Learn Python or R for Data Science

This blog will highlight the following key areas:

  • R vs Python
  • Comparison between R and Python on the basis of following:
    • Ease of Installation
    • Robustness and Flexibility
    • Ease of Learning
    • Speed of Processing
    • Statistical and Analytics Ability
    • Suitable Area
  • Reasons to Learn R and Python
  • Which is Better for Data Analysis and Data Science?
  • Is Python better than R?
  • Real-life Use Cases of R and Python
  • Career Opportunities in R and Python
  • Which Language to Go First?

What are R and Python?

R is an open-source programming language developed for statistical analysis and computations.

Like R, Python is also an open-source programming language that was initially developed as a general-purpose programming language, and later branched out to be a language for Statistical Analysis and Machine Learning Modeling.

Let’s understand the difference between these two highly popular Data Science languages:

Ease of Installation

Well to start with, R packages are solely managed by CRAN (The Comprehensive R Archive Network) repository that manages the updated versions, their installations, and related documentation of R Packages. All the packages you install in R are stored in CRAN. Also, any new package to be added in R should be submitted to CRAN. Currently CRAN has over 16000 additional statistical packages. This is why it is easier to install R.

On the other hand, Python has two package management platforms, Conda and PyPI (Python Package Index) that include over 100k Python packages. There have been inconsistencies found in Packages, Libraries, and Versions while installing Python due to two repositories. Due to this reason, it is a little tedious to install Python.


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Should I Learn Python or R for Data Science?

R vs Python

If you are someone who wishes to make a career in Data Science, then the ultimate question you have to face is, which programming language you should learn and why? There have been numerous discussions on public forums with people advocating for R or Python for plenty of reasons.

Though it completely depends on your choice, comparing both the languages on some grounds will surely help you make the right decision.

What are R and Python?

R is an open-source programming language developed for statistical analysis and computations.

Like R, Python is also an open-source programming language that was initially developed as a general-purpose programming language, and later branched out to be a language for Statistical Analysis and Machine Learning Modeling.

Let’s understand the difference between these two highly popular Data Science languages:

Ease of Installation

Well to start with, R packages are solely managed by CRAN (The Comprehensive R Archive Network) repository that manages the updated versions, their installations, and related documentation of R Packages. All the packages you install in R are stored in CRAN. Also, any new package to be added in R should be submitted to CRAN. Currently CRAN has over 16000 additional statistical packages. This is why it is easier to install R.

On the other hand, Python has two package management platforms, Conda and PyPI (Python Package Index) that include over 100k Python packages. There have been inconsistencies found in Packages, Libraries, and Versions while installing Python due to two repositories. Due to this reason, it is a little tedious to install Python.

Want to read about Python in detail? Read this riveting blog!

Robustness and Flexibility

R has all the features that Python has in terms of programming ability, statistical computing and modeling, but Python is more flexible and robust. Python is a better option when it comes to integrating it with web applications and production.

However, R is less robust and versatile, which is why it is limited to statistical computing and mathematical modeling.

Ease of Learning

One of the most frequently asked questions is “Which between R and Python is easy to learn?”.

Both R and Python have almost similar features, but when it comes to syntax, R is a little complicated and is better for someone who is already familiar with other programming language. On the other hand, Python has a relatively simpler and readable syntax and hence, for anyone who is about to start-off with a programming language, Python is a good option.

However, when the model building becomes complicated, it requires someone who is proficient in Python.

Speed of Processing

Usually Python is 8 times faster than R till there are up to 1000 iterations. When the number of iterations increases, R typically surpasses Python’s speed. In comparison to Python, R requires more lines of codes to perform a certain task, which make the programs more complex and bulkier.

Statistical and Analytics Ability

R was designed for statistical computation and Modeling purposes and hence it performs better for any level of complex computation. R has better statistical packages and libraries for dashboard than Python. Python being a general programming language somehow lacks the packages and libraries for Data Science. Python is better suited for modeling and machine learning, which is complicated in R.

Suitable Area

The focus of R is primarily into statistical analysis and hence it is better suited to academia and research. On the other hand, Python being the programming language for all purposes is suitable for tech industry. However, Python also comes with packages that can create an environment similar to R.

There are plenty of other grounds that differentiate R from Python. The following table will further clarify that!


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Python Career Guidelines: How To Become A Python Professional?

One of the familiar questions that we read these days on platforms such as Quora is: how do I become a professional Python programmer?

Yes, today many IT professionals are willing to pursue their career as Python programmer.The reason for the rise in such trend is because Python is emerging as the one of the most powerful programming languages of the present IT world.

Today we can see that more and more companies are relying on Python to develop their software projects across different industries. This programming language is being used in various fields such as Artificial Intelligence, Machine Learning and Data Science etc.

These are some of the factors that have led to offered huge career opportunities for young aspiring professionals across the world.

Source: Stack Overflow

We have also observed that many young IT professionals are looking for a right career guide that would help them to become a Python professional. Sowith an aim to help all such people all we are presenting here this blog to discuss a career guideline to become Python professional.

If you are new to the world of Python and are willing to learn it then we recommend to look into these online courses that contains a library of Python course that help you to learn this programming language efficiently.

In this blog, I will be covering the following topics.

  • Why Learn Python?
  • What are the career opportunities related to Python programming?
  • Top companies using Python Programming
  • Where Python developers can find jobs?
  • How Simpliv can help You to become a Python professional?
  • 5 Key Takeaways

Why Learn Python?

Python is a general purpose, object oriented, easy to learn programming language. There are many reasons why one needs to learn Python. Some of them are as follows:

  • Python supports Object-Oriented programming language
  • Python follows a easy syntax and hence has a simple coding structure
  • Python is considered as an easy programming language to learn for beginners
  • Python supports set of different libraries and API’s that will help the developers to build the software applications easily.

Now let us see some of the career opportunities of Python Programming.


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9 Reasons why you should Learn Python

Python is an important programming language that all developers should know. Many programmers use this language to make websites, produce learning algorithms, and perform different necessary tasks. The best way to learn Python begins with deciding what you want to build. Then you will want to find a course or resources to help you develop your idea. When learning Python, it is very important to start with an idea. If you try to create something that interests you, the process becomes more intense. Learn Python in just 9 simple steps with the Simpliv program.

1. Python is easy

Easy to learn, has a simple, even intuitive syntax (putting it simply: a way of writing the commands understood by a computer with a given programming language.) The syntax resembles the elements “from real life“ so the keywords are intelligible for every beginner, and at the same time, really close to these appearing in other programming languages (that’s why a switch to another language later is easier.)

When we specify the things to do, we often use a colon (“:“), and intersections — just like we give commands in Python blocks of code. By the way, it somehow forces us to build the good habits of making intersections. It makes our Python code look nice, legible, and clear.

First programme displaying “Hello World“


public class Main {
  public static void main(String[] args) {
    System.out.println("hello world");


print("hello world")

I leave it for individual judgement 😉 If you’ve installed Python already, check import this in a console, everything that inspires to code in Python in 19 lines.

2. Figure Out What Motivates You to Learn Python

Before you start learning Python, you have to ask yourself why you want to learn it. This is because the trip will be long and sometimes painful. Without sufficient motivation, you probably will not succeed. For example, I slept during high school and university when I had to remember the syntax and was not motivated. On the other hand, I stayed awake at night when I used Python to create an automated authoring site.

When you discover what motivates you, you will find a final goal and a path that will take you there without trouble. You do not have to define a specific project, but simply a general area that interests you when preparing Python.

Select an area that interests you, for example:

  • Data Science / Machine Learning
  • Mobile apps
  • Web sites
  • Games
  • Hardware / sensors / robots
  • Scripts to automate your work

Discover one or two areas that interest you and you are ready to stick to it. They will align their learning with them and eventually build projects.

3. Python is fast

Nope, I don’t mean to compare Python’s speed to other programming languages. There will be moaning that there are faster ones, for sure. Python is fast compared to interpeted languages but it’s not important for the beginner.

You can learn Python fast, and it’s available off-the-shelf.

You install Python, and you can immediately start writing your code. You run a console, write python, and you’re already welcomed with an encouraging sign “>>>“ — Write something, try me, come on! No need to read about choosing a programme, an environment, a compiler versions.

You don’t want to install Python but want to try your hand at a console? Go ahead: Python shell online or repl.it.


This GIF is here not accidentally. Mr. Robot is an excellent TV series about hackers, and there’s a big portion of IT world involved in it. It wasn’t directed with a lick and promise like most of productions of this kind. We can trace quite a lot of cybersecurity devices here. There’s a scene where a code in Python is quickly written straight in a console or fuxsocy.py file that Darlyn uses.

Creating penetration tests in Java — OK but how would hacking in a real life look like? There’s a scene in Mr. Robot: FBI cruises the corridors: Wait a sec, I’ll just compile this.

4. Python is productive

Working with the Big Data (collecting it, analysis, processing, usage) is the future. The more data you have to process, the more important is the management of used resources, and code’s effectiveness.

Python makes generators accessible, both as the expressions, and the functions. The generators enable iterative data processing — the element after the element. It doesn’t sound too attractive until you notice that “ordinary“ iterative data processing requires a list. A list takes up the memory. A really big list takes up a lot of memory. The generators allow to gather the data from a source one element at a time, and their transfer via a whole data processing chain, skipping a mechanism related to the storage of iterative list.

Even if working with the Big Data sounds like an abstraction for you for the time being, think of all these given consents to data processing, marketing, academic work or even the politics (e.g. Donald Trump won the elections thanks to Big Data.)

