[Py2.7] Data(.csv) from Internet w/ Matplotlib.pyplot

/*********************************************************************************************************
-- Title : [Py2.7] Data(.csv) from Internet w/ Matplotlib.pyplot
-- Reference : pythonprogramming.net
-- Key word : matplotlib pyplot numpy urllib csv urlopen split 
*********************************************************************************************************/

-- Figure

-- Script

 
# -*- coding: utf-8 -*-
 
import matplotlib.pyplot as plt
import numpy as np
import urllib
import matplotlib.dates as mdates
 
def bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter
 
def graph_data(stock):
 
    # http://chartapi.finance.yahoo.com/instrument/1.0/TSLA/chartdata;type=quote;range=10y/csv
    stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
 
    # # ----------------------------------------
    # # -- urllib.uropen 테스트
    # # ----------------------------------------
    # f = urllib.urlopen(stock_price_url)
    #
    # print f.headers    # 헤더출력
    # print '-'*300
    # print f.code
    # print '-'*300
    # print f.read()     # 실제 소스 출력
 
    source_code = urllib.urlopen(stock_price_url).read()
    #print source_code
 
    stock_data = []
    split_source = source_code.split('\n')        # 줄단위를 리스트로 변경
    #print split_source
 
    for line in split_source:
        split_line = line.split(',')
        if len(split_line) == 6:
            if 'values' not in line and 'labels' not in line:
                stock_data.append(line)           # 리스트에 추가
 
    #print stock_data
 
    # -- 항목별로 분리 저장(to 리스트)
    date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
                                                          delimiter=',',
                                                          unpack=True,
                                                          # %Y = full year. 2015
                                                          # %y = partial year 15
                                                          # %m = number month
                                                          # %d = number day
                                                          # %H = hours
                                                          # %M = minutes
                                                          # %S = seconds
                                                          # 12-06-2014
                                                          # %m-%d-%Y
                                                          converters={0: bytespdate2num('%Y%m%d')})
 
    plt.plot_date(date, closep, '-', label="Price")
    plt.xlabel('xlabel')
    plt.ylabel('ylabel')
    plt.title('Title: Data from the INternet for Matplotlib')
    plt.legend()
    plt.show()
 
graph_data('TSLA')