What exactly can you do with Python? Here are Python’s 3 main applications.

What exactly can you do with Python? Here are Python’s 3 main applications.

What exactly can you do with Python? Here are Python's 3 main applications. Web Development. Data Science — including machine learning, data analysis, and data visualization. Scripting.

What exactly can you do with Python? Here are Python's 3 main applications. Web Development. Data Science — including machine learning, data analysis, and data visualization. Scripting.

If you’re thinking of learning Python — or if you recently started learning it — you may be asking yourself:

“What exactly can I use Python for?”

Well that’s a tricky question to answer, because there are so many applications for Python.

But over time, I have observed that there are 3 main popular applications for Python:

Let’s talk about each of them in turn.

Web Development

Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

These web frameworks help you create server-side code (backend code) in Python. That’s the code that runs on your server, as opposed to on users’ devices and browsers (front-end code). If you’re not familiar with the difference between backend code and front-end code, please see my footnote below.

But wait, why do I need a web framework?

That’s because a web framework makes it easier to build common backend logic. This includes mapping different URLs to chunks of Python code, dealing with databases, and generating HTML files users see on their browsers.

Which Python web framework should I use?

Django and Flask are two of the most popular Python web frameworks. I’d recommend using one of them if you’re just getting started.

What’s the difference between Django and Flask?

There’s an excellent article about this topic by Gareth Dwyer, so let me quote it here:

Main contrasts:

You should probably choose:

</end quote>

In other words, If you’re a beginner, Flask is probably a better choice because it has fewer components to deal with. Also, Flask is a better choice if you want more customization.

On the other hand, if you’re looking to build something straight-forward, Django will probably let you get there faster.

Now, if you’re looking to learn Django, I recommend the book called Django for Beginners. You can find it here.

You can also find the free sample chapters of that book here.

Okay, let’s go to the next topic!

Data Science — including machine learning, data analysis, and data visualization

First of all, let’s review what machine learning is.

I think the best way to explain what machine learning is would be to give you a simple example.

Let’s say you want to develop a program that automatically detects what’s in a picture.

So, given this picture below (Picture 1), you want your program to recognize that it’s a dog.

Given this other one below (Picture 2), you want your program to recognize that it’s a table.

You might say, well, I can just write some code to do that. For example, maybe if there are a lot of light brown pixels in the picture, then we can say that it’s a dog.

Or maybe, you can figure out how to detect edges in a picture. Then, you might say, if there are many straight edges, then it’s a table.

However, this kind of approach gets tricky pretty quickly. What if there’s a white dog in the picture with no brown hair? What if the picture shows only the round parts of the table?

This is where machine learning comes in.

Machine learning typically implements an algorithm that automatically detects a pattern in the given input.

You can give, say, 1,000 pictures of a dog and 1,000 pictures of a table to a machine learning algorithm. Then, it will learn the difference between a dog and a table. When you give it a new picture of either a dog or a table, it will be able to recognize which one it is.

I think this is somewhat similar to how a baby learns new things. How does a baby learn that one thing looks like a dog and another a table? Probably from a bunch of examples.

You probably don’t explicitly tell a baby, “If something is furry and has light brown hair, then it’s probably a dog.”

You would probably just say, “That’s a dog. This is also a dog. And this one is a table. That one is also a table.”

Machine learning algorithms work much the same way.

You can apply the same idea to:

among other applications.

Popular machine learning algorithms you might have heard about include:

You can use any of the above algorithms to solve the picture-labeling problem I explained earlier.

Python for machine learning

There are popular machine learning libraries and frameworks for Python.

Two of the most popular ones are scikit-learn and TensorFlow.

If you’re just getting started with a machine learning project, I would recommend that you first start with scikit-learn. If you start running into efficiency issues, then I would start looking into TensorFlow.

How should I learn machine learning?

To learn machine learning fundamentals, I would recommend either Stanford’s or Caltech’s machine learning course.

Please note that you need basic knowledge of calculus and linear algebra to understand some of the materials in those courses.

Then, I would practice what you’ve learned from one of those courses with Kaggle. It’s a website where people compete to build the best machine learning algorithm for a given problem. They have nice tutorials for beginners, too.

What about data analysis and data visualization?

To help you understand what these might look like, let me give you a simple example here.

Let’s say you’re working for a company that sells some products online.

Then, as a data analyst, you might draw a bar graph like this.

From this graph, we can tell that men bought over 400 units of this product and women bought about 350 units of this product this particular Sunday.

As a data analyst, you might come up with a few possible explanations for this difference.

