引用数据透视表 table 数据帧中的多列位置
Referencing multi-column locations in a pivot table dataFrame
我有一些数据已导入到 dataFrame 中，我从中创建了一个多列数据透视表 table CokePepsi.pivot_table(index=['Date'], columns=['Brand', 'Type'], values=['cratesSold'], aggfunc='sum')
这是仅针对可口可乐筛选的结果数据透视表 table：
cratesSold
Brand Coke
Type Diet Regular
Date
4/14/21 9928 10729
4/15/21 8526 8556
4/16/21 7556 7788
4/19/21 9709 9765
4/20/21 13234 13588
4/21/21 11946 11401
4/22/21 14407 14956
4/23/21 10956 11776
4/26/21 8324 7487
4/27/21 7705 8166
4/28/21 6771 6471
4/30/21 10988 10594
5/4/21 11351 11635
5/7/21 8809 9454
5/12/21 12081 13249
5/13/21 12674 11942
5/14/21 8729 10869
5/17/21 6217 6900
5/18/21 10502 8306
5/19/21 8990 11061
5/20/21 8210 8316
我想在此数据透视表中创建两个新列 table：一个包含 Coke(Diet) * -1 的值（即 Coke-Diet 列中所有值的负值）和另一个计算饮食和常规之间差异（即增量）的列：Coke(Regular) – Coke(Diet)
我知道如何将列添加到基本数据框以及 运行 使用基本数据框中其他列的数据进行计算，但是我正在努力寻找正确引用多列位置的语法枢轴 table.
更新：
根据@sammywemmy 的要求，我将数据作为字典包含在内。
[{' Date': '25-Jun-21', ' Time': '23:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2330.6}, {' Date': '25-Jun-21', ' Time': '22:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.43}, {' Date': '25-Jun-21', ' Time': '21:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2330.9}, {' Date': '25-Jun-21', ' Time': '20:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '25-Jun-21', ' Time': '19:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.7}, {' Date': '25-Jun-21', ' Time': '18:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.0}, {' Date': '25-Jun-21', ' Time': '17:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2331.5}, {' Date': '25-Jun-21', ' Time': '16:00:00', ' cratesSold': 4.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2331.97}, {' Date': '25-Jun-21', ' Time': '15:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.03}, {' Date': '25-Jun-21', ' Time': '14:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '25-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '25-Jun-21', ' Time': '12:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '10:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '9:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.67}, {' Date': '25-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '25-Jun-21', ' Time': '7:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '25-Jun-21', ' Time': '6:00:00', ' cratesSold': 6.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.72}, {' Date': '25-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.87}, {' Date': '25-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.77}, {' Date': '25-Jun-21', ' Time': '3:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.63}, {' Date': '25-Jun-21', ' Time': '2:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '25-Jun-21', ' Time': '1:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.4}, {' Date': '25-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.27}, {' Date': '24-Jun-21', ' Time': '23:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2333.9}, {' Date': '24-Jun-21', ' Time': '22:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2333.8}, {' Date': '24-Jun-21', ' Time': '21:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2333.37}, {' Date': '24-Jun-21', ' Time': '20:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '24-Jun-21', ' Time': '19:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.43}, {' Date': '24-Jun-21', ' Time': '18:00:00', ' cratesSold': 7.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.9}, {' Date': '24-Jun-21', ' Time': '17:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '24-Jun-21', ' Time': '16:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.7}, {' Date': '24-Jun-21', ' Time': '15:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.0}, {' Date': '24-Jun-21', ' Time': '14:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.5}, {' Date': '24-Jun-21', ' Time': '13:00:00', ' cratesSold': 6.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.97}, {' Date': '24-Jun-21', ' Time': '12:00:00', ' cratesSold': 9.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '24-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '24-Jun-21', ' Time': '10:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '24-Jun-21', ' Time': '9:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '24-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '24-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '24-Jun-21', ' Time': '6:00:00', ' cratesSold': 8.