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法学Python——从 Pandas 数据框的多层列索引中移除一层
要从多层列索引中移除一层，请使用 columns.droplevel()。我们使用 Multiindex.from_tuples() 按列创建索引。
首先，按列创建索引 -
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，创建一个多索引数组并形成一个多索引数据框
arr = [np.array(['car', 'car', 'car','bike','bike', 'bike', 'truck', 'truck', 'truck']), np.array(['valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC','valueA', 'valueB', 'valueC'])] # forming multiindex dataframe dataFrame = pd.DataFrame(np.random.randn(9, 3), index=arr,columns=items)
标记索引 -
dataFrame.index.names = ['level 0', 'level 1']
在索引 0 处移除一层 -
dataFrame.columns = dataFrame.columns.droplevel(0)
示例
以下是代码
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)
# labelling index
dataFrame.index.names = ['level 0', 'level 1']
print"DataFrame...\n",dataFrame
print"\nDropping a level...\n";
dataFrame.columns = dataFrame.columns.droplevel(0)
print"Updated DataFrame..\n",dataFrame输出
这会产生如下输出
DataFrame... Col 1 Col 2 Col 3 Col 1 Col 2 Col 3 Col 1 Col 2 Col 3 level 0 level 1 car valueA 1.691127 0.315145 -0.695925 valueB -2.077182 -2.027643 -0.523965 valueC 1.021402 -0.384421 0.640215 bike valueA -2.271217 0.197185 0.304847 valueB 0.119615 -0.520491 -0.746547 valueC 1.856888 -0.491540 -1.754604 truck valueA 0.829854 -0.204102 -1.130511 valueB 0.310692 0.119087 -0.244919 valueC -0.245934 -2.141639 -1.298278 Dropping a level... Updated DataFrame.. Col 1 Col 2 Col 3 Col 1 Col 2 Col 3 level 0 level 1 car valueA 1.691127 0.315145 -0.695925 valueB -2.077182 -2.027643 -0.523965 valueC 1.021402 -0.384421 0.640215 bike valueA -2.271217 0.197185 0.304847 valueB 0.119615 -0.520491 -0.746547 valueC 1.856888 -0.491540 -1.754604 truck valueA 0.829854 -0.204102 -1.130511 valueB 0.310692 0.119087 -0.244919 valueC -0.245934 -2.141639 -1.298278
更新于： 2021-09-13
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