Guido van Rossum, a young programmer, was trying to find a way to keep himself busy during the Christmas week. It was likely a snowy and cold night in December 1989. He was out of work so he decided to pursue a new hobby. A new scripting language was something he had been thinking about for a while. It was a descendant from ABC language that would appeal to Unix/C maniacs. He was a huge fan of Monty Python's Flying Circus and decided to call the project "Python".
After working for Google, Dropbox and other companies for many years, Guido van Rossum has retired. And his hobby invented to avoid around-Christmas boredom grew to be the world's second most popular programming language according to the GitHub's Octoverse report, officially more popular than Java and slightly less than JavaScript.
It is also one of the few languages that keep being on the rise since 2015. One of the most important factors that influence the language's popularity is the machine-learning explosion and the fact that Python and Python's framework are leading technologies behind this business revolution.
Python is the perfect choice for both the experienced developer and the new developer. But why?
What is Python?
Python is a general-purpose language for programming. It is often called "battery included" because of the large number of libraries and frameworks that support multiple goals. Python's philosophy places a strong emphasis upon code readability and making developers' lives as easy as possible.
Python's support for multiple programming styles is what makes it so popular.Programming can be done in any combination of object-oriented, procedural or functional paradigms depending on the programmers preference and business goals. The language allows the developer to take a lot of freedom, which is sometimes in direct opposition with the more popular languages. One example is Ruby on Rails, one of Ideamotive’s favourite programming frameworks. Ruby provides clarity and order, but only supports one way of solving the problem. Python offers flexibility and freedom by taking the opposite approach. There is no right or wrong way to solve a problem. The coder can choose what they think is the best. Join data science with python training to learn in detailed.
A set of Python's core philosophical ideals are collected in the document called The Zen of Python. The most important elements of the "moral code", the programming language, are:
- Beautiful is better than bad.
- It is better to be explicit than to be implicit.
- Simpler is better than complicated
- Complexity is better than complexity.
- Readability is important.
It sounds as smart as it is cool, doesn't?
Why should you learn it.
Python, aside from being smart and cool, is on the rise. Python is used in many ways to support a variety of processes.Depending on the framework and set of libraries, it can be used in front-end, back-end, web development, machine learning and other usages that would be pointless to enumerate here.
Python is a master-of-all-trades, which makes it easier for developers to be flexible and takes less time to learn new skills. The web manager of the project does not require the developer to learn a new language. They only need to know a framework.
Machine learning is one of the most exciting and dynamic technologies in today's world, and Python is its main language.By 2020, 80% of customer interactions will take place without human intervention. However, 75 countries already use it.Artificial Intelligence technologyIn surveillance.
There is a high need for Python developers out there and the trends show it is not going to fade quickly. There is always money available for developers who are willing to work in companies that have a need. Both the Python developer salary and junior Python developers salary are very attractive.
What should you do to learn Python?
Learning Python is an excellent idea, considering current trends and the direction of technology. To keep in line with the general mood of programming, one could say:
"There are many ways to reach the top of the mountain but the view is the same."
This proverb is slightly updated to better suit the learning of programming languages. This is a reasonable guideline for learning Python:
- The obvious one is building the basics. Get comfortable with syntax and commands to write more complicated than "hello world!"
- Watch tutorials or read them. You will find that not all tutorials are the same. Start creating your own apps to help you with your everyday tasks. Many of the most successful startups began with a product that solved the owner’s problem.
- Read the documentation. At this point you should be able understand it. Frameworks and libraries can enrich your apps to make them more complex.
- You can always learn from other people, and attend events.
- You should read the source code. At this point you will not only be able understand the code but also be able distinguish between good and poor code.
- Don't think you're good enough. Programming language isn't something that can be learned overnight. It is important to improve your programming skills.
Conclusion
What about the "bullshit", as it is called in the title? It is the promise of a Python developer quickly and without any work that offers the greatest stack. There is no way.It takes a lot of commitment to become a Python developer.
In his famous book titled "Outliers", Malcolm Gladwell, a renowned Canadian journalist and author, analyzes multiple factors standing behind a success seen in a broad context. Gladwell follows Bill Gates, a famous ice hockey player, and many others as they journey to success. To become a master of a task, one must complete it for approximately 10 000 hours.
Although the theory was later challenged by others, one thing remains undisputed: there is a lot to be successful at any task. This includes software development in Python.
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In this article, I am going to talk about the most important libraries of Python used in the field of data science.Libraries are collection of functions and methods that enable you to perform a wide variety of actions without writing the code yourself.
First of all, there are over 137.000 libraries in Python.
In this article we are going to learn : Scientific Computing Libraries in PythonVisualization Libraries in Python High-Level Machine Learning and Deep Learning Libraries in Python Deep Learning Libraries in PythonPython Libraries for NLP ( Natural Language Processing ) Before we start explaining all of them, Let’s learn what data science is and why we use python programming language in this field.
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