Why Infrastructure As A Code Tools Used In Cloud Platforms?

IaC is a kind of methods used to control and describe centers of data processing with data sets for configuration rather than using manual methods of editing configurations on servers or interrelationships of infrastructures. Normally, the declarative method or imperative algorithm is used in writing code for operating with infrastructures.

Infrastructure as code (IaC) is a common set of tools used in cloud computing. The core principle of the IaC is to describe an infrastructure with a code in combination with an ordinary software development procedure. It is the main practice of developers and a component of a continuous software supply. IaC allows DevOps to work as a team promptly, reliably, and on large scales. They can use a single set of methods and tools to develop programs and servicing infrastructure.

From Hardware to Cloudformation tools

Many people probably no longer remember the iron age when we had to buy our own servers and computers. At that time no one had any idea what tools for cloud formation were. Now it already seems crazy when the hardware buying cycle can limit the infrastructure growth. A new server used to be delivered and installed for weeks! The software was available to developers many days after the hardware was installed.

The first cloud computing methods appeared only in the middle of the 2000s. This made it possible to run new instances of virtual machines quickly and brought businesses and developers not only benefits but also problems. First and foremost they had to maintain an increasing number of servers. However, these were still far from IaC tools.

Following that, only some large computers began to be replaced by smaller ones and the area of the infrastructure of an average engineering center began growing and became more cyclical. Ops had to support more and more things. In order to cope with the peak load, it was necessary to make up or down scaling at different times of the day.

To increase efficiency, many pickets had to be created in the morning to achieve the maximum power and many also at night to reduce that power. The whole process had to be managed manually, which became a challenge over time.

All abovementioned was the reason for the creation and introduction of the infrastructure as code tools. This allowed the systematization of the listed above task maximally. IaC solution made the management of the data processing centers and servers very sufficiently with the help of data readable by computers. They became an alternative to physical equipment and tool configuration under human supervision.

Amazon Web Cloud Formation Service (AWS) was the first tool that emerged in 2009. It became one of the best tools for DevOps allowing engineers to create versions of infrastructures as quickly as a normal code can make it as well. And that allows tracing the resulting versions of infrastructures in order, to make environments enough consistent.

Well-known Infrastructure As Code Tools

The TOP IaC tools that become famous recently among developers are:

• Terraform IaC
• Amazon Web Service Cloudformation tools (AWS)
• Azure Resource Manager
• Ansible
• Chef
• Puppet
• Pulumi
• Saltstack
• Google Cloud Deployment Manager
• Vagrant
• Crossplane

Now, we would like to compare all aforementioned infrastructure as code tools to understand what similarities or differences they have concerning the application area, writing method, or languages.

Main Infrastructure As Code Tools Tasks

At the present time, it is hard to imagine the work of major providers and services without a cloud automation tool application. A wide range of IaCs is dedicated to helping IT engineers to solve such challenges as:

• Deployment
• Instrumentation
• Configuration
• Provisioning

Earlier IT specialists set, configured, and updated software for cloud servers manually. Team participants stored and configured data also with the same method. It took much time and required the attraction of additional developers and influenced significantly the increase in expenses.

IaC infrastructure as code became a solution for professionals in addressing such problems as additional expenses for salary payment and solving problems with the scalability.

It is worth being aware that some IaC tools are already set inside the settings of the infrastructure and other kinds of tools manage applications and infrastructure in the environment.

Below, we would like to give a few words about AWS infrastructure as code and its advantages.

AWS Infrastructure As Code Advantages

The IaC is aimed at the provision and management of cloud resources using a template read by people, which can be consumed by machines in an easy way. AWS Cloud formation is considered a reliable solution to the DevOps cloud services, which uses the IaC for Amazon Web Services.

AWS type of cloud formation enables creating a personal account of a user on Amazon Web Services using the description requested by a user. Then, this description is realized upon request. A typical infrastructure as code example includes a fragment of the template, which describes the creation of resources for the Amazon Elastic Computed Web Services using YAML.

Therefore, when we create the code, we indicate the AWS, then ECS and Service gradually as a Type, then put the “Discovery of the service” as the Dependence, also indicate in the properties the “App” as a name, “Production” as the cluster, 200 maximal percent and 75 minimal percent in the deployment configuration and set 5 as the number of counts.

AWS cloud formation tools then take the template and after that becomes responsible for the creation, updating, and removing resources on a user’s Amazon Web Service account depending on the content of this template. If a user wants to add a new resource to his file, the Cloudautomation tool builds this resource in his account. In case this user wishes to update his recourse, the tool can update or replace all current existing resources. If a user wants to delete this resource from his template, it will be vanished or be removed from his account.

Tools IaC provides users many pros:

They are visible:

An IaC template plays a big role as a precise reference on what kinds of resources you have on your account and their indicators. To check settings there is no need to follow the web panel.

They are scalable:

You can write the infrastructure as a code one time and then use it multiple times. It means that you can use just a good quality template as the basis for different services in various areas of the world, which significantly simplifies horizontal scaling.

They are stable:

If a wrong parameter or a wrong resource has been removed from the web panel, you can break everything. IaC tools solve this problem, especially in combination with Git versions for control.

They have transaction ability:

Cloudformation tools can not only create resources in your AWS account, but they also wait until their stabilization during the starting process. IaCs check for a successful initialization and in case of any failure, they can roll carefully the infrastructure back to the previously known good condition.

They are secure:

It can be seen that the provides of IaC again you with a single template to deploy your architecture. As soon as your protected architecture has been created, you can use it many times and you will know that each deployed version can have the same settings.

Conclusions

IaCs are very popular instruments of the new generation introduced at the beginning of the new century to make the process of cloud service formation, deployment, and adjusting of the infrastructure easier using just a code. There is no need to make manual settings, which significantly simplified the tasks of developers and solve a problem with scalability as well. Terraform and its closest analog Pulumi is considered the most common tools used for cloud formation.

Binary Search Tree in Python (BST)

In this review, you will grasp more information about Binary search trees (BST). There will also be instances of BST in Python. Before starting to learn it, you need to familiarize yourself with what a Binary search tree is.

Python binary search tree uses a data structure that lets us keep values in a sorted list. Here each node is variable and this variable is bigger than its left child nodes. It is called binary sort in python.

There exists two reasons why this tree structure Python is called a Binary search tree. Firstly, each tree node has a max amount of child nodes of two (so that is why it is called a binary tree). Secondly, in O(log(n)) time search binary tree Python can be used to find a number (so that is why it is called a search tree).

