Pandas 在布尔索引中使用行标签
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Pandas using row labels in boolean indexing
提问by jason
So I have a DataFrame like this:
所以我有一个像这样的数据帧：
df = pd.DataFrame(np.random.randn(6, 3), columns=['a', 'b', 'c'])
a b c
0 1.877317 0.109646 1.634978
1 -0.048044 -0.837403 -2.198505
2 -0.708137 2.342530 1.053073
3 -0.547951 -1.790304 -2.159123
4 0.214583 -0.856150 -0.477844
5 0.159601 -1.705155 0.963673
We can boolean index it like this
我们可以像这样布尔索引它
df[df.a > 0]
a b c
0 1.877317 0.109646 1.634978
4 0.214583 -0.856150 -0.477844
5 0.159601 -1.705155 0.963673
We can also slice it via row labels like this:
我们也可以像这样通过行标签对其进行切片：
df.ix[[0,2,4]]
a b c
0 1.877317 0.109646 1.634978
2 -0.708137 2.342530 1.053073
4 0.214583 -0.856150 -0.477844
I would like to do both these operations at the same time (So I avoid making an unnecessary copy just to do the row label filter). How would I go about doing it?
我想同时执行这两个操作（因此我避免为了执行行标签过滤器而制作不必要的副本）。我将如何去做？
Pseudo code for what I am looking for:
我正在寻找的伪代码：
df[(df.a > 0) & (df.__index__.isin([0,2,4]))]
采纳答案by Andy Hayden
You nearly had it:
你几乎拥有它：
In [11]: df[(df.a > 0) & (df.index.isin([0, 2, 4]))]
Out[11]:
a b c
0 1.877317 0.109646 1.634978
4 0.214583 -0.856150 -0.477844