One obvious possible explanation is that this product is more popular with men than with women. Another possible explanation might be that the sample size is too small and this difference was caused just by chance. And yet another possible explanation might be that men tend to buy this product more only on Sunday for some reason.

To understand which of these explanations is correct, you might draw another graph like this one.

Instead of showing the data for Sunday only, we’re looking at the data for a full week. As you can see, from this graph, we can see that this difference is pretty consistent over different days.

From this little analysis, you might conclude that the most convincing explanation for this difference is that this product is simply more popular with men than with women.

On the other hand, what if you see a graph like this one instead?

Then, what explains the difference on Sunday?

You might say, perhaps men tend to buy more of this product only on Sunday for some reason. Or, perhaps it was just a coincidence that men bought more of it on Sunday.

So, this is a simplified example of what data analysis might look like in the real world.

The data analysis work I did when I was working at Google and Microsoft was very similar to this example — only more complex. I actually used Python at Google for this kind of analysis, while I used JavaScript at Microsoft.

I used SQL at both of those companies to pull data from our databases. Then, I would use either Python and Matplotlib (at Google) or JavaScript and D3.js (at Microsoft) to visualize and analyze this data.

Data analysis / visualization with Python

One of the most popular libraries for data visualization is Matplotlib.

It’s a good library to get started with because:

How should I learn data analysis / visualization with Python?

You should first learn the fundamentals of data analysis and visualization. When I looked for good resources for this online, I couldn’t find any. So, I ended up making a YouTube video on this topic:

Intro to Data Analysis / Visualization with Python and Matplotlib

I also ended up making a full course on this topic on Pluralsight, which you can take for free by signing up to their 10-day free trial.

I’d recommend both of them.

After learning the fundamentals of data analysis and visualization, learning fundamentals of statistics from websites like Coursera and Khan Academy will be helpful, as well.


What is scripting?

Scripting usually refers to writing small programs that are designed to automate simple tasks.

So, let me give you an example from my personal experience here.

I used to work at a small startup in Japan where we had an email support system. It was a system for us to respond to questions customers sent us via email.

When I was working there, I had the task of counting the numbers of emails containing certain keywords so we could analyze the emails we received.

We could have done it manually, but instead, I wrote a simple program / simple script to automate this task.

Actually, we used Ruby for this back then, but Python is also a good language for this kind of task. Python is suited for this type of task mainly because it has relatively simple syntax and is easy to write. It’s also quick to write something small with it and test it.

What about embedded applications?

I’m not an expert on embedded applications, but I know that Python works with Rasberry Pi. It seems like a popular application among hardware hobbyists.

What about gaming?

You could use the library called PyGame to develop games, but it’s not the most popular gaming engine out there. You could use it to build a hobby project, but I personally wouldn’t choose it if you’re serious about game development.

Rather, I would recommend getting started with Unity with C#, which is one of the most popular gaming engines. It allows you to build a game for many platforms, including Mac, Windows, iOS, and Android.

What about desktop applications?

You could make one with Python using Tkinter, but it doesn’t seem like the most popular choice either.

Instead, it seems like languages like Java, C#, and C++ are more popular for this.

Recently, some companies have started using JavaScript to create Desktop applications, too.

For example, Slack’s desktop app was built with something called Electron. It allows you to build desktop applications with JavaScript.

Personally, if I was building a desktop application, I would go with a JavaScript option. It allows you to reuse some of the code from a web version if you have it.

However, I’m not an expert on desktop applications either, so please let me know in a comment if you disagree or agree with me on this.

Python 3 or Python 2?

I would recommend Python 3 since it’s more modern and it’s a more popular option at this point.

Footnote: A note about back-end code vs front-end code (just in case you are not familiar with the terms):

Let’s say you want to make something like Instagram.

Then, you’d need to create front-end code for each type of device you want to support.

You might use, for example:

Each set of code will run on each type of device / browser. This will be the set of code that determines what the layout of the app will be like, what the buttons should look like when you click them, etc.

However, you will still need the ability to store users’ info and photos. You will want to store them on your server and not just on your users’ devices so each user’s followers can view his/her photos.

This is where the backend code / server-side code comes in. You’ll need to write some backend code to do things like:

So, this is the difference between backend code and front-end code.

By the way, Python is not the only good choice for writing backend / server-side code. There are many other popular choices, including Node.js, which is based on JavaScript.

