Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
Python is one of the languages that is witnessing incredible growth and popularity year by year. In 2017, Stackoverflow calculated that python would beat all other programming languages by 2020 as it has become the fastest-growing programming language in the world.
So why is Python so popular?
Let’s find out below:
1) Easy to Learn and Use
2) First-choice Language
3) Mature and Supportive Python Community
4)Support from Renowned Corporate Sponsors
5) Versatility, Efficiency, Reliability, and Speed
6) Big data, Machine Learning and Cloud Computing
7) Hundreds of Python Libraries and Frameworks
There are many frameworks and libraries are available for python language, such as:
>matplotib for plotting charts and graphs
>SciPy for engineering applications, science, and mathematics
>BeautifulSoup for HTML parsing and XML
>NumPy for scientific computing
>Django for server-side web development
8) The Flexibility of Python Language
Python language can help a lot in automation of tasks as there are lots of tools and modules available, which makes things much more comfortable.Python is the best performance booster in the automation of software testing also. One will be amazed at how much less time and few numbers of lines are required to write codes for automation tools.
Where do I begin?
We’ll put a rest to your confusion. You begin with Python.
Now, you might ask — Why learn Python? What’s so special about it?
Why choose Python? According to a recent study, Python is the most popular programming language choice among Developers,Data Analyst and Data Scientists.
How to learn Python?
Now let’s see how to learn Python in a few simple steps.
1. Set up your machine.
You cannot possibly learn Python without prepping up your machine for it, can you? The most convenient way to do it is to download Anaconda (or else you can use pycharm, python IDE or google colab according to your convenience)
2. Start with the basics of Python.
The best way to start learning Python would be to find a suitable Python course. (I’ll suggest https://letsupgrade.in/fcs from where I started my python journey!) Python courses introduce you to the fundamentals of Python, including variables, data types, functions, loops, operators, conditional statements, various libraries and frameworks, among other things. You will not only need to understand what these concepts are but also learn about their specific purpose.
3. Get comfortable with Python Libraries.
As we mentioned before, Python libraries are immensely helpful in programming. So, once you’ve mastered the fundamentals of the language, you must move on to the next best thing → Python libraries.Some of the widely used libraries are Pandas, NumPy, SciPy, PyTorch,Theano, Scikit-Learn, Keras, and Eli5. Your goal should be to find the best practices.
4. Work on mini Python projects.
By the time you will reach this step, you will have known all the basics of Python, its libraries and their uses. Now’s the time to put your theoretical knowledge to practical use — working on Python projects. You don’t have to build something too complicated; you can start working with APIs and developing small applications with Python. You could also try automating small routine tasks with Python.
It’s the same for Python as it for everything else. With regular practice, you’ll hone your programming skills.
Let’s see why python is becoming most popular language..?
Master Data Analysis, Manipulation, and Visualization with Pandas.
If you wish to work with Python, you must know the nitty-gritty of Pandas. It comes with a high-performance data structure, known as a “DataFrame” that works best for different types of tabular data. In addition to that, it also has many useful tools for reading/writing data, handling missing data, filtering data, cleaning raw data, merging datasets, and visualizing data. Once you
know Pandas inside-out, your efficiency will increase by leaps and bounds.
But there’s a catch — Pandas incorporates many functionalities for accomplishing the same task.
Python for data science
→ How Easy Is It?
With Data Science emerging as the hot new career option for the 21st century, it is attracting both young aspirants and professionals like a moth to a flame. While a career in Data Science is highly promising, the part where freshers tend to get astray at the beginning itself.
If you’re just starting with Data Science, the question that will first pop up in your mind is:
Where do I begin? Why learn Python? What’s so special about it?
Why choose Python for Data Science?
It might sound cliched, but Python is a perfect choice for beginners trying to get started in Data Science. There are numerous reasons for this. But before we dig in deeper into those reasons, let’s look at some stats to back our claim.
According to a recent study, Python is the most popular programming language choice among Data Scientists.
Data science is one of the hottest fields where Python is involved deeply in the roots. Python is used to process large amounts of data, clean the data, building machine learning models and visualize data.
