我有两个熊猫数据框，一个叫做“ orders”，另一个叫做“ daily_prices”。daily_prices如下：
AAPL GOOG IBM XOM 2011-01-10 339.44 614.21 142.78 71.57 2011-01-13 342.64 616.69 143.92 73.08 2011-01-26 340.82 616.50 155.74 75.89 2011-02-02 341.29 612.00 157.93 79.46 2011-02-10 351.42 616.44 159.32 79.68 2011-03-03 356.40 609.56 158.73 82.19 2011-05-03 345.14 533.89 167.84 82.00 2011-06-03 340.42 523.08 160.97 78.19 2011-06-10 323.03 509.51 159.14 76.84 2011-08-01 393.26 606.77 176.28 76.67 2011-12-20 392.46 630.37 184.14 79.97
订单如下：
direction size ticker prices 2011-01-10 Buy 1500 AAPL 339.44 2011-01-13 Sell 1500 AAPL 342.64 2011-01-13 Buy 4000 IBM 143.92 2011-01-26 Buy 1000 GOOG 616.50 2011-02-02 Sell 4000 XOM 79.46 2011-02-10 Buy 4000 XOM 79.68 2011-03-03 Sell 1000 GOOG 609.56 2011-03-03 Sell 2200 IBM 158.73 2011-06-03 Sell 3300 IBM 160.97 2011-05-03 Buy 1500 IBM 167.84 2011-06-10 Buy 1200 AAPL 323.03 2011-08-01 Buy 55 GOOG 606.77 2011-08-01 Sell 55 GOOG 606.77 2011-12-20 Sell 1200 AAPL 392.46
两个数据帧的索引均为datetime.date。通过使用列表解析来遍历所有订单并在“ daily_prices”数据框中查找特定日期的特定报价，然后将该列表作为列添加到“订单”数据框中的“价格”列。 “订单”数据框。我想使用数组操作而不是循环执行此操作。能做到吗 我尝试使用：
daily_prices.ix [日期，股票行情]
但这会返回两个列表的笛卡尔乘积矩阵。我希望它返回仅在指定日期的指定报价的价格的列向量。
lookup为此目的设计使用我们的朋友：
lookup
In [17]: prices Out[17]: AAPL GOOG IBM XOM 2011-01-10 339.44 614.21 142.78 71.57 2011-01-13 342.64 616.69 143.92 73.08 2011-01-26 340.82 616.50 155.74 75.89 2011-02-02 341.29 612.00 157.93 79.46 2011-02-10 351.42 616.44 159.32 79.68 2011-03-03 356.40 609.56 158.73 82.19 2011-05-03 345.14 533.89 167.84 82.00 2011-06-03 340.42 523.08 160.97 78.19 2011-06-10 323.03 509.51 159.14 76.84 2011-08-01 393.26 606.77 176.28 76.67 2011-12-20 392.46 630.37 184.14 79.97 In [18]: orders Out[18]: Date direction size ticker prices 0 2011-01-10 00:00:00 Buy 1500 AAPL 339.44 1 2011-01-13 00:00:00 Sell 1500 AAPL 342.64 2 2011-01-13 00:00:00 Buy 4000 IBM 143.92 3 2011-01-26 00:00:00 Buy 1000 GOOG 616.50 4 2011-02-02 00:00:00 Sell 4000 XOM 79.46 5 2011-02-10 00:00:00 Buy 4000 XOM 79.68 6 2011-03-03 00:00:00 Sell 1000 GOOG 609.56 7 2011-03-03 00:00:00 Sell 2200 IBM 158.73 8 2011-06-03 00:00:00 Sell 3300 IBM 160.97 9 2011-05-03 00:00:00 Buy 1500 IBM 167.84 10 2011-06-10 00:00:00 Buy 1200 AAPL 323.03 11 2011-08-01 00:00:00 Buy 55 GOOG 606.77 12 2011-08-01 00:00:00 Sell 55 GOOG 606.77 13 2011-12-20 00:00:00 Sell 1200 AAPL 392.46 In [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])
