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0

With the reticulate library, I'm using Python to gather market data and account positions for my r script. While most datasets convert cleanly between the 2 languages, I'm stuck on this particular dataset. How do I convert this particular dataset into an r data structure, preferably a data frame?

Here's my python code:

import pickle
import io
def getPythonData():
 with open('OpenPositions.dump','rb') as f:
 data = pickle.load(f)
 return (data)

Here's my r code:

library(reticulate)
use_python("/usr/local/bin/python3.7")
source_python("PyGetData.py")
data <- getPythonData()
print (data)
class(data)
class(data[[1]])
x <- data.frame(py_to_r(data))
x <- data.frame(py_to_r(data[[1]]))

Here's the output from the print (data) command, which is a list with 2 elements:

[[1]]
Position(account='DU1340125', contract=Future(conId=138979241, symbol='CL', lastTradeDateOrContractMonth='20190422', multiplier='1000', currency='USD', localSymbol='CLK9', tradingClass='CL'), position=1.0, avgCost=59832.37)

[[2]]
Position(account='DU1340125', contract=Future(conId=321454633, symbol='YM', lastTradeDateOrContractMonth='20190621', multiplier='5', currency='USD', localSymbol='YM JUN 19', tradingClass='YM'), position=1.0, avgCost=128087.05)

python r reticulate

share|improve this question

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

add a comment
 | 

0

With the reticulate library, I'm using Python to gather market data and account positions for my r script. While most datasets convert cleanly between the 2 languages, I'm stuck on this particular dataset. How do I convert this particular dataset into an r data structure, preferably a data frame?

Here's my python code:

import pickle
import io
def getPythonData():
 with open('OpenPositions.dump','rb') as f:
 data = pickle.load(f)
 return (data)

Here's my r code:

library(reticulate)
use_python("/usr/local/bin/python3.7")
source_python("PyGetData.py")
data <- getPythonData()
print (data)
class(data)
class(data[[1]])
x <- data.frame(py_to_r(data))
x <- data.frame(py_to_r(data[[1]]))

Here's the output from the print (data) command, which is a list with 2 elements:

[[1]]
Position(account='DU1340125', contract=Future(conId=138979241, symbol='CL', lastTradeDateOrContractMonth='20190422', multiplier='1000', currency='USD', localSymbol='CLK9', tradingClass='CL'), position=1.0, avgCost=59832.37)

[[2]]
Position(account='DU1340125', contract=Future(conId=321454633, symbol='YM', lastTradeDateOrContractMonth='20190621', multiplier='5', currency='USD', localSymbol='YM JUN 19', tradingClass='YM'), position=1.0, avgCost=128087.05)

python r reticulate

share|improve this question

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

add a comment
 | 

With the reticulate library, I'm using Python to gather market data and account positions for my r script. While most datasets convert cleanly between the 2 languages, I'm stuck on this particular dataset. How do I convert this particular dataset into an r data structure, preferably a data frame?

Here's my python code:

import pickle
import io
def getPythonData():
 with open('OpenPositions.dump','rb') as f:
 data = pickle.load(f)
 return (data)

Here's my r code:

library(reticulate)
use_python("/usr/local/bin/python3.7")
source_python("PyGetData.py")
data <- getPythonData()
print (data)
class(data)
class(data[[1]])
x <- data.frame(py_to_r(data))
x <- data.frame(py_to_r(data[[1]]))

Here's the output from the print (data) command, which is a list with 2 elements:

[[1]]
Position(account='DU1340125', contract=Future(conId=138979241, symbol='CL', lastTradeDateOrContractMonth='20190422', multiplier='1000', currency='USD', localSymbol='CLK9', tradingClass='CL'), position=1.0, avgCost=59832.37)

[[2]]
Position(account='DU1340125', contract=Future(conId=321454633, symbol='YM', lastTradeDateOrContractMonth='20190621', multiplier='5', currency='USD', localSymbol='YM JUN 19', tradingClass='YM'), position=1.0, avgCost=128087.05)

python r reticulate

share|improve this question

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

import pickle
import io
def getPythonData():
 with open('OpenPositions.dump','rb') as f:
 data = pickle.load(f)
 return (data)

library(reticulate)
use_python("/usr/local/bin/python3.7")
source_python("PyGetData.py")
data <- getPythonData()
print (data)
class(data)
class(data[[1]])
x <- data.frame(py_to_r(data))
x <- data.frame(py_to_r(data[[1]]))

[[1]]
Position(account='DU1340125', contract=Future(conId=138979241, symbol='CL', lastTradeDateOrContractMonth='20190422', multiplier='1000', currency='USD', localSymbol='CLK9', tradingClass='CL'), position=1.0, avgCost=59832.37)

[[2]]
Position(account='DU1340125', contract=Future(conId=321454633, symbol='YM', lastTradeDateOrContractMonth='20190621', multiplier='5', currency='USD', localSymbol='YM JUN 19', tradingClass='YM'), position=1.0, avgCost=128087.05)

share|improve this question

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

share|improve this question

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

edited Mar 28 at 20:03

dubbbdan

1,2731010 silver badges2424 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges

asked Mar 28 at 18:38

S NovogoratzS Novogoratz

1366 bronze badges