Originally published by *YK Sugi at *medium.com


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Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

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00:00:00 Introduction

00:01:49 Installing Python

00:06:10 Your First Python Program

00:08:11 How Python Code Gets Executed

00:11:24 How Long It Takes To Learn Python

00:13:03 Variables

00:18:21 Receiving Input

00:22:16 Python Cheat Sheet

00:22:46 Type Conversion

00:29:31 Strings

00:37:36 Formatted Strings

00:40:50 String Methods

00:48:33 Arithmetic Operations

00:51:33 Operator Precedence

00:55:04 Math Functions

00:58:17 If Statements

01:06:32 Logical Operators

01:11:25 Comparison Operators

01:16:17 Weight Converter Program

01:20:43 While Loops

01:24:07 Building a Guessing Game

01:30:51 Building the Car Game

01:41:48 For Loops

01:47:46 Nested Loops

01:55:50 Lists

02:01:45 2D Lists

02:05:11 My Complete Python Course

02:06:00 List Methods

02:13:25 Tuples

02:15:34 Unpacking

02:18:21 Dictionaries

02:26:21 Emoji Converter

02:30:31 Functions

02:35:21 Parameters

02:39:24 Keyword Arguments

02:44:45 Return Statement

02:48:55 Creating a Reusable Function

02:53:42 Exceptions

02:59:14 Comments

03:01:46 Classes

03:07:46 Constructors

03:14:41 Inheritance

03:19:33 Modules

03:30:12 Packages

03:36:22 Generating Random Values

03:44:37 Working with Directories

03:50:47 Pypi and Pip

03:55:34 Project 1: Automation with Python

04:10:22 Project 2: Machine Learning with Python

04:58:37 Project 3: Building a Website with Django

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Top 10 Python Frameworks for Web Development In 2019

Top 10 Python Frameworks for Web Development In 2019

In this article, we are going to share our list of the top 10 Python frameworks for web development which we believe will help you to develop awesome applications and your technical abilities.

In this article, we are going to share our list of the top 10 Python frameworks for web development which we believe will help you to develop awesome applications and your technical abilities.

Given how dynamic web development has become, the popularity of Python frameworks seems to be only increasing. This object-oriented, powerfully composed, interpreted, and interactive programming language is easy to **learn **and effectively lessens the development time with its easy-to-read syntax and simple compilation feature. That’s reason enough why it is continuously gaining popularity.

Also, it has a vast number of Python libraries that support data analysis, visualization, and manipulation. Consequently, it has advanced as the most favored programming language and is now considered the “Next Big Thing” for professionals.

Since **Python **does not accompany the built-in features required to accelerate custom web application development, many developers choose Python’s robust collection of frameworks to deal with the subtleties of execution.

**Python **gives a wide scope of frameworks to developers. There are two types of Python frameworks – **Full Stack Framework **and Non-Full Stack Framework. The full-stack frameworks give full support to Python developers including basic components like form generators, form validation, and template layouts.

There is a cluster of full stack options when we talk of Python frameworks. Listed below are the top 10 full-stack web frameworks for **Python **that you should be using in 2019 valuable for enhancing your technical abilities.


Django is a free and open-source Python framework that enables developers to develop complex code and applications effectively and quickly. This high-level framework streamlines web application development by giving different vigorous features. It has a colossal assortment of libraries and underscores effectiveness, less need for coding, and reusability of components.

A few of the key features of Django, such as authentication mechanism, URL routing, template engine, and database schema migration implements ORM (Object Relational Mapper) for mapping its objects to database tables. The framework underpins numerous **databases **including PostgreSQL, MySQL, Oracle, and SQLite, which implies that a similar coding works with various databases.

Django’s cutting-edge features help developers in achieving basic web development tasks like user authentication, RSS feeds, content services, and sitemap. Due to its incredible features, Django framework is extensively used in several high-traffic sites, which include Pinterest, Instagram, Bitbucket, Mozilla, Disqus, and The Washington Times.


**CherryPy **is an open source Python web development framework that implants its very own multi-strung server. It can keep running on any working framework that supports Python. **CherryPy **features incorporate thread-pooled web server, setup framework, and module framework.

A moderate web framework enables you to utilize any sort of technology for data access, templating, etc. Yet, it can do everything that a web framework can, for instance, handling sessions, static, file uploads, cookies, and so on.

Regardless of the accessible features and advantages like running on multiple platforms, built-in support for profiling, reporting, and testing, some developers may imagine that there is a requirement for easy and enhanced documentation. It doesn’t constrain you to use a specific template engine, ORM, so that you can use anything you wish to use.


**Pyramid **is a Python framework that underpins validation and directing. It is incredible for growing huge web applications, as CMSs, and it is valuable for prototyping an idea and for developers chipping away at API projects. Pyramid is adaptable and can be utilized for both easy as well as difficult projects.

