Списки Python

функция len(): длина списка
метод .append(): добавить в конец
метод .insert(): добавить в любое место
метод .pop(): удалить первый элемент
метод .pop(): удалить любой элемент
Похожие статьи


Чтобы создать список нужно воспользоваться квадратными скобками []

l = ['a','b','c']


Получился список из трёх элементов. У каждого есть уникальный индекс.

Индекс 'a' = 0, индекс 'b' = 1, индекс 'c' = 2.

Можно пользоваться индексом и в обратную сторону. Тогда индекс c = -1, индекс b = -2, индекс a = -3

Индекс --3-2-1


Длину списка можно узнать с помощью функции len()

>>> l = ["Pluralsight", "OTUS", "GeekBrains"]
>>> print(len(l))



Чтобы добавить в конец списка новый элемент применяется метод append()

l = ['a','b','c']





Чтобы добавить новый элемент в начало списка применяется метод insert()

l.insert(0, 'z')




С помощью insert() можно вставить элемент в любое место в списке

Нужно передать в insert() индекс того места в которое вы хотите поместить элемент

l.insert(3, 'j')




Чтобы удалить последний элемент списка применяется метод pop()






Чтобы удалить первый элемент списка





Чтобы удалить произвольный элемент списка передайте в pop() его индекс






С помощью функции zip() можно из двух списков создать список кортежей.

>>> languages = ['Java', 'Python', 'JavaScript'] >>> versions = [18, 3, 2015] >>> result = zip(languages, versions) >>> print(list(result))

[('Java', 14), ('Python', 3), ('JavaScript', 6)]

Как создавать списки

С помощью цикла for

Можно использовать цикл for для создания списка.

Это делается за три шага

  1. Создать пустой список
  2. Пройтись по итерируемому объекту или нескольким объектам.
  3. Дописать каждый элемент в конец списка

Если вы хотите создать список, содержащий первые десять полных квадратов, то вы можете выполнить эти шаги в трех строках кода:

>>> squares = [] >>> fori in range(10): ... squares.append(i * i) >>> squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Here, you instantiate an empty list, squares. Then, you use a for loop to iterate over range(10). Finally, you multiply each number by itself and append the result to the end of the list.

С помощью map()

map() provides an alternative approach that’s based in functional programming. You pass in a function and an iterable, and map() will create an object. This object contains the output you would get from running each iterable element through the supplied function. As an example, consider a situation in which you need to calculate the price after tax for a list of transactions:

>>> txns = [1.09, 23.56, 57.84, 4.56, 6.78] >>> TAX_RATE = .08 >>> def get_price_with_tax(txn): ... return txn * (1 + TAX_RATE) >>> final_prices = map(get_price_with_tax, txns) >>> list(final_prices)

[1.1772000000000002, 25.4448, 62.467200000000005, 4.9248, 7.322400000000001]

Here, you have an iterable txns and a function get_price_with_tax(). You pass both of these arguments to map(), and store the resulting object in final_prices. You can easily convert this map object into a list using list().

С помощью генераторов списков

List Comprehensions - списковые включения или генераторы списков.

List comprehensions are a third way of making lists. With this elegant approach, you could rewrite the for loop from the first example in just a single line of code:

>>> squares = [i * i fori in range(10)] >>> squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Rather than creating an empty list and adding each element to the end, you simply define the list and its contents at the same time by following this format:

new_list = [expression formember in iterable]

Every list comprehension in Python includes three elements: expression is the member itself, a call to a method, or any other valid expression that returns a value. In the example above, the expression i * i is the square of the member value. member is the object or value in the list or iterable. In the example above, the member value is i. iterable is a list, set, sequence, generator, or any other object that can return its elements one at a time. In the example above, the iterable is range(10). Because the expression requirement is so flexible, a list comprehension in Python works well in many places where you would use map(). You can rewrite the pricing example with its own list comprehension:

>>> txns = [1.09, 23.56, 57.84, 4.56, 6.78] >>> TAX_RATE = .08 >>> def get_price_with_tax(txn): ... return txn * (1 + TAX_RATE) >>> final_prices = [get_price_with_tax(i) fori in txns] >>> final_prices

[1.1772000000000002, 25.4448, 62.467200000000005, 4.9248, 7.322400000000001]

The only distinction between this implementation and map() is that the list comprehension in Python returns a list, not a map object.

