Vectorized look-up of values in Pandas dataframe

Use our friend lookup, designed precisely for this purpose:

In [17]: pricesOut[17]:               AAPL    GOOG     IBM    XOM2011-01-10  339.44  614.21  142.78  71.572011-01-13  342.64  616.69  143.92  73.082011-01-26  340.82  616.50  155.74  75.892011-02-02  341.29  612.00  157.93  79.462011-02-10  351.42  616.44  159.32  79.682011-03-03  356.40  609.56  158.73  82.192011-05-03  345.14  533.89  167.84  82.002011-06-03  340.42  523.08  160.97  78.192011-06-10  323.03  509.51  159.14  76.842011-08-01  393.26  606.77  176.28  76.672011-12-20  392.46  630.37  184.14  79.97In [18]: ordersOut[18]:                   Date direction  size ticker  prices0  2011-01-10 00:00:00       Buy  1500   AAPL  339.441  2011-01-13 00:00:00      Sell  1500   AAPL  342.642  2011-01-13 00:00:00       Buy  4000    IBM  143.923  2011-01-26 00:00:00       Buy  1000   GOOG  616.504  2011-02-02 00:00:00      Sell  4000    XOM   79.465  2011-02-10 00:00:00       Buy  4000    XOM   79.686  2011-03-03 00:00:00      Sell  1000   GOOG  609.567  2011-03-03 00:00:00      Sell  2200    IBM  158.738  2011-06-03 00:00:00      Sell  3300    IBM  160.979  2011-05-03 00:00:00       Buy  1500    IBM  167.8410 2011-06-10 00:00:00       Buy  1200   AAPL  323.0311 2011-08-01 00:00:00       Buy    55   GOOG  606.7712 2011-08-01 00:00:00      Sell    55   GOOG  606.7713 2011-12-20 00:00:00      Sell  1200   AAPL  392.46In [19]: prices.lookup(orders.Date, orders.ticker)Out[19]: array([ 339.44,  342.64,  143.92,  616.5 ,   79.46,   79.68,  609.56,        158.73,  160.97,  167.84,  323.03,  606.77,  606.77,  392.46])