In [1]:

    

from pandas import Series, DataFrame
import pandas as pd
from numpy.random import randn
import numpy as np
pd.options.display.max_rows = 12
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(12, 6))


    

In [2]:

    

%matplotlib inline


    

In [5]:

    

close_px = pd.read_csv('stock_px.csv', parse_dates=True, index_col=0)
volume = pd.read_csv('volume.csv', parse_dates=True, index_col=0)
prices = close_px.ix['2011-09-05':'2011-09-14', ['AAPL', 'JNJ', 'SPX', 'XOM']]
volume = volume.ix['2011-09-05':'2011-09-12', ['AAPL', 'JNJ', 'XOM']]


    

In [6]:

    

prices


    

    
Out[6]:






  

    

      

      
AAPL
      
JNJ
      
SPX
      
XOM
    
  
  

    

      
2011-09-06
      
379.74
      
64.64
      
1165.24
      
71.15
    
    

      
2011-09-07
      
383.93
      
65.43
      
1198.62
      
73.65
    
    

      
2011-09-08
      
384.14
      
64.95
      
1185.90
      
72.82
    
    

      
2011-09-09
      
377.48
      
63.64
      
1154.23
      
71.01
    
    

      
2011-09-12
      
379.94
      
63.59
      
1162.27
      
71.84
    
    

      
2011-09-13
      
384.62
      
63.61
      
1172.87
      
71.65
    
    

      
2011-09-14
      
389.30
      
63.73
      
1188.68
      
72.64
    
  

In [7]:

    

volume


    

    
Out[7]:






  

    

      

      
AAPL
      
JNJ
      
XOM
    
  
  

    

      
2011-09-06
      
18173500.0
      
15848300.0
      
25416300.0
    
    

      
2011-09-07
      
12492000.0
      
10759700.0
      
23108400.0
    
    

      
2011-09-08
      
14839800.0
      
15551500.0
      
22434800.0
    
    

      
2011-09-09
      
20171900.0
      
17008200.0
      
27969100.0
    
    

      
2011-09-12
      
16697300.0
      
13448200.0
      
26205800.0
    
  

In [8]:

    

prices * volume


    

    
Out[8]:






  

    

      

      
AAPL
      
JNJ
      
SPX
      
XOM
    
  
  

    

      
2011-09-06
      
6.901205e+09
      
1.024434e+09
      
NaN
      
1.808370e+09
    
    

      
2011-09-07
      
4.796054e+09
      
7.040072e+08
      
NaN
      
1.701934e+09
    
    

      
2011-09-08
      
5.700561e+09
      
1.010070e+09
      
NaN
      
1.633702e+09
    
    

      
2011-09-09
      
7.614489e+09
      
1.082402e+09
      
NaN
      
1.986086e+09
    
    

      
2011-09-12
      
6.343972e+09
      
8.551710e+08
      
NaN
      
1.882625e+09
    
    

      
2011-09-13
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
2011-09-14
      
NaN
      
NaN
      
NaN
      
NaN
    
  

In [9]:

    

vwap = (prices * volume).sum() / volume.sum()


    

In [10]:

    

vwap


    

    
Out[10]:





AAPL    380.655181
JNJ      64.394769
SPX            NaN
XOM      72.024288
dtype: float64

In [11]:

    

vwap.dropna()


    

    
Out[11]:





AAPL    380.655181
JNJ      64.394769
XOM      72.024288
dtype: float64

In [12]:

    

prices.align(volume, join='inner')


    

    
Out[12]:





(              AAPL    JNJ    XOM
 2011-09-06  379.74  64.64  71.15
 2011-09-07  383.93  65.43  73.65
 2011-09-08  384.14  64.95  72.82
 2011-09-09  377.48  63.64  71.01
 2011-09-12  379.94  63.59  71.84,
                   AAPL         JNJ         XOM
 2011-09-06  18173500.0  15848300.0  25416300.0
 2011-09-07  12492000.0  10759700.0  23108400.0
 2011-09-08  14839800.0  15551500.0  22434800.0
 2011-09-09  20171900.0  17008200.0  27969100.0
 2011-09-12  16697300.0  13448200.0  26205800.0)

In [13]:

    

s1 = Series(range(3), index=['a', 'b', 'c'])
s2 = Series(range(4), index=['d', 'b', 'c', 'e'])
s3 = Series(range(3), index=['f', 'a', 'c'])
DataFrame({'one': s1, 'two': s2, 'three': s3})


    

    
Out[13]:






  

    

      

      
one
      
three
      
two
    
  
  

    

      
a
      
0.0
      
1.0
      
NaN
    
    

      
b
      
1.0
      
NaN
      
1.0
    
    

      
c
      
2.0
      
2.0
      
2.0
    
    

      
d
      
NaN
      
NaN
      
0.0
    
    

      
e
      
NaN
      
NaN
      
3.0
    
    

      
f
      
NaN
      
0.0
      
NaN
    
  

In [14]:

    

DataFrame({'one': s1, 'two': s2, 'three': s3}, index=list('face'))


    

    
Out[14]:






  

    

      

      
one
      
three
      
two
    
  
  

    

      
f
      
NaN
      
0.0
      
NaN
    
    

      
a
      
0.0
      
1.0
      
NaN
    
    

      
c
      
2.0
      
2.0
      
2.0
    
    

      
e
      
NaN
      
NaN
      
3.0
    
  

In [ ]: