import urllib2
from datetime import datetime
import pandas as pd
import pandas.io.data as web
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
pd.set_option('max_columns', 50)
%matplotlib inline

url = "http://www.federalreserve.gov/datadownload/Output.aspx?rel=H15&series=bcb44e57fb57efbe90002369321bfb3f&lastObs=&from=&to=&filetype=csv&label=include&layout=seriescolumn"
res = urllib2.Request(url)
csvio = urllib2.urlopen(res)
data = pd.read_csv(csvio, header=5, index_col=0, parse_dates=True, na_values=["ND"])

data.info()
data.plot(figsize=(10, 10))

url = "http://www.federalreserve.gov/datadownload/Output.aspx?rel=FOR&series=5c8df3fd05e5b5ad4297328218040855&lastObs=&from=&to=&filetype=csv&label=include&layout=seriescolumn"
res = urllib2.Request(url)
csvio = urllib2.urlopen(res)
data1 = pd.read_csv(csvio, header=5, index_col=0, parse_dates=True, na_values=["ND"])

data1.info()
data1.plot(figsize=(10, 10))

url = "http://www.federalreserve.gov/datadownload/Output.aspx?rel=H15&series=40afb80a445c5903ca2c4888e40f3f1f&lastObs=&from=&to=&filetype=csv&label=include&layout=seriescolumn"
res = urllib2.Request(url)
csvio = urllib2.urlopen(res)
data2 = pd.read_csv(csvio, header=5, index_col=0, parse_dates=True, na_values=["ND"])

data2.info()
data2.plot(figsize=(10, 10))

url = "http://www.federalreserve.gov/datadownload/Output.aspx?rel=G17&series=38c557d559e8dd62aa18b8af9b626a25&lastObs=&from=&to=&filetype=csv&label=include&layout=seriescolumn"
res = urllib2.Request(url)
csvio = urllib2.urlopen(res)
data3 = pd.read_csv(csvio, header=5, index_col=0, parse_dates=True, na_values=["ND"])

data3.info()
data3.plot(figsize=(10, 10))

data4 = data3.append(other=data2)

data4.info()

data4.columns

plt.figure()
data4.plot(secondary_y=['GVIP.T50030.S'], figsize=(10, 10), style=['p','p'])