5. Data Structures¶
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.
5.1. More on Lists¶
The list data type has some more methods. Here are all of the methods of list objects:
Add an item to the end of the list. Equivalent to a[len(a):] = [x] .
list. extend ( iterable )
Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .
Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .
Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item.
Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)
Remove all items from the list. Equivalent to del a[:] .
Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError if there is no such item.
The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.
Return the number of times x appears in the list.
list. sort ( * , key = None , reverse = False )
Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).
Reverse the elements of the list in place.
Return a shallow copy of the list. Equivalent to a[:] .
An example that uses most of the list methods:
You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . 1 This is a design principle for all mutable data structures in Python.
Another thing you might notice is that not all data can be sorted or compared. For instance, [None, ‘hello’, 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.
5.1.1. Using Lists as Stacks¶
The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:
5.1.2. Using Lists as Queues¶
It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).
To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:
5.1.3. List Comprehensions¶
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.
For example, assume we want to create a list of squares, like:
Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:
which is more concise and readable.
A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:
and it’s equivalent to:
Note how the order of the for and if statements is the same in both these snippets.
If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.
List comprehensions can contain complex expressions and nested functions:
5.1.4. Nested List Comprehensions¶
The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.
Consider the following example of a 3×4 matrix implemented as a list of 3 lists of length 4:
The following list comprehension will transpose rows and columns:
As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:
which, in turn, is the same as:
In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:
See Unpacking Argument Lists for details on the asterisk in this line.
5.2. The del statement¶
There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:
del can also be used to delete entire variables:
Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.
5.3. Tuples and Sequences¶
We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.
A tuple consists of a number of values separated by commas, for instance:
As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.
Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.
A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:
The statement t = 12345, 54321, ‘hello!’ is an example of tuple packing: the values 12345 , 54321 and ‘hello!’ are packed together in a tuple. The reverse operation is also possible:
This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.
5.4. Sets¶
Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.
Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not <> ; the latter creates an empty dictionary, a data structure that we discuss in the next section.
Here is a brief demonstration:
Similarly to list comprehensions , set comprehensions are also supported:
5.5. Dictionaries¶
Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .
It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: <> . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.
The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.
Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.
Here is a small example using a dictionary:
The dict() constructor builds dictionaries directly from sequences of key-value pairs:
In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:
When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:
5.6. Looping Techniques¶
When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.
When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.
To loop over two or more sequences at the same time, the entries can be paired with the zip() function.
To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.
To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.
Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.
It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.
5.7. More on Conditions¶
The conditions used in while and if statements can contain any operators, not just comparisons.
The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.
Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .
Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.
The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,
Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.
5.8. Comparing Sequences and Other Types¶
Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:
Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.
Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort(); .
Создайте словарь из списка ключей и значений в Python
В этом посте мы обсудим, как создать словарь из списка ключей и значений в Python.
Например, keys = [‘A’, ‘B’, ‘C’] а также values = [1, 2, 3] должен привести к словарю <'A': 1, 'B': 2, 'C': 3>.
1. Использование zip() с dict() функция
Самый простой и элегантный способ построить словарь из списка ключей и значений — использовать метод zip() функция с конструктором словаря.
2. Использование словарного понимания
Другой подход заключается в использовании zip() с пониманием словаря. Это часто полезно, когда вам нужны пары ключ-значение вместо пар ключ-значение или вы применяете некоторую функцию сопоставления к каждому ключу или значению. Это может быть реализовано как:
Вот как выглядел бы код без zip() :
Если вам нужно создать новый словарь с ключами из iterable с тем же значением, вы можете сделать следующее.
В качестве альтернативы вы можете использовать fromkeys() функция класса:
Это все о создании словаря из списка ключей и значений в Python.
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Convert list into a dictionary [duplicate]
So basically, the evens will be keys whereas the odds will be values. I know that I can do it in a «non-pythonic» way such as a for loop with if statements but I believe that there should be a more «pythonic» way to accomplish this. So, I appreciate any help 🙂
4 Answers 4
If you are still thinking what the! You would not be alone, its actually not that complicated really, let me explain.
