7 Reasons Why You Should Learn Python in 2022
Believe it or not, Python has become one of the hottest topics in the field of programming in the past few years and has been widely used among big tech giants and developers today. Especially today when it’s all about data in almost every field, Python is giving a glorious touch to data mining, machine learning, and many other fields that were not common earlier. It is commonly known for its vast set of tools, and libraries that make it go smoother than any other language today.
Therefore, these specifications make it one of the most excellent and a language full of opportunities in the market today. As per records, Python is the second most widely used language and the simplicity of its syntax has enabled quick adaptability among non-programmers as well. Not only this as per a recent survey Python has also surpassed JAVA in terms of popularity around the globe, and interestingly today, if you’re reading this blog then you might be looking for a career change in development or you might be hearing chants of Python.
Here are the 7 reasons why you should learn python in 2022.
1. More Scope
When the world was fighting against the COVID-19 pandemic with chaos and layoffs from jobs, Python rose among all just like a strong pillar, and in fact, it was one of the highly paid and secured jobs at that time.
2. Easy to Integrate
You should not be surprised to see this among those reasons why you should learn python in 2022 this feature is lovable by everyone who has experience working in it. Python is also called a Glue language due to its adaptability to integrate with other languages. It runs on multiple platforms like Windows, Linux, and macOS and can work seamlessly for any project whether it could be gaming or data visualization, python will get it right.
In case you don’t know, it works both with frontend and backend by using its own (Plotly.dash) framework. This makes it easy to adopt and also promotes cost-cutting in any project. So next time, when there’s a requirement to switch the platform in any project then Python is the answer and with its simple defined syntax, it tops the chart when it comes to checking portability.
3. Vast Community Support
Since you’re looking out for a solid reason for learning python, how can it be possible without strong support? Well, you got this here too, python offers vast community support for its developers around the globe and users get quick responses in no time when stuck. As the demand rises, the number of active users is increasing tremendously every day, and to keep a smooth flow, a strong community was required which python provides. Today giants like Google, Microsoft, and Amazon are actively contributing to TensorFlow, PyTorch, etc.
A recent survey suggests that today we’ve around 9 million active developers who are using Python regularly for different purposes such as data mining, machine learning, automation, etc. Thus, enabling a strong community works just like a backbone so that it stands strong.
4. Collection of Libraries and Frameworks
Over the period, the more python started gaining popularity, the more updates and changes have been made and implemented every year. When it comes to libraries, we don’t think anyone can beat this up, wonder why? Well, this might surprise you but the overall count of the python libraries has about 2,80,000+ and it’s increasing every single day. Do you know what that means? You can create almost anything here with fewer efforts be its data visualization, or development tasks, it’s all there in python.
5. Quick Learning
When you’re going to learn any programming language, this might be the pillar that will decide your career pathway. We’ve talked about python and its feature but the fact is, it is popular primarily because of its easy structure which even takes care of your small mistakes and let you focus on the main logic. Python is being considered one of the most vocab languages like English, of all time and when it comes to new learners, it helps them by backing up with vast libraries, frameworks, community support, and whatnot.
The interesting part about python is its fascinating features, elegant plain syntax, and capability to explore the unearthed areas in development, data visualization, and so on (especially for beginners). Even it is so flexible that one can perform a cross-platform task without much hustle.
6. AI, BigData, and Data Science Friendly
Despite the fact that Python is one of the highly used programming languages, it is also suitable for using its application among these technologies i.e. artificial intelligence, big data, and data science. The journey began when noticeable libraries like NumPy, Pandas, and SymPy were introduced because these are the best tools and the first choice for fetching numeric information & calculations and being active in use by data scientists around the world. By now, you might be aware of the fact that python is an expert in handling a huge collection of raw data, and to manage that it also has certain libraries like Pyspark, Dask, etc that help data scientists to process big data.
On the other hand, AI has been on-trend for the past few years and with the help of pythonML, we can easily create human nature likewise properties that can interpret and interact just like us. (Keras library) is the perfect example of such AI experiments.
7. Interactive Jupyter Notebook & Pandas
Jupyter was introduced by IPython which is an interactive command-line terminal of python which allows a developer to connect with other data scientists using a web browser. In case you don’t know, working on a command line is not that easy task so Jupyter offers a simple yet powerful interface to Python Language and is being extensively used to present data science projects by allowing to integrate of code and generating output in a single document along with the data visualization.
On the other hand, Panda allows users to create a neat and clean analysis of data and the data can be easily loaded from different file extensions (.CSV, Excel, etc.). It contains a vast collection of features which also includes data manipulation so it can be a perfect fit for reshaping, merging, splitting, and aggregating data in one go, and that’s what makes it more handy and comfortable to use among data scientists.