Planet Python

Installing private python packages into Docker container can be tricky because container does not have access to private repositories and you do not want to leave trace of private ssh key in docker…

"Django app name translation in admin" is small drop-in django application that overrides few admin templates thus allowing app names in Django admin to be translated.

create documentation for django website

Telling Django to rename all uploaded files in ASCII encoding is easy and takes only two steps.

Dictionary comprehensions are a concise and quick way to create, transform, and filter dictionaries in Python. They can significantly enhance your code’s conciseness and readability compared to using regular for loops to process your dictionaries.

Understanding dictionary comprehensions is crucial for you as a Python developer because they’re a Pythonic tool for dictionary manipulation and can be a valuable addition to your programming toolkit.

In this tutorial, you’ll learn how to:

• Create dictionaries using dictionary comprehensions
• Transform existing dictionaries with comprehensions
• Filter key-value pairs from dictionaries using conditionals
• Decide when to use dictionary comprehensions

To get the most out of this tutorial, you should be familiar with basic Python concepts, such as for loops, iterables, and dictionaries, as well as list comprehensions.

Get Your Code: Click here to download the free sample code that you’ll use to learn about dictionary comprehensions in Python.

Creating and Transforming Dictionaries in Python

In Python programming, you’ll often need to create, populate, and transform dictionaries. To do this, you can use dictionary literals, the dict() constructor, and for loops. In the following sections, you’ll take a quick look at how to use these tools. You’ll also learn about dictionary comprehensions, which are a powerful way to manipulate dictionaries in Python.

Creating Dictionaries With Literals and dict()

To create new dictionaries, you can use literals. A dictionary literal is a series of key-value pairs enclosed in curly braces. The syntax of a dictionary literal is shown below:

Python Syntax {key_1: value_1, key_2: value_2,..., key_N: value_N} Copied!

The keys must be hashable objects and are commonly strings. The values can be any Python object, including other dictionaries. Here’s a quick example of a dictionary:

Python >>> likes = {"color": "blue", "fruit": "apple", "pet": "dog"} >>> likes {'color': 'blue', 'fruit': 'apple', 'pet': 'dog'} >>> likes["hobby"] = "guitar" >>> likes {'color': 'blue', 'fruit': 'apple', 'pet': 'dog', 'hobby': 'guitar'} Copied!

In this example, you create dictionary key-value pairs that describe things people often like. The keys and values of your dictionary are string objects. You can add new pairs to the dictionary using the dict[key] = value syntax.

Note: To learn more about dictionaries, check out the Dictionaries in Python tutorial.

You can also create new dictionaries using the dict() constructor:

Python >>> dict(apple=0.40, orange=0.35, banana=0.25) {'apple': 0.4, 'orange': 0.35, 'banana': 0.25} Copied!

In this example, you create a new dictionary using dict() with keyword arguments. In this case, the keys are strings and the values are floating-point numbers. It’s important to note that the dict() constructor is only suitable for those cases where the dictionary keys can be strings that are valid Python identifiers.

Using for Loops to Populate Dictionaries

Sometimes, you need to start with an empty dictionary and populate it with key-value pairs dynamically. To do this, you can use a for loop. For example, say that you want to create a dictionary in which keys are integer numbers and values are powers of 2.

Here’s how you can do this with a for loop:

Python >>> powers_of_two = {} >>> for integer in range(1, 10): ... powers_of_two[integer] = 2**integer ... >>> powers_of_two {1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64, 7: 128, 8: 256, 9: 512} Copied!

In this example, you create an empty dictionary using an empty pair of curly braces. Then, you run a loop over a range of integer numbers from 1 to 9. Inside the loop, you populate the dictionary with the integer numbers as keys and powers of two as values.

The loop in this example is readable and clear. However, you can also use dictionary comprehension to create and populate a dictionary like the one shown above.

Introducing Dictionary Comprehensions Read the full article at https://realpython.com/python-dictionary-comprehension/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Textual-cogs is a collection of Textual dialogs that you can use in your Textual application. You can see a quick demo of the dialogs below:

Dialogs included so far:

• Generic MessageDialog – shows messages to the user
• SaveFileDialog – gives the user a way to select a location to save a file
• SingleChoiceDialog – gives the user a series of choices to pick from
• TextEntryDialog – ask the user a question and get their answer using an Input widget
• and more

You can check out textual-cogs on GitHub.

