However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis. It is no longer a back-room utility language, but a major force in web application creation and systems management, and a key driver of the explosion in big data analytics and machine intelligence. Python is easy to learn python programming language use The number of features in python programming language language itself is modest, requiring relatively little investment of time or effort to produce your first programs. The Python syntax is designed to be readable and straightforward. This simplicity makes Python an ideal teaching language, and it lets newcomers pick it up quickly. Python is broadly adopted and supported Python is both popular and python programming language used, as the high rankings in surveys like the and the attest. Python runs on every major operating system and platform, and most minor ones too. What Python is used for The most basic use case for Python is as a scripting and automation language. But scripting and automation represent only the tip of the iceberg with Python. The vast majority of the libraries used for data science or machine learning have Python interfaces, making the language the most popular high-level command interface to for machine learning libraries and other numerical algorithms. Metaprogramming and code generation in Python In Python, everything in the language is an object, including Python modules and libraries themselves. This lets Python work as a highly efficient code generator, making it possible to write applications that manipulate their own functions and have the kind of extensibility that would be difficult or impossible to pull off in other languages. Python can also be used to drive code-generation systems, such asto efficiently create code in other languages. Where Python falls short Also worth noting are the sorts of tasks Python is not well-suited for. Finally, Python is not the best choice when speed is an absolute priority in every aspect of the application. This takes the place of several lines of boilerplate to open the file, read individual lines from it, then close it up. You could write such a thing as a formal for… loop in Python programming language, much as you would in another language. The point is that Python has a way to economically express things like loops that iterate over multiple objects and perform a simple operation on each element in the loop, or to work with things that require explicit instantiation and disposal. Constructions like this let Python developers balance terseness and readability. Most modern object types—Unicode strings, for example—are built directly into the language. Data structures—like lists, dictionaries that is, hashmapstuples for storing immutable collections of objectsand sets for storing collections of unique objects —are available as standard-issue items. Python 2 versus Python 3 Python is available in two versions, which are different enough to trip up many new users. Python 3 adoption was slowed for the longest time by the relative lack of third-party library support. Many Python libraries supported only Python 2, making it difficult to switch. But over the last couple of years, the number of libraries supporting only Python 2 has dwindled; with both versions. Today, there are few reasons for not using Python 3. Python benefits from both a strong standard library and a generous assortment of easily obtained and readily used libraries from third-party developers. Python programming language has been enriched by decades of expansion and contribution. Normally, garbage collection happens automatically in the background, but if that poses a performance problem, you can. An important aspect of Python is its dynamism. Everything in the language, including functions and modules themselves, are handled as objects. This comes at the expense of speed more on that laterbut makes it far easier to write high-level code. Developers can perform complex object manipulations with only a few instructions, and even treat parts of an application as abstractions that can be altered if needed. But strict indentation rules are far less obtrusive in practice than they might seem in theory, even with the most minimal of code editors, and the result is code that is cleaner and more readable. Another potential turnoff, especially for those coming from languages like C or Java, is how Python handles variable typing. That said, Python has recently added support for optional compile-timeso projects that might benefit python programming language static typing can use it. Some Python programs will be slower by an order of magnitude or more. It is also due to the fact that the inherent dynamism and the malleability of objects in Python make it difficult to optimize the language for speed, even when it is compiled. Math and statistics operations can be boosted tremendously by using libraries such as NumPy and Pandas, and the PyPy runtime can provide orders-of-magnitude speedups for many Python apps.
