Python for Finance: Analyze Big Financial Data

Further Reading

The following web resources are helpful with regard to the topics covered in this chapter:

http://docs.continuum.io/anaconda/ for the Anaconda documentation

http://conda.pydata.org/docs/ for the conda documentation

http://ipython.org/ipython-doc/stable/ for the IPython documentation

http://daringfireball.net/projects/markdown/ for the Markdown language used by

IPython Notebook

http://code.google.com/p/spyderlib for information about Spyder

A good introduction to Python deployment and the use of IPython as a development

environment is provided in:

Wes McKinney (2012): Python for Data Analysis. O’Reilly, Sebastopol, CA.

[ 5 ]

They can, for example, in general be executed even on a Raspberry Pi for about 30 USD (cf.

http://www.raspberrypi.org), although memory issues quickly arise for some applications. Nevertheless, this can be

considered a rather low requirement when it comes to hardware.

[ 6 ]

For those who want to control which libraries and packages get installed, there is Miniconda, which comes with a

minimal Python installation only. Cf. http://conda.pydata.org/miniconda.html.

[ 7 ]

There is also an Anaconda version available that contains proprietary packages from Continuum Analytics called

Accelerate. This commercial version, whose main goal is to improve the performance of typical operations with

Python, has to be licensed.

[ 8 ]

This is only one subtle, but harmless, change in the Python syntax from 2.7.x to 3.x that might be a bit confusing to

someone new to Python.

[ 9 ]

For Windows users and developers, the full integration of Python in Visual Studio is a compelling alternative. There

is even a whole suite of Python tools for Visual Studio available (cf. http://pytools.codeplex.com).

[ 10 ]

From IPython 2.0 on, these cells are called Raw NBConvert.

[ 11 ]

However, you can configure your favorite editor for IPython and invoke it by the magic command %editor

FILENAME.