In [1]:

    

%matplotlib inline


    

In [2]:

    

import pandas as pd
import numpy as np


    

In [3]:

    

%timeit range(1000)


    

    




100000 loops, best of 3: 7.38 µs per loop

In [4]:

    

%%timeit x = range(10000)
max(x)


    

    




10000 loops, best of 3: 162 µs per loop

In [5]:

    

from datetime import datetime

now = datetime.now()
num = 1234567890

print("{:%Y-%m %H:%M:%S} - {:,}".format(now, num))


    

    




2015-01 18:24:04 - 1,234,567,890

In [6]:

    

normals = pd.Series(np.random.normal(size=10))
normals.plot()


    

    
Out[6]:





<matplotlib.axes.AxesSubplot at 0x7f686b3d70d0>






    

In [7]:

    

data = pd.read_csv("data.csv")


    

In [8]:

    

data.head()


    

    
Out[8]:






  

    

      

      
Date
      
Open
      
High
      
Low
      
Close
      
Volume
      
Adj Close
    
  
  

    

      
0
      
 2012-06-01
      
 569.16
      
 590.00
      
 548.50
      
 584.00
      
 14077000
      
 581.50
    
    

      
1
      
 2012-05-01
      
 584.90
      
 596.76
      
 522.18
      
 577.73
      
 18827900
      
 575.26
    
    

      
2
      
 2012-04-02
      
 601.83
      
 644.00
      
 555.00
      
 583.98
      
 28759100
      
 581.48
    
    

      
3
      
 2012-03-01
      
 548.17
      
 621.45
      
 516.22
      
 599.55
      
 26486000
      
 596.99
    
    

      
4
      
 2012-02-01
      
 458.41
      
 547.61
      
 453.98
      
 542.44
      
 22001000
      
 540.12
    
  

5 rows × 7 columns

In [9]:

    

data.plot(x="Date", y="High")


    

    
Out[9]:





<matplotlib.axes.AxesSubplot at 0x7f686afad690>






    

In [ ]: