如何使用 Pandas 重命名重置索引上的多个列
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How to Rename Multiple Columns on a Reset Index with Pandas
提问by slee
I'm trying to figure out if there is a way to rename Pandas columns when you try to reset the index. I see in the documentation that you can use the "name" parameter to set the column name of a reset index if there is only one column, but I'm curious if there is a way to do this for multiple columns.
我试图弄清楚当您尝试重置索引时是否有一种方法可以重命名 Pandas 列。我在文档中看到，如果只有一列，您可以使用“name”参数来设置重置索引的列名，但我很好奇是否有办法为多列执行此操作。
For example:
例如：
df1 = pd.DataFrame({
'A' : ['a1', 'a1', 'a2', 'a3'],
'B' : ['b1', 'b2', 'b3', 'b4'],
'D1' : [1,0,0,0],
'D2' : [0,1,1,0],
'D3' : [0,0,1,1],
})
df1.set_index(['B','A']).stack().reset_index()
The result leaves you with:
结果给你留下：
Out[82]:
B A level_2 0
0 b1 a1 D1 1
1 b1 a1 D2 0
2 b1 a1 D3 0
3 b2 a1 D1 0
4 b2 a1 D2 1
You could do:
你可以这样做：
df1.set_index(['B','A']).stack().reset_index(name='my_col')
In order to set the name of the last column but I'm wondering if there is a way to use the parameter to set the name of the 'level_2' column as well.
为了设置最后一列的名称，但我想知道是否有一种方法可以使用该参数来设置“level_2”列的名称。
The first thing that came to my mind was to try:
我想到的第一件事是尝试：
df1.set_index(['B','A']).stack().reset_index(name=['my_col2','my_col'])
However, that did not work so looking for another way around. I realize I could always just rename the columns in the next line but was hoping there'd be a cleaner way to do it in one line.
然而，这不起作用，所以寻找另一种方法。我意识到我总是可以重命名下一行中的列，但希望有一种更简洁的方法可以在一行中做到这一点。
Thanks! Sam
谢谢！山姆
采纳答案by Psidom
reset_indexis not smart enough to do this, but we could leverage methods rename_axisand renameto give names to the index and columns/series before resetting the index; once the names are set up properly, reset_indexwill automatically convert these names to the column names in the result:
reset_index不够聪明，无法做到这一点，但我们可以利用方法rename_axis并rename在重置索引之前为索引和列/系列命名；一旦名称设置正确，reset_index将自动将这些名称转换为结果中的列名称：
Here rename_axisgives names to index which is somewhat equivalent to df.index.names = ...except in a functional style; renamegives name to the Series object:
这里rename_axis给出了索引的名称，这有点等同df.index.names = ...于功能风格的除外；rename为 Series 对象命名：
df1.set_index(['B','A']).stack().rename_axis(['B','A','col2']).rename('col').reset_index()
# B A col2 col
#0 b1 a1 D1 1
#1 b1 a1 D2 0
#2 b1 a1 D3 0
#3 b2 a1 D1 0
#4 b2 a1 D2 1
# ..
