So I have a DataFrame like this:
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
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:
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?
Pseudo code for what I am looking for:
df[(df.a > 0) & (df.__index__.isin([0,2,4]))]
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