Python List .remove() — How to Remove an Item from a List in Python
Dionysia Lemonaki
In this article, you’ll learn how to use Python’s built-in remove() list method.
By the end, you’ll know how to use remove() to remove an item from a list in Python.
Here is what we will cover:
The remove() Method — A Syntax Overview
The remove() method is one of the ways you can remove elements from a list in Python.
The remove() method removes an item from a list by its value and not by its index number.
The general syntax of the remove() method looks like this:
Let’s break it down:
- list_name is the name of the list you’re working with.
- remove() is one of Python’s built-in list methods.
- remove() takes one single required argument. If you do not provide that, you’ll get a TypeError – specifically you’ll get a TypeError: list.remove() takes exactly one argument (0 given) error.
- value is the specific value of the item that you want to remove from list_name .
The remove() method does not return the value that has been removed but instead just returns None , meaning there is no return value.
If you need to remove an item by its index number and/or for some reason you want to return (save) the value you removed, use the pop() method instead.
How to Remove an Element from a List Using the remove() Method in Python
To remove an element from a list using the remove() method, specify the value of that element and pass it as an argument to the method.
remove() will search the list to find it and remove it.
If you specify a value that is not contained in the list, then you’ll get an error – specifically the error will be a ValueError :
To avoid this error from happening, you could first check to see if the value you want to remove is in the list to begin with, using the in keyword.
It will return a Boolean value – True if the item is in the list or False if the value is not in the list.
Another way to avoid this error is to create a condition that essentially says, «If this value is part of the list then delete it. If it doesn’t exist, then show a message that says it is not contained in the list».
Now, instead of getting a Python error when you’re trying to delete a certain value that doesn’t exist, you get a message returned, saying the item you wanted to delete is not in the list you’re working with.
The remove() Method Removes the First Occurrence of an Item in a List
A thing to keep in mind when using the remove() method is that it will search for and will remove only the first instance of an item.
This means that if in the list there is more than one instance of the item whose value you have passed as an argument to the method, then only the first occurrence will be removed.
Let’s look at the following example:
In the example above, the item with the value of Python appeared three times in the list.
When remove() was used, only the first matching instance was removed – the one following the JavaScript value and preceeding the Java value.
The other two occurrences of Python remain in the list.
What happens though when you want to remove all occurrences of an item?
Using remove() alone does not accomplish that, and you may not want to just remove the first instance of the item you specified.
How to Remove All Instances of an Item in A List in Python
One of the ways to remove all occurrences of an item inside a list is to use list comprehension.
List comprehension creates a new list from an existing list, or creates what is called a sublist.
This will not modify your original list, but will instead create a new one that satisfies a condition you set.
In the example above, there is the orginal programming_languages list.
Then, a new list (or sublist) is returned.
The items contained in the sublist have to meet a condition. The condition was that if an item in the original list has a value of Python , it would not be part of the sublist.
Now, if you don’t want to create a new list, but instead want to modify the already existing list in-place, then use the slice assignment combined with list comprehension.
With the slice assignment, you can modify and replace certain parts (or slices) of a list.
To replace the whole list, use the [:] slicing syntax, along with list comprehension.
The list comprehension sets the condition that any item with a value of Python will no longer be a part of the list.
Conclusion
And there you have it! You now know how to remove a list item in Python using the remove() method. You also saw some ways of removing all occurrences of an item in a list in Python.
I hope you found this article useful.
To learn more about the Python programming language, check out freeCodeCamp’s Scientific Computing with Python Certification.
You’ll start from the basics and learn in an interacitve and beginner-friendly way. You’ll also build five projects at the end to put into practice and help reinforce what you’ve learned.
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(); .
Списки в Python
Всем привет! В этой статье мы познакомимся с методами для работы со списками в python . Но сначала вспомним, что такое список? Список — это изменяемый и последовательный тип данных. Это значит, что мы можем добавлять, удалять и изменять любые элементы списка.
Начнем с метода append() , который добавляет элемент в конец списка:
# Создаем список, состоящий из четных чисел от 0 до 8 включительно
numbers = list ( range ( 0 , 10 , 2 ))
# Добавляем число 200 в конец списка
numbers. append ( 200 )
print (numbers)
# [0, 2, 4, 6, 8, 200]
numbers. append ( 1 )
numbers. append ( 2 )
numbers. append ( 3 )
print (numbers)
# [0, 2, 4, 6, 8, 200, 1, 2, 3]
Мы можем передавать методу append() абсолютно любые значения:
all_types = [ 10 , 3.14 , ‘Python’ , [ ‘I’ , ‘am’ , ‘list’ ]]
all_types. append ( 1024 )
all_types. append ( ‘Hello world!’ )
all_types. append ([ 1 , 2 , 3 ])
print (all_types)
# [10, 3.14, ‘Python’, [‘I’, ‘am’, ‘list’], 1024, ‘Hello world!’, [1, 2, 3]]
Метод append() отлично выполняет свою функцию. Но, что делать, если нам нужно добавить элемент в середину списка? Это умеет метод insert () . Он добавляет элемент в список на произвольную позицию. insert() принимает в качестве первого аргумента позицию, на которую нужно вставить элемент, а вторым — сам элемент.
