Create A Custom Direct ‘Add to Checkout Link For WooCommerce Products?

In today's video tutorial, we'll learn how to create a custom link for your store checkout page with some specific products already added to it in a simple, fast, and easy method to make the purchasing process easier and faster for the customer. 
 

#woocommerce 

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PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
2. The Tumblr API console at https://api.tumblr.com/console
3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

• state - a string, the state of the post. Supported types are published, draft, queue, private
• tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
• tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
• date - a string, the customized GMT that you want
• format - a string, the format that your post is in. Support types are html or markdown
• slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

• title - a string, the title of post that you want. Supports HTML entities.
• url - a string, the url that you want to create a link post for.
• description - a string, the desciption of the link that you have

#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

• Lists
• Sets
• Dictionaries
• User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

• Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
• Strings
• Tuples
• Frozen Sets
• User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

• Identity – This refers to the address that the object refers to in the computer’s memory.
• Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
• Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10
 

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick check – Python Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable Object	Immutable Object
State of the object can be modified after it is created.	State of the object can’t be modified once it is created.
They are not thread safe.	They are thread safe
Mutable classes are not final.	It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

• Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

• Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

Join Great Learning Academy’s free online courses and upgrade your skills today.

---

Original article source at: https://www.mygreatlearning.com

#python 

Woo your customers with WooCommerce in 2021 - features and benefits - TopDevelopers.co

Are you looking for the best plugins for the eCommerce portal to skyrocket the sales on your store? Then this is the ideal place for you to know the WooCommerce plugin features and use.

#woocommerce development service #woocommerce development #woocommerce benefits #woocommerce features #woocommerce services #woocommerce

Create A Custom Direct ‘Add to Checkout Link For WooCommerce Products?

In today's video tutorial, we'll learn how to create a custom link for your store checkout page with some specific products already added to it in a simple, fast, and easy method to make the purchasing process easier and faster for the customer. 
 

#woocommerce 

Como anexar A Uma Lista Ou Matriz Em Python Como Um Profissional

Neste artigo, você aprenderá sobre o .append()método em Python. Você também verá como .append()difere de outros métodos usados ​​para adicionar elementos a listas.

Vamos começar!

O que são listas em Python? Uma definição para iniciantes

Uma matriz na programação é uma coleção ordenada de itens e todos os itens precisam ser do mesmo tipo de dados.

No entanto, ao contrário de outras linguagens de programação, os arrays não são uma estrutura de dados embutida no Python. Em vez de arrays tradicionais, o Python usa listas.

Listas são essencialmente arrays dinâmicos e são uma das estruturas de dados mais comuns e poderosas em Python.

Você pode pensar neles como contêineres ordenados. Eles armazenam e organizam tipos semelhantes de dados relacionados juntos.

Os elementos armazenados em uma lista podem ser de qualquer tipo de dados.

Pode haver listas de inteiros (números inteiros), listas de floats (números de ponto flutuante), listas de strings (texto) e listas de qualquer outro tipo de dados interno do Python.

Embora seja possível que as listas contenham apenas itens do mesmo tipo de dados, elas são mais flexíveis do que as matrizes tradicionais. Isso significa que pode haver uma variedade de tipos de dados diferentes dentro da mesma lista.

As listas têm 0 ou mais itens, o que significa que também pode haver listas vazias. Dentro de uma lista também pode haver valores duplicados.

Os valores são separados por uma vírgula e colocados entre colchetes, [].

Como criar listas em Python

Para criar uma nova lista, primeiro dê um nome à lista. Em seguida, adicione o operador de atribuição ( =) e um par de colchetes de abertura e fechamento. Dentro dos colchetes, adicione os valores que você deseja que a lista contenha.

#create a new list of names
names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#print the list to the console
print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny']

Como as listas são indexadas em Python

As listas mantêm uma ordem para cada item.

Cada item na coleção tem seu próprio número de índice, que você pode usar para acessar o próprio item.

