Python for high-frequency data

High-frequency data is referred to as second-by-second or millisecond-by-millisecond transaction and quotation data. The New York Stock Exchange's Trade and Quotation (TAQ) database is a typical example (http://www.nyxdata.com/data-products/daily-taq). The following program can be used to retrieve high-frequency data from Google Finance:

import tempfile
import re, string 
import pandas as pd 
ticker='AAPL'                    # input a ticker 
f1="c:/temp/ttt.txt"             # ttt will be replace with above sticker
f2=f1.replace("ttt",ticker) 
outfile=open(f2,"w") 
#path="http://www.google.com/finance/getprices?q=ttt&i=300&p=10d&f=d,o, h,l,c,v" 
path="https://www.google.com/finance/getprices?q=ttt&i=300&p=10d&f=d,o,%20h,l,c,v"
path2=path.replace("ttt",ticker) 
df=pd.read_csv(path2,skiprows=8,header=None) 
fp = tempfile.TemporaryFile()
df.to_csv(fp) 
print(df.head...