Most finance data is in the format of time-series, see the following several examples. The first one shows how to download historical, daily stock price data from Yahoo!Finance for a given ticker's beginning and ending dates:
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
x = getData("IBM",(2016,1,1),(2016,1,21),asobject=True, adjusted=True)
print(x[0:4])The output is shown here:
The type of the data is numpy.recarray as the type(x) would show. The second example prints the first several observations from two datasets called ffMonthly.pkl and usGDPquarterly.pkl, and both are available from the author's website, such as http://canisius.edu/~yany/python/ffMonthly.pkl:
import pandas as pd
GDP=pd.read_pickle("c:/temp/usGDPquarterly.pkl")
ff=pd.read_pickle("c:/temp/ffMonthly.pkl")
print(GDP.head())
print(ff.head())The related output is shown here:
There is one end of chapter problem which is designed to merge discrete data with the daily...