Índices em Python (e em qualquer outra linguagem de programação moderna) começam em 0 e aumentam para cada item da lista.

Por exemplo, a lista criada anteriormente tinha 4 valores:

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

O primeiro valor na lista, "Jimmy", tem um índice de 0.

O segundo valor na lista, "Timmy", tem um índice de 1.

O terceiro valor na lista, "Kenny", tem um índice de 2.

O quarto valor na lista, "Lenny", tem um índice de 3.

Para acessar um elemento na lista por seu número de índice, primeiro escreva o nome da lista, depois entre colchetes escreva o inteiro do índice do elemento.

Por exemplo, se você quisesse acessar o elemento que tem um índice de 2, você faria:

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

print(names[2])

#output
#Kenny

Listas em Python são mutáveis

Em Python, quando os objetos são mutáveis , significa que seus valores podem ser alterados depois de criados.

As listas são objetos mutáveis, portanto, você pode atualizá-las e alterá-las depois de criadas.

As listas também são dinâmicas, o que significa que podem crescer e diminuir ao longo da vida de um programa.

Os itens podem ser removidos de uma lista existente e novos itens podem ser adicionados a uma lista existente.

Existem métodos internos para adicionar e remover itens de listas.

Por exemplo, para add itens, há as .append(), .insert()e .extend()métodos.

Para remove itens, há as .remove(), .pop()e .pop(index)métodos.

O que o .append()método faz?

O .append()método adiciona um elemento adicional ao final de uma lista já existente.

A sintaxe geral se parece com isso:

list_name.append(item)

Vamos decompô-lo:

• list_name é o nome que você deu à lista.
• .append()é o método de lista para adicionar um item ao final de list_name.
• item é o item individual especificado que você deseja adicionar.

Ao usar .append(), a lista original é modificada. Nenhuma nova lista é criada.

Se você quiser adicionar um nome extra à lista criada anteriormente, faça o seguinte:

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#add the name Dylan to the end of the list
names.append("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'Dylan']

Qual é a diferença entre os métodos .append()e .insert()?

A diferença entre os dois métodos é que .append()adiciona um item ao final de uma lista, enquanto .insert()insere um item em uma posição especificada na lista.

Como você viu na seção anterior, .append()irá adicionar o item que você passar como argumento para a função sempre no final da lista.

Se você não quiser apenas adicionar itens ao final de uma lista, poderá especificar a posição com a qual deseja adicioná-los .insert().

A sintaxe geral fica assim:

list_name.insert(position,item)

Vamos decompô-lo:

• list_name é o nome da lista.
• .insert() é o método de lista para inserir um item em uma lista.
• positioné o primeiro argumento para o método. É sempre um número inteiro - especificamente é o número de índice da posição onde você deseja que o novo item seja colocado.
• itemé o segundo argumento para o método. Aqui você especifica o novo item que deseja adicionar à lista.

Por exemplo, digamos que você tenha a seguinte lista de linguagens de programação:

programming_languages = ["JavaScript", "Java", "C++"]

print(programming_languages)

#output
#['JavaScript', 'Java', 'C++']

Se você quisesse inserir "Python" no início da lista, como um novo item da lista, você usaria o .insert()método e especificaria a posição como 0. (Lembre-se de que o primeiro valor em uma lista sempre tem um índice de 0.)

programming_languages = ["JavaScript", "Java", "C++"]

programming_languages.insert(0, "Python")

print(programming_languages)

#output
#['Python', 'JavaScript', 'Java', 'C++']

Se, em vez disso, você quisesse que "JavaScript" fosse o primeiro item da lista e, em seguida, adicionasse "Python" como o novo item, você especificaria a posição como 1:

programming_languages = ["JavaScript", "Java", "C++"]

programming_languages.insert(1,"Python")

print(programming_languages)

#output
#['JavaScript', 'Python', 'Java', 'C++']

O .insert()método oferece um pouco mais de flexibilidade em comparação com o .append()método que apenas adiciona um novo item ao final da lista.

