Python has gained a reputation over the past decade as an excellent language for beginners to the world of programming. Why is this? It boils down to two primary reasons: it is easy to read and write, and it comes with a great ecosystem of open-source libraries. Python’s syntax is known for being clear and concise, making it easy to read and write. Unlike C++ and Java, Python doesn’t require the programmer to specify the types of variables and there is no need to end lines with semi-colons or demarcate code blocks with curly braces. This keeps the code minimal, and combined with Python’s plain English keywords, reading and writing Python often feels like reading and writing pseudocode. One must be careful, since strict variable typing and punctuation often act as guardrails, and their absence leaves the Python programmer open to different types of errors, but for the careful programmer this is usually not an issue. In addition to the large collection of libraries that come installed with Python, Python has a vast ecosystem of open-source libraries. Python’s data science ecosystem is especially powerful, with libraries for every part of the data science stack. NumPy and Pandas give programmers tools for numerical operations and data processing, Scikit-learn and PyTorch give programmers access to machine learning models, and Seaborn gives programmers access to beautiful visualizations with just a few lines of code. As a result, if you are looking to work with data in just about any way, chances are Python can fulfill your needs. And in addition to its powerful data science libraries, Python also has tools like Flask and Django for building web applications. This means that if a programmer is looking to build a full-stack web application that takes in data, processes it, models it, visualizes it, and presents analysis to an end-user, they can do all of this in Python using well-established, easy-to-use libraries. This is not true of any other language; while it’s true that other languages have libraries for all of these things, none of them provide the simple interface and extensive capabilities of their Python equivalents, making Python the language of choice for many full-stack developers. The case for Python is strong. Python is easy to read and write, and comes with a wider and deeper ecosystem of libraries than every other language. It is true that for certain types of applications, like those where safety or speed is of critical importance, Python might not be the best tool; but if you are a novice programmer looking to build something cool quickly, it’s incredibly likely that Python is the best language for the job.Simple syntax
Depth of libraries
Breadth of libraries
Summary
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