Some of the important libraries for data science are:
The more you practice, the better you’ll get. Apart from developing personal
Data Science projects, you could always take part in Kaggle competitions, enroll in advanced online courses, attend Data Science and tech conferences/seminars, read journals and books, etc. There are many ways of learning — you have to be open to the idea of learning!
Best choice for Machine Learning
Programming is a pivotal aspect of Machine Learning. After all, ML applications and ML algorithms are written and designed using programming languages. However, there’s often much confusion surrounding the question, “what are the best programming languages for Machine Learning?”
And the answer is Python — Python is extremely popular in the developer and coding community. It is considered one of the best programming languages for machine learning. It is a highly dynamic,open-source language that supports object-oriented, imperative, functional, as well as procedural development paradigms. Python comes with an assortment of excellent libraries and tools for ML, including Scikit Learn,TensorFlow, ChatterBot, and much more.
Python is used for web development and internet applications. Web frameworks like Django and Flask are one of the most popular frameworks. They allow you to write server-side code in Python language.
With a framework, it becomes easier to build backend logic like mapping different URLs to Python code, dealing with databases and generating HTML files to view on the user’s device.
The standard library of Python supports many internet protocols like:
- JSON and XML
- Email processing
- FTP and IMAP
- Socket interface.
Desktop GUI Applications
You can build desktop applications along with their GUI all in Python. Python has a simple syntax, modular architecture, and a rich set of tools that can work on various operating systems. This is the reason why it is suitable for desktop applications.
The platform-independent GUI toolkits that you can use are:
Scientific and Numeric Calculations
A lot of scientists, researchers, programmers, and statisticians use Python for scientific and numeric calculations. Python’s simplicity and ability to handle operations with large numbers makes it pretty useful in scientific calculations. There are ample of libraries available for Scientific and numeric calculations.
Some of these are:
Games and 3D Graphics
This is an interesting part for everyone, making games are very exciting as you can create anything with your imagination. Python has some amazing libraries, some of them are PyGame and PyOpenGL. You can build wonderful games with these libraries.
You can create 3D graphics with Python OpenGL and Blender 3d API.
If you love game development, then you should check out PyWeek which hosts interesting gaming contests.
In software development, Python is mostly used as a support language for building control and management, testing and automating the workflow.
- SCons — build-control.
- Buildbot and Apache Gump — automation and continuous compilation and testing.
- Roundup or Trac — project management and bug-tracking.
- Tools like Selenium can be used with Python for testing web applications.
Python is the most preferred language for beginners and for teaching purposes. It is a good choice to learn about programming and computer science concepts.
Python has fantastic features like special libraries, scalability, extensibility, and simplicity. Larger applications can be easily customized using Python. A popular website Reddit which was originally written in Common Lisp was rewritten with Python in 2005. Youtube also uses Python for some of its functionalities.
Python is a great choice for developing ERP and E-commerce systems.
- Tryton — A three-tier, high-level general-purpose application platform.
- Odoo — It’s an all-rounder management software with a range of business applications. With that, it forms a complete suite of enterprise-management applications in-effect.
Python is also used in making connections between client and server, socket programming and network programming. It supports lower-level network programming.
- Twisted Python — Framework for asynchronous network programming.
- Easy to use socket interface for socket programming.
After all these possibilities, how can Python lack behind in connectivity with databases? Python is also used in server-side programming where it connects to SQL or NoSQL databases and performs database operations. Some of the libraries for working with databases are:
- PyMySQL — to work with MySQL database.
- PyMongo — to work with MongoDB NoSQL database.
How’s the Future of Python Developer?
It is bright! A general-purpose programming language, Python is used for web development, as a support language for software development, in applications used in scientific computing, data analysis and machine learning. You name the sector and Python developer will find their way to effect.
In particular, the growth and demand of Artificial Intelligence, Machine Learning and Deep Learning is on the rise among enterprises. In an era where technological innovation and digital transformation has been prevailing over due to these disruptive technologies, the requirement for Python programmers will continue to surge ensuring a bright future for them.
Bottom line — try to put your knowledge to good use and build something!
Keep practising and upskilling.
“Practice makes a man perfect.”
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Have a good journey with python!