In [1]:

    

import pandas as pd
import datetime
import numpy as np
import scipy as sp
import os
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
# font = {'size'   : 18}
# matplotlib.rc('font', **font)
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
os.chdir("/Users/yulongyang/Envs/btc-analysis/btc-price-analysis")


    

In [24]:

    

time_format = "%d/%m/%Y %H:%M:%S"
price_data = pd.read_csv("./data/info-price.txt", header=False, names=["time", "price"], index_col='time',
                         parse_dates=[0], date_parser=lambda x: datetime.datetime.strptime(x, time_format))
tra_data = pd.read_csv("./data/info-transactions.txt", header=False, names=["time", "transaction"], index_col='time',
                       parse_dates=[0], date_parser=lambda x: datetime.datetime.strptime(x, time_format))


    

In [25]:

    

data = pd.merge(price_data, tra_data, how="left", left_index=True, right_index=True)
data.head()


    

    
Out[25]:






  

    

      

      
price
      
transaction
    
    

      
time
      

      

    
  
  

    

      
2013-03-26 18:15:05
      
 78.00000
      
 47247
    
    

      
2013-03-27 18:15:05
      
 88.90000
      
 51993
    
    

      
2013-03-28 18:15:05
      
 93.56701
      
 57170
    
    

      
2013-03-29 18:15:05
      
 89.05000
      
 66205
    
    

      
2013-03-30 18:15:05
      
 90.99999
      
 59787
    
  

In [34]:

    

data.plot(secondary_y="transaction", figsize=(15,7))


    

    
Out[34]:





<matplotlib.axes.AxesSubplot at 0x10b011390>






    

In [28]:

    

data.corr()


    

    
Out[28]:






  

    

      

      
price
      
transaction
    
  
  

    

      
price
      
 1.000000
      
 0.151185
    
    

      
transaction
      
 0.151185
      
 1.000000