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.22}, {' Date': '24-Jun-21', ' Time': '5:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '24-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.77}, {' Date': '24-Jun-21', ' Time': '3:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.93}, {' Date': '24-Jun-21', ' Time': '2:00:00', ' cratesSold': 4.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.07}, {' Date': '24-Jun-21', ' Time': '1:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.73}, {' Date': '24-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.73}, {' Date': '23-Jun-21', ' Time': '23:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.7}, {' Date': '23-Jun-21', ' Time': '22:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '21:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '20:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '19:00:00', ' cratesSold': 12.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.83}, {' Date': '23-Jun-21', ' Time': '18:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.17}, {' Date': '23-Jun-21', ' Time': '17:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.14}, {' Date': '23-Jun-21', ' Time': '16:00:00', ' cratesSold': 6.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.1}, {' Date': '23-Jun-21', ' Time': '15:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.97}, {' Date': '23-Jun-21', ' Time': '14:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.6}, {' Date': '23-Jun-21', ' Time': '12:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2337.0}, {' Date': '23-Jun-21', ' Time': '11:00:00', ' cratesSold': 11.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2337.0}, {' Date': '23-Jun-21', ' Time': '10:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.6}, {' Date': '23-Jun-21', ' Time': '9:00:00', ' cratesSold': 7.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '8:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.97}, {' Date': '23-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.1}, {' Date': '23-Jun-21', ' Time': '6:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2337.14}, {' Date': '23-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2337.17}, {' Date': '23-Jun-21', ' Time': '4:00:00', ' cratesSold': 6.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.83}, {' Date': '23-Jun-21', ' Time': '3:00:00', ' cratesSold': 9.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '2:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '1:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.93}, {' Date': '22-Jun-21', ' Time': '23:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.77}, {' Date': '22-Jun-21', ' Time': '22:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '22-Jun-21', ' Time': '21:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.22}, {' Date': '22-Jun-21', ' Time': '20:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.4}, {' Date': '22-Jun-21', ' Time': '19:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '18:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '17:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '16:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '15:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '14:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '12:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '10:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '9:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '22-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.22}, {' Date': '22-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '22-Jun-21', ' Time': '6:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.77}, {' Date': '22-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '3:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '2:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '1:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}]
这是我想要实现的想法（在 excel 中产生），两个彩色列是根据现有数据透视列计算的新添加的列。
更新 2：
经过 大量 的搜索，我拼凑出一个部分有效的解决方案——但我希望能帮助解决剩余的错误。
使用一些简化的源数据：
np.random.seed(1)
df = pd.DataFrame(np.random.random((10, 12)))
df.columns = pd.MultiIndex.from_product(
[
['one', 'two', 'three'],
['A', 'B', 'C', 'D']
]
)
我能够添加一个新列和子列，并根据现有的多索引列使用计算填充这些新列（从而解决了我最初问题的第一部分，即获取包含的新列现有列的负值):
df[('newMeta', 'newSub')] = df.loc[0:len(df.index), [('one', 'C')]] * -1
one ... three newMeta
A B C ... C D newSub
0 0.209685 0.719405 0.313690 ... 0.223464 0.177383 -0.313690