How to turn a list into a dictionary using built-in functions only
We want to turn the following list into a dictionary using the odd entries (counting from 1) as keys mapped to their consecutive even entries.
To create a dictionary we can use the built in dict function for Mapping Types as per the manual the following methods are supported.
The last option suggests that we supply a list of lists with 2 values or (key, value) tuples, so we want to turn our sequential list into:
We are also introduced to the zip function, one of the built-in functions which the manual explains:
returns a list of tuples, where the i-th tuple contains the i-th element from each of the arguments
In other words if we can turn our list into two lists a, c, e and b, d then zip will do the rest.
slice notation
Slicings which we see used with Strings and also further on in the List section which mainly uses the range or short slice notation but this is what the long slice notation looks like and what we can accomplish with step:
Even though this is the simplest way to understand the mechanics involved there is a downside because slices are new list objects each time, as can be seen with this cloning example:
Even though b looks like a they are two separate objects now and this is why we prefer to use the grouper recipe instead.
grouper recipe
Although the grouper is explained as part of the itertools module it works perfectly fine with the basic functions too.
Some serious voodoo right? =) But actually nothing more than a bit of syntax sugar for spice, the grouper recipe is accomplished by the following expression.
Which more or less translates to two arguments of the same iterator wrapped in a list, if that makes any sense. Lets break it down to help shed some light.
zip for shortest
As you can see the addresses for the two iterators remain the same so we are working with the same iterator which zip then first gets a key from and then a value and a key and a value every time stepping the same iterator to accomplish what we did with the slices much more productively.
You would accomplish very much the same with the following which carries a smaller What the? factor perhaps.
What about the empty key e if you’ve noticed it has been missing from all the examples which is because zip picks the shortest of the two arguments, so what are we to do.
Well one solution might be adding an empty value to odd length lists, you may choose to use append and an if statement which would do the trick, albeit slightly boring, right?
Now before you shrug away to go type from itertools import izip_longest you may be surprised to know it is not required, we can accomplish the same, even better IMHO, with the built in functions alone.
map for longest
I prefer to use the map() function instead of izip_longest() which not only uses shorter syntax doesn’t require an import but it can assign an actual None empty value when required, automagically.
Comparing performance of the two methods, as pointed out by KursedMetal, it is clear that the itertools module far outperforms the map function on large volumes, as a benchmark against 10 million records show.
However the cost of importing the module has its toll on smaller datasets with map returning much quicker up to around 100 thousand records when they start arriving head to head.
Python Tips, Tricks, and Hacks (часть 2)
К сожалению, нельзя написать программу только с помощью генераторов списков. (Я шучу… конечно, можно.) Они могут отображать и фильтровать, но нет простого способа для свертки списка. Под этим понятием я подразумеваю применение функции к первым двум элементам списка, а затем к получившемуся результату и следующему элементу, и так до конца списка. Можно реализовать это через цикл for:
А можно воспользоваться встроенной функцией reduce, принимающей в качестве аргументов функцию от двух параметров и список:
Не так красиво, как генераторы списков, но короче обычного цикла. Стоит запомнить этот способ.
2.3 Прохождение по списку: range, xrange и enumerate
Помните, как в языке C для цикла for вы использовали переменную-счетчик вместо элементов списка? Возможно, вы уже знаете, как имитировать это поведение в Python с помощью range и xrange. Передавая число value функции range, мы получим список, содержащий элементы от 0 до value-1 включительно. Другими словами, range возвращает индексы списка указанной длины. xrange действует похоже, но более эффективно, не загружая весь список в память целиком.
Проблема в том, что обычно вам всё равно нужны элементы списка. Что толку от индексов без них? В Python есть потрясающая встроенная функция enumerate, которая воозвращает итератор для пар индекс → значение:
Еще один плюс состоит в том, что enumerate выглядит более читаемо, чем xrange(len()). Поэтому range и xrange полезны, наверно, только для создания списка с нуля, а не на основе других данных.
