Analyzing Bitcoin Data with PQP and Quandl.com

In [1]:

    

import pandas as pd
import urllib
import json
import matplotlib as mpl
mpl.use('agg')
import seaborn as sns; sns.set()
%matplotlib inline


    

In [2]:

    

def read_quandl(url):
    d = json.loads(urllib.urlopen(url).read())
    df = pd.DataFrame(d['data'], columns=d['column_names']).set_index('Date').sort()
    return df


    

In [3]:

    

url = "https://www.quandl.com/api/v1/datasets/BCHAIN/TOTBC.json"


    

In [4]:

    

number = read_quandl(url)


    

In [5]:

    

number.plot(figsize=(10, 5))


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f9e82e38450>






    

In [6]:

    

url = "https://www.quandl.com/api/v1/datasets/BAVERAGE/USD.json"


    

In [7]:

    

quotes = read_quandl(url)


    

In [8]:

    

quotes[['24h Average', 'Total Volume']][quotes.index > '2012-12-31'].plot(
        subplots=True, figsize=(10, 5), color='b')


    

    
Out[8]:





array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9e82581690>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f9e825eec50>], dtype=object)






    

In [9]:

    

quotes[['24h Average', 'Total Volume']][quotes.index > '2014-12-31'].plot(
        subplots=True, figsize=(10, 5), color='b')


    

    
Out[9]:





array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9e8244b910>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f9e822d5e50>], dtype=object)