# Создадим список чисел от 0 до 9
numbers = list ( range ( 10 ))
# Добавление элемента 999 на позицию с индексом 0
numbers. insert ( 0 , 999 )
print (numbers)
# [999, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
numbers. insert ( 2 , 1024 )
print (numbers)
# [999, 0, 1024, 1, 2, 3, 4, 5, 6, 7, 8, 9]
numbers. insert ( 5 , ‘Засланная строка-шпион’ )
print (numbers)
# [999, 0, 1024, 1, 2, ‘Засланная строка-шпион’, 3, 4, 5, 6, 7, 8, 9]
Отлично! Добавлять элементы в список мы научились, осталось понять, как их из него удалять. Метод pop() удаляет элемент из списка по его индексу:
numbers = list ( range ( 10 ))
print (numbers)
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# Удаляем первый элемент
numbers. pop ( 0 )
print (numbers)
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
numbers. pop ( 0 )
print (numbers)
# [2, 3, 4, 5, 6, 7, 8, 9]
numbers. pop ( 2 )
print (numbers)
# [2, 3, 5, 6, 7, 8, 9]
# Чтобы удалить последний элемент, вызовем метод pop без аргументов
numbers. pop ()
print (numbers)
# [2, 3, 5, 6, 7, 8]
numbers. pop ()
print (numbers)
# [2, 3, 5, 6, 7]
Теперь мы знаем, как удалять элемент из списка по его индексу. Но что, если мы не знаем индекса элемента, но знаем его значение? Для такого случая у нас есть метод remove() , который удаляет первый найденный по значению элемент в списке.
all_types = [ 10 , ‘Python’ , 10 , 3.14 , ‘Python’ , [ ‘I’ , ‘am’ , ‘list’ ]]
all_types. remove ( 3.14 )
print (all_types)
# [10, ‘Python’, 10, ‘Python’, [‘I’, ‘am’, ‘list’]]
all_types. remove ( 10 )
print (all_types)
# [‘Python’, 10, ‘Python’, [‘I’, ‘am’, ‘list’]]
all_types. remove ( ‘Python’ )
print (all_types) # [10, ‘Python’, [‘I’, ‘am’, ‘list’]]
А сейчас немного посчитаем, посчитаем элементы списка с помощью метода count()
numbers = [ 100 , 100 , 100 , 200 , 200 , 500 , 500 , 500 , 500 , 500 , 999 ]
print (numbers. count ( 100 ))
# 3
print (numbers. count ( 200 ))
# 2
print (numbers. count ( 500 ))
# 5
print (numbers. count ( 999 ))
# 1
В программировании, как и в жизни, проще работать с упорядоченными данными, в них легче ориентироваться и что-либо искать. Метод sort() сортирует список по возрастанию значений его элементов.
numbers = [ 100 , 2 , 11 , 9 , 3 , 1024 , 567 , 78 ]
numbers. sort ()
print (numbers)
# [2, 3, 9, 11, 78, 100, 567, 1024]
fruits = [ ‘Orange’ , ‘Grape’ , ‘Peach’ , ‘Banan’ , ‘Apple’ ]
fruits. sort ()
print (fruits)
# [‘Apple’, ‘Banan’, ‘Grape’, ‘Orange’, ‘Peach’]
Мы можем изменять порядок сортировки с помощью параметра reverse . По умолчанию этот параметр равен False
fruits = [ ‘Orange’ , ‘Grape’ , ‘Peach’ , ‘Banan’ , ‘Apple’ ]
fruits. sort ()
print (fruits)
# [‘Apple’, ‘Banan’, ‘Grape’, ‘Orange’, ‘Peach’]
fruits. sort ( reverse = True )
print (fruits)
# [‘Peach’, ‘Orange’, ‘Grape’, ‘Banan’, ‘Apple’]
Иногда нам нужно перевернуть список, не спрашивайте меня зачем. Для этого в самом лучшем языке программирования на этой планете JavaScr..Python есть метод reverse() :
numbers = [ 100 , 2 , 11 , 9 , 3 , 1024 , 567 , 78 ]
numbers. reverse ()
print (numbers)
# [78, 567, 1024, 3, 9, 11, 2, 100]
fruits = [ ‘Orange ‘, ‘Grape’ , ‘Peach’ , ‘Banan’ , ‘Apple’ ]
fruits. reverse ()
print (fruits)
# [‘Apple’, ‘Banan’, ‘Peach’, ‘Grape’, ‘Orange’]
Допустим, у нас есть два списка и нам нужно их объединить. Программисты на C++ cразу же кинулись писать циклы for , но мы пишем на python , а в python у списков есть полезный метод extend() . Этот метод вызывается для одного списка, а в качестве аргумента ему передается другой список, extend() записывает в конец первого из них начало второго:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
vegetables = [ ‘Tomato’ , ‘Cucumber’ , ‘Potato’ , ‘Carrot’ ]
fruits. extend (vegetables)
print (fruits)
# [‘Banana’, ‘Apple’, ‘Grape’, ‘Tomato’, ‘Cucumber’, ‘Potato’, ‘Carrot’]
В природе существует специальный метод для очистки списка — clear()
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
vegetables = [ ‘Tomato’ , ‘Cucumber’ , ‘Potato’ , ‘Carrot’ ]
fruits. clear ()
vegetables. clear ()
print (fruits)
# []
print (vegetables)
# []
Осталось совсем чуть-чуть всего лишь пара методов, так что делаем последний рывок! Метод index() возвращает индекс элемента. Работает это так: вы передаете в качестве аргумента в index() значение элемента, а метод возвращает его индекс:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
print (fruits. index ( ‘Apple’ ))
# 1
print (fruits. index ( ‘Banana’ ))
# 0
print (fruits. index ( ‘Grape’ ))
# 2
Финишная прямая! Метод copy() , только не падайте, копирует список и возвращает его брата-близнеца. Вообще, копирование списков — это тема достаточно интересная, давайте рассмотрим её по-подробнее.