1 0.280559 0.788768 0.679686 ... 0.332770 0.866036 -0.679686
2 0.763944 0.990348 0.103789 ... 0.067546 0.134955 -0.103789
3 0.945861 0.220135 0.929363 ... 0.669494 0.743312 -0.929363
4 0.455665 0.903104 0.652752 ... 0.610537 0.887649 -0.652752
5 0.070355 0.401810 0.841703 ... 0.288189 0.711274 -0.841703
6 0.700615 0.213194 0.156292 ... 0.127861 0.363358 -0.156292
7 0.738146 0.878900 0.317653 ... 0.354384 0.492631 -0.317653
8 0.766122 0.518608 0.247643 ... 0.736120 0.952106 -0.247643
9 0.669162 0.474207 0.599781 ... 0.658298 0.913724 -0.599781
我同样能够创建另一个多索引列，它将现有多索引列的值添加到自身（shorthand：new multi-column [('Meta2', 'Sub2')] = [('one', 'C')] + [('one', 'C')]
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + (df.loc[0:len(df.index), [('one', 'C')]])
one ... three newMeta Meta2
A B C ... D newSub Sub2
0 0.209685 0.719405 0.313690 ... 0.177383 -0.313690 0.627380
1 0.280559 0.788768 0.679686 ... 0.866036 -0.679686 1.359372
2 0.763944 0.990348 0.103789 ... 0.134955 -0.103789 0.207578
3 0.945861 0.220135 0.929363 ... 0.743312 -0.929363 1.858727
4 0.455665 0.903104 0.652752 ... 0.887649 -0.652752 1.305503
5 0.070355 0.401810 0.841703 ... 0.711274 -0.841703 1.683407
6 0.700615 0.213194 0.156292 ... 0.363358 -0.156292 0.312584
7 0.738146 0.878900 0.317653 ... 0.492631 -0.317653 0.635306
8 0.766122 0.518608 0.247643 ... 0.952106 -0.247643 0.495286
9 0.669162 0.474207 0.599781 ... 0.913724 -0.599781 1.199561
然而，非常令人沮丧且看似无法解释的障碍是无法更改 单个 索引参考来自之前的 [('one', 'C')] + [('one', 'C')].
如果我尝试类似 [('one', 'C')] + [('two', 'C')] 的操作，我会遇到一系列错误（最后复制）。
谁能解释为什么 [('one', 'C')] + [('one', 'C')] 应该在成功添加的新列中产生正确计算的值，而 [('one', 'C')] + [('two', 'C')] 会引发错误？
这是官方调用的问题语句：
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + (df.loc[0:len(df.index), [('two', 'C')]])
以及完整的错误列表：
Traceback (most recent call last):
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3081, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'Meta2'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "pandas/_libs/index.pyx", line 705, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3083, in get_loc
raise KeyError(key) from err
KeyError: 'Meta2'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 3826, in _set_item
loc = self._info_axis.get_loc(key)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/multi.py", line 2886, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 708, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
KeyError: ('Meta2', 'Sub2')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/user/Desktop/ScratchSpace/ExtractIce/multiIndexTests.py", line 156, in <module>
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + \
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 3163, in __setitem__
self._set_item(key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 3243, in _set_item
NDFrame._set_item(self, key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 3829, in _set_item
self._mgr.insert(len(self._info_axis), key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 1203, in insert
block = make_block(values=value, ndim=self.ndim, placement=slice(loc, loc + 1))
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2751, in make_block
return klass(values, ndim=ndim, placement=placement)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 142, in __init__
raise ValueError(
ValueError: Wrong number of items passed 2, placement implies 1
如果您将这两列转换为 numpy 数组而不是 pandas 数据帧，这应该可以解决您的问题。您可以使用以下内容：
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]).to_numpy() \
+ (df.loc[0:len(df.index), [('two', 'C')]]).to_numpy()
需要注意的重要部分是 .to_numpy() 函数。这会将列的值加在一起，而不是列本身。
我有一些数据已导入到 dataFrame 中，我从中创建了一个多列数据透视表 table CokePepsi.pivot_table(index=['Date'], columns=['Brand', 'Type'], values=['cratesSold'], aggfunc='sum')
这是仅针对可口可乐筛选的结果数据透视表 table：
cratesSold
Brand Coke
Type Diet Regular
Date
4/14/21 9928 10729
4/15/21 8526 8556
4/16/21 7556 7788
4/19/21 9709 9765
4/20/21 13234 13588
4/21/21 11946 11401
4/22/21 14407 14956
4/23/21 10956 11776
4/26/21 8324 7487
4/27/21 7705 8166
4/28/21 6771 6471
4/30/21 10988 10594
5/4/21 11351 11635
5/7/21 8809 9454
5/12/21 12081 13249
5/13/21 12674 11942
5/14/21 8729 10869
5/17/21 6217 6900
5/18/21 10502 8306
5/19/21 8990 11061
5/20/21 8210 8316
我想在此数据透视表中创建两个新列 table：一个包含 Coke(Diet) * -1 的值（即 Coke-Diet 列中所有值的负值）和另一个计算饮食和常规之间差异（即增量）的列：Coke(Regular) – Coke(Diet)
我知道如何将列添加到基本数据框以及 运行 使用基本数据框中其他列的数据进行计算，但是我正在努力寻找正确引用多列位置的语法枢轴 table.
