Python for Finance: Analyze Big Financial Data

(Elle) #1

dependencies for which no current version is installed. For our newly created


environment, the updating would take the form:


$   conda   update  -n  py33test    pandas

Finally, conda makes it easy to remove packages with the remove command from the main


installation or a specific environment. The basic usage is:


$   conda   remove  scipy

For an environment it is:


$   conda   remove  -n  py33test    scipy

Since the removal is a somewhat “final” operation, you might want to dry run the


command:


$   conda   remove  —dry-run    -n  py33test    scipy

If you are sure, you can go ahead with the actual removal. To get back to the original


Python and Anaconda version, deactivate the environment:


$   source  deactivate

Finally, we can clean up the whole environment by use of remove with the option —all:


$   conda   remove  —all    -n  py33test

The package manager conda makes Python deployment quite convenient. Apart from the


basic functionalities illustrated in this section, there are also a number of more advanced


features available. Detailed documentation is found at http://conda.pydata.org/docs/.


Python Quant Platform


There are a number of reasons why one might like to deploy Python via a web browser.


Among them are:


No need for installation


Local installations of a complete Python environment might be both complex (e.g., in


a large organization with many computers), and costly to support and maintain;


making Python available via a web browser makes deployment much more efficient


in certain scenarios.


Use of (better) remote hardware


When it comes to complex, compute- and memory-intensive analytics tasks, a local


computer might not be able to perform such tasks; the use of (multiple) shared


servers with multiple cores, larger memories, and maybe GPGPUs makes such tasks


possible and more efficient.


Collaboration


Working, for example, with a team on a single or multiple servers makes


collaboration simpler and also increases efficiency: data is not moved to every local


machine, nor, after the analytics tasks are finished, are the results moved back to


some central storage unit and/or distributed among the team members.


The Python Quant Platform is a web- and browser-based financial analytics and


collaboration platform developed and maintained by The Python Quants GmbH. You can

Free download pdf