Python for Finance

Sources of Data
Appendix F –how to generate a Python
dataset with an extension of .pkl or .pickle
First, let look at the simplest dataset:
>>>import pandas as pd
>>>import numpy.ranom as random
>>>x=random.randn(10)
>>>y=pd.DataFrame(x)
>>>y.to_pickle("c:/temp/test.pkl")
Reading a Python dataset with an extension of .pkl or .pickle, we use thepd.
read_pickle() function:
>>> import pandas as pd
>>>kk=pd.read_pickle("c:/temp/test.pkl")
Next, the Python program is shown to generate theffMonthly.pkl dataset:
import pandas as pd
import numpy as np
file=open("c:/temp/ffMonthly.txt","r")
data=file.readlines()
dd=mkt=smb=hml=rf=[]
n=len(data)
index=range(1,n-3)
#
for i in range(4,n):
t=data[i].split()
dd.append(pd.to_datetime(t[0]+'01', format='%Y%m%d').date())
mkt.append(float(t[1])/100)
smb.append(float(t[2])/100)
hml.append(float(t[3])/100)
rf.append(float(t[4])/100)
#
d=np.transpose([dd,mkt,smb,hml,rf])
ff=pd.DataFrame(d,index=index,columns=['DATE','MKT_
RF','SMB','HML','RF'])
ff.to_pickle("c:/temp/ffMonthly.pkl")
The first and last several observations are shown here:
>>>ff.head(2)
DATE MKT_RFSMBHML
1 1926-10-01 -0.0324 0.0004 0.0051
2 1926-11-01 0.0253 -0.002 -0.0035
[ 144 ]