在MultiIndex数据框中按特定的索引级别过滤行 [英] Filtering rows by a particular index level in a MultiIndex dataframe

问题描述

给出一个multIndex数据框:

Given a multIndex dataframe:

mux = pd.MultiIndex.from_arrays([
    list('aaaabbbbbccdddddd'),
    list('tuvwlmnopxyfghijk')
], names=['one', 'two'])

df = pd.DataFrame({'col': np.arange(len(mux))}, mux)

df

         col
one two     
a   t      0
    u      1
    v      2
    w      3
b   l      4
    m      5
    n      6
    o      7
    p      8
c   x      9
    y     10
d   f     11
    g     12
    h     13
    i     14
    j     15
    k     16

是否可以保持对应于数据帧第0级的第i个值的行?

Is it possible to keep rows corresponding to upto the ith value of the 0th level of the dataframe?

对于i = 2，我的预期输出是:

For i = 2, my expected output is:

         col
one two     
a   t      0
    u      1
    v      2
    w      3
b   l      4
    m      5
    n      6
    o      7
    p      8

请注意，仅保留与a和b有关的行，其他所有内容均被删除.我希望这个问题很清楚，但如果不是这样，请随时进行澄清.

Note that only rows pertaining to a and b are retained, everything else is dropped. I hope the problem is clear, but if it isn't, please feel free to ask for clarifications.

我尝试过:

idx = pd.IndexSlice
df.iloc[(idx[:2], slice(None))]

但是，这只给了我整个df中的前两行，而不是第0级中前两个值的所有行.

But that gives me only the first two rows in the entire df, not all rows of the first two values in the 0th level.

推荐答案

一种解决方法是返回第0级的索引值，然后使用这些值索引到原始数据帧中:

One way to go about this is to return the index values for the 0th level and then index into the original data frame with those:

df.loc[df.index.levels[0][:2].values]

         col
one two     
a   t      0
    u      1
    v      2
    w      3
b   l      4
    m      5
    n      6
    o      7
    p      8

如评论中所述，这仅适用于第0级，不适用于第1级.可能会有一个更通用的解决方案，可以与其他级别一起使用.

As mentioned in the comments this only works for the 0th level and not the 1st. There may be a more a generalizable solution that would work with other levels.

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