5. Professional skills

There are many languages for educational purposes such as Scratch or Logo. Surely, they can help you with learning the logics of programming, some of them gets to the schools, and it’s a good trend. However, no matter how advanced is the stuff you do with them, nobody will take it seriously (unless you’re a teacher, and you want to introduce programming lessons to your students.)

So reach for Python! It’s really approachable, and will immediately give you a concrete professional asset — programming.

After all, you don’t want to develop your skills with Python? Chill, you’ll easily “get lower“ to C, jump to Ruby (its syntax is really similar) or move towards front-end, straight into JavaScript arms. Python integration with other languages? No problem. Additional solutions? Sure, there are many options. Jython (Python implemented in Java) works everywhere where Java does. IronPython is a Python implemented in .Net.

6. Remuneration

Let’s talk about money. It’s not an interview so let’s put it bluntly — the main reason people change their field is a wish to earn more, and the sums in IT world may impress.

Python is second on a list of well-paid languages in USA. We analyse an average annual wage, the fact that Python is an easy language to learn, and things become clear.
Despite the fact that these statistics doesn’t correspond with Polish trends, Python programmers can’t complain about their earnings. I see a bright future for them, especially because the trends usually come to us “from the West.“

7. Possibilities

As I said, you can make use of Python in every way. It’s high time for examples.

Arduino or Raspberry Pi

In both cases you can code in Python. A lot of fun, immense possibilities. DIY projects are easily accessible on YouTube, and really rewarding.


Ethical hacking, penetration tests, security systems analysis, software development — these might be your tasks as a Security Specialist

Internet of Things

Actually, you can make the gadgets for your house on your own or work in this field profesionally.


Collecting information about the users and its analysis with your own data or Facebook API,  Google, Twitter, better ads targetting.


Data processing on mathematical and statistical level, working with results of laboratory experiments in the field of genomics, chemistry, geoinformation, etc.


Software testing, automated testing, debugging, everywhere where you can — out of laziness — write the code that would carry out the code the tests for a tester.


As far as Data Scientist positions are concerned, Python is one of the most often required languages.

Machine learning, AI

The fields that involve processing of a huge amount of data. Python is the future of machine learning, they say.

Web development

More effective backend than popular PHP, and the frameworks that make you do your work faster, e.g. Django or Flask.

Many, many more could come to our mind. Even in a field of games which isn’t, at least at first, associated with Python, one can find a suitable position (gameplay programmer).

8. Python III The Mighty

Because Python is easy, you cannot do with it more? By no means! It’s application really varies. Python has the power so the companies such as Google, Dropbox, Spotify or Netflix use it in their applications.


Dropbox is completely written in Python , and it ensures its compatibility with every operation system. It has around 400 millions of users. For many of them, it’s one of the first applications they install on their computers. Not only a desktop application but also Dropbox server side code is written in Python.


Google uses a huge amount of technologies: C++, Python, and Go among them. Supposedly, someone said in Google office: Python where we can, C++ when we have to.


Spotify and Netflix

Similarly to Google, Spotify and Netflix employ different languages. In Spotify, it’s mainly Java but Python is used for things like their Web API, data analysis which is not only related to users (DNS server’s recovery system, payment system, content management system.) Netflix uses a mix of Java, Scala and Python, simultaneously giving their programmers the autonomy of choosing the language that is most proper where a given problen occurs. Where we can find Python there? In analytical groups, and real-time event service.

Where else Python is used?

Facebook, Instagram, Yahoo, Quora, Pinterest, Disqus.

9. Materials and community



You’ll easily find a lot of learning materials, mainly in English. Python documentation is rich, and really coherently written. The books doesn’t become outdated as quickly as in the case of web technologies.

The beginners like support, and Python community is active, also in Poland (numerous events, Facebook groups such as Python Poland, Python: Pierwsze kroki, Python szukam pracy, and also my group, Python: nauka). There’s also a strong female community: PyLadies, PyCode Carrots, Django Girls.

Useful Resources to Learn Python

If you decide to learn Python in 2019 then here are some of the useful Python books, courses, and tutorials to start your journey in the beautiful world of Python.

Learn Python GUI with Tkinter: The Complete Guide

Python 1200: Practice for BEGINNERS

Learn Python from Basic to Advance with Projects in a day

Python For Beginners With Exercises

Python Programming Tutorials For Beginners

Learn Python in a Day

Learn Programming with Python in 100 Steps

Python for Beginners: A Python Mega Course with 10 Projects

Learn Python Programming – Easy as Pie

Spark for Data Science with Python

Machine Learning, NLP & Python-Cut to the Chase

Image Processing Applications on Raspberry Pi – From Scratch

Python for Beginners 2017

Selenium with Python

Guide to Python Programming Language

Python GUI Programming Projects using Tkinter and Python 3

Complete Python Course Go from zero to hero in Python

The Python 3 New Features from Python Enhancement Proposal

Learn Python Programming

Selenium WebDriver With Python 3.x – Novice To Ninja

Learn Python 3 from scratch to become a developer in demand

Learn Python Django – A Hands-On Course

Python Programming & Data Handling

Python for Beginners

Create Your Calculator: Learn Python Programming Basics Fast

The Complete Python Training for 2019: Work on 10 Projects

Fundamentals of Python for Data Mining

Python for Data Science, Data Analysis & Visualization: 2019

Python For Beginners In Arabic تعلم لغة البايثون

Curso Completo De Machine Learning: Data Science en Python

Complete Python Beginners Bootcamp: Python Deluxe Edition

Python Acelerado

Python Pro – Python Basics for Machine Learning

GUI Automation using Python| Python Automation

Data Structures and Algorithms in Python

Machine Learning Basics: Classification models in Python

Python Automation for Everyone – Learn Python 3

COMPLETE Python Bootcamp 2019

NEW Python 3.7 Mastery course [FAST TRACK] – Programming language

Build Full Download Manager | Python & PyQt5

Learn Python 3 Programming in සිංහල

Python Programming for Absolute Beginners: Quickly learn python

Building Movies Site With Python & Django – IMDB Clone

That’s all for this article on the important reasons to learn Python in 2019. As I said, it’s important to know programming and coding in today’s world and if you don’t know coding you are missing something and Python is a great way to start learning to code.

For programmers who already know Java or C++, learning Python not only will make you a polyglot programmer but also gives you a powerful tool in your arsenal to write scripts, create a web application, and open the door to the exciting fields of data science and machine learning.

In short, if you could learn just one programming language in 2019 then make it to Python and to start with, The Complete Python MasterClass is the best course.


So these are my 9 reasons why it’s worth learning Python. Surely, there are more. What are yours?



15 Best Programming Languages to Learn in 2019 (for Job & Future)

Table of Contents

The most important skill to learn in today’s world is to know how to write a computer program. Today, computers have entered in almost every industry. Be it the autopilot in an aircraft or digital speedometer in your bike, computers in various forms surround us. Computers are extremely useful for an organization to scale up well. Gone are the days of pen and paper. Today, in order to store and access your information, you absolutely need computers.

The programming and developer community are emerging at a rate faster than ever before. Various new programming languages are coming up that are suited for different categories of developers (beginners, intermediate, and experts) as well as for different use cases (web application, mobile applications, game development, distributed system, etc).

Let us take a look at best Programming Languages to learn in 2019 for a job and for future prospects:



Python undoubtedly tops the list. It is widely accepted as the best programming language to learn first. Python is fast, easy-to-use, and easy-to-deploy programming language that is being widely used to develop scalable web applications. YouTube, Instagram, Pinterest, SurveyMonkey are all built-in Python. Python provides excellent library support and has a large developer community. The programming language provides a great starting point for beginners. Talking about those who are looking for a better job, you should definitely learn Python ASAP! A lot of startups are using Python as their primary backend stack and so, this opens up a huge opportunity for full-stack Python developers. Here is a sample Python “Hello World!” program:

  print “Hello World!"

Yes, Python is that simple! Anyone who wishes to join a startup should master Python programming.



Java is another popular choice in large organizations and it has remained so for decades. Java is widely used for building enterprise-scale web applications. Java is known to be extremely stable and so, many large enterprises have adopted it. If you are looking for a development based job at a large organization, Java is the language that you should learn.

Java is also widely used in Android App Development. Almost any business today needs an Android Application owing to the fact that there are billions of Android users today. This opens up a huge opportunity for Java developers given the fact that Google has created an excellent Java-based Android development framework – Android Studio.



C/C++ is like the bread and butter of programming. Almost all low-level systems such as operating systems, file systems, etc are written in C/C++. If you wish to be a system-level programmer, C/C++ is the language you should learn.

C++ is also widely used by competitive programmers owing to the fact that it is extremely fast and stable. C++ also provides something called as STL – Standard Template Library. STL is a pool of ready-to-use libraries for various data structures, arithmetic operations, and algorithms. The library support and speed of the language make it a popular choice in the High-frequency trading community as well.


JavaScript is the “frontend” programming language. JavaScript is widely used to design interactive frontend applications. For instance, when you click on a button which opens up a popup, the logic is implemented via JavaScript.

These days, many organizations, particularly startups, are using NodeJS which is a JavaScript-based run-time environment. Node.js lets developers use JavaScript for server-side scripting—running scripts server-side to produce dynamic web page content before the page is sent to the user’s web browser. Hence now with JS, you can use a single programming language for server-side and client-side scripts. If you are looking for that cool tech job at your favorite startup, you should seriously consider learning JavaScript.

Go programming language

Go programming language

Go, also known as Golang, is a programming language built by Google. Go provides excellent support for multithreading and so, it is being used by a lot of companies that rely heavily on distributed systems. Go is widely used in startups in Silicon Valley. However, it is yet to be adopted by Indian companies/startups. Those who wish to join a Valley-based startup specializing in core systems should master Golang.


