极客教程Python ŌĆō Pandas数据框中删除多级列索引的多个级别
要从多级列索引中删除多个级别，请重复使用 columns.droplevel() 方法。 我们使用 Multiindex.from_tuples() 创建逐列索引。
首先，按列创建多级索引−
items = pd.MultiIndex.from_tuples([("Col 1", "Col 1", "Col 1"),("Col 2", "Col 2", "Col 2"),("Col 3", "Col 3", "Col 3")])
接下来，创建一个多级索引数组并形成多级数据框−
arr = [np.array(['car', 'car', 'car','bike','bike', 'bike', 'truck', 'truck', 'truck']),
np.array(['valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC'])]
# 形成多级数据框
dataFrame = pd.DataFrame(np.random.randn(9, 3), index=arr,columns=items)
为索引添加标签−
dataFrame.index.names = ['level 0', 'level 1']
删除索引 0 上的一级−
dataFrame.columns = dataFrame.columns.droplevel(0)
我们删除了索引 0 上的一级。 删除后，级别 1 现在变成了级别 0。 要删除另一个级别，只需再次使用上面的方法，即：
dataFrame.columns = dataFrame.columns.droplevel(0)
以下是代码
例子
import numpy as np
import pandas as pd
items = pd.MultiIndex.from_tuples([("Col 1", "Col 1", "Col 1"),("Col 2", "Col 2", "Col 2"),("Col 3", "Col 3", "Col 3")])
# multiindex array
arr = [np.array(['car', 'car', 'car','bike','bike', 'bike', 'truck', 'truck', 'truck']),
np.array(['valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC'])]
# forming multiindex dataframe
dataFrame = pd.DataFrame(np.random.randn(9, 3), index=arr,columns=items)
# labeling index
dataFrame.index.names = ['one', 'two']
print"DataFrame...\n",dataFrame
print"\nDropping a level...\n";
dataFrame.columns = dataFrame.columns.droplevel(0)
print"Updated DataFrame..\n",dataFrame
print"\nDropping another level...\n";
dataFrame.columns = dataFrame.columns.droplevel(0)
print"Updated DataFrame..\n",dataFrame
输出
这将产生以下输出−
DataFrame...
Col 1 Col 2 Col 3
Col 1 Col 2 Col 3
Col 1 Col 2 Col 3
one two
car valueA 0.425077 0.020606 1.148156
valueB -1.720355 0.502863 1.184753
valueC 0.373106 1.300935 -0.128404
bike valueA -0.648708 0.944725 0.593327
valueB -0.613921 -0.238730 -0.218448
valueC 0.313042 -0.628065 0.910935
truck valueA 0.286377 0.478067 -1.000645
valueB 1.151793 -0.171433 -0.612346
valueC -1.358061 0.735075 0.092700
Dropping a level...
Updated DataFrame..
Col 1 Col 2 Col 3
Col 1 Col 2 Col 3
one two
car valueA 0.425077 0.020606 1.148156
valueB -1.720355 0.502863 1.184753
valueC 0.373106 1.300935 -0.128404
bike valueA -0.648708 0.944725 0.593327
valueB -0.613921 -0.238730 -0.218448
valueC 0.313042 -0.628065 0.910935
truck valueA 0.286377 0.478067 -1.000645
valueB 1.151793 -0.171433 -0.612346
valueC -1.358061 0.735075 0.092700
Dropping another level...
Updated DataFrame..
Col 1 Col 2 Col 3
one two
car valueA 0.425077 0.020606 1.148156
valueB -1.720355 0.502863 1.184753
valueC 0.373106 1.300935 -0.128404
bike valueA -0.648708 0.944725 0.593327
valueB -0.613921 -0.238730 -0.218448
valueC 0.313042 -0.628065 0.910935
truck valueA 0.286377 0.478067 -1.000645
valueB 1.151793 -0.171433 -0.612346
valueC -1.358061 0.735075 0.092700