No question about it: Python is a crucial part of modern data science. Convenient and powerful, Python connects data scientists and developers with a whole galaxy of tools and functionality, in convenient and programmatic ways.
Still, those tools sometimes come with a little—or a lot—of assembly required. Because Python is a general-purpose programming language, how it’s packaged and delivered doesn’t speak specifically to data scientists. But various folks have delivered Python to that audience in a way that’s prepackaged, with little to no assembly required—a project that regular Python users can benefit from, too.
[ The essentials from InfoWorld: Julia vs. Python: Julia language rises for data science • 5 essential Python tools for data science—now improved • What’s new in the Anaconda distribution for Python. | Keep up with hot topics in programming with InfoWorld’s App Dev Report newsletter. ]Continuum Analytics’s Anaconda distribution is a repackaging of Python aimed at developers who use Python for data science. It provides a management GUI, a slew of scientifically oriented work environments, and tools to simplify the process of using Python for data crunching. It can also be used as a general replacement for the standard Python distribution, but only if you’re conscious of how and why it differs from the stock version of Python.
What’s included in Anaconda
CPython, the reference version of Python, includes a few things to make life easier—the standard library, the IDLE mini-IDE, and the Tkinter user-interface library. But everything you might need for data science is an add-on—even the most basic tools. Anaconda, by contrast, tries to include a decent selection of data-science tools out of the box.
https://www.infoworld.com/article/3245814/python/get-started-with-anaconda-python-the-distro-for-data-science.html#tk.rss_applicationdevelopment