The qualities that Binary search tree in Python has, and which make them different from regular binary trees:

• Nodes which count as lower than the root node can be found in a node’s left subtree.
• Nodes which count as bigger than the root node can be found in the right subtree of a node.
• The both subtrees must be Binary search trees and possess the aforementioned characteristics.
• There cannot be any duplicate nodes in binary search trees.

A tree has a right subtree with one value smaller than the root is shown to demonstrate that it is not a valid binary search tree program in Python

The binary tree above isn’t a valid  Python binary search tree because the node “10” right subtree contains a value “9” that is smaller than it. The second property of BST Python is violated. But if we change the value “9” to “11”, it will be a Binary Python search tree. So let’s explain binary search tree with example:

Now, what can we do with a binary search python program? So actually binary tree search Python can be used for storing something like data. The plus will be that it will be organized so you can without a doubt insert something, delete, and update. And also these operations will be as fast as lightning. As we said before BST can provide binary tree search complexity that is called Big-O of O(log(n)) when you are searching, updating or deleting data. Actually linear O(n) is slower than log(n) and it needs some time to find elements. And fact says that a lot of productions use binary trees in their databases like MySQL or PostgreSQL to speed them up when they use CRUD operations.

There are basic operations that you can perform on a binary search tree. Here is the binary search tree implementation. Below we will show basic operations that you can use with a binary search tree.

Let’s create a binary tree python. Tree node class python code can be used as a pointer so you can point to a root node that will connect to the child nodes. Here is the binary tree program in python:

Here we will have a value that can function as a key. Also in the line of tree in python code:’value=None’ we can see that if a value will not be created, it will count as None. And in the next two lines, we create two child nodes that equal ‘None’.

Linear search in Python Binary search tree

Binary search algorithm in python depends on the quality of the Binary search treе that all of the left subtrees have values below the root node and all of the right subtrees have values above the root node.

Searching in Python for a value in a binary tree involves comparing the incoming value with the nodes. If this value is below the root node, it means that the value is not in the right subtree, so the search will take place in the left subtree accordingly if this value is above the root node, it means that the search will take place in the right subtree.

So now, let’s try to show steps on how it works. We are finding the number ‘11’:

Here our search method in Python has not found the number eleven, so it transfers to the right subtree to find it:

Our search program in python has not found the number eleven here, so it transfers to the right node again because ‘10’ is a higher number than eleven:

The search program in python has not found again the number ‘11’, and the number ‘12’ is higher than ‘11’ so it transfers to the left subtree:

Finally, the number ‘11’ was found. Well done, binary tree search!

When the binary tree in Python finds our value, it returns. The returned value will spread in every step, so that means that if there will not be, for example, ‘12’, it will find it anyway. In situations when a binary tree can not find the value you need, it returns NULL. Here are the binary search algorithm steps:

Let’s write a program for binary search in a data structure.

So here we see the easy search code in python that returns the boolean value True or False. Binary search python code tree depends on whether the value that we want to find exists or not.

Insert operation in Python Binary search tree

When we want to insert something, for example, a value, we use the same technique as searching because we obey the only rule that in the right subtree there must be larger values and in the left subtree there must be lower values than in our root node.

So the insert technique differs from searching in putting the new node. It looks like that we go through each subtree depending on the value and we put the new node when the left or right subtree is NULL.

Example of binary tree code in python:

So now, let’s try to show steps on how it works. We are going to add the number ‘11’:

Here we can see that ‘11’ is higher than our root ‘7’ so the Python binary search program transfers to the right subtree:

Now again ‘10’ is higher than ‘11’ so we transfer to the right:

Here is the last transfer, because the number ‘11’ is lower than ‘12’ so which means we’re gonna transfer to the left subtree. And we see that the left subtree is NULL so we add ‘11’ here. Hooray!

So we put the node with the number ‘11’, but we did not exit the operation, because we needed to return the value. So here the return node comes in hand. The process of returning a node works when we have NULL and create the new node that is returned and attached to the parent node. If we have not found the position for a new node, we without change come back to the root in reverse order.

That means that we move in reverse order in the tree without changing other connections.

Here we see how in each step we come back to the root without changing any value.

Also, let`s try to create a Python binary search code for this operation:

Here we see that we created a python program for binary search where the given value sets and returns if the node does not own a value. But if our node contains the value, it returns it. Also, we used the rule that if the value is lower than the value that is in our node and we have a left child, we recursively call insert on this child. In the opposite case, if we do not have the left child, we just create it with the value that was given. And all the same, is for the right child, but only if our value is higher.

Delete operation in Python Binary search tree

The operation of deleting the node has three variations:

First BST variation:

Binary search in Python program deletes the node that was located in the end of the tree and does not change any connections. For example, we want to delete the number ‘11’:

Here python binary search algorithm detected the value and deleted it:

Second BST variation:

When we delete the node that has a child node. For example, we need to delete the number ‘12’:

Here we detected the number ‘12’. Next, we copy the value of its child. Our child is number ‘14’. So we copy this value:

We have already copied the child ‘14’.Then we do the same step as in ‘First variation’, we delete the last node:

Third BST variation:

When we have more than one child below the node we are searching for deleting. For instance, we need to delete the number ‘10’ from the tree:

Here we detected the value. Now we must find the successor of the number ‘10’ in the tree. It is ‘11’, so we copy the value:

Then we finally delete the node of successor ‘11’:

Let’s try to make the program for binary search in python with delete operation:

So here we see this huge tree in Python data structure. But do not worry, it is simple. Actually, this operation works recursively, but it returns the new shape of the node that was given after doing the delete. And it helps in giving access to the parent node whose child node was deleted so we can properly set where the data must be in the left node and where it must be in the right node.

Getting minimum and maximum

with Python Binary search tree

Here we see a very straightforward tree class in Python that can be helpful. They transfer the edges of the data structure tree Python so we can find the largest and the smallest values in our storage.

Why would you need binary trees?

Let’s define a python tree node class. Binary search trees Python can provide very fast O(log(n)) operations that were mentioned before. They are useful for binary sorting in Python. It is very simple to understand and very useful. A simple node class in python without any operations needs only a few lines of code to get to work.

What about the cons? Binary search trees in data structure python are slow for force-brute search. If you want to have iterations through each node, you must better use arrays. Also, BST requires more memory than arrays in implementation.

Actually, there are a lot of appliances where the data tree python will be useful. So the easiest solution to the problem of storing indexes and keys in the database will be BST.

