CODEWITHSUNDEEP
pythonpandas
briant57
briant57

Reputation: 21

Python map() function output into Pandas DataFrame

I utilize python's map() function to pass parameters to a trading model and output the results. I use itertools.product to find all the possible combinations of the two parameters, then pass the combination to my function named "run". The function run returns a pandas dataframe of returns. The Column header is a tuple of the two parameters and the sharpe ratio of the returns. See below:

def run((x,y)): 
    ENTRYMULT = x
    PXITR1PERIOD = y

    create_trade()
    pull_settings()    
    pull_marketdata()
    create_position()
    create_pnl_output()

    return DataFrame(DF3['NETPNL'].values, index=DF3.index, columns=[(ENTRYMULT,PXITR1PERIOD,SHARPE)])

My main() function uses the Pool() capability to run map() on all 8 cores:

if __name__ == '__main__':    
global DF3
pool = Pool()    
test1 =pool.map(run,list(itertools.product([x * 0.1 for x in range(10,12)], range(100,176,25))))
print test1

I realize the map function can only output lists. The output is a list of the header from the returned dataframe My output from print test1 looks like this:

[(1.0, 150, -8.5010673966997263)
2011-11-17  18.63                          
2011-11-18  17.86                          
2011-11-21  17.01                          
2011-11-22  15.92                          
2011-11-23  15.56                          
2011-11-24  15.56                          
2011-11-25  15.36                          
2011-11-28  15.18                          
2011-11-29  15.84                          
2011-11-30  NaN                            ,             (1.0, 175, -9.4016837593189102)
2011-11-17  22.63                          
2011-11-18  22.03                          
2011-11-21  21.36                          
2011-11-22  19.93                          
2011-11-23  19.77                          
2011-11-24  19.77                          
2011-11-25  19.68                          
2011-11-28  19.16                          
2011-11-29  19.56                          
2011-11-30  NaN                            ,             (1.1, 100, -20.255968672741457)
2011-11-17  12.03                          
2011-11-18  10.95                          
2011-11-21  10.03                          
2011-11-22  9.003                          
2011-11-23  8.221                          
2011-11-24  8.221                          
2011-11-25  7.903                          
2011-11-28  7.709                          
2011-11-29  6.444                          
2011-11-30  NaN                            ,             (1.1, 125, -18.178187305758119)
2011-11-17  14.64                          
2011-11-18  13.76                          
2011-11-21  12.89                          
2011-11-22  11.85                          
2011-11-23  11.34                          
2011-11-24  11.34                          
2011-11-25  11.16                          
2011-11-28  11.06                          
2011-11-29  10.14                          
2011-11-30  NaN                            ,             (1.1, 150, -14.486791104380069)
2011-11-17  26.25                          
2011-11-18  25.57                          
2011-11-21  24.76                          
2011-11-22  23.74                          
2011-11-23  23.48                          
2011-11-24  23.48                          
2011-11-25  23.43                          
2011-11-28  23.38                          
2011-11-29  22.93                          
2011-11-30  NaN                            ,             (1.1, 175, -12.118290962161304)
2011-11-17  24.66                          
2011-11-18  24.21                          
2011-11-21  23.57                          
2011-11-22  22.14                          
2011-11-23  22.06                          
2011-11-24  22.06                          
2011-11-25  22.11                          
2011-11-28  21.64                          
2011-11-29  21.24                          
2011-11-30  NaN                            ]

My end goal is to have a pandas dataframe with an index (same for all returns), column headers of the (ENTRYMULT, PXITR1PERIOD, SHARPE) with the corresponding returns below. I will then be doing a pairwise correlation calculation across all of the return series.

Upvotes: 2

Views: 5381

Answers (1)

Wes McKinney
Wes McKinney

Reputation: 105591

I think all you need to do is:

data = DataFrame(dict(test1))

that will result in a DataFrame whose columns are the elements like (1.1, 175, -12.118290962161304)

in pandas 0.6.1 (to be released soon) you'll also be able to do:

data = DataFrame.from_items(test1)

Upvotes: 4

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