import os import pandas as pd import matplotlib.pyplot as plt def test_run(): start_date='2017-01-01' end_data='2017-12-15' dates=pd.date_range(start_date, end_data) # Create an empty data frame df=pd.DataFrame(index=dates) symbols=['SPY', 'AAPL', 'IBM', 'GOOG', 'GLD'] for symbol in symbols: temp=getAdjCloseForSymbol(symbol) df=df.join(temp, how='inner') return df def getAdjCloseForSymbol(symbol): # Load csv file temp=pd.read_csv("data/{0}.csv".format(symbol), index_col="Date", parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan']) # rename the column temp=temp.rename(columns={'Adj Close': symbol}) return temp def plot_data(df, title="Stock prices"): ax=df.plot(title=title, fontsize=10) ax.set_xlabel("Date") ax.set_ylabel("Price") plt.show() if __name__ == '__main__': df=test_run() # data=data.ix['2017-12-01':'2017-12-15', ['IBM', 'GOOG']] plot_data(df) """ IBM GOOG 2017-12-01 154.759995 1010.169983 2017-12-04 156.460007 998.679993 2017-12-05 155.350006 1005.150024 2017-12-06 154.100006 1018.380005 2017-12-07 153.570007 1030.930054 2017-12-08 154.809998 1037.050049 2017-12-11 155.410004 1041.099976 2017-12-12 156.740005 1040.479980 2017-12-13 153.910004 1040.609985 2017-12-15 152.500000 1064.189941 """