2.4 Проверка всех элементов списка на выполнение условия
Допустим, нам надо проверить, выполняется ли условие хотя бы для одного элемента. До Python 2.5 можно было писать так:
Аналогично, может возникнуть задача проверки, что все элементы удовлетворяют условию. Без Python 2.5 придется писать так:
Здесь мы фильтруем список и проверяем, уменьшилась ли его длина. Если нет, то все его элементы удовлетворяют условию. Опять же, без Python 2.5 это единственный способ уместить всю логику в одно выражение.
В Python 2.5 есть более простой путь — встроенная функция all. Легко догадаться, что она прекращает проверку после первого элемента, не удовлетворяющего условию. Эта функция работает абсолютно аналогично предыдущей.
2.5 Группировка элементов нескольких списков
Встроенная функция zip используется для сжимания нескольких списков в один. Она возвращает массив кортежей, причем n-й кортеж содержит n-е элементы всех массивов, переданных в качестве аргументов. Это тот случай, когда пример — лучшее объяснение:
Эта вещь часто используется как итератор для цикла for, извлекающая три значения за одну итерацию («for letter, number, squares in zipped_list»).
2.6 Еще несколько операторов для работы со списками
Ниже перечислены встроенные функции, в качестве аргумента принимающие любой итерируемый объект.
max и min возвращают наибольший и наименьший элемент соответственно.
sum возвращает сумму всех элементов списка. Опциональный второй параметр задает начальную сумму (по умолчанию 0).
2.7 Продвинутые логические операции с типом set.
Я понимаю, что в разделе, посвященном спискам, не положено говорить о сетах (sets). Но пока я не использовал их, мне не хватало некоторых логических операций в списках. Сет отличается от списка тем, что его элементы уникальны и неупорядоченны. Над сетами также можно выполнять множество логических операций.
Довольно распространенная задача — убедиться, что элементы списка уникальны. Это просто, достаточно преобразовать его в сет и проверить, изменилась ли длина:
Конечно, вы можете преобразовать сет обратно в список, но не забывайте, что порядок элементов мог не сохраниться. Больше информации об операциях с сетами вы найдете в документации.
3 Словари
3.1 Создание словаря с помощью именованных аргументов
Когда я начал изучать Python, я полностью пропустил альтернативный способ создания словаря. Если передать конструктору dict именованные аргументы, они будут добавлены в возращаемый словарь. Конечно, на его ключи накладываются те же ограничения, что и на имена переменных. Вот пример:
Этот способ немного очищает код, избавляя вас от летающих вокруг кавычек. Я часто использую его.
3.2 Преобразование словаря в список
Чтобы получить список ключей, достаточно привести словарь к типу list. Но лучше использовать .keys() для получения списка ключей или .iterkeys() для получения итератора. Если вам нужны значения, используйте .values() и .itervalues(). Но помните, что словари не упорядочены, поэтому полученные значения могут быть перемешаны любым мыслимым образом.
Чтобы получить и ключи, и значения в виде списка кортежей, можно использовать .items() или .iteritems(). Возможно, вы часто пользовались этим захватывающим методом:
3.3 Преобразование списка в словарь
Обратная операция — превращение списка, содержащего пары ключ-значение, в словарь — делается так же просто:
Вы можете комбинировать методы, добавив именованные аргументы:
Превращать списка и словари друг в друга довольно удобно. Но следующий совет просто потрясающий.
3.3 «Dictionary Comprehensions»
Хотя в Python нет встроенного генератора словарей, можно сделать нечто похожее ценой небольшого беспорядка в коде. Используем .iteritems() для превращения словаря в список, передадим его выражению-генератору или генератору списков, а затем преобразуем список обратно в словарь.
Допустим, у нас есть словарь пар name:email, а мы хотим получить словарь пар name:is_email_at_a_dot_com (проверить каждый адрес на вхождение подстроки .com):
Конечно, необязательно начинать и заканчивать словарем, можно в некоторых местах использовать и списки.
Это чуть менее читабельно, чем строгие генераторы списков, но я считаю, что это лучше, чем большой цикл for.