original_docstring: Main function to see the results
docstring: Main function to see the results
docstring_tokens: Main function to see the results
code: def main():
      """Main function to see the results"""
      dates = pandas.date_range("2021-01-01","2021-10-30")
      symbols = ["AAPL","FB","WMT"]
      data_frame = getting_data(symbols,dates)
      print(data_frame)
      print(data_frame.mean())
      print(data_frame.median())
      # standard deviation , understanding how risky is stock, depends on its volatility . 
      print(data_frame.std())
      # rolling 5 days - understanding last five days changes in stock prices 
      ax = data_frame["AAPL"].plot(title="Apple rolling mean",label="Apple stock price")
      ax.legend(loc="upper left")
      ax.set_xlabel("dates")
      ax.set_ylabel("Prices")
      rm_apple = data_frame["AAPL"].rolling(window=10).mean()
      rm_apple.plot(label="Rolling mean",ax=ax)
      ax.legend()
      plt.savefig("graphs/apple.png")
code_tokens: def main ( ) : dates = pandas . date_range ( "2021-01-01" , "2021-10-30" ) symbols = [ "AAPL" , "FB" , "WMT" ] data_frame = getting_data ( symbols , dates ) print ( data_frame ) print ( data_frame . mean ( ) ) print ( data_frame . median ( ) ) print ( data_frame . std ( ) ) ax = data_frame [ "AAPL" ] . plot ( title = "Apple rolling mean" , label = "Apple stock price" ) ax . legend ( loc = "upper left" ) ax . set_xlabel ( "dates" ) ax . set_ylabel ( "Prices" ) rm_apple = data_frame [ "AAPL" ] . rolling ( window = 10 ) . mean ( ) rm_apple . plot ( label = "Rolling mean" , ax = ax ) ax . legend ( ) plt . savefig ( "graphs/apple.png" )
short_docstring: Main function to see the results
short_docstring_tokens: Main function to see the results
comment: """Main function to see the results"""$SEP$# standard deviation , understanding how risky is stock, depends on its volatility . 
  $SEP$# rolling 5 days - understanding last five days changes in stock prices 
  