R Programming Language

R programming language is one of the most commonly used programming languages for Data Analysis and Machine Learning. R provides an excellent framework and built-in libraries to develop powerful Machine Learning algorithms. R is also used for general statistical computing as well as graphics. R has been well adopted by enterprises. Those who wish to join “Analytics” team of a large organization should definitely learn R.


Swift is the programming language that is used to develop iOS applications. iOS-based devices are becoming increasingly popular. Apple iPhone, for instance, has captured a significant market share and is giving a tough competition to Android. Therefore, those who want to serve this community can learn Swift programming.



PHP is among the most popular backend programming language. Though PHP is facing a tough competition from Python and JavaScript, the market still needs a large number of PHP developers. Those who wish to join a reasonably well old organization as a backend developer should aim to learn PHP programming.



C# is a general-purpose programming language developed by Microsoft. C# is widely used for backend programming, building games (using Unity), building Window mobile phone apps and lots of other use cases.




MATLAB is a statistical analysis tool that is used in various industries for Data Analysis. MATLAB is used widely in the Computer Vision and Image processing industry as well.




Ruby is another scripting language that’s commonly used for web development. In particular, it’s used as the basis for the popular Ruby on Rails web application framework.

Beginners often gravitate to Ruby because it has a reputation for having one of the friendliest and most helpful user communities. The Ruby community even has an unofficial saying, “Matz is nice and so we are nice,” encouraging members to model their kind and considerate behavior on Ruby’s chief inventor Yukihiro Matsumoto.

In addition to the active community and its straightforward syntax, Ruby is also a good language to pick up thanks to its association with great tech businesses. Twitter, Airbnb, Bloomberg, Shopify and countless other startups have all built their websites using Ruby on Rails at some point.


SQL (es-que-el) stands for Structured Query Language, is a programming language to operate databases. It includes storing, manipulating and retrieving data stored in a relational database.

SQL keeps data precise and secure, and it also helps in maintaining the integrity of databases, irrespective of its size.

SQL is used today across web frameworks and database applications. If you are well versed in SQL, you can have better command over data exploration, and effective decision 


Rust is a bit of an upstart among the other languages on this list, but that doesn’t mean it’s not a valuable language to learn. Stack Overflow’s 2019 Developer Survey found that Rust was the most loved programming language among developers for the third year in a row, with 78 percent of Rust developers saying that they want to continue working with it.

Developed by the Mozilla Corporation, Rust, like C and C++, is intended primarily for low-level systems programming. What Rust adds to the mix, however, is an emphasis on speed and security. Rust emphasizes writing “safe code” by preventing programs from accessing parts of memory that they shouldn’t, which can cause unexpected behavior and system crashes.

The advantages of Rust mean that other big tech companies, such as Dropbox and Coursera, are already starting to use it internally. While it may be a bit more difficult to master than other beginner languages, Rust programming skills are likely to pay off handsomely as the language’s popularity will only continue to rise in the near future.


Objective-C (ObjC) is an object-oriented programming language. It is used by Apple for the OS X and iOS operating systems and their application programming interfaces (APIs). It was developed in the 1980s and came in usage by some of the earliest operating systems.

Objective-C is object-oriented, general purpose. You can call it hybrid C because of the features it adds to C programming language.


If you are thinking seriously about Android App development, then Kotlin is the programming language to learn this year. It is definitely the next big thing happening in the Android world.

Even though Java is my preferred language, Kotlin has got native support, and many IDEs like IntelliJ IDEA and Android Studio are supporting Kotlin for Android development.


Even if you learn just one programming language apart from the one you use on a daily basis, you will be in good shape for your career growth. The most important thing right now is to make your goal and do your best to stick with it. Happy learning!






Future Scope Of Python Programming

Python is a high level and multi-paradigm programming language designed by Guido van Rossum, a Dutch programmer, having all the features as conventional programming languages such as C, C++ and Java have.

It is one of the fastest growing languages and has undergone a successful span of more than 25 years as far as its adoption is concerned. This success also reveals a promising future scope of python programming language.

In fact, it has been continuously serving as the best programming language for application development, web development, game development, system administration, scientific and numeric computing, GIS and Mapping etc.

Why Is Python So Popular?

The reason behind the immense popularity of python programming language across the globe is the features it provides. Have a look at the features of python language.

Future Scope Of Python Programming.jpg

(1) Python Supports Multiple Programming Paradigms

Python is a multi-paradigm programming language including features such as object-oriented, imperative, procedural, functional, reflective etc.

(2) Python Has Large Set Of Library and Tools

Python has very extensive standard libraries and tools that enhance the overall functionality of python language and also helps python programmers to easily write codes. Some of the important python libraries and tools are listed below.

  • Built-in functions, constants, types, and exceptions.
  • File formats, file and directory access, multimedia services.
  • GUI development tools such as Tkinter
  • Custom Python Interpreters, Internet protocols and support, data compression and archiving, modules etc.
  • Scrappy, wxPython, SciPy, matplotlib, Pygame, PyQT, PyGTK etc.

(3) Python Has a Vast Community Support

This is what makes python a favorable choice for development purposes. If you are having problems writing python a program, you can post directly to python community and will get the response with the solution of your problem. You will also find many new ideas regarding python technology and change in the versions.

(4) Python is Designed For Better Code Readability

Python provides a much better code readability as compared to another programming language. For example, it uses whitespace indentation in place of curly brackets for delimiting the block of codes. Isn’t it awesome?

(5) Python Contains Fewer Lines Of Codes

Codes are written in python programming language complete in fewer lines thus reducing the efforts of programmers. Let’s have a look on the following “Hello World” program written in C, C++, Java, and Python.


While, C, C++, and Java take six, seven and five lines respectively for a simple “Hello World” program. Python takes only a single line which means, less coding effort and time is required for writing the same program.

Future Technologies Counting On Python

Generally, we have seen that python programming language is extensively used for web development, application development, system administration, developing games etc.

But do you know there are some future technologies that are relying on python? As a matter of fact, Python has become the core language as far as the success of these technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

(1) Artificial Intelligence (AI)

Python programming language is undoubtedly dominating the other languages when future technologies like Artificial Intelligence(AI) comes into the play.

There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes.

It is only the Artificial Intelligence that has made it possible to develop speech recognition system, autonomous cars, interpreting data like images, videos etc.

We have shown below some of the python libraries and tools used in various Artificial Intelligence branches.

  • Machine Learning- PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy etc.
  • General AI- pyDatalog, AIMA, EasyAI, SimpleAI etc.
  • Neural Networks- PyAnn, pyrenn, ffnet, neurolab etc.
  • Natural Language & Text Processing- Quepy, NLTK, gensim

(2) Big Data

The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.

Let’s have a look at the python libraries and toolkits used for Data analysis and handling other big data issues.

  • Pandas
  • Scikit-Learn
  • NumPy
  • SciPy
  • GraphLab Create
  • IPython
  • Bokeh
  • Agate
  • PySpark
  • Dask

(3) Networking

Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.

For these purposes, there are many libraries and tools that are built on the top of the python language. Here we have listed some of these python libraries and tools especially used by network engineers for network automation.

  • Ansible
  • Netmiko
  • NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)
  • Pyeapi
  • Junos PyEZ
  • PySNMP
  • Paramiko SSH

Real-Life Python Success Stories

Python has seemingly contributed as a core language for increasing productivity regarding various development purposes at many of the IT organizations. We have shown below some of the real-life python success stories.

  • Australia’s RMA Department D-Link has successfully implemented python for creating DSL Firmware Recovery System.
  • Python has helped Gusto.com, an online travel site, in reducing development costs and time.
  • ForecastWatch.com also uses python in rating the accuracy of weather forecast reports provided by companies such as Accuweather, MyForecast.com and The Weather Channel.
  • Python has also benefitted many product development companies such as Acqutek, AstraZeneca, GravityZoo, Carmanah Technologies Inc. etc in creating autonomous devices and software.
  • Test&Go uses python scripts for Data Validation.
  • Industrial Light & Magic(ILM) also uses python for batch processing that includes modeling, rendering and compositing thousands of picture frames per day.

There is a huge list of success stories of many organizations across the globe which are using python for various purposes such as software development, data mining, unit testing, product development, web development, data validation, data visualization etc.

These success stories directly point towards a promising future scope of python programming language.

Top Competitors Of Python

The future scope of python programming language also depends on its competitors in the IT market. But, due to the fact that it has become a core language for future technologies such as artificial intelligence, big data, etc., it will surely gonna rise further and will be able to beat its competitors.

Tiobe Index

According to Tiobe Index for October 2017, python is among the top five popular programming languages and has left behind Php, Swift, Javascript, Perl, Ruby, R.

The only languages which are slightly ahead of python in terms of popularity ratings are Java, C, C++, and C#. These figures will shortly be going to change after seeing the growing popularity and high adoption of Python programming language.

PYPL Index

Another Index that measures the popularity of programming languages is PYPL. And according to PYPL(PopularitY of Programming Language) index, Python has secured the second position in India and Germany, Java being the only language ahead of it.

But in other countries like U.K, U.S.A, and France, Python has seized the top position beating its toughest competitor Java in terms of popularity.


According to datanyze.com, python is at the 5th position in the list of 31 frameworks and programming languages in India with a market share of 1.6 percent.

The top three competitors of Python in India are listed below along with their market shares and current websites.

  1. ASP.NET
    Market Share- 39.53%
    Current Websites- 41,052
  2. Java
    Market Share- 4.03%
    Current Websites- 4,186
  3. C#
    Market Share- 1.97%
    Current Websites- 2,042

Websites Developed Using Python

As you already know that python programming language is used for web development, so here are some of the world’s most popular websites that are created using python.