Let’s imagine the example when you need to create a main key column in SQL databases. So here you can without a doubt use a binary python class tree where keys are values in the column and you can point to the rows with nodes. In this situation your SQL base will be easy to search by using keys.

For example, you need to create a game where there must be a list of records of users. It is easy to find the nickname of the user by using binary tree search. And there are more apps where class tree python will be useful. Other simple uses for binary trees you can find in Google.

But what should I use? Binary search in python using list or the binary trees? For this question there are a lot of different answers. They are both positive and negative. Firstly, python node tree use the same pointers to track where the node is as linked lists. But node class python is more efficient and faster while you are searching.

What is Getter and Setter in Python?

After using C++ or other languages you can know how to define getters and setters. But if you start learning the first language Python, you can see that they are not so popular. Often people use them for attributes for classes. They help in creating access for private attributes while preserving encapsulation. But in Python you usually use them to uncover attributes partially in public API and use things when you need attributes with useful actions.

Although it is in Pythonic style to use properties, they can anyway have disadvantages. That’s why people start using getters and setters Python more than properties. And also you must know some OOP or in long: object-oriented programming.

After specifying the class in object-oriented programming, usually you use class attributes and instances. And the attributes that you have created are variables that you can use using instances or the class or both.

Firstly, let’s try to create a class in Python.

How to Create Class

If you need to create the class first you need to use the class name and variables. Here is an example:

Here we have created the class with the name Example. Let’s create objects from this class:

So here the object was created which is called Example1 and it is from the Example class.

That all means that when we printed Example1.first, we had got the value that was stored in a variable in the class that we had created. This calling and these variables are our attributes of the class.

Python Class Attributes

So the basic getter definition of attributes in class is that variables in class can be inherited by objects in the class. The next examples will show how it looks in code, but the only thing we must say is that those attributes are written outside the function __init__().

Let’s see the example:

So here we created the variable called ”office” in the class called Example. Then we used ”init” to create two more variables that are “name” and “position”. “Self” parameter inside init helped us to initialize them.

Python Setter Getter Strategies

So what are a getter and setter? Subsequently, the nation of the objects is held in attributes. In case you want to get admission to this kingdom, you need to have an entry to these attributes. So to get entry to those attributes you can use techniques or access them without delay.

After revealing them to different users, these attributes become part of the elegance’s API. So public attributes imply that other customers can have entry to the attributes in their snippets of codes.

Whilst you are in this example, languages like C++ want to use some techniques to address attributes in lessons. And as you can determine, they may be setters and getters.

Strategies of getter and setter Python have become properly appreciated in programming languages. Human beings use them for item-oriented programming, so surely there are a lot of human beings who’ve already heard about them.

So what are getters setters? If we need to talk about the usage of easy definitions, we are able to say that getter is the method that allows you to have an opportunity to have access to the values in a characteristic. And setter is the method that helps you to change or insert the cost inside the attribute.

Our everyday techniques submit that attrs which are public are operated when you have decided that nobody from builders will ever contact them. While you need to change the inner implementation of the attribute, you will use setter getter Python methods. To enforce the setter and getter it’s required to make the attributes non-public and write each characteristic with setter and getter. For example, say that you want to jot down a Room magnificence with width, length, and height attributes. Permit’s use getters and setters:

For instance, say that you need to jot down a python class getter setter with width, length, and height attributes. Let’s use getters and setters:

Here we can see that the constructor of our class uses three arguments that are width, height, and length. And we saved them in “._width”, “._length”, “._height” non-public attributes.

Often, you can use getter Python methods for returning the focused value of an attribute. And setter methods you use to make a new value and change it in this attribute.

Also, when you are used to programming C++ you can see that Python does not have private, protected, or public modifiers. Python has only public or non-public.

When you want to mark the attribute as non-public, you can use Python in-built syntax that is written before the name. It looks like the underscore “_”. This is only syntax, so it does not ruin all your plans of using dot notation like “object._attribute”. Dot notation is used often in “self.name” or anything else.

Also to use your code and make it work you can use “import”:

Let’s explain what we have done here. As we know the Pythonic way to add behavior for attributes is to make them a property. Properties percent together strategies for getting, setting, deleting, and documenting the underlying data. In short, properties we can call as attributes that have additional behavior.

Actually, it’s not a problem to use properties in the same way as stock attributes. Whilst you get entry to a property, its attached getter method is robotically called. And whilst you mutate the property, its setter technique is called.

The room gives us the opportunity to make instances so we can make changes to the associated height, width, and length.

Features of Setter and Getter

Using a setter and getter only for accessing or changing the values is not the only way. When you want to save all values in uppercase you can also use them. Well, you may use it by attribute ”.text” but you can’t. So if you want to use it, it seems that you have come from Java and C++ because they do not support constructs that are like properties. So that’s the issue why you can not use your attributes here as public. You should use setter and getter and provide implementation without public.

Python properties have plenty of capacity use cases. For instance, we can make properties to form read-write, read-only, and write-only attributes. Properties will let you delete and record the underlying attributes and also more interesting is that you can make them act like controlled attributes with attached behavior without converting the way you figure with them.

Because of Python possibilities when you use properties, people generally tend to use them. So if you need to add some new processing with attributes you can use them. But when you want to change all your attributes, you will waste a lot of time. And even it can overload your code and make it less efficient.

Descriptors in Python

Now after we get to know the base of topics about getter setter Python, also about properties that are written in a Pythonic way and also dealing with problems with how to add more functionality, we can learn other tools and techniques that we may use when we want to change getter and setter in Python on other methods.

So a descriptor is a feature in Python that helps you in creating attributes with behaviors. If you want to create a descriptor, you must use the specified protocol. In other words, it is a syntax that helps you. They are “.__get__()” and “.__set__()” methods.

Factual descriptors are very similar to properties in Python. Or in other words, Python class property setter is a type of descriptor that is more powerful than an in-built property. To clarify a case of how to use descriptors to create attributes with useful behavior, let’s use the example before:

Here we have used “@property” and “@.setter” which allowed us to change the height, width, and length for each room. In general, in case you locate yourself disordering your instructions with comparable property definitions, you ought to not forget to use a descriptor instead.

Methods __setattr__() and __getattr__()

Except traditional getters Python and setters you can use .__setattr__() and .__getattr__() special methods that help you to manage the attributes. Let’s make a class named Position that will change the type of x and y into float:

Here we can see that __init__ takes our position coordinates “.__getattr__()” method returns the firstname that is the coordinate. To make this done we used “.__dict__”. Take note that Python use  “.__getattr__()” automatically when you try to access an attribute with dot notation. What about the “.__setattr__()” method is that adds or changes the attributes. For example we used it for operating with each positional coordinate and converting it into float type. And also as with “.__getattr__()”, “.__setattr__()” runs automatically when you use assignment operation.

Here in our class we have to get or set accessors expected with our positions as regular attributes.

So here we used a very interesting example and showed how it works. But actually, you will not use similar. The tools that we have used help you to make validations and transformations with access to our attribute.

And in short, “.__getattr__()’’ and “.__setattr__()” are like an implementation that is like a Python getter and setter pattern. But actually, they work the same as regular methods getters and setters.

What Does Inheritance Look Like?

If you want to inherit some classes in Python you will meet up with one problem. For instance, you want to change the Python property method in a subclass. And if you try to Google it, you do not find any safe ways to do this. In short, it is hard to overturn the regular getter and wait that this property getter and setter will have the same functions as in the parent class. So that means that when you try to change the method in the inherited class you change the whole property including its other methods as a setter. Let’s see the example:

Here we can see that in class “Room” we changed the @property. This case shows us that we changed the whole .title property, including example setter too. Let’s add some functions:

Here we can see how Python can not find the setter of the inherited class. This is because we have changed it before and we are now trying to use a false setter, but he was not inherited. So, what is the solution to this situation?

Let’s use regular get set in Python methods:

Here we see that no issues were found. Python has an in-built independent get and set in Python methods. Room is a subclass of Goods and it overrides the getter. We can see that this line of code does not change the functionality of setter and successfully it was inherited from the parent class.

Let’s make an example with get set Python and functions:

Now our class can work properly. The changed getter method in the inherited class is functional. And the setter method also works well. Well done!

What is Better? Set and Get in Python or Properties?

If you start coding, you know the answer. There are some situations where methods of setter and getter are preferred over properties, but also there are other situations where Pythonic properties play better.

Let’s try to distribute by points why the get and set in Python are dealing with better, we can do this. When we talk about transformations, the methods setter and getter run better on attribute Python accessors and mutation Python. Also, they use inheritance and this is very good. Getter and setter can raise Python exception attributes when they are accessing the attributes. And finally, their integrations are being facilitated in heterogeneous enhancement teams.

When we want to talk about properties in Python, the problem can be seen when you are making a huge amount of lines of code. In other words, take note that you should avoid making slow Python mutations with Python properties. When somebody repeatedly tries to make access to a given attribute, users can suffer from waiting because it can take a long time, and the efficiency of your code can suffer a lot. So when your plans are to use Pythonic properties to change values in attributes, be sure that your mutations are fast, because if you will not do that and use slow get set accessors, you will change your code to use a simple setter and getter.

Another situation is that getter and setter methods are more flexible than you would think. Let’s make an example with the name Goods and its expiration date attribute:

Our date was a constant value and we made it read-only, so it means non-public. Also because of the fact that human error exists, so in life, you will face a lot of cases where somebody writes letters instead of numbers in the date section. Here we provided regular getter and setter methods for the “.expiration_date” attribute. Here our setter method has taken the “force” argument so that we can be ready for mistakes made by people. Also factually, traditional setters do not use more than one argument in code, so we can say that our code is wrong.

Here we have written the code without the “force” argument and got a message. Let’s make it work properly:

In the first example we tried to change the expiration date by using .set_expiration_date. But the problem was that we did not use force argument – we have not changed its setting to True. That is why we have got an AttributeError. So in the next example we replaced its setting and said to Python that we are sure that there are no mistakes. So it worked.

As we said before, the Python property setter getter does not accept more than one argument in the setter, because it accepts only that variable that you want to change or make.

Usually, you won’t accept a work message like “objects.attributes = value” to heighten an exception. Especially, you may accept messages that heighten exceptions in response to mistakes. Here regular Python getter setter methods were extra specific than properties.

Let`s look like on line: “phone.number = ‘360’”. Undoubtedly, it looks like something that will work in a solid way and will not raise an exception. It is an ordinary attribute. But if we use a setter like here: “phone.set_number(‘360’)”, we can see that it can heighten a message about a value error. “inserted phone number is not a phone number.”. In this case, the setter method is a better way to solve this. It simply expresses the code’s beneficial behavior.

Generally, keep away from raising exceptions from your Python properties. You can do that only if you want to use a property to offer attributes that will be only for reading. If you ever need to heighten messages on access or mutation, then you absolutely shouldn’t forget to use getter and setter methods in place of properties.

So in short, using a regular setter and getter can help in solving troubles when they make their code.

In the end, we can wrap up that we know better what is setter and getter methods. But they are not so popular because Python has in-built properties that allow you to mutate and get access to the attributes. But genuinely, as we have before in some situations they have drawbacks that you can fix only by changing properties in regular Python get and set methods.

How to use HTTPS with Azure and install SSL

HTTPS (Hypertext Transfer Protocol Secure) is a secure version of the HTTP protocol used for transmitting data over the internet. It encrypts communication between a website and its visitors to protect sensitive data from being intercepted. In today’s internet landscape, it is becoming increasingly important for website owners to use HTTPS to secure their websites and protect their visitors’ data.

HTTPS connection with Azure

One way to implement HTTPS on a website is through Azure, a cloud computing platform and infrastructure created by Microsoft. Azure offers a range of tools and services that can be used to secure and manage a website, including options for implementing HTTPS.

There are several ways to enable HTTPS on a website hosted on Azure. One option is to use Azure App Service, which is a platform-as-a-service (PaaS) offering that allows developers to build and host web applications. With Azure App Service, website owners can enable HTTPS by simply turning on the “HTTPS Only” option in the App Service configuration.

Instructions for setting up HTTPS with Azure App Service

To enable HTTPS with Azure App Service, follow these steps:

1. Navigate to the App Service page in the Azure portal.
2. Select the app for which you want to enable HTTPS.
3. In the left-hand menu, click on “SSL certificates.”
4. Click on the “HTTPS Only” option.
5. Click on the “Save” button to apply the changes.

Once HTTPS is enabled for an app, Azure App Service will automatically provision and bind a certificate to the app. This certificate will be valid for one year and will be renewed automatically by Azure App Service.