更新：
根据@sammywemmy 的要求，我将数据作为字典包含在内。
[{' Date': '25-Jun-21', ' Time': '23:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2330.6}, {' Date': '25-Jun-21', ' Time': '22:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.43}, {' Date': '25-Jun-21', ' Time': '21:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2330.9}, {' Date': '25-Jun-21', ' Time': '20:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '25-Jun-21', ' Time': '19:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.7}, {' Date': '25-Jun-21', ' Time': '18:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.0}, {' Date': '25-Jun-21', ' Time': '17:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2331.5}, {' Date': '25-Jun-21', ' Time': '16:00:00', ' cratesSold': 4.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2331.97}, {' Date': '25-Jun-21', ' Time': '15:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.03}, {' Date': '25-Jun-21', ' Time': '14:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '25-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '25-Jun-21', ' Time': '12:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '10:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.8}, {' Date': '25-Jun-21', ' Time': '9:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.67}, {' Date': '25-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '25-Jun-21', ' Time': '7:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '25-Jun-21', ' Time': '6:00:00', ' cratesSold': 6.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.72}, {' Date': '25-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.87}, {' Date': '25-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.77}, {' Date': '25-Jun-21', ' Time': '3:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.63}, {' Date': '25-Jun-21', ' Time': '2:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '25-Jun-21', ' Time': '1:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.4}, {' Date': '25-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.27}, {' Date': '24-Jun-21', ' Time': '23:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2333.9}, {' Date': '24-Jun-21', ' Time': '22:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2333.8}, {' Date': '24-Jun-21', ' Time': '21:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2333.37}, {' Date': '24-Jun-21', ' Time': '20:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '24-Jun-21', ' Time': '19:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.43}, {' Date': '24-Jun-21', ' Time': '18:00:00', ' cratesSold': 7.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.9}, {' Date': '24-Jun-21', ' Time': '17:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.6}, {' Date': '24-Jun-21', ' Time': '16:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2330.7}, {' Date': '24-Jun-21', ' Time': '15:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.0}, {' Date': '24-Jun-21', ' Time': '14:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.5}, {' Date': '24-Jun-21', ' Time': '13:00:00', ' cratesSold': 6.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2331.97}, {' Date': '24-Jun-21', ' Time': '12:00:00', ' cratesSold': 9.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '24-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '24-Jun-21', ' Time': '10:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '24-Jun-21', ' Time': '9:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '24-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '24-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '24-Jun-21', ' Time': '6:00:00', ' cratesSold': 8.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.22}, {' Date': '24-Jun-21', ' Time': '5:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '24-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.77}, {' Date': '24-Jun-21', ' Time': '3:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.93}, {' Date': '24-Jun-21', ' Time': '2:00:00', ' cratesSold': 4.