Во-первых, если мы просто присвоим уже существующий список новой переменной, то на первый взгляд всё выглядит неплохо:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
new_fruits = fruits
print (fruits)
# [‘Banana’, ‘Apple’, ‘Grape’]
print (new_fruits)
# [‘Banana’, ‘Apple’, ‘Grape’]
Но есть одно маленькое «НО»:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
new_fruits = fruits
fruits. pop ()
print (fruits)
# [‘Banana’, ‘Apple’]
print (new_fruits)
# Внезапно, из списка new_fruits исчез последний элемент
# [‘Banana’, ‘Apple’]
При прямом присваивании списков копирования не происходит. Обе переменные начинают ссылаться на один и тот же список! То есть если мы изменим один из них, то изменится и другой. Что же тогда делать? Пользоваться методом copy() , конечно:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ ]
new_fruits = fruits. copy ()
fruits. pop ()
print (fruits)
# [‘Banana’, ‘Apple’]
print (new_fruits)
# [‘Banana’, ‘Apple’, ‘Grape’]
Отлично! Но что если у нас список в списке? Скопируется ли внутренний список с помощью метода copy() — нет:
fruits = [ ‘Banana’ , ‘Apple’ , ‘Grape’ , [ ‘Orange’ , ‘Peach’ ]]
new_fruits = fruits. copy ()
fruits[ — 1 ]. pop ()
print (fruits)
# [‘Banana’, ‘Apple’, ‘Grape’, [‘Orange’]]
print (new_fruits)
# [‘Banana’, ‘Apple’, ‘Grape’, [‘Orange’]]
Как удалить элемент массива в Python?
В этой статье мы поговорим о том, как удалить элемент из массива в Python. Для демонстрации одного из примеров воспользуемся модулем array, определяющим массивы в «Питоне». Перед началом работы необходимо импортировать соответствующую библиотеку. Это делается путём добавления в файл программы строки from array import *.
Итак, представим, что у нас есть массив со следующим содержимым:
Обратите внимание, что функция array принимает 2 аргумента. Первый — тип создаваемого массива, второй — начальный список значений (в качестве начального списка значений задали простейший набор чисел от 1 до 5). Что касается i, то в нашем случае — это целое знаковое число, которое занимает 2 байта памяти. Кстати, код типа может быть и другим, например, однобайтовым символом (с) или 4-байтовым числом с плавающей точкой (f).
Идём дальше. Удаление элементов в массиве Python может выполняться двумя способами: • pop() — удаление элемента по индексу; • remove() — удаление элемента по значению.
Рассмотрим каждый из них подробнее.
Удаляем элемент в массиве Python с помощью pop()
Операция по удалению выполняется предельно просто:
Вывод в терминале будет следующим:
Обратите внимание, что код выше удаляет число 3, т. к. индексация элементов в Python-массиве начинается с нуля.
Если же нам нужно удалить последний элемент из массива, просто не пишите никакой индекс в методе pop() :
А теперь давайте проделаем ту же операцию, но без модуля array, представив одномерный массив в виде простейшего списка элементов в Python:
Как видите, здесь тоже всё предельно просто.
Удаляем элемент в массиве Python с помощью remove()
В некоторых случаях мы не знаем точный индекс элемента в массиве в Python, зато знаем имя элемента. Допустим, речь идёт о следующем одномерном массиве (для удобства сразу отобразим его в виде списка):
Как удалить здесь, к примеру, элемент “online”? Для этого нам пригодится метод remove() :
В результате слово “online” из нашего массива будет удалено:
На этом всё, надеюсь, этот материал был вам полезен.
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