Python Pandas: 添加一级多索引

Question

我有以下数据框：

我想添加的列有额外的索引级别，如红色所示。

我创建的 MultiIndex 如下：

MI = pd.MultiIndex(levels=[['trade_input', 'mae_function'], list(df)],
                   labels=[[0, 0, 1, 1, 1, 1], range(len(list(df)))],
                   names=['first', 'second'])

如何将 MultiIndex 添加到现有的 DataFrame？我如何指定它应该应用于列？

下面是重新创建原始 DataFrame 的数据和命令：

df = pd.DataFrame(data = dict, columns = ['entry_index', 'exit_index', 'direction', 'high', 'low', 'compar_tuples'])

dict = {'compar_tuples': {0: [(1, slice('1', '1', None))],
      1: [(1, slice('1', '2', None)), (2, slice('2', '2', None))],
      2: [(1, slice('1', '3', None)),
       (2, slice('2', '3', None)),
       (3, slice('3', '3', None))],
      3: [(1, slice('1', '4', None)),
       (2, slice('2', '4', None)),
       (3, slice('3', '4', None)),
       (4, slice('4', '4', None))],
      4: [(1, slice('1', '5', None)),
       (2, slice('2', '5', None)),
       (3, slice('3', '5', None)),
       (4, slice('4', '5', None)),
       (5, slice('5', '5', None))],
      5: [(1, slice('1', '6', None)),
       (2, slice('2', '6', None)),
       (3, slice('3', '6', None)),
       (4, slice('4', '6', None)),
       (5, slice('5', '6', None)),
       (6, slice('6', '6', None))],
      6: [(1, slice('1', '7', None)),
       (2, slice('2', '7', None)),
       (3, slice('3', '7', None)),
       (4, slice('4', '7', None)),
       (5, slice('5', '7', None)),
       (6, slice('6', '7', None)),
       (7, slice('7', '7', None))]},
     'direction': {0: 1, 1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: -1},
     'entry_index': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0},
     'exit_index': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7},
     'high': {0: 1209.75,
      1: 1211.0,
      2: 1211.25,
      3: 1207.25,
      4: 1206.25,
      5: 1205.75,
      6: 1205.5},
     'low': {0: 1207.25,
      1: 1207.5,
      2: 1205.75,
      3: 1206.0,
      4: 1201.0,
      5: 1202.75,
      6: 1203.75}}

Answer 1

最简单的方法是使用 pd.concat 和 keys 参数

ti_cols = df.columns[:2]
mae_cols = df.columns[2:]
pd.concat([df[ti_cols], df[mae_cols]], axis=1, keys=['trade_inputs', 'mae_function'])

但是，如果您费尽心思制作了多重索引，则可以将其分配给 columns 属性

df.columns = MI
df

Answer 2

df.index = index

对我有用。

Python Pandas: 添加一级多索引

Python Pandas: Add a level of MultIndexing

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