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.07}, {' Date': '24-Jun-21', ' Time': '1:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.73}, {' Date': '24-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.73}, {' Date': '23-Jun-21', ' Time': '23:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.7}, {' Date': '23-Jun-21', ' Time': '22:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '21:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '20:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '19:00:00', ' cratesSold': 12.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.83}, {' Date': '23-Jun-21', ' Time': '18:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.17}, {' Date': '23-Jun-21', ' Time': '17:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.14}, {' Date': '23-Jun-21', ' Time': '16:00:00', ' cratesSold': 6.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.1}, {' Date': '23-Jun-21', ' Time': '15:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.97}, {' Date': '23-Jun-21', ' Time': '14:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.6}, {' Date': '23-Jun-21', ' Time': '12:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2337.0}, {' Date': '23-Jun-21', ' Time': '11:00:00', ' cratesSold': 11.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2337.0}, {' Date': '23-Jun-21', ' Time': '10:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.6}, {' Date': '23-Jun-21', ' Time': '9:00:00', ' cratesSold': 7.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '8:00:00', ' cratesSold': 5.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.97}, {' Date': '23-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2337.1}, {' Date': '23-Jun-21', ' Time': '6:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2337.14}, {' Date': '23-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2337.17}, {' Date': '23-Jun-21', ' Time': '4:00:00', ' cratesSold': 6.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2336.83}, {' Date': '23-Jun-21', ' Time': '3:00:00', ' cratesSold': 9.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '2:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '1:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2336.8}, {' Date': '23-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.93}, {' Date': '22-Jun-21', ' Time': '23:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.77}, {' Date': '22-Jun-21', ' Time': '22:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '22-Jun-21', ' Time': '21:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.22}, {' Date': '22-Jun-21', ' Time': '20:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.4}, {' Date': '22-Jun-21', ' Time': '19:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '18:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '17:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '16:00:00', ' cratesSold': 1.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '15:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '14:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '13:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Pepsi', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '12:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '11:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '10:00:00', ' cratesSold': 8.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '9:00:00', ' cratesSold': 3.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.4}, {' Date': '22-Jun-21', ' Time': '8:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.22}, {' Date': '22-Jun-21', ' Time': '7:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2335.3}, {' Date': '22-Jun-21', ' Time': '6:00:00', ' cratesSold': 4.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.77}, {' Date': '22-Jun-21', ' Time': '5:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.47}, {' Date': '22-Jun-21', ' Time': '4:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2335.27}, {' Date': '22-Jun-21', ' Time': '3:00:00', ' cratesSold': 3.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.95}, {' Date': '22-Jun-21', ' Time': '2:00:00', ' cratesSold': 5.0, ' Type': 'Diet', ' Brand': 'Pepsi', ' cratePrice': 2334.57}, {' Date': '22-Jun-21', ' Time': '1:00:00', ' cratesSold': 2.0, ' Type': 'Diet', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': '22-Jun-21', ' Time': '0:00:00', ' cratesSold': 2.0, ' Type': 'Regular', ' Brand': 'Coke', ' cratePrice': 2334.03}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}, {' Date': nan, ' Time': nan, ' cratesSold': nan, ' Type': nan, ' Brand': nan, ' cratePrice': nan}]