HTTPS with Azure CDN and SSL certificate

Another option for enabling HTTPS on a website hosted on Azure is to use Azure CDN (Content Delivery Network), which is a global network of edge nodes that helps to deliver content faster and more reliably to users. With Azure CDN, website owners can enable HTTPS by creating a custom domain and purchasing a SSL/TLS certificate. The certificate can then be configured to work with Azure CDN by following the steps in the Azure documentation.

Instructions for setting up HTTPS with Azure CDN

To enable HTTPS with Azure CDN, follow these steps:

1. Navigate to the Azure CDN page in the Azure portal.
2. Select the CDN profile for which you want to enable HTTPS.
3. In the left-hand menu, click on “Custom domains.”
4. Click on the “Add custom domain” button.
5. Enter your custom domain name and select the desired protocol (HTTP or HTTPS).
6. Click on the “Validate” button to verify that you own the domain.
7. Once the domain is validated, click on the “Add” button to add the custom domain to your CDN profile.
8. Click on the “Add binding” button to bind the custom domain to your CDN endpoint.
9. In the “Add binding” window, select the custom domain and the desired protocol (HTTP or HTTPS).
10. Click on the “Add” button to add the binding.

After the custom domain is added and bound to the CDN endpoint, website owners can purchase a SSL/TLS certificate and configure it to work with their custom domain. This can be done through Azure or through a third-party provider. Once the certificate is configured, website owners can enable HTTPS by turning on the “HTTPS Only” option in the CDN endpoint configuration.

In addition to these options, Azure also offers other tools and services that can be used to secure a website and enable HTTPS, such as Azure Traffic Manager and Azure Key Vault. These tools can be used to manage and secure traffic to a website.

Installing SSL and Azure

After the custom domain is added and bound to the CDN endpoint, website owners can purchase a SSL/TLS certificate and configure it to work with their custom domain. This can be done through Azure or through a third-party provider. Once the certificate is configured, website owners can enable HTTPS by turning on the “HTTPS Only” option in the CDN endpoint configuration.

In addition to these options, Azure also offers other tools and services that can be used to secure a website and enable HTTPS, such as Azure Traffic Manager and Azure Key Vault. These tools can be used to manage and secure traffic to a website.

Conclusion

This article will show you how to enable HTTPS in Azure web applications using Azure App Service. We also discussed some best practices and its up to you to choose which one should you use.

Introduction to System.Threading.Channels

Problems of producer/consumer are all around us, in every sphere of our lives. A fast food line cook cuts tomatoes and then passes them to another cook to make a hamburger. The register worker will fulfill your order. You happily eat the burger. You may see postal drivers delivering mail along their routes. If you’re home at night, you might check your mailbox later to make sure. A flight attendant unloads suitcases from a plane’s cargo hold and places them on a conveyor belt. Then another employee transfers them to a van, which then drives them to another conveyor that will transport them to you. A happy couple is getting ready to send their invites. One partner addresses an envelope, and the other hand it to the other.

Software developers often see everyday happenings in our software. “Producer/consumer” problems are no exception. Anyone who has piped together commands from a command line has used producer/consumer. The stdout of one program is fed as the stdin. Anybody who has launched multiple workers to calculate discrete values, or download data from multiple sources has used producer/consumer. A consumer aggregates the results and displays them for further processing. Anybody who has tried to parallelize an entire pipeline has explicitly used producer/consumer. So on.

These scenarios, in real life or in software, all have one thing in common: they all use a vehicle to transfer the results from the producer back to the consumer. Fast food worker places the burgers on a stand, which the register worker pulls out to fill the customer’s bag. The postal worker puts mail in a mailbox. To transfer the materials between the engaged couple, the postal worker places mail into a mailbox. A hand-off in software requires some data structure to facilitate the transaction. This can be used by the producer to transfer results and possibly buffer more. It also allows the consumer to be notified when one or more results have been made available. Enter System.Threading.Channels.

What is a Channel?

Sometimes, it is easier to grasp technology when I implement it myself. This allows me to learn about the problems that implementers of the technology might have to face, the trade-offs they had to make, as well as the best way to use the functionality. To that end, let’s start learning about System.Threading.Channels by implementing a “channel” from scratch.

A channel is simply a data format that stores data that consumers can retrieve. It also allows for safe synchronization and appropriate notifications in both directions. There are many design options available. Is it possible for a channel to store an unlimited number of items? What should you do if it is full? Performance is critical. Is it possible to reduce synchronization? Are there any assumptions that allow us to make about the number of consumers and producers allowed simultaneously? To quickly create a channel, we will assume that there is no need to set any specific limit and that overheads are not an issue. A simple API will also be created.

We need to know our type first. To that we will add some simple methods

Our Write Method gives us a way to create data into the channel using a method that we can use. ReadAsync Method gives us a way to consume it. Because our channel is unlimited, data will be produced into it successfully and synchronously. This is just like calling. Add On a Liste We have made it non-asynchronous, void-returning and therefore, Our method of consuming is, however, ReadAsyncThis is because data may not be available at the moment we need it. We’ll have to wait until it arrives if there is no data available. While we don’t care about performance in the initial design, we do not want excessive overheads. Our expectations are that we will be reading often, and to read data when it is available, so our overheads won’t be excessive. ReadAsync Method returns a ValueTask Instead of a Task So that it can be made allocation-free once it is completed synchronously.

We now need to implement the two methods. We’ll start by adding two fields to our type. One to store the product and one to coordinate between producers and consumers.

We use a ConcurrentQueue The data can be stored, which eliminates the need to lock the buffering data structure. ConcurrentQueue It is thread-safe enough for all producers to have simultaneous access as well as any number of consumers. We use a SempahoreSlim To coordinate between consumers and producers, and to notify consumers who might be waiting for additional information to arrive.

The Write procedure is very simple. It simply needs to “release” the SemaphoreSlim data and store it in the queue.

Our ReadAsync process is nearly as easy. It will wait for the data to become available, then it will take it out.

We note that no code can manipulate the queue or the semaphore. Once we have successfully waited on it, we know that the queue will have data. This is why we can simply assert that the TryDequeue method returned one. These assumptions could change, so this implementation would have to be more complex.

That’s all. We now have our basic channel. This implementation works well if you only need the core features described here. However, there are more important requirements, in terms of performance as well as APIs that enable more scenarios.

Now that we understand the basics of what a channel provides, we can switch to looking at the actual System.Threading.Channel APIs.

