相当于Excel中使用Pythonpandas的数组

我有一个大pandas数据框如下。 需要添加两个更多的列与特定品牌的销售最大值和最小值。

date brand price quantity sales vat 31-May-13 Reebok 10 23 230 3.5 31-May-13 Adidas 10 25 250 2.8 31-May-13 Campus 8 21 168 3.5 31-May-13 Nike 10 20 200 6.5 31-May-13 Woods 2 7 14 2.8 01-Jun-13 Reebok 4 27 108 2.2 01-Jun-13 Adidas 7 28 196 3.8 01-Jun-13 Campus 7 41 287 4.2 01-Jun-13 Nike 2 39 78 7.2 01-Jun-13 Woods 5 26 130 3.3 02-Jun-13 Reebok 10 5 50 2.2 02-Jun-13 Adidas 10 15 150 3.8 02-Jun-13 Woods 6 30 180 3.3 

在这里，我的date栏不是按顺序排列的，所有的品牌数据在date列中都不可用。 结果应该看起来像，

  date brand price quantity sales vat Max Min 31-May-13 Reebok 10 23 230 3.5 230 50 31-May-13 Adidas 10 25 250 2.8 250 150 31-May-13 Campus 8 21 168 3.5 287 168 31-May-13 Nike 10 20 200 6.5 200 78 31-May-13 Woods 2 7 14 2.8 180 14 01-Jun-13 Reebok 4 27 108 2.2 230 50 01-Jun-13 Adidas 7 28 196 3.8 250 150 01-Jun-13 Campus 7 41 287 4.2 287 168 01-Jun-13 Nike 2 39 78 7.2 200 78 01-Jun-13 Woods 5 26 130 3.3 180 14 02-Jun-13 Reebok 10 5 50 2.2 230 50 02-Jun-13 Adidas 10 15 150 3.8 250 150 02-Jun-13 Woods 6 30 180 3.3 180 14 

Answer 1

你可以使用groupby.transform：

 df['max'] = df.groupby('brand')['sales'].transform('max') df['min'] = df.groupby('brand')['sales'].transform('min') df Out: date brand price quantity sales vat max min 0 2013-05-31 Reebok 10 23 230 3.5 230 50 1 2013-05-31 Adidas 10 25 250 2.8 250 150 2 2013-05-31 Campus 8 21 168 3.5 287 168 3 2013-05-31 Nike 10 20 200 6.5 200 78 4 2013-05-31 Woods 2 7 14 2.8 180 14 5 2013-06-01 Reebok 4 27 108 2.2 230 50 6 2013-06-01 Adidas 7 28 196 3.8 250 150 7 2013-06-01 Campus 7 41 287 4.2 287 168 8 2013-06-01 Nike 2 39 78 7.2 200 78 9 2013-06-01 Woods 5 26 130 3.3 180 14 10 2013-06-02 Reebok 10 5 50 2.2 230 50 11 2013-06-02 Adidas 10 15 150 3.8 250 150 12 2013-06-02 Woods 6 30 180 3.3 180 14 

Answer 2

您可以使用groupby，然后join原始数据框：

 >>> g = df.groupby('brand')['sales'].agg([np.min, np.max]) >>> g amin amax brand Adidas 150 250 Campus 168 287 Nike 78 200 Reebok 50 230 Woods 14 180 >>> df.join(g, on='brand') date brand price quantity sales vat amin amax 0 31-May-13 Reebok 10 23 230 3.5 50 230 1 31-May-13 Adidas 10 25 250 2.8 150 250 2 31-May-13 Campus 8 21 168 3.5 168 287 3 31-May-13 Nike 10 20 200 6.5 78 200 4 31-May-13 Woods 2 7 14 2.8 14 180 5 01-Jun-13 Reebok 4 27 108 2.2 50 230 6 01-Jun-13 Adidas 7 28 196 3.8 150 250 7 01-Jun-13 Campus 7 41 287 4.2 168 287 8 01-Jun-13 Nike 2 39 78 7.2 78 200 9 01-Jun-13 Woods 5 26 130 3.3 14 180 10 02-Jun-13 Reebok 10 5 50 2.2 50 230 11 02-Jun-13 Adidas 10 15 150 3.8 150 250 12 02-Jun-13 Woods 6 30 180 3.3 14 180