这是我想要实现的想法（在 excel 中产生），两个彩色列是根据现有数据透视列计算的新添加的列。
更新 2：
经过 大量 的搜索，我拼凑出一个部分有效的解决方案——但我希望能帮助解决剩余的错误。
使用一些简化的源数据：
np.random.seed(1)
df = pd.DataFrame(np.random.random((10, 12)))
df.columns = pd.MultiIndex.from_product(
[
['one', 'two', 'three'],
['A', 'B', 'C', 'D']
]
)
我能够添加一个新列和子列，并根据现有的多索引列使用计算填充这些新列（从而解决了我最初问题的第一部分，即获取包含的新列现有列的负值):
df[('newMeta', 'newSub')] = df.loc[0:len(df.index), [('one', 'C')]] * -1
one ... three newMeta
A B C ... C D newSub
0 0.209685 0.719405 0.313690 ... 0.223464 0.177383 -0.313690
1 0.280559 0.788768 0.679686 ... 0.332770 0.866036 -0.679686
2 0.763944 0.990348 0.103789 ... 0.067546 0.134955 -0.103789
3 0.945861 0.220135 0.929363 ... 0.669494 0.743312 -0.929363
4 0.455665 0.903104 0.652752 ... 0.610537 0.887649 -0.652752
5 0.070355 0.401810 0.841703 ... 0.288189 0.711274 -0.841703
6 0.700615 0.213194 0.156292 ... 0.127861 0.363358 -0.156292
7 0.738146 0.878900 0.317653 ... 0.354384 0.492631 -0.317653
8 0.766122 0.518608 0.247643 ... 0.736120 0.952106 -0.247643
9 0.669162 0.474207 0.599781 ... 0.658298 0.913724 -0.599781
我同样能够创建另一个多索引列，它将现有多索引列的值添加到自身（shorthand：new multi-column [('Meta2', 'Sub2')] = [('one', 'C')] + [('one', 'C')]
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + (df.loc[0:len(df.index), [('one', 'C')]])
one ... three newMeta Meta2
A B C ... D newSub Sub2
0 0.209685 0.719405 0.313690 ... 0.177383 -0.313690 0.627380
1 0.280559 0.788768 0.679686 ... 0.866036 -0.679686 1.359372
2 0.763944 0.990348 0.103789 ... 0.134955 -0.103789 0.207578
3 0.945861 0.220135 0.929363 ... 0.743312 -0.929363 1.858727
4 0.455665 0.903104 0.652752 ... 0.887649 -0.652752 1.305503
5 0.070355 0.401810 0.841703 ... 0.711274 -0.841703 1.683407
6 0.700615 0.213194 0.156292 ... 0.363358 -0.156292 0.312584
7 0.738146 0.878900 0.317653 ... 0.492631 -0.317653 0.635306
8 0.766122 0.518608 0.247643 ... 0.952106 -0.247643 0.495286
9 0.669162 0.474207 0.599781 ... 0.913724 -0.599781 1.199561
然而，非常令人沮丧且看似无法解释的障碍是无法更改 单个 索引参考来自之前的 [('one', 'C')] + [('one', 'C')].
如果我尝试类似 [('one', 'C')] + [('two', 'C')] 的操作，我会遇到一系列错误（最后复制）。
谁能解释为什么 [('one', 'C')] + [('one', 'C')] 应该在成功添加的新列中产生正确计算的值，而 [('one', 'C')] + [('two', 'C')] 会引发错误？
这是官方调用的问题语句：
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + (df.loc[0:len(df.index), [('two', 'C')]])
以及完整的错误列表：
Traceback (most recent call last):
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3081, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'Meta2'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "pandas/_libs/index.pyx", line 705, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3083, in get_loc
raise KeyError(key) from err
KeyError: 'Meta2'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 3826, in _set_item
loc = self._info_axis.get_loc(key)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/multi.py", line 2886, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 708, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
KeyError: ('Meta2', 'Sub2')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/user/Desktop/ScratchSpace/ExtractIce/multiIndexTests.py", line 156, in <module>
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]) + \
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 3163, in __setitem__
self._set_item(key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 3243, in _set_item
NDFrame._set_item(self, key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 3829, in _set_item
self._mgr.insert(len(self._info_axis), key, value)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 1203, in insert
block = make_block(values=value, ndim=self.ndim, placement=slice(loc, loc + 1))
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2751, in make_block
return klass(values, ndim=ndim, placement=placement)
File "/Users/user/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 142, in __init__
raise ValueError(
ValueError: Wrong number of items passed 2, placement implies 1
如果您将这两列转换为 numpy 数组而不是 pandas 数据帧，这应该可以解决您的问题。您可以使用以下内容：
df[('Meta2', 'Sub2')] = (df.loc[0:len(df.index), [('one', 'C')]]).to_numpy() \
+ (df.loc[0:len(df.index), [('two', 'C')]]).to_numpy()
需要注意的重要部分是 .to_numpy() 函数。这会将列的值加在一起，而不是列本身。