Installation

 

You can install textual-cog using pip:

python -m pip install textual-cog

You also need Textual to run these dialogs.

Example Usage

 

Here is an example of creating a small application that opens the MessageDialog immediately. You would normally open the dialog in response to a message or event that has occurred, such as when the application has an error or you need to tell the user something.

from textual.app import App from textual.app import App, ComposeResult from textual_cogs.dialogs import MessageDialog from textual_cogs import icons class DialogApp(App): def on_mount(self) -> ComposeResult: def my_callback(value: None | bool) -> None: self.exit() self.push_screen( MessageDialog( "What is your favorite language?", icon=icons.ICON_QUESTION, title="Warning", ), my_callback, ) if __name__ == "__main__": app = DialogApp() app.run()

When you run this code, you will get something like the following:

 

Creating a SaveFileDialog

 

The following code demonstrates how to create a SaveFileDialog:

from textual.app import App from textual.app import App, ComposeResult from textual_cogs.dialogs import SaveFileDialog class DialogApp(App): def on_mount(self) -> ComposeResult: self.push_screen(SaveFileDialog()) if __name__ == "__main__": app = DialogApp() app.run()

When you run this code, you will see the following:

Wrapping Up

The textual-cogs package is currently only a collection of reusable dialogs for your Textual application. However, this can help speed up your ability to add code to your TUI applications because the dialogs are taken care of for you.

Check it out on GitHub or the Python Package Index today.

The post ANN – The textual-cogs Package – Creating Reusable Dialogs for Textual appeared first on Mouse Vs Python.

In this quiz, you’ll test your understanding of how to use Python’s built-in functions input() and print() for basic input and output operations.

You’ll also revisit how to use readline to improve the user experience when collecting input, and how to format output using the sep and end keyword arguments of print().

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Introduction

eGenix PyRun™ is our open source, one file, no installation version of Python, making the distribution of a Python interpreter to run Python based scripts and applications to Unix based systems simple and efficient.

eGenix PyRun's executable only needs 4-6MB on disk, but still supports most Python applications and scripts.

Compared to a regular Python installation of typically 100MB on disk, eGenix PyRun is ideal for applications and scripts that need to be distributed to containers, VMs, clusters, client installations, customers or end-users.

It makes "installing" Python on a Unix based system as simple as copying a single file.

eGenix has been using eGenix PyRun as run-time for the Linux version of mxODBC Connect Server product since 2008 with great success and decided to make it available as a stand-alone open-source product.

We provide the source archive to build your own eGenix PyRun on Github, as well as a few binary distributions to get you started on Linux x86_64. In the future, we will set up automated builds for several other platforms.

Please see the product page for more details:

    >>> eGenix PyRun - One file Python Runtime

News

This major release of eGenix PyRun comes with the following enhancements:

Enhancements / Changes

• Added support for Python 3.12
• Added support for LTO release builds
• Added dev build targets for development; these don't use PGO and thus build faster

For a complete list of changes, please see the eGenix PyRun Changelog. Downloads

Please visit the eGenix PyRun product page for downloads, instructions on installation and documentation of the product.

Support

Commercial support for this product is available directly from eGenix.com.

Please see the support section of our website for details.

More Information

For more information on eGenix PyRun, licensing and download instructions, please write to sales@egenix.com.

Enjoy !

Marc-Andre Lemburg, eGenix.com

Web scraping as an API service 2024-11-13, by Dariusz Suchojad Overview

In systems-to-systems integrations, there comes an inevitable time when we have to employ some kind of a web scraping tool to integrate with a particular application. Despite its not being our first choice, it is good to know what to use at such a time - in this article, I provide a gentle introduction to my favorite tool of this kind, called Playwright, followed by sample Python code that integrates it with an API service.

Naturally, in the context of backend integrations, web scraping should be avoided and, generally, it should be considered the last resort. The basic issue here is that while the UI term contains the "interface" part, it is not really the "Application Programming" Interface that we would like to have.

It is not that the UI cannot be programmed against. After all, a web browser does just that, it takes a web page and renders it as expected. Same goes for desktop or mobile applications. Also, anyone integrating with mainframe computers will recognize that this is basically what 3270 can be used for too.

Rather, the fundamental issue is that web scraping goes against the principles of separation of layers and roles across frontend, middleware and backend, which in turn means that authors of resources (e.g. HTML pages) do not really expect for many people to access them in automated ways.

Perhaps they actually should expect it, and web pages should finally start to resemble genuine knowledge graphs, easy to access by humans, be it manually or through automation tools, but the reality today is that it is not the case and, in comparison with backend systems, the whole of the web scraping space is relatively brittle, which is why we shun this approach in integrations.

Yet, another part of reality, particularly in enterprise integrations, is that people may be sometimes given access to a frontend application on an internal network and that is it. No API, no REST, no JSON, no POST data, no real data formats, and one is simply supposed to fill out forms as part of a business process.

Typically, such a situation will result in an integration gap. There will be fully automated parts in the business process preceding this gap, with multiple systems coordinated towards a specific goal and there will be subsequent steps in the process, also fully automated.

Or you may be given access only to a specific frontend and only through VPN via a single remote Windows desktop. Getting access to a REST API may take months or may be never realized because of some high level licensing issues. This is not uncommon in the real life.

Such a gap can be a jarring and sore point, truly ruining the whole, otherwise fluid, integration process. This creates a tension and to resolve the tension, we can, should all the attempts to find a real API fail, finally resort to web scraping.

It is mostly in this context that I am looking at Playwright below - the tool is good and it has many other uses that go beyond the scope of this text, and it is well worth knowing it, for instance for frontend testing of your backend systems, but, when we deal with API integrations, we should not overdo with web scraping.

Needless to say, if web scraping is what you do primarily, your perspective will be somewhat different - you will not need any explanation of why it is needed or when, and you may be only looking for a way to enclose up your web scraping code in API services. This article will explain that too.

Introducing Playwright

The nice part of Playwright is that we can use it to visually prepare a draft of Python code that will scrape a given resource. That is, instead of programming it in Python, we go to an address, fill out a form, click buttons and otherwise use everything as usually and Playwright generates for us code that will be later used in integrations.

That code will require a bit of clean-up work, which I will talk about below, but overall it works very nicely and is certainly useful. The result is not one of these do-not-touch auto-generated pieces of code that are better left to their own.

While there are better ways to integrate with Jira, I chose that application as an example of Playwright's usage simply because I cannot show you any internal application in a public blog post.

Below, there are two windows. One is Playwright's emulating a Blackberry device to open a resource. I was clicking around, I provided an email address and then I clicked the same email field once more. To the right, based on my actions, we can find the generated Python code, which I consider quite good and readable.

The Playwright Inspector, the tool that gave us the code, will keep recording all of our actions until we click the "Record" button which then allows us to click the button next to "Record" which is "Copy code to clipboard". We can then save the code to a separate file and run it on demand, automatically.

But first, we will need to install Playwright.

Installing and starting Playwright

The tools is written in TypeScript and can be installed using npx, which in turn is part of NodeJS.

Afterwards, the "playwright install" call is needed as well because that will potentially install runtime dependencies, such as Chrome libraries.

Finally, we install Playwright using pip as well because we want to access with Python. Note that if you are installing Playwright under Zato, the "/path/to/pip" will be typically "/opt/zato/code/bin/pip".

npx -g --yes playwright install playwright install /path/to/pip install playwright

We can now start it as below. I am using BlackBerry as an example of what Playwright is capable of. Also, it is usually more convenient to use a mobile version of a site when the main window and Inspector are opened side by side, but you may prefer to use Chrome, Firefox or anything else.

playwright codegen https://example.atlassian.net/jira --device "BlackBerry Z30"

That is practically everything as using Playwright to generate code in our context goes. Open the tool, fill out forms, copy code to a Python module, done.

What is still needed, though, is cleaning up the resulting code and embedding it in an API integration process.

Code clean-up

After you keep using Playwright for a while with longer forms and pages, you will note that the generated code tends to accumulate parts that repeat.

For instance, in the module below, which I already cleaned up, the same "[placeholder=\"Enter email\"]" reference to the email field is used twice, even if a programmer developing this could would prefer to introduce a variable for that.

There is not a good answer to the question of what to do about it. On the one hand, obviously, being programmers we would prefer not to repeat that kind of details. On the other hand, if we clean up the code too much, this may result in too much of a maintenance burden because we need to keep it mind that we do not really want to invest to much in web scraping and, should there be a need to repeat the whole process, we do not want to end up with Playwright's code auto-generated from scratch once more, without any of our clean-up.

A good compromise position is to at least extract any kind of credentials from the code to environment variables or a similar place and to remove some of the code comments that Playwright generates. The result as below is what it should like at the end. Not too much effort without leaving the whole code as it was originally either.

Save the code below as "play1.py" as this is what the API service below will use.

# -*- coding: utf-8 -*- # stdlib import os # Playwright from playwright.sync_api import Playwright, sync_playwright class Config: Email = os.environ.get('APP_EMAIL', 'zato@example.com') Password = os.environ.get('APP_PASSWORD', '') Headless = bool(os.environ.get('APP_HEADLESS', False)) def run(playwright: Playwright) -> None: browser = playwright.chromium.launch(headless=Config.Headless) # type: ignore context = browser.new_context() # Open new page page = context.new_page() # Open project boards page.goto("https://example.atlassian.net/jira/software/projects/ABC/boards/1") page.goto("https://id.atlassian.com/login?continue=https%3A%2F%2Fexample.atlassian.net%2Flogin%3FredirectCount%3D1%26dest-url%3D%252Fjira%252Fsoftware%252Fprojects%252FABC%252Fboards%252F1%26application%3Djira&application=jira") # Fill out the email page.locator("[placeholder=\"Enter email\"]").click() page.locator("[placeholder=\"Enter email\"]").fill(Config.Email) # Click #login-submit page.locator("#login-submit").click() with sync_playwright() as playwright: run(playwright) Web scraping as a standalone activity

We have the generated code so the first thing to do with it is to run it from command line. This will result in a new Chrome window's accessing Jira - it is Chrome, not Blackberry, because that is the default for Playwright.

The window will close soon enough but this is fine, that code only demonstrates a principle, it is not a full integration task.

python /path/to/play1.py

It is also useful that we can run the same Python module from our IDE, giving us the ability to step through the code line by line, observing what changes when and why.

Web scraping as an API service

Finally, we are ready to invoke the standalone module from an API service, as in the following code that we are also going to make available as a REST channel.

A couple of notes about the Python service below:

• We invoke Playwright in a subprocess, as a shell command
• We accept input through data models although we do not provide any output definition because it is not needed here
• When we invoke Playwright, we set the APP_HEADLESS to True which will ensure that it does not attempt to actually display a Chrome window. After all, we intend for this service to run on Linux servers, in backend, and such a thing will be unlikely to work in this kind of an environment.

Other than that, this is a straightforward Zato service - it receives input, carries out its work and a reply is returned to the caller (here, empty).

# -*- coding: utf-8 -*- # stdlib from dataclasses import dataclass # Zato from zato.server.service import Model, Service # ########################################################################### @dataclass(init=False) class WebScrapingDemoRequest(Model): email: str password: str # ########################################################################### class WebScrapingDemo(Service): name = 'demo.web-scraping' class SimpleIO: input = WebScrapingDemoRequest def handle(self): # Path to a Python installation that Playwright was installed under py_path = '/path/to/python' # Path to a Playwright module with code to invoke playwright_path = '/path/to/the-playwright-module.py' # This is a template script that we will invoke in a subprocess command_template = """ APP_EMAIL={app_email} APP_PASSWORD={app_password} APP_HEADLESS=True {py_path} {playwright_path} """ # This is our input data input = self.request.input # type: WebScrapingDemoRequest # Extract credentials from the input .. email = input.email password = input.password # .. build the full command, taking all the config into account .. command = command_template.format( app_email = email, app_password = password, py_path = py_path, playwright_path = playwright_path, ) # .. invoke the command in a subprocess .. result = self.commands.invoke(command) # .. if it was not a success, log the details received .. if not result.is_ok: self.logger.info('Exit code -> %s', result.exit_code) self.logger.info('Stderr -> %s', result.stderr) self.logger.info('Stdout -> %s', result.stdout) # ###########################################################################

Now, the REST channel:

The last thing to do is to invoke the service - I am using curl from the command line below but it could very well be Postman or a similar option.

curl localhost:17010/demo/web-scraping -d '{"email":"hello@example.com", "password":"abc"}' ; echo

There will be no Chrome window this time around because we run Playwright in the headless mode. There will be no output from curl either because we do not return anything from the service but in server logs we will find details such as below.

We can learn from the log that the command took close to 4 seconds to complete, that the exit code was 0 (indicating success) and that is no stdout or stderr at all.

INFO - Command ` APP_EMAIL=hello@example.com APP_PASSWORD=abc APP_HEADLESS=True /path/to/python /path/to/the-playwright-module.py ` completed in 0:00:03.844157, exit_code -> 0; len-out=0 (0 Bytes); len-err=0 (0 Bytes); cid -> zcmdc5422816b2c6ff9f10742134

We are now ready to continue to work on it - for instance, you will notice that the password is visible in logs and this should not be allowed.

But, all such works are extra in comparison with the main theme - we have Playwright, which is a a tool that allows us to quickly integrate with frontend applications and we can automate it through API services. Just as expected.

More resources

➤ Python API integration tutorial
➤ What is an integration platform?
➤ Python Integration platform as a Service (iPaaS)
➤ What is an Enterprise Service Bus (ESB)? What is SOA?

More blog posts➤

Simpleadmindoc is django application that allows you to quickly create help for modules in Django admin. Goal is to be flexible enough, fast to create and easy to integrate.

Middleware that intialize specific locale for admin pages.

Importing and exporting data with included admin integration.

django-cookie-consent is a reusable application for managing various cookies and visitors consent for their use in Django project.

I find Linux virtualization stack confusing. KVM? ibvirt? QEMU? Xen? What does that even mean?

This post is my attempt at making sense of that all. I don’t claim it to be correct, but that’s the way I understand it. If I badly missed a mark somewhere, please reach out and tell me how wrong I am.

Virtualization primer

Virtual machine is a box inside which programs think they are running on different hardware and operating system. That box is like a full computer, running inside a computer. Virtualization is process of creating these boxes. Virtual machine is called guest, while operating system that runs virtual machine is called host.

Two main reasons to virtualize are security and resource management. Security - because if virtual machine is done correctly, then program inside it won’t even know it’s inside a virtual machine, and that means there’s a good chance it won’t escape to interfere with other programs. Resource management - so you can buy huge server and assign slice of resources to each box as you see fit, and dynamically change it as your needs shift.

Virtual machine pretends to be the entire computer, but most discussions revolve around CPU. Each CPU architecture has a specific set of instructions, and programs generally target only one of them. It is technically possible to translate instructions from one architecture to another, but that’s usually slow. Most modern CPUs provide extensions that allow virtual machines to run at speed comparable to non-virtualized installations. Using these extensions is only possible when the guest and the host target the same CPU architecture.

Conceptually, there is no significant difference between virtualization and emulation. Practically, emulation usually refers to guest thinking it has different CPU architecture than host, and to virtual machine pretending to be some very specific physical hardware - like a video game console. Virtualization in turn usually refers to specific case where guest thinks it has the same CPU architecture as the host.

KVM

KVM is part of the kernel, responsible for talking with hardware. As I mentioned before, most modern CPUs have special extensions that allow guests to achieve near-native performance. KVM makes it possible for Linux host to use these extensions.

QEMU

QEMU is weird, because it’s multiple different things.

First, it can run virtual machines and pass instructions from guest to KVM, where they will be handled by virtualization CPU extension. So you can think about QEMU as user space component for KVM.

Second, it can run virtual machines while emulating different CPU architecture. So you can think about QEMU as user space virtualization solution, which can run without KVM at all.

Third, it can run programs targeting one CPU architecture on a computer with another CPU architecture. It’s like lightweight virtualization, where only single program is virtualized.

Finally, it is set of standalone utilities related to virtualization, but not directly tied to anything in particular. Most of these programs work with disk image files in some way.

QEMU is probably the hardest part to understand, because it can do things that other parts of the stack are responsible for. This results in some overlap between different components and encountering program names in contexts where you would not expect them.