Introducing System.Threading.Channels

The core abstractions exposed from the System.Threading.Channels library are a writer:

A reader

We are familiar with most of the API surface area, having just completed our channel design and implementation. ChannelWriter Provides a TryWrite This method is very similar to the Write method, but it’s abstracted and a Try method that returns an answer. Boolean To account for the possibility that some implementations might be limited in the number of items they can store and if there was a channel full enough that writing could not occur simultaneously, TryWrite Would need to return False To indicate that writing was unsuccessful. However, ChannelWriter Also, the WriteAsync Method; In such cases where the channel is full, writing would be required to wait (often called “back pressure”). WriteAsync Can be used with the producer waiting for the result WriteAsync You will only be allowed to continue if there is enough room.

There are times when code might not want to immediately produce a result. For example, if the value is too expensive or if it represents a costly resource (e.g. a large object that takes up much of the memory or has many open files), the producer may delay producing the value until it is certain it will succeed immediately. WaitToWriteAsync is available for this and other situations. Producers can wait for WaitToWriteAsync to return true and then decide to produce a value that TryWrites, or WriteAsyncs for the channel.

WriteAsync can be virtual. While some implementations might choose to offer a more optimized implementation than others, with abstract TryWrite or WaitToWriteAsync the base type can offer a reasonable implementation that is slightly less complex than these:

It also shows how WaitToWriteAsync And TryWrite This highlights some additional interesting facts. The while loop is used because channels can be used simultaneously by both producers and consumers. Two threads can be told via the while loop “yes, there is space”, if a channel has a limit on the number of items it can store. WaitToWriteAsync However, it is possible for one of them to lose the race condition and to have TryWrite Return False This is why it is necessary to keep trying again and again. This is another example of why WaitToWriteAsync Returns a ValueTask Instead of only ValueTask These include situations that exceed a buffer. TryWrite Could be back False. The notion of completion is supported by channels. A producer can signal to consumers that no more items will be produced. This allows them to gracefully end their attempts to consume. This is accomplished via the Complete Or TryComplete Methods that were previously demonstrated ChannelWriter ( Complete It is only implemented to call TryComplete Throw it if it comes back False ). However, if one producer considers the channel complete, all producers should be aware that they are not welcome to post into the channel. TryWrite Returns False, WaitToWriteAsync Also, returns False And WriteAsync throws a ChannelCompletedException.

The majority of members are ChannelReader They are also likely to be self-explanatory. TryRead It will attempt to extract the next element from each channel synchronously, and return whether it succeeded or not. ReadAsync It will also extract the next element in the channel. However, if the element is not synchronized, it will return a task. And WaitToReadAsync Returns a ValueTask This serves as an indicator that the element is available for consumption. Similar to ChannelWriter ‘s WriteAsync, ReadAsync Virtual, the base implementation is possible in terms of the abstract TryRead And WaitToReadAsync This is not the exact implementation of the base class, but it is close.

There are many ways to consume food from one source. ChannelReader. One way to view a channel as an endless stream of value is to simply consume via infinity. ReadAsync :

However, the stream of values may not be infinite, and the channel will be marked complete at some point. After consumers have cleared the channel of all data, subsequent attempts to ReadAsync will throw. TryRead and WaitToReadAsync will return false. A nested loop is a common way to consume alcohol.

Although the inner “while” could be a simple “if”, the tight inner loop allows a cost-conscious developer avoid small overheads. WaitToReadAsync If an item is available, TryRead will consume it. This is actually the exact method used by the ReadAllAsync method. ReadAllAsync .NET Core 3.0 introduced this feature, and it returns an IAsyncEnumerable. It allows all data to be read via a channel that uses familiar language constructs.

The base implementation of the virtual method uses the same pattern nested loop pattern as the WaitToReadAsync or TryRead.

The last member of ChannelReader Is Completion. This just returns a Task This will make the channel complete once the channel reader has been completed.

Built-In Channel Implementations

Okay, we’re able to read from readers and write to them… but where can we find those readers and writers?

The Channel Type exposes a Writer property, and a Reader property which returns a ChannelWriter And a ChannelReader, respectively:

This base abstract class can be used for niche use cases, where a channel may transform written data into another type for consumption. However, the vast majority of use cases have TWrite as well as TRead being identical. That is why most use occurs via the derived Channel type which is basically:

This non-generic channel type allows for factories to be used in multiple implementations. Channel:

The CreateUnbounded Method creates a channel that allows for unlimited storage. However, it is possible to store more than one item at a time. Liste It is very similar to the Channel-like type we used at the beginning. It’s TryWrite Will always return True Both it’s WriteAsync Its. WaitToWriteAsync Will always be completed synchronously.

The CreateBounded method, on the other hand, creates a channel with a limit that is explicitly maintained by the implementation. Just like CreateUnbounded before reaching this limit, TryWrite will return true, and WriteAsync or WaitToWriteAsync both will finish synchronously. However, TryWrite won’t return false. WriteAsync or WaitToWriteAsync both will finish asynchronously.

They will only complete their tasks when there is enough space, or if another producer signals that the channel has finished. It should be noted that these APIs accept a CancellationToken and can be interrupted by cancellation.

Both CreateUnbounded and CreateBounded have overloads that accept a ChannelOptions-derived type. The base channelOptions allows for options to control the behavior of any channel. It exposes SingleWriter, SingleReader, and other properties that allow creators to specify constraints they are willing to accept. A creator can set SingleWriter to true so that only one producer has access to the writer, and SingleReader to true so that only one consumer has access to the reader at any given time. Factory methods can then specialize the implementation created by the creator, optimizing it according to the provided options.

For example, CreateUnbounded sets SingleWriter to true to indicate that only one producer will be accessing the writer at a time, and singleReader to true to indicate that only one consumer will have access to the reader at a time. This implementation not only avoids locks while reading but also avoids interlocked operations during reading, greatly reducing overheads The base ChannelOptions also exposes an AllowSynchronousContinuations property. This property is similar to SingleReader or SingleWriter.

A creator can set it to true to get some optimizations. These optimizations have significant implications on how code is produced and consumed. Specifically, AllowSynchronousContinuations in a sense allows a producer to temporarily become a consumer.

Let’s suppose there is no data in a channel. A consumer calls ReadAsync. The consumer hooks up a callback that will be invoked when data has been written to the channel by waiting for the task to be returned from . This callback will by default be invoked asynchronously. The producer writes the data to the channel, then queues the invocation of the callback. This allows the producer and consumer to simultaneously go about their business while the consumer is being processed by another thread. In some cases, however, it might be beneficial for performance for the producer writing the data and then processing the callback itself, e.g.

