Python Data Science Handbook



There are many development options for Python and I am often asked which one I use in my business. My answer sometimes surprises people: my favorite environment is IPython plus a text editor (in my case Emacs or Atom depending on the mood). IPython (short for Interactive Python) was started in 2001 by Fernando Perez as an improved Python interpreter and has since grown into a project whose goal, according to Perez, is to provide "Tools for the full research computing lifecycle." If Python is the driving force behind our data science mission, you might think of Python as an interactive dashboard.

Python Data Science Handbook

In addition to being a useful interactive interface to Python, IPython also provides a number of useful syntactic additions to the language; here we will cover the most useful of these add-ons. Additionally, IPython is closely related to the Jupiter project, which provides a browser-based notebook useful for developing, collaborating, sharing and even publishing data science results. The IPython notebook is actually a special case of the larger Jupiter notebook structure, which includes notebooks for Julia, R, and other programming languages. As an example of the usefulness of the notebook format, look no further than the page you are reading - the entire manuscript of this book has been compiled as a set of IPython notebooks.

IPython deals with the efficient use of Python for interactive and intensive scientific computing. This chapter will begin by examining some of the Python functions useful in the practice of data science, focusing specifically on the syntax it offers in addition to the standard Python functions. So we'll take a closer look at some of the more useful "magic commands" that can speed up common tasks in creating and using data science code. Finally, we will touch on some of the features of a laptop that make it useful for understanding data and sharing results.

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