I think this overlap is mostly a historical artifact. The oldest commit in QEMU git repository is dated 2003, while KVM was included in Linux kernel in 2007. So it seems to me that QEMU was originally intended as software virtualization solution that did not depend on any hardware or kernel driver. Later Linux gained drivers for virtualization CPU extensions, but they were useless without something in user space that could work with them. So instead of creating completely new thing, QEMU was extended to work with KVM.

Technically, the journey towards understanding Linux virtualization stack could end here - you can use QEMU to create, start, stop and modify virtual machines. But QEMU commands tend to be verbose and long, so people rarely use it directly.

libvirt

I have ignored that so far, but KVM is not the only virtualization driver in kernel. There are many, some of them closed-source.

libvirt is intended as unified layer that abstracts virtual machine management for application developers. So if you would like your application to create, start, stop or delete virtual machine, you don’t have to support each of these operations on KVM, QEMU, Xen and VirtualBox - you can just support libvirt and trust it will do the right thing.

libvirt doesn’t do any work directly, and instead asks other programs to do it. System administrator is responsible for setting up the host and ensuring virtualization may work. This makes libvirt great for application developers, who can forget about details of different solutions; but most of libvirt users are in less fortunate position, because they have to take a role of system administrator.

libvirt will talk with QEMU, which in turn may pass CPU instructions to KVM. Some sources online claim that libvirt can talk with KVM directly. libvirt itself perpetuates this confusion, as KVM is listed alongside QEMU on the main page, giving the impression they are two equivalent components. But detailed documentation page makes it clear - libvirt only talks with QEMU. It can detect KVM and allow user to create “hardware accelerated guests”, but that case is still handled through QEMU.

It’s also worth noting that libvirt supports multiple virtualization drivers across multiple operating systems, including FreeBSD and macOS.

virsh

virsh is a command line interface for libvirt. That’s it, there’s nothing more to it. If it was created today, it might have been called libvirtctl.

Vagrant

Vagrant abstracts virtual machine management across operating systems. It’s similar to libvirt, in the way that it allows application developers to target Vagrant and leave all the details to someone else. And just like libvirt, Vagrant doesn’t do anything on its own, but pushes all the work to tools lower in the stack.

Vagrant primary audience is software developers. It allows to succinctly define multiple virtual machines, and then start and provision them all with a single command. These machines are usually considered disposable - they might be deleted and re-created multiple times a day. If you want to manually modify the virtual machine and keep these changes for longer period of time, Vagrant is probably not a tool for you.

On Linux, Vagrant usually works with libvirt, but may also work with VirtualBox or Xen.

VirtualBox

VirtualBox is full virtualization solution. It consists of kernel driver and user space programs, including one with graphical interface. You can think of VirtualBox as something parallel to all that we’ve discussed so far.

The main reason to use VirtualBox is ease of use. It offers simpler mental model for virtualization - you just install VirtualBox, start VirtualBox UI and create and run virtual machines. While it does require a kernel module, and kernel is notoriously bad at breaking modules that are not in tree, VirtualBox has a lot of tooling and integrations that will ensure that module is built every time you install or update kernel image. On distributions like Ubuntu you don’t have to think about this at all.

Technically speaking, libvirt supports VirtualBox, so you can use libvirt and virsh to manage VirtualBox machines. In practice that seems to be rare.

Xen

Xen is another virtualization stack. You can think about it as something parallel to KVM/QEMU/libvirt and VirtualBox. What sets Xen apart is that instead of running inside your operating system, it runs below your operating system.

Xen manages scheduling, interrupts and timers - that is, it manages who gets access to computer resources, when, and for how long. Xen is the very first thing that starts when you boot your computer.

But Xen is not an operating system. So right after starting, it starts the guest with one. That guest is special, because it provides drivers to hardware and is the only one with a privilege to talk with Xen. Only through that special guest virtual machine you can create other virtual machines, assign them resources etc.

Xen is one of the oldest virtualization solutions, with first release back in 2003. However, it was included in kernel only in 2010. So while many people prefer Xen and think favorably of its architecture, it seems to lost the popularity contest to KVM and QEMU.

Xen provides the tools required to manage guest virtual machines, but you can also manage them through libvirt.

Summary

I thought to put a visual aid in place of summary. Each column represents a possible stack - you generally want to pick one of them. Items with dashed line style are optional - you can use them, but don’t have to. Click to see the bigger image.