It invokes the callback by itself, rather than TryWrite waiting for the invocation. This can significantly cut down on overheads, but also requires great understanding of the environment, as, for example, if you were holdling a lock while calling TryWrite, with AllowSynchronousContinuations set to true, you might end up invoking the callback while holding your lock, which (depending on what the callback tried to do) could end up observing some broken invariants your lock was trying to maintain.

The BoundedChannelOptions passed to CreateBounded layers on additional options specific to bounding. In addition to the maximum capacity supported by the channel, it also exposes a BoundedChannelFullMode enum that indicates the behavior writes should experience when the channel is full:

public enum BoundedChannelFullMode  Wait, DropNewest, DropOldest, DropWrite

Wait is the default mode. This has the semantics discussed above: TryWrite on full channels returns false. WriteAsync returns a task that can only be completed when there is enough space available. WaitToWriteAsync also returns a task that can only be completed when space becomes available. Instead, the other three modes allow writes to complete synchronously and drop an element if there is not enough space. DropOldest will remove an “oldest” item from the queue (wall-clock time), meaning that the next element to be dequeued would be removed by a consumer. DropNewest on the other hand will remove the newest item. This is the element that was written most recently to the channel. DropWrite drops any item that is currently being written. TryWrite will return true, but the item will be immediately removed.

Performance

This is the API perspective. The library’s abstractions are very simple which is a big part of its power. A few simple implementations and abstracts should be sufficient to meet 99.9% of developers’ use cases. Although the library’s surface might seem simple, it is actually quite complex in its implementation. The implementation is complex, with a lot of focus on high throughput and simple consumption patterns. For example, the implementation takes great care to minimize allocations. Many of the surface area methods have a return. ValueTask And ValueTask Instead of Task And Task. We can use, as we have seen in the trivial example at the beginning of this article. ValueTask to avoid allocations when methods complete synchronously, but the System.Threading.Channels implementation also takes advantage of the advanced IValueTaskSource And IValueTaskSource Interfaces can be used to prevent allocations, even if the different methods are completed synchronously and return tasks.

This is a benchmark:

This is a test of the throughput and memory allocation for an unbounded channel. When writing an element, and then reading it out 10 million times, this means that an element will always have the ability to be read. The following results were obtained on my machine. (The 72 bytes in the Allocated column are for WriteThenRead’s single Task).

Let’s make it a little more simple: first issue the read, then write the element that will fulfill it. This will ensure that reads are completed asynchronously, since the data required to complete them is not available.

This is what I got after running it for 10,000,000 writes and readings.

So, there’s some more overhead when every read completes asynchronously, but even here we see zero allocations for the 10 million asynchronously-completing reads (again, the 72 bytes shown in the Allocated column is for the Task returned from ReadThenWrite)!

Combinators

The majority of channels can be consumed using one of these methods. However, it is possible to execute different types of operations across channels in order to achieve a specific goal. Let’s take, for example, that I want to wait until the first element arrives from one of two readers. I could write this:

We’re only calling here WaitToReadAsync Both channels and return the reader for the one that is completed first. This example has one interesting thing to notice. ChannelReader Many similarities exist between them. IEnumerator This example is not a good idea. IEnumerator (or IAsyncEnumerator ). IAsyncEnumerator Exposes a MoveNextAsync Method, which moves your cursor to the next item. This is where you can see it. Current. We could implement this if we tried. WhenAny On top of IAsyncEnumerator Invoke the following: MoveNextAsync Each. We could move each item ahead by doing this. We could end up with missing items if we used the looped method, as we might have advanced an enumerator we didn’t return to.

Relationship to the rest.NET Core

System.Threading.Channels is part of the .NET Core shared framework, meaning a .NET Core app can start using it without installing anything additional. You can also download it as a NuGet package. However, the separate implementation does not have the same optimizations as the built-in one. This is due to the fact that the built-in implementation can take advantage of additional library and runtime support in.NET Core.

It is also used in a number of other systems within.NET. ASP.NET, for example, uses channels in SignalR and its Libuv-based Kestrel transportation. The upcoming QUIC implementation for.NET 5 will also use channels.

If you squint, the System.Threading.Channels library also looks a bit similar to the System.Threading.Tasks.Dataflow library that’s been available with .NET for years. The dataflow library can be thought of as a superset or the channels library. In particular, it includes the BufferBlock The dataflow library provides much of the same functionality as type. The dataflow library is focused on a different programming paradigm. It links blocks together so that data flows from one to another. Advanced functionality is also included, such as a two-phase commit that allows multiple blocks to be linked to the same consumer and consumers to be able to atomically withdraw from multiple blocks without deadlocking. These mechanisms are more complex and more costly, but they are more powerful. You can see this by simply writing the same benchmark. BufferBlock As we did for Channels.

This is in no way meant to suggest that the System.Threading.Tasks.Dataflow library shouldn’t be used. It allows developers to communicate succinctly large numbers of concepts and can show very high performance when used to solve the best problems. However, when all one needs is a hand-off data structure between one or more producers and one or more consumers you’ve manually implemented, System.Threading.Channels is a much simpler, leaner bet.

At this point, hopefully, you have a better grasp of the System.Threading.Channels enough channels in the library to see how they might fit in and enhance your applications.

How to Read, Write and Parse CSV in Python

A popular format that exchanges details through text files is the CSV format. It is easy to use CSV because you don’t have to build your own CSV parser. Python contains several suitable libraries you can use. One of them is a Python csv module that will work on many occasions. Also, there is a pandas library that has CSV parsing capabilities. This library will be useful if you need numerical analysis or your work requires lots of data.

What is CSV

Comma Separated Values file is a type of text file that allows data to be saved in a tabular format. In a CSV file in Python information is separated by commas, which is given away by his name. CSV file is used to move information between the programs that aren’t able to exchange data. Two programs can exchange data only if they both can open a CSV file. A sample CSV file for Python:

A typical CSV file in python example:

The divider symbol is named a “delimiter”. In a Python CSV file, there can be anything as the separator. Here are other delimiters that are less popular: colon (:), semi-colon (;) and the tab (\t).

Parsing CSV Files by Using Python csv Library

To parse CSV Python means read the data from CSV. The csv library allows you to read from and write to CSV Python. This csv package Python you can describe the CSV formats understood by other applications or even define your own CSV format. The csv module contains reader and Python writer objects that help you manipulate CSV Python. Also, you can use Python DictReader and DictWriter classes to read and write data in dictionary form. Let’s see Python working with csv library.