Выгода от испльзования List Comprehensions

List comprehensions are often described as being more Pythonic than loops or map().

But rather than blindly accepting that assessment, it’s worth it to understand the benefits of using a list comprehension in Python when compared to the alternatives.

Later on, you’ll learn about a few scenarios where the alternatives are a better choice.

One main benefit of using a list comprehension in Python is that it’s a single tool that you can use in many different situations.

In addition to standard list creation, list comprehensions can also be used for mapping and filtering.

You don’t have to use a different approach for each scenario.

This is the main reason why list comprehensions are considered Pythonic, as Python embraces simple, powerful tools that you can use in a wide variety of situations.

As an added side benefit, whenever you use a list comprehension in Python, you won’t need to remember the proper order of arguments like you would when you call map().

List comprehensions are also more declarative than loops, which means they’re easier to read and understand.

Loops require you to focus on how the list is created.

You have to manually create an empty list, loop over the elements, and add each of them to the end of the list.

With a list comprehension in Python, you can instead focus on what you want to go in the list and trust that Python will take care of how the list construction takes place.

How to Supercharge Your Comprehensions

In order to understand the full value that list comprehensions can provide, it’s helpful to understand their range of possible functionality. You’ll also want to understand the changes that are coming to the list comprehension in Python 3.8.

Using Conditional Logic

Earlier, you saw this formula for how to create list comprehensions:

new_list = [expression formember in iterable]

While this formula is accurate, it’s also a bit incomplete. A more complete description of the comprehension formula adds support for optional conditionals. The most common way to add conditional logic to a list comprehension is to add a conditional to the end of the expression:

new_list = [expression formember in iterable (if conditional)]

Here, your conditional statement comes just before the closing bracket.

Conditionals are important because they allow list comprehensions to filter out unwanted values, which would normally require a call to filter():

>>> sentence = 'the rocket came back from mars' >>> vowels = [i fori in sentence if i in 'aeiou'] >>> vowels

['e', 'o', 'e', 'a', 'e', 'a', 'o', 'a']

In this code block, the conditional statement filters out any characters in sentence that aren’t a vowel. The conditional can test any valid expression. If you need a more complex filter, then you can even move the conditional logic to a separate function:

>>> sentence = 'The rocket, who was named Ted, came back \ ... from Mars because he missed his friends.' >>> def is_consonant(letter): ... vowels = 'aeiou' ... return letter.isalpha() and letter.lower() not in vowels >>> consonants = [i fori in sentence if is_consonant(i)]

['T', 'h', 'r', 'c', 'k', 't', 'w', 'h', 'w', 's', 'n', 'm', 'd', \ 'T', 'd', 'c', 'm', 'b', 'c', 'k', 'f', 'r', 'm', 'M', 'r', 's', 'b', \ 'c', 's', 'h', 'm', 's', 's', 'd', 'h', 's', 'f', 'r', 'n', 'd', 's']

Here, you create a complex filter is_consonant() and pass this function as the conditional statement for your list comprehension. Note that the member value i is also passed as an argument to your function. You can place the conditional at the end of the statement for simple filtering, but what if you want to change a member value instead of filtering it out? In this case, it’s useful to place the conditional near the beginning of the expression:

new_list = [expression (if conditional) formember in iterable]

With this formula, you can use conditional logic to select from multiple possible output options. For example, if you have a list of prices, then you may want to replace negative prices with 0 and leave the positive values unchanged:

>>> original_prices = [1.25, -9.45, 10.22, 3.78, -5.92, 1.16] >>> prices = [i if i > 0 else 0 fori in original_prices] >>> prices

[1.25, 0, 10.22, 3.78, 0, 1.16]

Here, your expression i contains a conditional statement, if i > 0 else 0. This tells Python to output the value of i if the number is positive, but to change i to 0 if the number is negative. If this seems overwhelming, then it may be helpful to view the conditional logic as its own function:

>>> def get_price(price): ... return price if price > 0 else 0 >>> prices = [get_price(i) fori in original_prices] >>> prices

[1.25, 0, 10.22, 3.78, 0, 1.16]

Now, your conditional statement is contained within get_price(), and you can use it as part of your list comprehension expression. Using Set and Dictionary Comprehensions While the list comprehension in Python is a common tool, you can also create set and dictionary comprehensions. A set comprehension is almost exactly the same as a list comprehension in Python. The difference is that set comprehensions make sure the output contains no duplicates. You can create a set comprehension by using curly braces instead of brackets:

>>> quote = "life, uh, finds a way" >>> unique_vowels = {i fori in quote if i in 'aeiou'} >>> unique_vowels

{'a', 'e', 'u', 'i'}

Your set comprehension outputs all the unique vowels it found in quote. Unlike lists, sets don’t guarantee that items will be saved in any particular order. This is why the first member of the set is a, even though the first vowel in quote is i. Dictionary comprehensions are similar, with the additional requirement of defining a key:

>>> squares = {i: i * i fori in range(10)} >>> squares

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}

To create the squares dictionary, you use curly braces ({}) as well as a key-value pair (i: i * i) in your expression. Using the Walrus Operator Python 3.8 will introduce the assignment expression, also known as the walrus operator. To understand how you can use it, consider the following example. Say you need to make ten requests to an API that will return temperature data. You only want to return results that are greater than 100 degrees Fahrenheit. Assume that each request will return different data. In this case, there’s no way to use a list comprehension in Python to solve the problem. The formula expression for member in iterable (if conditional) provides no way for the conditional to assign data to a variable that the expression can access. The walrus operator solves this problem. It allows you to run an expression while simultaneously assigning the output value to a variable. The following example shows how this is possible, using get_weather_data() to generate fake weather data:

>>> import random >>> def get_weather_data(): ... return random.randrange(90, 110) >>> hot_temps = [temp for_ in range(20) if (temp := get_weather_data()) >= 100] >>> hot_temps

[107, 102, 109, 104, 107, 109, 108, 101, 104]

You won’t often need to use the assignment expression inside of a list comprehension in Python, but it’s a useful tool to have at your disposal when necessary. When Not to Use a List Comprehension in Python List comprehensions are useful and can help you write elegant code that’s easy to read and debug, but they’re not the right choice for all circumstances. They might make your code run more slowly or use more memory. If your code is less performant or harder to understand, then it’s probably better to choose an alternative. Watch Out for Nested Comprehensions Comprehensions can be nested to create combinations of lists, dictionaries, and sets within a collection. For example, say a climate laboratory is tracking the high temperature in five different cities for the first week of June. The perfect data structure for storing this data could be a Python list comprehension nested within a dictionary comprehension:

>>> cities = ['Austin', 'Tacoma', 'Topeka', 'Sacramento', 'Charlotte'] >>> temps = {city: [0 for_ in range(7)] forcity in cities} >>> temps

{ 'Austin': [0, 0, 0, 0, 0, 0, 0], 'Tacoma': [0, 0, 0, 0, 0, 0, 0], 'Topeka': [0, 0, 0, 0, 0, 0, 0], 'Sacramento': [0, 0, 0, 0, 0, 0, 0], 'Charlotte': [0, 0, 0, 0, 0, 0, 0] }

You create the outer collection temps with a dictionary comprehension. The expression is a key-value pair, which contains yet another comprehension. This code will quickly generate a list of data for each city in cities. Nested lists are a common way to create matrices, which are often used for mathematical purposes. Take a look at the code block below:

>>> matrix = [[i fori in range(5)] for_ in range(6)] >>> matrix

[ [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4] ]

The outer list comprehension [... for_ in range(6)] creates six rows, while the inner list comprehension [i fori in range(5)] fills each of these rows with values. So far, the purpose of each nested comprehension is pretty intuitive. However, there are other situations, such as flattening nested lists, where the logic arguably makes your code more confusing. Take this example, which uses a nested list comprehension to flatten a matrix:

matrix = [ ... [0, 0, 0], ... [1, 1, 1], ... [2, 2, 2], ... ] >>> flat = [num forrow in matrix fornum in row] >>> flat

[0, 0, 0, 1, 1, 1, 2, 2, 2]

The code to flatten the matrix is concise, but it may not be so intuitive to understand how it works. On the other hand, if you were to use for loops to flatten the same matrix, then your code will be much more straightforward:

>>> matrix = [ ... [0, 0, 0], ... [1, 1, 1], ... [2, 2, 2], ... ] >>> flat = [] >>> forrow in matrix: ... fornum in row: ... flat.append(num) ... >>> flat

[0, 0, 0, 1, 1, 1, 2, 2, 2]

Now you can see that the code traverses one row of the matrix at a time, pulling out all the elements in that row before moving on to the next one. While the single-line nested list comprehension might seem more Pythonic, what’s most important is to write code that your team can easily understand and modify. When you choose your approach, you’ll have to make a judgment call based on whether you think the comprehension helps or hurts readability. Choose Generators for Large Datasets A list comprehension in Python works by loading the entire output list into memory. For small or even medium-sized lists, this is generally fine. If you want to sum the squares of the first one-thousand integers, then a list comprehension will solve this problem admirably:

>>> sum([i * i fori in range(1000)])


But what if you wanted to sum the squares of the first billion integers? If you tried then on your machine, then you may notice that your computer becomes non-responsive. That’s because Python is trying to create a list with one billion integers, which consumes more memory than your computer would like. Your computer may not have the resources it needs to generate an enormous list and store it in memory. If you try to do it anyway, then your machine could slow down or even crash. When the size of a list becomes problematic, it’s often helpful to use a generator instead of a list comprehension in Python. A generator doesn’t create a single, large data structure in memory, but instead returns an iterable. Your code can ask for the next value from the iterable as many times as necessary or until you’ve reached the end of your sequence, while only storing a single value at a time. If you were to sum the first billion squares with a generator, then your program will likely run for a while, but it shouldn’t cause your computer to freeze. The example below uses a generator:

>>> sum(i * i fori in range(1000000000))


You can tell this is a generator because the expression isn’t surrounded by brackets or curly braces. Optionally, generators can be surrounded by parentheses. The example above still requires a lot of work, but it performs the operations lazily. Because of lazy evaluation, values are only calculated when they’re explicitly requested. After the generator yields a value (for example, 567 * 567), it can add that value to the running sum, then discard that value and generate the next value (568 * 568). When the sum function requests the next value, the cycle starts over. This process keeps the memory footprint small. map() also operates lazily, meaning memory won’t be an issue if you choose to use it in this case:

>>> sum(map(lambda i: i*i, range(1000000000)))


It’s up to you whether you prefer the generator expression or map(). Profile to Optimize Performance So, which approach is faster? Should you use list comprehensions or one of their alternatives? Rather than adhere to a single rule that’s true in all cases, it’s more useful to ask yourself whether or not performance matters in your specific circumstance. If not, then it’s usually best to choose whatever approach leads to the cleanest code! If you’re in a scenario where performance is important, then it’s typically best to profile different approaches and listen to the data. timeit is a useful library for timing how long it takes chunks of code to run. You can use timeit to compare the runtime of map(), for loops, and list comprehensions:

>>> import random >>> import timeit >>> TAX_RATE = .08 >>> txns = [random.randrange(100) for_ in range(100000)] >>> def get_price(txn): ... return txn * (1 + TAX_RATE) ... >>> def get_prices_with_map(): ... return list(map(get_price, txns)) ... >>> def get_prices_with_comprehension(): ... return [get_price(txn) fortxn in txns] ... >>> def get_prices_with_loop(): ... prices = [] ... fortxn in txns: ... prices.append(get_price(txn)) ... return prices ... >>> timeit.timeit(get_prices_with_map, number=100)


>>> timeit.timeit(get_prices_with_comprehension, number=100)


>>> timeit.timeit(get_prices_with_loop, number=100)


Here, you define three methods that each use a different approach for creating a list. Then, you tell timeit to run each of those functions 100 times each. timeit returns the total time it took to run those 100 executions. As the code demonstrates, the biggest difference is between the loop-based approach and map(), with the loop taking 50% longer to execute. Whether or not this matters depends on the needs of your application. Conclusion In this tutorial, you learned how to use a list comprehension in Python to accomplish complex tasks without making your code overly complicated. Now you can: Simplify loops and map() calls with declarative list comprehensions Supercharge your comprehensions with conditional logic Create set and dictionary comprehensions Determine when code clarity or performance dictates an alternative approach Whenever you have to choose a list creation method, try multiple implementations and consider what’s easiest to read and understand in your specific scenario. If performance is important, then you can use profiling tools to give you actionable data instead of relying on hunches or guesses about what works the best. Remember that while Python list comprehensions get a lot of attention, your intuition and ability to use data when it counts will help you write clean code that serves the task at hand. This, ultimately, is the key to making your code Pythonic!

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