  • Youtube
  • Quora
  • Instagram
  • Pinterest
  • Spotify
  • Flipkart
  • Slack
  • Uber
  • Cloudera
  • Zenefits

Organizations Using Python Language

There are many small and big organizations and startups as well that are immensely using Python to improve their productivity and meet customer requirements.

Even the governmental organizations are using python to maintain and add more functionality to their website. USA’s CIA(Central Intelligence Agency) is one of them.

We have jotted down some of the world’s biggest organizations that are continuously deploying python and its development frameworks to deal with their chief areas of production.

(1) NASA-

It uses Workflow Automation System(WAS), an application written in python and developed by NASA’s shuttle support contractor USA(United Space Alliance).

NASA also uses Python for its various open source projects such as APOD(Astronomy Picture of the Day) API, PyTransit, PyMDP Toolbox, EVEREST etc.

(2) Google-

It uses python for its internal systems and API’s and for reports generation, log analysis, A/Q and testing, writing core search algorithms, just to name a few.

Youtube which is subsidiary of Google, Inc also uses python for viewing a video, accessing canonical data, controlling templates of the website etc.

(3) Walt Disney Feature Animation

Walt Disney Feature Animation uses python as a scripting language for most of its animation tasks and related production.

(4) AlphaGene, Inc.

AlphaGene is a biotechnology company based in the United States which deals in gene and protein discovery. It uses python for its bioinformatics and tracking system.

(5) Red Hat

It is a multinational computer software company based in the United States. It uses an installer, Anaconda, written in python for installing RHEL(Red Hat Enterprise Linux) and Fedora operating systems.

Apart from using python-based installer Anaconda, most of the system configuration tools in RHEL and Fedora operating systems are written in python. These tools are used to change the state of the newly installed operating system.

For example, Firewalld is a configuration tool used for the dynamic management of the firewall and provides an essential support for network/firewall zones.

(6) Nokia

Well, you all are already familiar with this popular vendor of mobile phones in the world. It is basically a Finnish IT, consumer electronics, and telecommunication industry.

It uses PyS60(Python for S60) and PyMaemo(Python for Maemo) for its S60(Symbian) and Maemo(Linux) software platforms.

(7) IBM

IBM is an American-based multinational computer manufacturing company. It is using python for its factory tool control applications at its micrus semiconductor plant in East Fishkill. These tools are used to handle data collection, material entry etc.

(8) SGI, Inc.

SGI(Silicon Graphics International) is a U.S-based computer hardware and software company. It also provides high-performance computing, data analytics, and data management solutions.

It uses python for its Linux installer being derived from Red Hat’s Anaconda installer.

This Linux installer is used in several Linux-based products of SGI such as ISP, workstations, system console, clustering, servers etc.

(9) Yahoo! Maps

It is an online mapping portal developed at Yahoo!. Many of its mapping lookup services and addresses were written in python.

This clearly shows that python programming language is currently one of the most popular and widely used languages which is influencing the IT sector and has a vast scope in the future.

Career Prospects In Python Technology

With the advent of Information Technology, the career opportunities associated with python programming language have grown significantly. In fact, IT organizations are looking for candidates having an excellent core and advanced python skills.

This has resulted in an increased demand for python professionals who can easily perform the programming tasks given to them. This also depicts a better career scope for python programmers in the future.

Here we have listed some of the python job profiles along with their respective salaries(according to payscale.com and indeed.com) in India.

Python Developer- Rs. 336k per year

Software Engineer- Rs. 543,840 per year

Senior Software Engineer- Rs. 909,651

Software Developer- Rs. 524,032 per year

DevOps Engineer- Rs. 634,345 per year

Data Scientist- Rs. 816,147 per year

Why Python Programming Language Has Bright Future?

  1. Python has been voted as most favorite programming language beating C, C++ and java programming. Python programming is open source programming language and used to develop almost every kind of application.
  2. Python is being used worldwide as a wide range of application development and system development programming language. Big brands and search engine giants are using python programming to make their task easier. Google, Yahoo, Quora, Facebook are using python programming to solve their complex programming problems.
  3. Python programming is versatile, robust and comprehensive. Python is high-level programming language and easy to learn as well as it reduces the coding effort compare to other programming languages.
  4. Python programming is used to write test scripts and tests mobile devices performance. It is one of the most versatile languages these days. Python programmers are most demandable in the IT industry these days and get paid more compared to another language programmer.

Resources to lean Python


Best Way to Learn Python (Step-by-Step Guide)

Python is a very popular language.

It’s also one of the languages that I recommend for beginners to start with.

But how do you go about learning this language?

The best way to learn Python is to understand the big picture of all what you need to learn before you dive in and start learning.

In this article, I divide the path of learning Python into 6 levels.

Each level covers a subset of the language that you need to master before you move on to the next one.

My focus on this article is for you to be a competent well-rounded programmer so you can easily get a job at any tech company that you choose.

But don’t worry, you don’t need to go all the way to level 6 in order to get your first job 🙂

Let’s get started.

Level 0: The Beginnings

This is the level you begin at if you are an absolute beginner.

And by absolute beginner, I mean someone who has never coded before in Python or any other programming language for that matter.

If you are coming from a different programming language, then you should skip to level 1.

In this level, most of the concepts you will be learning are general programming concepts. The fundamental skills that will bootstrap you as a programmer.

This means that these concepts are not really exclusive to Python but can be extended to other programming languages as well.

You see, a lot of programming languages are very similar and knowing what’s common (and what’s not) between programming languages will help you transition into a different one in the future.

So what are some of these general programming concepts that I am talking about?

Some of these fundamental concepts are variables, data types, operations, functions, conditionals, and loops.

If you understand what these concepts are, then skip to level 1.

Otherwise, Let me give you a very brief introduction about what these concepts mean.


Variables are essentially storage for data in your program.

More accurately, it’s a way of giving a name for data for later use.

Let’s look at an example.

# variables
msg = "Hello World!"
# this code outputs Hello World! on the screen

In the Python snippet above, we define a variable msg that stores the value Hello World!

This allows us to later print Hello World! on the screen by just using the variable name that stores this value instead of having to type the value Hello World! every time we want to use it.

Data Types

We talked about variables as storage for data, now let’s talk about data.

In Python, data has types.

For example, in the code snippet above, the data Hello World! has a specific type that Python (and other programming languages) call string.

String is simply a sequence of characters.

But strings aren’s the only data type in Python, there are also integersfloating-point numbersbooleanliststuples, and dictionaries.

By the end of level 0, you need to be comfortable with these data types and understand when (and how) to use them in your program.


Operations is how you manipulate and change data in your program.

In other words, your programs needs to operate on data and produce more data, that you also operate on, until you reach the final outcome.

This is just the lifecycle of any program.

In Python, and all programming languages, there exists at least ArithmeticComparison, and Logic operations.

# an example of an arithmetic operation
x = 5 + 2

# an example of a comparison operation
y = 3 > 4

# an example of a logic operation
z = True or False


In order to write any program that is useful, you almost always will need the ability to check conditions and change the behavior of the program accordingly.

Conditional statements using ifif else, or if elsif else gives you this ability.

Here is an example of an if-else statement in Python.

>>> if 3 > 5:
...   print('3 is greater than 5')
... else:
...   print('3 is not greater than 5')
3 is not greater than 5


A function is essentially a block of Python code that only runs when it is called.

You can pass parameters into a function as input and a function can return data as output.

In Python you define a function using the def keyword.

Here is an example of a hello world program using a function say_hello

def say_hello(msg):
  # this is the function
  # msg is the input parameter
  print(f'hello {msg}')

# calling the say_hello function

# output:
# hello world

So this was an example of the fundamental concepts that you should learn at this level.

But most importantly, what you really need to do in order to master this level is to use the above concepts to solve problems.

You will never be a good programmer if all what you do is read books or take courses.

You need to practice solving problems so get your hands dirty and start solving simple problems using Python. You can start by solving Project Euler problems.

I can’t stress enough the importance of mastering level 0.

The reason for that is, this level lays the foundation and the fundamental concepts for not only mastering Python, but mastering any other programming language as well.

So even though this is level 0, don’t take it lightly.

Level 1: Object-oriented Programming

Everything in Python is an object.

You either heard this already, or you are destined to hear about it 🙂

But wait a minute, what exactly is an object?

There are many different ways, models, or paradigms to write computer programs.

One of the most popular programming paradigms is called object-oriented programming (OOP).

In object-oriented programming, an object refers to a particular instance of a Class.

And a Class is like a blueprint of the state and actions that an object can take.

For example, in Python a Person Class might look something like this.

class Person:
  def __init__(self, name, age):
    self.name = name
    self.age = age
  def get_name(self):
    return self.name

The class declared above describes the state and actions of any Person object.

For example, any Person object will have a name and an age.

In OOP’s terminology, name and age are called the object attributes.

You can also call get_name() on any Person object to return the name of the person.

We call get_name an object method.

In other words, a Python object has attributes and methods that are defined in the object’s Class.

Here’s how to create a Person object

>>> p = Person('Alice', 22)
>>> p.get_name()

Object-oriented programming is essentially one way of structuring and designing your code.

However, I want you to understand that it is not the only way, and it is not necessarily the best way.

In order to learn OOP in Python, you need to progress through a few steps.

Step 1: Learn the concepts of OOP

As I mentioned earlier, OOP is a programming paradigm, a way of structuring and designing your code.

OOP concepts are not exclusive to Python so the concepts you will learn will easily transition to any other programming language.

Some Examples of these concepts are inheritanceencapsulation, and polymorphism.