Qual é a diferença entre os métodos .append()e .extend()?

E se você quiser adicionar mais de um item a uma lista de uma só vez, em vez de adicioná-los um de cada vez?

Você pode usar o .append()método para adicionar mais de um item ao final de uma lista.

Digamos que você tenha uma lista que contém apenas duas linguagens de programação:

programming_languages = ["JavaScript", "Java"]

print(programming_languages)

#output
#['JavaScript', 'Java']

Você então deseja adicionar mais dois idiomas, no final dele.

Nesse caso, você passa uma lista contendo os dois novos valores que deseja adicionar, como argumento para .append():

programming_languages = ["JavaScript", "Java"]

#add two new items to the end of the list
programming_languages.append(["Python","C++"])

print(programming_languages)

#output
#['JavaScript', 'Java', ['Python', 'C++']]

Se você observar mais de perto a saída acima, ['JavaScript', 'Java', ['Python', 'C++']], verá que uma nova lista foi adicionada ao final da lista já existente.

Então, .append() adiciona uma lista dentro de uma lista .

Listas são objetos, e quando você usa .append()para adicionar outra lista em uma lista, os novos itens serão adicionados como um único objeto (item).

Digamos que você já tenha duas listas, assim:

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

E se você quiser combinar o conteúdo de ambas as listas em uma, adicionando o conteúdo de more_namesa names?

Quando o .append()método é usado para essa finalidade, outra lista é criada dentro de names:

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

#add contents of more_names to names
names.append(more_names)

print(names)

#output
#['Jimmy', 'Timmy', ['Kenny', 'Lenny']]

Então, .append()adiciona os novos elementos como outra lista, anexando o objeto ao final.

Para realmente concatenar (adicionar) listas e combinar todos os itens de uma lista para outra , você precisa usar o .extend()método.

A sintaxe geral fica assim:

list_name.extend(iterable/other_list_name)

Vamos decompô-lo:

• list_name é o nome de uma das listas.
• .extend() é o método para adicionar todo o conteúdo de uma lista a outra.
• iterablepode ser qualquer iterável, como outra lista, por exemplo, another_list_name. Nesse caso, another_list_nameé uma lista que será concatenada com list_name, e seu conteúdo será adicionado um a um ao final de list_name, como itens separados.

Então, tomando o exemplo anterior, quando .append()for substituído por .extend(), a saída ficará assim:

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

names.extend(more_names)

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny']

Quando usamos .extend(), a nameslista foi estendida e seu comprimento aumentado em 2.

A maneira como .extend()funciona é que ele pega uma lista (ou outro iterável) como argumento, itera sobre cada elemento e, em seguida, cada elemento no iterável é adicionado à lista.

Há outra diferença entre .append()e .extend().

Quando você deseja adicionar uma string, como visto anteriormente, .append()adiciona o item inteiro e único ao final da lista:

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#add the name Dylan to the end of the list
names.append("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'Dylan']

Se, em .extend()vez disso, você adicionasse uma string ao final de uma lista, cada caractere na string seria adicionado como um item individual à lista.

Isso ocorre porque as strings são iteráveis ​​e .extend()iteram sobre o argumento iterável passado para ela.

Então, o exemplo acima ficaria assim:

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#pass a string(iterable) to .extend()
names.extend("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'D', 'y', 'l', 'a', 'n']

Conclusão

Resumindo, o .append()método é usado para adicionar um item ao final de uma lista existente, sem criar uma nova lista.

Quando é usado para adicionar uma lista a outra lista, cria uma lista dentro de uma lista.

Se você quiser saber mais sobre Python, confira a Certificação Python do freeCodeCamp . Você começará a aprender de maneira interativa e amigável para iniciantes. Você também construirá cinco projetos no final para colocar em prática o que aprendeu.

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fonte: https://www.freecodecamp.org

#python