%matplotlib inline
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt

    def get_max_close(symbol):
        """
        return the max close value of the specified stock
        """
        df = pd.read_csv("stock/data/{}.csv".format(symbol))
        #print df.head(5)
        df[['Close','Adj Close']].plot()
        plt.show()
        return df['Close'].max()
    
    def test_run():
        for symbol in ['AAPL','IBM']:
            print symbol,get_max_close(symbol)
            
    test_run()

    dt = pd.date_range('2010-01-22','2010-01-26')
    #print dt, dt[0]
    df1=pd.DataFrame(index=dt)
    
    dfspy=pd.read_csv('stock/data/SPY.csv',index_col='Date',parse_dates=True,usecols=['Date','Adj Close'],na_values=['nan'])
    #print dfspy.head()
    df2=df1.join(dfspy)
    #print df1
    df2=df2.dropna()
    print df2

                Adj Close
    2010-01-22  95.201720
    2010-01-25  95.689887
    2010-01-26  95.288892

    df2=df1.join(dfspy,how='inner')
    print df2

                Adj Close
    2010-01-26  95.288892
    2010-01-25  95.689887
    2010-01-22  95.201720

    print df2.head()
    df2 = df2.rename(columns={'Adj Close':'SPY'})
    print df2

                Adj Close
    2010-01-26  95.288892
    2010-01-25  95.689887
    2010-01-22  95.201720
                      SPY
    2010-01-26  95.288892
    2010-01-25  95.689887
    2010-01-22  95.201720

    import os
    
    def symbol_to_path(symbol, base_dir="stock/data"):
        """Return CSV file path given ticker symbol."""
        return os.path.join(base_dir, "{}.csv".format(str(symbol)))
    
    
    def get_data(symbols, dates):
        """Read stock data (adjusted close) for given symbols from CSV files."""
        df = pd.DataFrame(index=dates)
        if 'SPY' not in symbols:  # add SPY for reference, if absent
            symbols.insert(0, 'SPY')
    
        for symbol in symbols:
            # TODO: Read and join data for each symbol
            df1 = pd.read_csv(symbol_to_path(symbol),index_col='Date', usecols=['Date','Adj Close'],parse_dates=True,na_values=['nan'])
            df1=df1.rename(columns={'Adj Close':symbol})
            df = df.join(df1)
            if symbol=='SPY':
                df=df.dropna()
        return df
    
    
    def test_run():
        # Define a date range
        dates = pd.date_range('2010-01-01', '2010-10-31')
    
        # Choose stock symbols to read
        symbols = ['GOOG', 'IBM', 'AAPL']
        
        # Get stock data
        df = get_data(symbols, dates)
        fig = df.plot(title='stock graph')
        fig.set_xlabel('month')
        fig.set_ylabel('price')
        plt.show()
        df = df.ix['2010-01-01':'2010-01-08']
        print df.head(20)#.ix['2010-01-20':'2010-02-28']
        
        df = df.ix[:,['SPY','GOOG']] # or df = df[['SPY','GOOG']]
        print df
    
    test_run()

                      SPY        GOOG         IBM       AAPL
    2010-01-04  98.793254  313.062468  112.285875  27.847252
    2010-01-05  99.054769  311.683844  110.929466  27.895396
    2010-01-06  99.124509  303.826685  110.208865  27.451683
    2010-01-07  99.542943  296.753749  109.827375  27.400936
    2010-01-08  99.874198  300.709808  110.929466  27.583106
                      SPY        GOOG
    2010-01-04  98.793254  313.062468
    2010-01-05  99.054769  311.683844
    2010-01-06  99.124509  303.826685
    2010-01-07  99.542943  296.753749
    2010-01-08  99.874198  300.709808