So make sure you understand these concepts at an abstract level first before you jump into Python’s OOP.

Step 2: Learn about Python’s Classes and Objects

In this step, you need to apply the abstract concepts you learned in the previous step but specifically in Python.

Get comfortable with writing Classes and creating Objects.

Write classes that inherit from other classes and investigate the attributes and methods of the objects created.

Step 3: Solve Python problems using OOP

This is a crucial step.

In this step you want to learn how to use OOP to design and structure your code.

And as a matter of fact, this step is more of an art than a science. That means the only way to get better is through practice, practice, and more practice.

Again keep solving more problems using Python, but try to structure your solutions in an object-oriented way.

The more you practice, the more you will feel at ease with OOP.

Here is a good course on Simplivicon that pretty much covers level 0 and level 1.

Level 2: Concurrent and Parallel Programming

The days of single core processors are far gone.

Nowadays whether you are buying an off-the-shelf laptop or a high-end server for your business, your processor will definitely have multiple cores.

And sometimes, your program needs to take advantage of these multiple cores to run things in parallel.

This can potentially lead to an increased throughput, higher performance, and better responsiveness.

But let me be clear about one thing here, if high performance and increased throughput is of high importance, Python isn’t really the best language out there that supports parallel programming.

In this situation, I would personally go for golang instead (or good old C).

But since this is an article about Python, let’s keep our focus on Python.

Before you dive in and write your first parallel program, there are some parallel processing concepts that you should learn about first.

Here are some of these concepts.

Mutual Exclusion

When you have some data that is shared across multiple threads or processes, it is important to synchronize access to these shared resources.

If you don’t, a race condition can happen which might lead to unexpected and sometimes disastrous consequences. I will talk more about race conditions later.

Mutual exclusion means that one thread blocks the further progress of other concurrent threads that require the use of the shared resource.


Locks is one of various implementations of mutual exclusion.

To understand what locks are, you can think about them from a conceptual perspective.

If a thread wants to access a shared resource, this thread must grab a lock before it’s granted access to that resource.

And after it’s done with the resource, it releases this lock.

If the lock is not available because it is grabbed by another thread, then the thread has to wait for the lock to be released first.

This simple concept guarantees that at most one thread can have access to a shared resource at a time.


A deadlock is when your program comes to a complete halt because some of the threads can’t progress further because they can’t acquire a lock.

For example, imagine Thread A is waiting on Thread B to release a lock. At the same time, Thread B is waiting on Thread A to release another lock that Thread A is currently holding.

In this dire situation, neither Thread A nor Thread B can progress any further so your program is hosed!

This is what a deadlock is.

And it happens more often than you think.

To make the situation worse, it’s also one of the hardest problems to debug.

Race conditions

As I mentioned earlier, a race condition is a situation that arises when accessing a shared resource isn’t protected (for example, by locks).

This can lead to disastrous unexpected outcomes.

Take a look at this example.

import threading
# x is a shared value
x = 0
COUNT = 1000000

def inc():
    global x
    for _ in range(COUNT):
        x += 1

def dec():
    global x
    for _ in range(COUNT):
        x -= 1

t1 = threading.Thread(target=inc)
t2 = threading.Thread(target=dec)


Here is what the code above does. There is a shared global variable x that is initialized to 0.

Two functions inc and dec run in parallel. inc() increments the value of x 1 million times whereas dec() decrements the value of x 1 million times.

By quickly going through the code, it can be concluded that the final value of x should be 0… but is it?

Here is what I get when I run the above code.

 $ python3 race.py
 $ python3 race.py
 $ python3 race.py
 $ python3 race.py

The reason why this is happening is because the shared resource x is not protected (by locks for example).

Python’s Parallel Programming

Only after you’re comfortable with the concepts discussed above that you are ready to learn how to write concurrent programs in Python.

First you should learn how Python’s definition of multiprocessing is different from multithreading. (By the way, this is completely unrelated to threads and processes from an OS perspective).

To understand this distinction between multiprocessing and multithreading from Python’s view, you will need to learn and understand the global interpreter lock (GIL).

You will also need to learn about the threadingqueue, and multiprocessing Python modules.

All of these modules provide you with the primitives you need to write parallel programs.

Level 3: Socket Programming

By now you should be very comfortable writing Python code that runs on a single machine.

But what if you want to write code that communicates with other machines over a network?

If you want to do that, then you need to learn about socket programming.

And for that I highly recommend you learn about the basics of computer networks first. Here’s my favorite book.

After you learn the basic networking concepts, you can use Python’s libraries to write code on one machine that communicates with code on another.

It’s like magic. I still remember the exhilaration I felt the first time I had two laptops communicating back and forth to each other over a Wifi network.

Follow these three steps to get started.

Step 1: Write an Echo Program

In this step, you will use Python’s socket module to write a simple TCP server on one machine and a TCP client on another.

Make sure they are two different computers and that both of them are connected to your home network.

The idea of the Echo program is simple. The client side reads a message from the user and sends this message to the server over the network.

At the server side, when this message is received, the server echoes the same message back to the client.

Think of the Echo program as the Hello World program but for socket programming.

After that you can move on to more complex programs.

Step 2: Play around with HTTP

Once you’re comfortable with writing simple TCP client-server applications, you can start using Python’s requests module to send and receive HTTP messages.

This is especially useful because the vast majority of web services these days provide an HTTP API interface that you can interact with programmatically. For example, Facebook, Twitter, and Google maps all have HTTP API interfaces that your code can communicate with.

And if you feel a little more adventurous and want to take this a bit further, you can also scrape the web with BeautifulSoup.

Step 3: Know thy tools

When you write a networking program, sometimes your program will work at the first time.

But sometimes it won’t.

When that happens you need to equip yourself with the tools necessary to troubleshoot what’s going on.

Here are some of the most popular networking tools that you will need.

ping is used to check the connectivity between your machine and another one.

netstat is a versatile networking tool that allows you to, among other things, monitor network connections both incoming and outgoing.

tcpdump is one of my favorite tools for learning networks. It tools allows you to listen to, capture, and analyze real packets going into and out of your computer through any network interface.

Wireshark is a nice GUI interface that does pretty much everything that tcpdump can do. I recommend starting out with Wireshark before moving on to tcpdump just because it’s a little more user-friendly.

And like I said, to understand what all these Get, SYNSYN ACKFIN mean you need to learn networking fundamentals first.

Level 4: Data Structures and Algorithms in Python

If you reached this level, give yourself a pat on the shoulder.

Because by now, you have the skills that enable you to solve a wide variety of problems.

However, something is missing.

You are still not seasoned enough at writing efficient code.

What do I mean by that?

For example, you don’t know how to modify your code to make it run faster. You can’t even analyze why it is slow in the first place.

This is normal.

The knowledge you have learned so far in the previous levels are not enough for you to have a solid understanding of what performance really is, and how to modify your existing code to make it run faster.

Don’t believe me? Look at this simple code that calculates the nth Fibonacci number.

def fib(n):
    if n < 2:
        return n
    return fib(n-2) + fib(n-1)


The code looks simple enough and very straightforward, right?

Try using this code to calculate fib(100) [SPOILER ALERT: it will take an extremely long time]

Now let’s make a simple modification to the code.

def fib(n, d):
    if n < 2:
        return n
    if n not in d:
        d[n] = fib(n-2, d) + fib(n-1, d)
    return d[n]

print(fib(100, {}))

This time all it took was a few milliseconds and you will get the answer, which is 354224848179261915075 just in case you’re wondering 🙂

I used what’s called dynamic programming to solve this problem and make it run astronomically faster.

Well I hope you are convinced by now that you should learn data structures and algorithms.

The skills that you are going to learn at this level are some of the major differentiators between average coders and solid programmers.

You will need to learn about linked liststreesstacksqueuesgraphshash tablesrecursiondynamic programming, searching and sorting algorithms, etc…

Once you master these concepts, you are steps away from getting a software engineering job at any tech company of your choice.

I really mean it!

Level 5: Python Coding Interview Practice

Congratulations! Now you have what it takes to apply for any software engineering job in any tech company in the whole world.

You only need to pass this dreaded coding interview.

In fact a series of them.

If you are at this level, I have written an in-depth article about how you can prepare for a coding interview.

A typical coding interview will assess your problem solving skills, communication skills, knowledge of data structures and algorithms, in addition to how good and efficient you are at translating your thoughts into code.

The best way to pass coding interviews is to give yourself an ample amount of time to prepare.

The more you prepare, the better your interview experience will be, and the more likely you will land your dream job.

Simpliv is an excellent resource with a ton of coding interview questions.

Simpliv allows you to submit your Python solutions to the coding questions and get an instant feedback about the validity and the efficiency of your solutions.

After you start working, you will learn a lot on the job and you will start gaining extensive experience in a very short amount of time.

This is when level 6 starts.

Level 6: Advanced Python

If you want to venture into the territory of Python fluency and take your skills to the next level, then I highly recommend the “Fluent Python” book.

This book assumes you already have a solid understanding of the basics of Python.

In Fluent Python, some of the concepts that you already learned from introductory books are covered from a different angle, in more detail, and with greater depth.

In addition to that, you will learn some new concepts as well.

For example, some of the new concepts that you will learn in this book are

  1. Higher-order Functions: explains how functions can be used as first class objects in Python
  2. Control Flow: covers the topic of generators, context managers, coroutines, and concurrency
  3. Metaprogramming: essentially this is writing code that manipulates code. Some of the topics discussed here are decorators and meta-classes

Optional 1: Python Libraries and Frameworks

Now you have all the basics covered, you are a Python pro.

Well done.

But the journey doesn’t end here, Python has a ton of useful libraries that can help you even more.

Knowing what libraries to use and when to use them can save you a lot of time and effort and enables you to have the breadth of knowledge that is required to choose the right tools for the right job.

So let’s talk about some of the most popular Python libraries and frameworks.

1. Building API services with Python (Flask)

These days, the way large and scalable web applications are built is by creating a bunch of smaller applications that communicate with each other.

This architecture is called a micro-services architecture [buzzword alert] and each of these smaller applications is called a service or micro-service.

These micro-services can communicate in various ways but one of the most popular methods is HTTP.

In other words, each one of these services will expose an HTTP API that other services will be able to talk to.

With that said, it’s a very good investment to learn how to create API services in Python.

And one of the most popular Python libraries that make this super easy is Flask.

Here is a good tutorial about Flask.

2. Building Web applications with Django

Django is a full-fledged web framework that allows you to create an entire web application (both front-end and back-end) in Python.

By learning Django, you will also be introduced to some concepts that are very popular in other web frameworks in other languages like MVC (model-view-controller) and ORM(object-relational mapper).

MVC is a way of structuring and organizing your web application whereas ORM is a technique that bridges the gap between object-oriented programming and accessing data in a database.

And while we’re at the topic of ORM, It’s worth mentioning that you should take a look at SQLAlchemy which is a very popular, and widely-used ORM library in Python.

So roll up your sleeves and go ahead, create your first web application 🙂

3. Machine Learning Libraries

Python has become the de-facto language for machine learning and data science.

This comes as no surprise given the maturity of Python’s machine learning libraries.

If you want to be a data scientist, I highly recommend learning the mathematical and statistical fundamentals of machine learning first before learning the ML libraries in Python.

Introduction to Statistical Learning is an excellent place to start.

If you prefer a video course instead, then you should take Andrew Ng’s ML course on Simpliv.

Once you have the basics covered, start playing around with these Python libraries.

1- scikit-learn This library has everything under the sun when it comes to ML algorithms.

2- Tensorflow Another very popular open source machine learning framework.

3- pandas A popular data analysis library.

Optional 2: Python Implementation (CPython)

Python in an interpreted language.

This means that your Python code doesn’t get compiled down to a machine code directly, but first it is compiled to an intermediate language, called byte code, which is later interpreted by another piece of software called the interpreter.

Do you want to see how the bytecode looks like for a simple Hello World program?

Let’s create a source file helloworld.py

# helloworld.py
print("hello world")

Here is how to view the bytecode for the above source code

$ python3 -m dis helloworld.py
2           0 LOAD_NAME                0 (print)
            2 LOAD_CONST               0 ('hello world')
            4 CALL_FUNCTION            1
            6 POP_TOP
            8 LOAD_CONST               1 (None)
           10 RETURN_VALUE

This bytecode will then be interpreted by an interpreter. This is when it gets executed and you finally see hello world printed on your screen.

There are various Python implementations for the compiler and the interpreter.

However, CPython is the default and most widely-used one. It’s written entirely in C.

It is both an interpreter and a compiler as it compiles Python code into bytecodebefore interpreting it.

So why am I talking about Python implementation?

Do you really need to know this nitty gritty details of Python to be a Python master?

Honestly, the answer is no.

But if you are curious about how Python’s list, tuples, functions,.. etc are implemented, and if you are willing to learn a new language (C) along the way, then may be you should consider contributing to CPython.

And if you don’t know how to get started, then I highly recommend Philip Guo’s 10-hour course on CPython.

Finally, whatever level you’re at, good luck in your Python learning journey :).


Featured Posts

Top Steps to Learning Python the Right Way

What is Python? Why Programmers Should Learn Python in 2019?

Top Steps to Learning Python the Right Way


Top Steps to Learning Python the Right Way

Python is an important programming language that any developer should know. Many programmers use this language to build websites, create learning algorithms, and perform other important tasks. Learn Python in just five steps when you take advantage of the program offered through Dataquest.

One of the things that I found most frustrating when I was learning Python was how generic all the learning resources were. I wanted to learn how to make websites using Python, but it seemed like every learning resource wanted me to spend 2 long, boring, months on Python syntax before I could even think about doing what interested me.

This mismatch made learning Python quite intimidating for me. I put it off for months. I got a couple of lessons into the Simpliv tutorials, then stopped. I looked at Python code, but it was foreign and confusing:

from django.http import HttpResponse
def index(request):
return HttpResponse("Hello, world. You're at the polls index.")

The above code is from the tutorial for Django, a popular Python website development framework. Experienced programmers will often throw snippets like the above at you. “It’s easy!”, they’ll promise. But even a few seemingly simple lines of code can be incredibly confusing. For instance, why are some lines indented? What’s django.http? Why are some things in parentheses? Understanding how everything fits together when you don’t know much Python can be very hard.

The problem is that you need to understand the building blocks of the Python language to build anything interesting. The above code snippet creates a view, which is one of the key building blocks of a website using the popular MVC architecture. If you don’t know how to write the code to create a view, it isn’t really possible to make a dynamic website.

Most tutorials assume that you need to learn all of Python syntax before you can start doing anything interesting. This is what leads to months spent just on syntax, when what you really want to be doing is analyzing data, or building a website, or creating an autonomous drone. This is what leads to your motivation ebbing away, and to you just calling the whole thing off. I like to think of this as the “cliff of boring”. You need to be able to climb the “cliff of boring” to make it to the “land of interesting stuff you work on” (better name pending).

After facing the “cliff of boring” a few times and walking away, I found a process that worked better for me. What worked was blending learning the basics with building interesting things. I spent as little time as possible learning the basics, then immediately dove into creating things that interested me. In this blog post, I’ll walk you through step by step how to replicate this process, regardless of why you want to learn Python.

1. Figure Out What Motivates You to Learn Python

Before you start diving into learning Python online, it’s worth asking yourself why you want to learn it. This is because it’s going to be a long and sometimes painful journey. Without enough motivation, you probably won’t make it through. For example, I slept through high school and college programming classes when I had to memorize syntax and I wasn’t motivated. On the other hand, when I needed to use Python to build a website to automatically score essays, I stayed up nights to finish it.

Figuring out what motivates you will help you figure out an end goal, and a path that gets you there without boredom. You don’t have to figure out an exact project, just a general area you’re interested in as you prepare to learn Python.

Pick an area you’re interested in, such as:

  • Data science / Machine learning
  • Mobile apps
  • Websites
  • Games
  • Hardware / Sensors / Robots
  • Scripts to automate your work

Yes, you can make robots using Python! From the Raspberry Pi Cookbook.

Figure out one or two areas that interest you, and you’re willing to stick with. You’ll be gearing your learning towards them, and eventually will be building projects in them.

2. Learn the Basic Syntax

Unfortunately, this step can’t be skipped. You have to learn the very basics of Python syntax before you dive deeper into your chosen area. You want to spend the minimum amount of time on this, as it isn’t very motivating. I personally made it about 30% into the Codecademy Python tutorials, which was enough.

Here are some good resources to help you learn the basics:

  • Simpliv — does a good job of teaching basic syntax, and builds on itself well.
  • Learn Python the Hard Way — a book that teaches Python concepts from the basics to more in-depth programs.
  • Dataquest – Python Programming: Beginner Course — I started Dataquest to make learning Python and data science easier. Dataquest teaches Python syntax in the context of learning data science. For example, you’ll learn about for loops while analyzing weather data.
  • The Python Tutorial — the tutorial on the main Python site.

I can’t emphasize enough that you should only spend the minimum amount of time possible on basic syntax. The quicker you can get to working on projects, the faster you will learn. You can always refer back to the syntax when you get stuck later. You should ideally only spend a couple of weeks on this phase, and definitely no more than a month.

3. Make Structured Projects

Once you’ve learned the basic syntax, it’s possible to start making projects on your own. Projects are a great way to learn, because they let you apply your knowledge. Unless you apply your knowledge, it will be hard to retain it. Projects will push your capabilities, help you learn new things, and help you build a portfolio to show to potential employers.

However, very free form projects at this point will be painful — you’ll get stuck a lot, and need to refer to documentation. Because of this, it’s usually better to make more structured projects until you feel comfortable enough to make projects completely on your own. Many learning resources offer structured projects, and these projects let you build interesting things in the areas you care about while still preventing you from getting stuck.

Let’s look at some good resources for structured projects in each area:

Data science / Machine learning

  • Dataquest — Teaches you Python and data science interactively. You analyze a series of interesting datasets ranging from CIA documents to NBA player stats. You eventually build complex algorithms, including neural networks and decision trees.
  • Python for Data Analysis — written by the author of a major Python data analysis library, it’s a good introduction to analyzing data in Python.
  • Scikit-learn documentation — Scikit-learn is the main Python machine learning library. It has some great documentation and tutorials.
  • CS109 — this is a Harvard class that teaches Python for data science. They have some of their projects and other materials online.

Mobile Apps

  • Kivy guide — Kivy is a tool that lets you make mobile apps with Python. They have a guide on how to get started.


  • Flask tutorial — Flask is a popular web framework for Python. This is the introductory tutorial.
  • Bottle tutorial — Bottle is another web framework for Python. This is how to get started with it.
  • How To Tango With Django — A guide to using Django, a complex Python web framework.


An example of a game you can make with Pygame. This is Barbie Seahorse Adventures 1.0, by Phil Hassey.

Hardware / Sensors / Robots

Scripts to Automate Your Work

Once you’ve done a few structured projects in your own area, you should be able to move into working on your own projects. But, before you do, it’s important to spend some time learning how to solve problems.

