Python's expansive library of open source data analysis tools, web frameworks, and testing instruments make its ecosystem one of the largest out of any programming community.
Python is an accessible language for new programmers because the community provides many introductory resources. The language is also widely taught in universities and used for working with beginner-friendly devices such as the Raspberry Pi.
Several programming language popularity rankings exist. While it's possible to criticize that these guides are not exact, every ranking shows Python as a top programming language within the top ten, if not the top five of all languages.
The TIOBE Index, a long-running language ranking, has Python steady at #8.
The PopularitY of Programming Language (PYPL), based on leading indicators from Google Trends search keyword analysis, shows Python at #3.
GitHut, a visualization of GitHub language popularity, pegs Python at #3 overall as well.
These rankings provide a rough measure for language popularity. They are not intended as a precise measurement tool to determine exactly how many developers are using a language. However, the aggregate view shows that Python remains a stable programming language with a growing ecosystem.
Programming languages have unique ecosystems, cultures and philosophies built around them. You will find friction with a community and difficulty in learning if your approach to programming varies from the philosophy of the programming language you've selected.
Python's culture values open source software, community involvement with local, national and international events and teaching to new programmers. If those values are also important to you and/or your organization then Python may be a good fit.
The philosophy for Python is so strongly held that it's even embedded in the language as shown when the interpreter executes "import this" and displays The Zen of Python.
>>> import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!
The Python documentation has a HOWTO section specifically for Python advocacy.
How to argue for Python’s use explains that choosing a programming language can be complicated but that Python is a very good option for many use cases.
Why I Push for Python gives one professor's rationale for promoting Python to teach programming to undergraduates.
If you're wondering about the differences in Python's dynamically typed system versus statically typed languages, be sure to read this thorough explanation of the topic.
Why I swapped C#.NET for Python as my default language and platform (and won’t be going back) provides a viewpoint from someone who is not a professional developer but uses coding to hack out some projects. He found Microsoft's .NET ecosystem lacking when it came to satisfying his needs and Python filled the gap for him with its wide array of open source code libraries, package management and ability to work well on platforms other than Windows.
Python, Machine Learning, and Language Wars compares Python with R, MATLAB and Julia for data science work. While Python is great for deployment automation and web development, many non-developers are first introduced to the language and ecosystem while getting data analysis work done.