Python is an immensely popular and one of the most highly-demanded programming languages in the world. Why?Â
Simply because of its universal appeal. Whether it is Data Science or Big Data, or be it for coding and app development, Python finds applications everywhere. That’s how versatile it is. The language has become so popular in recent times that aspirants are flocking to learn the language and acquire Python programming skills.
If you are one such aspirant who wishes to learn Python but is sceptical about it, wondering, “Should I learn Python?”
Today, we’ll put those doubts to rest!Â
Why Learn Python?
To make it easier for you, we’ve listed the top reasons why to learn Python.
1. It couldn’t get simpler than Python!
The main reason why Python is an excellent choice for beginners is its innate simplicity. Often beginners seeking to enter the coding/Data Science domain think “why to learn Python?” and our answer to that is – why not learn Python?
Python’s simple syntax (it almost resembles English!) and high readability factor make it a beginner-friendly language. Naturally, the learning curve of Python is way shorter than that of any other language (Jave, C, C++, etc.). Moreover, Python lets you head straight to your research part without worrying about the documentation.Â
This is why Python is widely used in both development and Data Science fields for web development, text processing, data analysis, and statistical analysis, among other things.Â
2. Python is highly flexible and extensible
Python is highly scalable and extensible. This flexibility of Python allows you to perform cross-language operations without any hassle. Not only can you integrate it with Java and .NET components, but you can also use Python to invoke C/C++ libraries.Â
Also, almost all modern platforms like Windows, Linux, Macintosh, Solaris, etc., support Python.
3. Python has a library to cater to your every need.
No other language can boast of as many useful libraries as Python. The programming language comes with the choicest assortment of libraries that come in handy for development and Data Science tasks. It has NumPy, SciPy, Scikit-Learn, Matplotlib, Pandas, StatsModels, and so much more. Thanks to the vast collection and inclusion of libraries over the years, Python’s functionalities and capabilities have significantly multiplied. Read: Python’s most popular machine learning libraries
NumPy is one of the earliest Python libraries that incorporates high-level mathematical functions operating on multi-dimensional arrays and matrices. It is the perfect choice for scientific computing. SciPy, the scientific equivalent of NumPy, is equipped with everything you need for numerical integration and analysis of scientific data.
Pandas is another popular Python library that was built on top of NumPy. It is primarily used for data analysis. Scikit-Learn, PyBrain, PyLearn2, and PyMC are Python’s ML libraries.Â
You name the need – Python has a library for it!
4. Python makes web development a breeze
Another reason why to learn Python is that it makes the web development process so much easier. Python comes with a wide variety of web development frameworks such as Django, Flask, Pyramid, TurboGears, Web2Py, Bottle, CherryPy, Hug, Falcon, Sanic, and FastAPI, to name a few.Â
These Python frameworks help developers write stable code much faster. They can automate the implementation of common (standard) solutions, thereby reducing the development time. This enables developers to focus on more critical elements like application logic. Apart from this, Python frameworks can also perform web scraping tasks.
Explore our Popular Data Science Courses
5. There’s plenty for Data Visualization
As we mentioned earlier, Python has something for every need. It packs in plenty of options for data visualization. Some of the most popular data visualization tools in Python are Matplotlib (the foundation library based on which Pandas Plotting, Seaborn, and ggplot were developed), Plotly, Altair, Seaborn, Bokeh, Pygal, Geoplotlib, Gleam, and Missingno.Â
With these data visualization frameworks, you can easily make sense of complex datasets. Not just that, you can also visualize your findings through various representation options like graphs, pie charts, graphical plots, web-ready interactive plots, and much more.
6. Python comes with numerous testing frameworks
When it comes to testing or validating ideas/products, Python is the way to go. It comprises several built-in testing frameworks that help in debugging & speeding up workflows.Â
Python supports both cross-platform and cross-browser testing with frameworks like PyTest and Robot. There are also other testing frameworks like UnitTest, Behave, and Lettuce.
