筛选并按数据框列中的标签数组分组 [英] filter and group by array of tags in column of a dataframe

问题描述

在下面的示例中，如何仅保留列tags中存在的数组中具有"a"的行?

In the following example, how do I keep only rows that have "a" in the array present in column tags?

df = pd.DataFrame(columns=["val", "tags"], data=[[5,["a","b","c"]]])
df[3<df.val]            # this works
df["a" in df.tags]      # is there an equivalent for filtering on tags?

推荐答案

我认为使用集合很直观.然后，您可以使用>=作为设置包含项

I think using sets is intuitive. Then you can use >= as set containment

df[df.tags.apply(set) >= {'a'}]

   val       tags
0    5  [a, b, c]

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一个笨拙的选择是

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A Numpy alternative would be

tags = df['tags']
n = len(tags)
out = np.zeros(n, np.bool8)
i = np.arange(n).repeat(tags.str.len())
np.logical_or.at(out, i, np.concatenate(tags) == 'a')

df[out]

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每个 @JonClements

您可以在map(非常聪明)中使用set.issubset

You can use set.issubset in a map (very clever)

df[df.tags.map({'a'}.issubset)]

   val       tags
0    5  [a, b, c]

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