基于 Python pandas 中索引的补充挑选元素
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picking out elements based on complement of indices in Python pandas
提问by Zelazny7
I have a dataframe out of which I pick two subset dfs, df_aand df_b. For example in irisdataset:
我有一个数据框，从中我选择了两个子集 dfsdf_a和df_b. 例如在iris数据集中：
df_a = iris[iris.Name == "Iris-setosa"]
df_b = iris[iris.Name == "Iris-virginica"]
What's the best way to get all elements of iristhat are neither in df_anor in df_b? I prefer not to refer to the original conditions that defined df_aand df_b. I just assume that df_aand df_bare subsets of iris, so I'd like to pull out elements from irisbased on the indices of df_aand df_b. Basically, assume that:
获得iris既不在df_a也不在的所有元素的最佳方法是df_b什么？我不想参考定义df_a和的原始条件df_b。我只是假设df_a和df_b是的子集iris，所以我想从拔出元素iris基础上的指数df_a和df_b。基本上，假设：
df_a = get_a_subset(iris)
df_b = get_b_subset(iris)
# retrieve the subset of iris that
# has all elements not in df_a or in df_b
# ...
EDIT:here is a solution that seems inefficient and inelegant and I'm sure pandas has a better way:
编辑：这是一个看起来效率低下且不优雅的解决方案，我相信大Pandas有更好的方法：
# get subset of iris that is not in a nor in b
df_rest = iris[map(lambda x: (x not in df_a.index) & (x not in df_b.index), iris.index)]
And a second one:
还有第二个：
df_rest = iris.ix[iris.index - df_a.index - df_b.index]
how can this be done most efficiently/elegantly in pandas? thanks.
如何在Pandas中最有效/最优雅地做到这一点？谢谢。
回答by Zelazny7
This seems a bit faster than your second solution. There's a bit more overhead when indexing with .ix:
这似乎比您的第二个解决方案快一点。使用以下方法进行索引时会产生更多开销.ix：
df[~df.index.isin(df_a.index+df_b.index)]