Top Data Science Skills to Learn in 2022
SL. No
Top Data Science Skills to Learn in 2022
1
Data Analysis Course
Inferential Statistics Courses
2
Hypothesis Testing Programs
Logistic Regression Courses
3
Linear Regression Courses
Linear Algebra for Analysis
7. Python is excellent for Enterprise Application Integration (EAI)
Python is a fantastic choice for EAI. It can be embedded in applications seamlessly, and it also applies to applications written in other languages. Case in point, not only can Python invoke CORBA/COM components, but it can also directly call from and to Java, C++, or C code. The language features strong integration bonding with Java, C, and C++, which makes it perfect for application scripting.Â
Python’s text processing and integration capabilities are highly commendable. It can be used for developing GUI and desktop applications as well.Â
8. Python is great for scripting
Yes, Python is not just a programming language – it can be used for scripting too! The feature that sets scripting languages apart from programming languages is that scripting languages don’t require any compilation; they are directly interpreted. In Python, you can write code in the script from and directly execute it.
The machine will read and interpret your code and also perform error checking during runtime itself. Once the code is error-free, you can use it multiple times.
The Linux Journal hails Python as the best programming and scripting language.
Read our popular Data Science Articles
9. Python is backed by an active community
Python boasts of a dynamic and well-knit community that you can rely on. In case of any coding-related or Data Science issues, you can always seek help from the Python community. They are ever-ready to help people. Since it is an open-source language, everyday new progress is being made in the community – developers and coders regularly contribute to enriching the language by developing new tools and libraries. Learn why Python is so popular among developers.
upGrad’s Exclusive Data Science Webinar for you –
Watch our Webinar on How to Build Digital & Data Mindset?
10. Python skills can command high salaries
If you have Python skills, you can command high salaries in the industry. Since Python rules the development and Data Science fields at present, it promises a high growth graph with huge salary prospects.Â
According to Daxx’s research, Python Engineers, Developers, and Programmers fetch some of the highest salaries in the US. The average annual salary of a Python Developer salary in the US is around $110,021, with New York and California having the highest salaries, $122,135, and $121,443, respectively.
Explore our Popular Data Science Courses
ConclusionÂ
Well, there you have ten reasons to learn Python!Â
Python has emerged as the number one programming language in the industry, and if not now, then when to take advantage of this?
If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Data Science Courses from upGrad.
Python is one of the most commonly used languages, and among its many applications are data mining, AI, web development, embedded systems, and many others. Data analysis and machine learning tools have advanced significantly in recent years thanks to new Python packages. Additionally, there are packages like numpy and pandas that make data comprehension and transformation possible. There is also pyspark, which serves as an API for working with Spark, a framework that makes it easy to work with big data sets. Python is a popular choice for doing rapid prototypes, which means it's utilised by start-ups to quickly create their initial minimum viable product (MVP). Python is one of the most highly scalable languages and is therefore used by many of the world's largest and most advanced businesses. Netflix recently described how they used Python in a variety of systems, from their CDN to their monitoring systems. Python programming, which goes hand-in-hand with rapid growth, is in demand for jobs. Python is predicted to be the second most sought-after programming language of 2021, according to job listings found on LinkedIn.com.
Python and Java are the most popular and reliable programming languages, both of which have millions of users. Python's speed and efficiency are behind Java's because it is an interpreted language. Python is a simpler, more concise language than Java, because it is an interpreted language. It has the same capabilities as Java but is more concise. The bugs introduced by programmers aren't caught in Python until the code is executed. This could potentially create operational difficulties and put additional time between cycles. In Java, Python's mutable objects can't be changed, whereas in Python, everything is mutable. Secure software development occurs with this.
Some disadvantages of Python that are worth noting are: Python uses an interpreter to run instead of a compiler. Unlike C, C++, Java, and numerous other languages in being comparatively slow at executing code. Python structures require additional memory. Why do people like using Python?
Which is faster, Java or Python?
What is Python not good at?
![42 Exciting Python Project Ideas & Topics for Beginners in 2023 [Latest]](https://www.upgrad.com/blog/wp-content/uploads/2020/02/195-300x170.png)