How to Read CSV Files by Using the Python csv Package

As already mentioned, the CSV reader Python object is used for reading from a CSV file. Python open() CSV function opens the CSV program as a text. Here is an example:

Here is a code to read this file:

Python Read CSV Files Into a Dictionary

Each string returned by the reader is a list of string elements containing the data that is found by removing the delimiters. The first row returned contains the column names that are treated in a special way.

Instead of creating a list of string elements, you can read the CSV data into a dictionary. Our input file is the same as last time:

Here is Python csv DictReader example:

In the code above we used python csv open function. Keys for the dictionary are contained in the first line of the CSV file Python. If you don’t have them, you should determine your own keys by setting the fieldnames (optional parameter).

Python CSV Reader Parameters

In the csv library, the reader object can deal with different types of CSV files in Python by defining additional parameters. For example:

• delimiter identifies the character that is used to separate each field. The comma is a default separator.
• quotechar identifies the character that is used to surround areas that contain a delimiter character. A double quote is a default quotechar.
• escapechar identifies the character that is used to escape the delimiter character, when there are no quotes. The default escapechar is no escape character. The following examples will help to better understand these parameters.

There are two fields name and birthday in this Python 3 CSV file, which are separated by commas. The data in the birthday field also contains a comma to signify the year. So we can’t use the default separator (comma).

• Write the data in quotes

In quoted strings, the nature of your chosen delimiter is ignored. If you want to use different characters for quoting the quotechar optional parameter will help you with this.

• Use a different delimiter

You can use the delimiter optional parameter to identify the new delimiter. In this case, the comma can be used in the data.

• Escape the delimiter characters

To use escape characters, you must identify them using the escapechar optional parameter.

How to Write CSV Files by Using the CSV Module Python

Python CSV writer object and the .write_row() method can help you to write data to a CSV file. Here we also unfold the file with open csv python function.

The quotechar optional parameter defines which symbol to use in quote fields. No difference in using quoting or not, however, it is decided by the quoting parameter:

• csv.QUOTE_MINIMAL

On this occasion .writerow() will quote fields which contain the delimiter or the quotechar. This quoting is a default case.

• csv.QUOTE_NONE

On this occasion .writerow() won’t quote fields. Instead of this .writerow() function will escape delimiters. There you also must provide a value for the escapechar optional parameter.

• csv.QUOTE_NONNUMERIC

On this occasion .writerow() will quote fields which contain text data and convert numeric fields to the float data type.

• csv.QUOTE_ALL

On this occasion .writerow() will quote all fields.

So Python save to CSV file following data:

Python write CSV File Into a Dictionary

You should be able to write data out from a dictionary. For this you need to use  csv.DictWriter and the following two methods .writeheader() (is used to write a row of column field names to the CSV program) and .writerow() (is used to write a single row of data into the file).

Here is Python csv DictWriter example:

This code makes a file save as CSV Python:

CSV pandas

One more open-source Python library that can read and write CSV files is pandas. People often use pandas if their CSV files are huge to analyze.

pandas provides a lot of high-performance tools that can analyze data and also pandas provides easy-to-use data structures.

You can implement the pandas library in PyCharm as all other libraries, but the best option to work with it is using Anaconda’s Jupyter notebooks.

What is Anaconda? The in-built Python library has a lot of methods but there are a lot of other methods that are more useful and not included in it. That is where Anaconda distribution is useful. It is a free, open-source platform that allows you to execute code.

We need to talk about Jupyter notebook more. This application is web based so it allows you to use all the libraries that are included in Anaconda in your default web browser. Also there are useful features like: titles, comments and another text that you can write beyond the code.

So why do we need a Jupyter notebook? Because pandas is available in it as an in-built library and it works extremely well in Jupyter Notebook. You can share code, analyze results, and see graphs made in it by CSV. And also it is very easy to use it here.

Read CSV Files by pandas

Firstly, to try working with pandas we need to create a CSV file to work with. So let’s create one:

The example of code will look like that:

We have operated with ‘import’ to show that we use the panda’s library and also we used ‘as’ for making it shorter in code. Anyway, that means that only 3 lines of code are needed to make our CSV file readable for us. ‘read_csv()’ operation reads. So here pandas have read the first line of our CSV file and used them in the output correctly.

The only note is that our ‘Fire date’ is the string type. We can check all our types by using this code:

parse_dates=[]

So we need to fix this. Let’s try to fix this issue. The best way is to use ‘parse_dates’ the optional parameter that convinces pandas to turn things into datetime types. This parameter receives only a list of columns(or multiple columns).

Also if you use Jupyter notebook you can see this message after using ‘parse_dates’:

So here Anaconda helps us that we can use different formats for editing our dataframe.

After our example let`s check the data type of our ‘Fire date’:

So here Anaconda helps us that we can use different formats for editing our dataframe.

After our example let`s check the data type of our ‘Fire date’:

And here we see that our column is formatted properly. Hooray!

index_col=””

Also, we see here that we have numbers on the left. But if we don’t need them, we can remove them.

The only way to do that is using an optional parameter called ‘index_col’. Index_col allows us to set which cols will be used as the index in our dataframe. Index_col receives the column name or its index that will be used as index column. 

If your CSV files do not contain column names on the first line, there is a way to provide it by the ‘names’ optional parameter. It is also used when you want to replace the column names specified in the first line. In this case, you must also use ‘pandas.read_csv()’ to ignore existing column names with the optional ‘header=0’ parameter.
Let’s take this CSV file:

Here we have seen, we have the wrong first line. We can change it as in next example:

But take note that if we change our first line, we must take care of our ‘index_col’ and ‘parse_dates’ too!

How to Write CSV Python by pandas

So after getting knowed how to read CSV files by pandas, here you can Python write to CSV. It is much easier now. Let’s see the example:

 Here we got a new function ‘to_csv’. All it does is that it creates the new file called as you named in quotes and saves the data to CSV python. And the data2.csv will be:

We got a copy of the file ‘data.csv’. So if you need to use ‘print’ as the writer for the new file you can use ‘to_csv’ for this.

How to Save CSV Python by pandas

To save CSV file Python to a folder you need to import one more package os.path.

How to Convert Python CSV to List by pandas

To convert a CSV file into a list you need to read the file using read_csv and convert it into a dataframe. Then you should convert each row into a list. Here is an example: