Python for Finance: Analyze Big Financial Data
elle
(Elle)
#1
Further Reading
This chapter focuses on those issues that might be of particular importance for finance
algorithms and applications. However, it can only represent a starting point for the
exploration of data structures and data modeling in Python. There are a number of
valuable resources available to go deeper from here.
Here are some Internet resources to consult:
The Python documentation is always a good starting point:
For details on NumPy arrays as well as related methods and functions, see
The SciPy lecture notes are also a good source to get started: http://scipy-
lectures.github.io/.
Good references in book form are:
Goodrich, Michael et al. (2013): Data Structures and Algorithms in Python. John
Wiley & Sons, Hoboken, NJ.
Langtangen, Hans Petter (2009): A Primer on Scientific Programming with Python.
Springer Verlag, Berlin, Heidelberg.
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The Cython library brings static typing and compiling features to Python that are comparable to those in C. In fact,
Cython is a hybrid language of Python and C.
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Here and in the following discussion, terms like float, float object, etc. are used interchangeably, acknowledging
that every float is also an object. The same holds true for other object types.
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It is not possible to go into details here, but there is a wealth of information available on the Internet about regular
expressions in general and for Python in particular. For an introduction to this topic, refer to Fitzgerald, Michael (2012):
Introducing Regular Expressions. O’Reilly, Sebastopol, CA.
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