4. Work on Projects on Your Own

Once you’ve completed some structured projects, it’s time to work on projects on your own to continue to learn Python better. You’ll still be consulting resources and learning concepts, but you’ll be working on what you want to work on. Before you dive into working on your own projects, you should feel comfortable debugging errors and problems with your programs. Here are some resources you should be familiar with:

  • StackOverflow — a community question and answer site where people discuss programming issues. You can find Python-specific questions here.
  • Google — the most commonly used tool of every experienced programmer. Very useful when trying to resolve errors. Here’s an example.
  • Python documentation — a good place to find reference material on Python.

Once you have a solid handle on debugging issues, you can start working on your own projects. You should work on things that interest you. For example, I worked on tools to trade stocks automatically very soon after I learned programming.

Here are some tips for finding interesting projects:

  • Extend the projects you were working on previously, and add more functionality.
  • Go to Python meetups in your area, and find people who are working on interesting projects.
  • Find open source packages to contribute to.
  • See if any local nonprofits are looking for volunteer developers.
  • Find projects other people have made, and see if you can extend or adapt them. Github is a good place to find these.
  • Browse through other people’s blog posts to find interesting project ideas.
  • Think of tools that would make your every day life easier, and build them.

Remember to start very small. It’s often useful to start with things that are very simple so you can gain confidence. It’s better to start a small project that you finish that a huge project that never gets done. At Dataquest, we have guided projects that give you small data science related tasks that you can build on.

It’s also useful to find other people to work with for more motivation.

If you really can’t think of any good project ideas, here are some in each area we’ve discussed:

Data Science / Machine Learning

  • A map that visualizes election polling by state.
  • An algorithm that predicts the weather where you live.
  • A tool that predicts the stock market.
  • An algorithm that automatically summarizes news articles.

You could make a more interactive version of this map. From RealClearPolitics.

Mobile Apps

  • An app to track how far you walk every day.
  • An app that sends you weather notifications.
  • A realtime location-based chat.


  • A site that helps you plan your weekly meals.
  • A site that allows users to review video games.
  • A notetaking platform.


  • A location-based mobile game, where you capture territory.
  • A game where you program to solve puzzles.

Hardware / Sensors / Robots

  • Sensors that monitor your home temperature and let you monitor your house remotely.
  • A smarter alarm clock.
  • A self-driving robot that detects obstacles.

Scripts to automate your work

  • A script to automate data entry.
  • A tool to scrape data from the web.

My first project on my own was adapting my automated essay scoring algorithm from R to Python. It didn’t end up looking pretty, but it gave me a sense of accomplishment, and started me on the road to building my skills.

The key is to pick something and do it. If you get too hung up on picking the perfect project, there’s a risk that you’ll never make one.

5. Keep working on harder projects

Keep increasing the difficulty and scope of your projects. If you’re completely comfortable with what you’re building, it means it’s time to try something harder.

Here are some ideas for when that time comes:

  • Try teaching a novice how to build a project you made.
  • Can you scale up your tool? Can it work with more data, or can it handle more traffic?
  • Can you make your program run faster?
  • Can you make your tool useful for more people?
  • How would you commercialize what you’ve made?

Going forward

At the end of the day, Python is evolving all the time. There are only a few people who can legitimately claim to completely understand the language, and they created it.

You’ll need to be constantly learning and working on projects. If you do this right, you’ll find yourself looking back on your code from 6 months ago and thinking about how terrible it is. If you get to this point, you’re on the right track. Working only on things that interest you means that you’ll never get burned out or bored.

Python is a really fun and rewarding language to learn, and I think anyone can get to a high level of proficiency in it if they find the right motivation.

I hope this guide has been useful on your journey. If you have any other resources to suggest, please let us know!

Find out more about how you can learn Python and add this skill to your portfolio by visiting Dataquest.

Have Another Resource You Recommend?

Last month I shared seven reasons from my personal experience why you should learn the Python programming language. My goal here today was to help provide a list of resources that you can use on your own Python programming journey.

Do you have personal experience with any of the resources I listed above?

Did I miss a book or course that you recommend?

Be sure to leave a comment in the form below and let us know!

What is Python? Why Programmers Should Learn Python in 2019?

What is Python?

Python is an object-oriented programming language created by Guido Rossum in 1989. It is ideally designed for rapid prototyping of complex applications. It has interfaces to many OS system calls and libraries and is extensible to C or C++. Many large companies use the Python programming language include NASA, Google, YouTube, BitTorrent, etc.
Python programming is widely used in Artificial Intelligence, Natural Language Generation, Neural Networks and other advanced fields of Computer Science. Python had deep focus on code readability & this class will teach you python from basics.

Digital binary code concept.

Why Programmers Should Learn Python in 2019?

If you are thinking to learn Python but not sure why you should do that then here are 10 reasons which highlight the benefits of learning Python in 2019.

Though, the questions depend upon who is asking that i.e. for a beginner, learning Python makes sense because its simple and main reason for learning Python is simplicity.

Similarly, for an experienced programmer who is looking to go into Data Science and Machine learning, learning Python makes sense because it’s quickly becoming the most used programming language and there are powerful APIs and library available for AI, Data Science, and Machine learning.

Anyway, without any further ado, here are my 10 reasons to learn Python in 2019:

1. Data Science

This is the single, biggest reason why many programmers are learning Python in 2019. I know many of my friends who are bored with their Java programming jobs in Investment banks are learning Python on Simpliv to make a career in Data Science due to exciting work and high pay.

But, what makes Python a preferred language for Data Science and Machine Learning?Didn’t R was considered best for that not too long ago? Well, I think the libraries and framework Python offers e.g. PyBrain, NumPy and PyMySQL on AI, DataScience, and Machine learning are one of that reason.

Another reason is diversity, Python experience allows you to do a lot more than R e.g. you can create scripts to automate stuff, go into web development and so much more.

If you are interested in becoming a Data Scientist in 2019 and looking for pointers, I suggest you check out Data Science, Deep Learning, & Machine Learning with Python course on Simpliv. I have purchased this course and it’s one of the awesome resources. You can get it in less than $9 sometimes.

best data science course in Python

And if you need more choices, you can also take a look at this list of best Python Data Science courses for programmers.

2. Machine Learning

This is another reason why programmers are learning Python in 2019. The growth of machine learning is phenomenal in last a couple of years and it’s rapidly changing everything around us. Algorithms become sophisticated day by day, the best example is Google which can now answer what you are expecting.

If you are interested in machine learning, want to do a pet project or just want to play around, Python is the only major programming language which makes it easy.

Though there are machine learning libraries available in Java, you will find more content around Python as developer community is preferring Python over anything else on Data Science and Machine learning.

If you are interested in machine learning with Python, I suggest you to further check Machine Learning A-Z™: Hands-On Python & R In Data Science course on Simpliv

best machine learning course in Python

And if you need more options, here is another comprehensive list of machine learning courses for programmers.

3. Web Development

The good old development is another reason for learning Python. It offers so many good libraries and frameworks e.g. Django and Flask which makes web development really easy.

The task which takes hours in PHP can be completed in minutes on Python. Python is also used a lot for web scrapping. In fact, there is a Free Python course on Simpliv which will teach you that while teaching Python.

There are a lot of using Python web development frameworks like Django and Flask which can help you quickly create your web application in no time.


4. Simplicity

This is the single biggest reason for beginners to learn Python. When you first start with programming and coding, you don’t want to start with a programming language which has tough syntax and weird rules.

Python is both readable and simple. It also easier to setup, you don’t need to deal with any class path problems like Java or compiler issues like C++.

Just install Python and you are done. While installing it will also ask you to add Python in PATH which means you can run Python from anywhere on your machine.

5. Big Community

You need a community to learn a new technology and friends are your biggest asset when it comes to learning a programming language. You often get stuck with one or other issue and that time you need helping hand.

Thanks to Google, you can find the solution of your any Python related problem in minutes. Communities like StackOverflow also brings many Python experts together to help newcomers.

6. Libraries and Frameworks

One of the similarities between Python and Java is the sheer number of open source libraries, frameworks, and modules available to do whatever you want to do. It makes application development really easy.

Just imagine creating a web application without Spring in Java or Django and Flask in Python. It makes your job simple as you only need to focus on business logic.

Python has numerous libraries for different needs. Django and Flask are two of the most popular for web development and NumPy and SciPy are for Data Science.  If you want to learn more, here is a list of 8 Useful Python Machine learning libraries.


7. Automation

When I first come to know about Python was due to one of my scripting need. I was working with an application which receives messages over UDP and there was a problem, we were not seeing messages in the log.

I wanted to check if we are receiving any UDP traffic on that box and that port or not but I couldn’t find a handy UNIX command to do that. My friend who sits next to me was learning Python and he wrote a utility in just 5 minutes to intercept UDP message using one of the Python modules.

Obviously, I was impressed with the time it took for him to write such a tool but that just highlights the power of Python when it comes to writing scripts, tool and automating stuff.

If you seriously want to know how much Python help with automation, my favorite place is the Automate boring stuff with Python book, simply awesome book.

best book to learn Python


8. Multipurpose

One of the things I like about Python is its Swiss Army knife nature. It’s not tied to just one thing e.g. R which is good on Data Science and Machine learning but nowhere when it comes to web development. Learning Python means you can do many things.

You can create your web applications using Django and Flask, Can do Data Analysis using NumPy, Scipy, Scikit-Learn, and NLTK. At a bare minimum, you can use Python to write scripts to automate many of your days to day tasks.

9. Jobs and Growth

Python is growing really fast and big time and it makes a lot of sense to learn a growing programming major programming language if you are just starting your programming career.

It not only help you to get a job quickly but also it will also accelerate your career growth. IMHO, for beginners, after simplicity, this should be the most important reason to learn Python

10. Salary

Python developers are one of the highest paid developers, particularly in the Data Science, Machine learning and web development. On average also, they are very good paying, ranging from 70K USD to 150K USD depending upon their experience, location, and domain.

Why learn Python in 2019

Useful Resources to Learn Python

If you decide to learn Python in 2019 then here are some of the useful Python books, courses, and tutorials to start your journey in the beautiful world of Python.

Top 10 Python Books for Beginners & Advanced Programmers 2019

And if you are still not convinced about learning Python then look at this image, it correctly shows the life of a Python developer:

10 Reasons to Learn Python Programming in 2018

That’s all about some of the important reasons to learn Python in 2019. As I said, it’s important to know to code in today’s world and if you don’t know coding you are missing something and Python is a great way to start learning to code.

For programmers who already know Java or C++, learning Python not just make you a Polyglot programmer but also gives you a powerful tool in your arsenal to write scripts, create a web application and open door on exciting field of Data Science and Machine Learning.

In short, if you could learn just one programming language in 2019 then make it to Python and to start with, The Complete Python MasterClass is the best course.

Thanks for reading this article so far. If you decide to learn Python in 2019 than its a great decision and I wish you all the best for your journey.

Top 10 Python Books for Beginners & Advanced Programmers 2019

Python is a general-purpose interpreted programming language used for web development, machine learning, and complex data analysis. Python is a perfect language for beginners as it is easy to learn and understand. As the popularity of the language is soaring, the opportunities in Python programming are amplifying as well. If you wish to learn Python programming, there are plenty of books available in the market. Books provide you the ability to learn at your on time even if you are on the go and they go really in detail. We bring to you a list of 10 best Python books for beginners and advanced programmers. These books will help programmers of all skill levels, from amateurs to code wizards. The list also includes a few free Python books for beginners.

Best Python Books for Beginners

Python Crash Course

‘Python Crash Course’ by Eric Matthews is a fast-paced and comprehensive introduction to Python language for beginners, who wish to learn Python programming and write useful programs. The book aims to get you up to speed fast enough and have you writing real programs in no time at all. This book is also for programmers who have a vague understanding of the language and wish to brush up their knowledge before trying their hands on Python programming. As you work through the book, you will learn the use of libraries and tools such as Numpy and matplotlib and work with data to create stunning visualizations. You will also learn about the idea behind 2d games and Web applications and how to create them.

This 560 pages long book is majorly dissected into two parts. The first part of the book discusses the basics of Python programming and sheds lights on concepts such as dictionaries, lists, loops, and classes. You will understand the working of a Python program and learn how to write clean and readable code which creates interactive programs. The part ends with the topic of how to test your code before you add it to a project. The second part of the book follows a practical approach and will help you test your knowledge by presenting three different projects, an arcade game, a simple web application and data visualizations using Python’s libraries.

Head-First Python (2nd edition)

‘Head-First Python’ by Paul Barry is a quick and easy fix for you if you wish to learn the basics of Python programming without having to slog through counterproductive tutorials and books. The book will help you in gaining a quick grasp of the fundamentals of Python programming and working with built-in functions and data structures. The book then moves to help you build your own web application, exception handling, data wrangling, and other concepts. Head first Python makes use of a visual format rather than a text-based approach, helping you to see and learn better.

The author is Paul Barry, a lecturer at the Institute of Technology, Carlow, Ireland. Before entering the academic world, he worked for over a decade in the IT industry. He is the author of certain well-known programming books, such as Programming the Network with Perl, Head First Programming and Head First Python.


Learn Python the Hard Way (3rd Edition)

‘Learn Python the Hard Way’ by Zed A. Shaw (3rd Edition) is a collection of 52 perfectly collated exercises. You will have to read the code and type it precisely. Once typed, you will have to fix the mistakes in the code for a better understanding and watch the programs run. These exercises will help you understand the working of software, structure of a well-written program and how to avoid and find common mistakes in code using some tricks that professional programmers have up their sleeves.

The book begins it all by helping you install a complete Python environment, which helps you in writing optimized code. The book then discusses various topics, such as basic mathematics, variables, strings, files, loops, program design, and data structures among many others. The book is ideal for beginners who wish to learn Python programming through the crux of the language. The author is Zed A. Shaw, who is the creator of the Hard Way series which includes books on C, Python and Ruby programming language.

Python Programming: An Introduction to Computer Science (3rdEdition)

‘Python Programming’ by John Zelle is the third edition of the original Python programming book published in 2004, the second edition of which was released in 2010. Instead of treating this book as a source to Python programming, it should be taken as an introduction to the art of programming. This book will introduce you to computer science, programming, and other concepts, only using Python language as the medium for beginners. The book will discuss its contents in a style that is most suitable for beginners, who will find the concepts in the book easy to understand and interesting.

The third edition of this extremely successful book follows the path paved by the first edition and continues to test students through a time-tested approach while teaching introductory computer science. The most notable change in this edition is the removal of nearly every use of python eval() library and the addition of a section which discusses its negatives. The latest version also uses new graphic examples.

Free Python Books for Beginners

Learning with Python: How to Think Like a Computer Scientist

‘Learning with Python’ by Allen Downey, Jeff Elkner and Chris Meyers is an introduction to Python programming and using the language to create wonderful real-world programs. The book is divided into 20 sections and also includes a contributors list and a way forward. The initial sections discuss the basics of programming and what makes up a program. Then it moves on to basic Python concepts such as variables, functions, conditionals, fruitful functions and iteration. Towards the end, the book discusses the core concepts such as objects, inheritance, lists, stacks, queues, trees and debugging.

The book is available for free in a variety of formats, which include PDF, Postscript, Gzipped Rar and HTML. Users are free to download and print these files as the book is licensed under the GNU Free Documentation License. The book has also been translated in Spanish, Italian, German and Czech, and available for download.

A Byte of Python

‘A Byte of Python’ by C.H. Swaroop is a free book on Python programming with an aim to guide the beginner audience to an understanding of the Python language. The book will discuss the Python 3 version majorly, but will also help you adapt to the older versions of the language. The book is available in over 26 languages including Turkish, Swedish, French, Chinese, German, Spanish, Russian, Ukrainian, Portuguese and Korean. The translations have been provided by active community members who vigorously work to keep the edits going on as the book is updated.

The book initiates its approach with an introduction to what the book is about and what it demands from the readers concerning dedication. Then it describes Python and how it has emerged as one of the most powerful languages in the programming world. It then moves on to Python concepts and describes them in detail along with examples at every step. It culminates with how you can continue learning Python after reading this book and leaves you with a problem to solve, testing your skills even at the last step.


Best Python Books Advanced Programmers

Introduction to Machine Learning with Python: A Guide for Data Scientists

Many commercial applications and projects have employed machine learning as an integral ingredient, and the number of applications doing so has only risen over the years. This book by Sarah Guido and Andreas C. Muller will teach you how to use Python programming language to build your own machine learning solutions. As the amount of data usage increases with the second, the limitation to machine learning applications is only our imagination.

Throughout the course of this book, you will learn about the steps required to create a rich machine-learning application using Python and scikit-learn library. The book will introduce you to the fundamental concepts and uses of machine learning, before moving on to the pros and cons of popular machine learning algorithms. You will then learn about the advanced methods for model evaluation and the concept of pipelines, which is used for encapsulating your workflow and chaining models. In conclusion, the book will provide suggestions to help you improve your data science skills.


Fluent Python: Clear, Concise, and Effective Programming

‘Fluent Python’ by Luciano Ramalho will be your hands-on-guide that will help you learn how to write effective Python code by using the most neglected yet best features of the language. The author will take you through the features and libraries of the language, and will help you make the code shorter, faster and readable.

The book covers various concepts including python data model, data structures, functions as objects, object-oriented idioms, control flow, and metaprogramming. Using this book, advanced Python programmers will learn about Python 3 and how to become proficient in this version of the language. The author is Luciano Ramalho, a Web Developer who has worked with some of the largest news portals in Brazil using Python and has his own Python training company.

Python Cookbook: Recipes for Mastering Python 3

‘Python Cookbook’ by David Beazley and Brian K. Jones will help you master your programming skills in Python 3 or help you update older Python 2 code. This cookbook is filled with recipes tried and tested with Python 3.3 is the ticket for experienced Python programmers who wish to take the approach to modern tools and idioms rather than just standard coding. The book has complete recipes for a variety of topics, covering Python language and its uses, along with tasks common to a large number of application domains.

Some of the topics covered in the book are but not limited to strings, data structures, iterators, functions, classes, modules, packages, concurrency, testing, debugging and exceptions. Throughout the book, the recipes mentioned above will presuppose that you have the necessary knowledge to understand the topics in the book. Each recipe contains sample code the reader can use in their projects. The code is followed by a discussion about the working of the code and why the solution works.


Programming Python: Powerful Object-Oriented Programming

‘Programming Python’ by Mark Lutz is ideal for programmers who have understood the fundamentals of Python programming and ready to learn how to use their skills to get real work done. This book includes in-depth tutorials on various application domains of Python, such as GUIs, the Web and system administration. The book will also discuss how the language is used in databases, text processing, front-end scripting layers, networking and much more.

he book will explain the commonly used tools, language syntax, and programming techniques through a brief yet clear approach. The book is filled with many examples that show the correct usage and common idioms. The book also digs into the language as a software development tool, along with multiple examples illustrated particularly for that purpose.


If you are looking for online Python tutorials or courses, then http://www.simpliv.com has a great list of community-curated and recommended top Python tutorials: Python tutorials and courses