Pyramid is enhanced with features without driving a specific method for completing things, lightweight without abandoning you all alone as your app develops. It is a most valued web framework among experienced Python developers by virtue of its transparency and measured quality. It has been used by a moderate team and tech giants like Mozilla, Yelp, Dropbox, and SurveyMonkey.

The pyramid is reliably known for its security arrangements, which makes it easy to set up and check access control records. Another inventive functionality worth uncovering is Pyramid’s Traversal framework for mapping URLs to code, which makes it simple to develop RESTful APIs.


**TurboGears **is an open-source, free, and data-driven full-stack web application Python framework. It is designed to overcome the inadequacies of various extensively used web development frameworks. It empowers software engineers to begin developing web applications with an insignificant setup.

**TurboGears **enables web developers to streamline web application development utilizing diverse JavaScript development tools. You can develop web applications with the help of elements such as SQLAlchemy, Repoze, WebOb, and Genshi, much faster than other existing frameworks. It supports different **databases **and web servers like Pylons.

The framework pursues an MVC (Model-View-Controller) design and incorporates vigorous formats, an incredible Object Relational Mapper (ORM) and Ajax for the server and program. Organizations using TurboGears incorporate Bisque, ShowMeDo, and SourceForge.


**Web2py **is a free, open source Python framework for web application development. The framework accompanies a debugger, code editor as well as a deployment tool to enable you to build and debug the code, as well as test and keep up web applications.

It’s a cross platform framework that underpins Windows, Unix/Linux, Mac, Google App Engine, and different other platforms. It pursues the MVC (Model-View-Controller) design. The framework streamlines web application development procedure via a web server, SQL database, and an online interface. It enables clients to build, revise, deploy, and manage web applications via web browsers.

The key component of Web2py is a ticketing framework, which issues a ticket when a mistake occurs. This encourages the client to follow the mistake and its status. Also, it has in-built components to manage HTTP requests, reactions, sessions, and cookies.


Another interesting Python web framework is Bottle, which falls under the class of small-scale frameworks. Originally, it was developed for building web APIs. Also, Bottle tries to execute everything in a single document, which should give you a short perspective on how small it is designed to be.

The out-of-the-box functionalities include templating, utilities, directing, and some fundamental abstraction over the WSGI standard. Like Flask, you will be coding significantly closer to the metal than with a full-stack framework. Regardless of their, Bottle has been used by Netflix to create web interfaces.


Tornado is a Python web framework and offbeat framework library. It utilizes a non-blocking framework I/O and unravels the C10k issue (which means that, whenever configured properly, it can deal with 10,000+ simultaneous connections).

Tornado’s main features comprise of built-in support for user confirmation, superior quality, real-time services, non-blocking HTTP customer, Python-based web templating language, and support for interpretation and localization.

This makes it an extraordinary tool for building applications that require superior and a huge number of simultaneous clients.


Flask is a Python framework accessible under the BSD license, which is inspired by the Sinatra Ruby framework. Flask relies upon the Werkzeug WSGI toolbox and Jinja2 template. The main purpose is to help develop a strong web application base.

**Developers **can **develop backend frameworks **any way they need, however, it was designed for applications that are open-ended. Flask has been used by big companies, which include LinkedIn and Pinterest. As compared to Django, Flask is best suited for small and easy projects. Thus, you can expect a web server development, **support **for Google App Engine as well as in-built unit testing.


**Grok framework **has been created, depending on Zope toolbox for giving an agile development experience to developers by concentrating on convention over configuration and DRY (Don’t Repeat Yourself). It is an open-source framework, developed to speed up the application development process.

Developers can choose from a wide scope of network and independent libraries as indicated by the task needs. Grok’s UI (user interface) is like other full-stack frameworks such as **Pylons **and TurboGears.

The Grok component architecture helps developers lessen the unpredictability of development by availing views, content objects, and controller. Grok, likewise, provides the building blocks and other essential assets required to develop custom web applications for business needs.


**BlueBream **is also an open source web application framework, server, and library for website developers. It has been developed by the Zope team which was formerly known as Zope 3.

This framework is best suited for both medium and substantial activities apportioned into various re-usable and well-suited segments.

**BlueBream **relies upon Zoop Toolkit (ZTK). It holds extensive periods of experience ensuring that it meets the main essential for enduring, relentless, and adaptable programming.


Though there are many python web development frameworks that will be popular and in-demand in the coming years, especially in 2019, every framework has its own pros and cons. Every developer has different coding styles and preferences. They will assess every framework as per the requirements of an individual task. In this way, the choice of python web development framework will change from one developer onto the next.

The above-listed are some of the Python frameworks that are widely used as a full-stack backend web application development. Which one are you picking for your next project? Do let us know in the comments section given below.