Python for Finance: Analyze Big Financial Data

Further Reading

For all libraries and solutions presented in this chapter, there are helpful web resources

available:

For xlrd and xlwt, see http://www.python-excel.org for the online documentation;

there is also a tutorial available in PDF format at

http://www.simplistix.co.uk/presentations/python-excel.pdf.

xlsxwriter is nicely documented on the website http://xlsxwriter.readthedocs.org.

OpenPyxl has its home here: http://pythonhosted.org/openpyxl/.

For detailed information about PyXLL, see https://www.pyxll.com.

Free trials and detailed documentation for DataNitro can be found at

http://www.datanitro.com.

You can find the documentation and everything else you need regarding xlwings at

http://xlwings.org.

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Note that a simple mechanism to generate Excel spreadsheets from Python is to export data in the form of a

comma-separated value (CSV) file and to import this with Excel. This might sometimes be more efficient than the ways

presented in the following discussion.

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Cf. Chapter 4 for a similar discussion in the context of NumPy ndarray objects and the benefits of vectorization. The

rule of thumb there as well as here is to avoid loops on the Python level.