pandas 仅在多索引中的第二个索引上使用 .loc
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Using .loc on just second index in multiindex
提问by snapcrack
I have multiindex dataframe that looks like this:
我有如下所示的多索引数据框：
value
year name
1921 Ah 40
1921 Ai 90
1922 Ah 100
1922 Ai 7
in which yearand nameare the indices. I want to select every row where the name Aiappears. I have tried df.loc[(:,'Ai')]and df.loc['Ai']but both give errors. How do I index only using the name column?
其中year和name是指数。我想选择名称Ai出现的每一行。我试过df.loc[(:,'Ai')]，df.loc['Ai']但都出现错误。如何仅使用名称列进行索引？
采纳答案by sacuL
I would use .xson the first level of your multiindex (note: level=1refers to the "second" index (name) because of python's zero indexing: level 0 is yearin your case):
我会.xs在你的多重level=1索引的第一级使用（注意：指的是“第二”索引 ( name) 因为 python 的零索引：级别 0year在你的情况下）：
df.xs('Ai', level=1, drop_level=False)
# or
df.xs('Ai', level='name', drop_level=False)
value
year name
1921 Ai 90
1922 Ai 7
回答by cs95
@sacul has the most idiomatic answer, but here are a few alternatives.
@sacul 有最惯用的答案，但这里有一些替代方案。
MultiIndex.get_level_values
MultiIndex.get_level_values
df[df.index.get_level_values('name') == 'Ai']
value
year name
1921 Ai 90
1922 Ai 7
DataFrame.query
DataFrame.query
df.query('name == "Ai"')
value
year name
1921 Ai 90
1922 Ai 7
DataFrame.loc(axis=0)with pd.IndexSlice
DataFrame.loc(axis=0)和 pd.IndexSlice
Similar to @liliscent's answer, but does not need the trailing :if you specify axis=0.
类似于@liliscent 的答案，但:如果您指定axis=0.
df.loc(axis=0)[pd.IndexSlice[:, 'Ai']]
value
year name
1921 Ai 90
1922 Ai 7
回答by llllllllll
If you prefer loc, you can use:
如果您愿意loc，可以使用：
In [245]: df.loc[(slice(None), 'Ai'), :]
...:
Out[245]:
value
year name
1921 Ai 90
1922 Ai 7
