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Working with Pandas DataFrame returned by Pipeline
blade bread
posted

My aim is to perform some operations on the DataFrame returned by the Pipeline, but I am struggling to write most efficient code.

As I understand, Pipeline returns a MultiIndexed Pandas DataFrame (indexed on datetime and symbol) like this :
-----------------------------Equity (xyz1)
-----------------------------Equity (xyz2)
2015-11-01----------------Equity (xyz3)
-----------------------------Equity (xyz4)
-----------------------------Equity (xyz5)

Lets say I want to operate on a 10 day window on each stock, what is the most efficient way to do that? Using groupby(['symbol']).apply(lambda x : something) is too slow and iterating using .xs(symbol) seems redundant (using loops with pandas is a big no no).

Can someone point me to some resource where I can learn how to do operations on this MultiIndexed DataFrame returned by Pipeline?, particularly applying a function to data of each symbol.
I am fairly advanced with python, so pointing to snippets in zipline project will also work.

2 responses
Michael Matthews

I use a combination of df.unstack() and df.rolling(). Note, I used the mean function for simplicity in my example, but you can substitute a customized function of your choosing by using df.rolling().apply( some_function).

You can also take a look at the Pandas MultiIndex/Advanced Indexing documentation for a general overview. Note, Quantopian currently uses Pandas version 0.18.1.

blade bread

Thank you so much Michael.... I completely missed the unstack() function of pandas.
And thanks for the really helpful notebook.

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