module editor: …, то есть подробную справку по модулю editor, который хранится в файле editor.py

Обратите внимание, что в окне среды разработки IDLE команды имеют сиреневый цвет, а строковые аргументы — зелёный. Это облегчает чтение программ.

help(str) выдаёт информацию о классе str в модуле builtins

По команде help() получите такой ответ Интерпретатора

Welcome to Python 3.6′s help utility!

If this is your first time using Python, you should definitely check out the tutorial on the Internet at http://docs.python.org/3.6/tutorial/

Enter the name of any module, keyword, or topic to get help on writing Python programs and using Python modules. To quit this help utility and return to the interpreter, just type «quit«.

To get a list of available modules, keywords, symbols, or topics, type «modules», «keywords», «symbols», or «topics». Each module also comes with a one-line summary of what it does; to list the modules whose name or summary contain a given string such as «spam», type «modules spam».

Последнее хвостатое слово help> приглашает ввести конкретный запрос.

Например, help> editor или help> str или какой-нибудь иной

Чтобы оценить мощность Python Справки, рекомендую выполнить команды

help> modules

help> keywords

help> symbols

help> topics

По любому слову из 4-х списков, полученных по этим командам, существуют Python Справки!

Чтобы прервать эту справочную утилиту и вернуться в Интерпретатор, достаточно исполнить quit

print (function_name.__doc__) выдаёт информацию о function_name — пример привожу такой:

>>> import math
>>> print (math.__doc__)
This module is always available. It provides access to the
mathematical functions defined by the C standard.

Информация весьма скудная, а вот help (math) выдаёт весьма обширную информацию.

pydoc topic_name выдаёт информацию о topic_name — Команду pydoc в Python 3.6 мне запустить не удалось.

Итак, запоминаем три основных способа получения Python Справки в окне Интерпретатора:

help (‘module_name’) или help (class_name)

pydoc topic_name — надо бы проверить в Python 2.6

К сожалению, в большинстве случаев справочная информация выдаётся на английском языке — радуйтесь тому, что не на голландском! И находите способы понять английские тексты — вот Вам повод в совершенстве выучить английский язык.

Кроме того, для получения Python Справки всегда можно обратиться к учебникам и справочникам на русском языке, упомянутым на странице Python Книги, причём нужно постараться обзавестись электронными копиями учебников и справочников на русском языке — их есть в Интернете в доступной форме и в хорошем качестве.

При запросе Python Справки избегайте без необходимости заглавных символов: help(STR) выдаст ошибку.

Приглашаю всех высказываться в Комментариях. Критику и обмен опытом одобряю и приветствую. В хороших комментариях сохраняю ссылку на сайт автора!

И не забывайте, пожалуйста, нажимать на кнопки социальных сетей, которые расположены под текстом каждой страницы сайта.
Продолжение тут…

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