1. import matplotlib.pyplot as plt
    2. import numpy as np
    3. import urllib
    4. import matplotlib.dates as mdates
    6. def bytespdate2num(fmt, encoding='utf-8'):
    7.     strconverter = mdates.strpdate2num(fmt)
    8.     def bytesconverter(b):
    9.         s = b.decode(encoding)
    10.         return strconverter(s)
    11.     return bytesconverter
    14. def graph_data(stock):
    16.     source_code = urllib.request.urlopen(stock_price_url).read().decode()
    17.     stock_data = []
    18.     split_source = source_code.split('\n')
    19.     for line in split_source:
    20.         split_line = line.split(',')
    21.         if len(split_line) == 6:
    22.             if 'values' not in line and 'labels' not in line:
    23.                 stock_data.append(line)
    25.     date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
    26.                                                           delimiter=',',
    27.                                                           unpack=True,
    28.                                                           # %Y = full year. 2015
    29.                                                           # %y = partial year 15
    30.                                                           # %m = number month
    31.                                                           # %H = hours
    32.                                                           # %M = minutes
    33.                                                           # %S = seconds
    34.                                                           # 12-06-2014
    35.                                                           # %m-%d-%Y
    36.                                                           converters={0: bytespdate2num('%Y%m%d')})
    38.     plt.plot_date(date, closep,'-', label='Price')
    40.     plt.xlabel('Date')
    41.     plt.ylabel('Price')
    42.     plt.title('Interesting Graph\nCheck it out')
    43.     plt.legend()
    44.     plt.show()
    46. graph_data('TSLA')