如何在 Pandas 数据框中选择基于行的类别
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How to select rows based categories in Pandas dataframe
提问by Skywalker326
this is really trivial but can't believe I have wandered around for an hour and still can find the answer, so here you are:
这真的很微不足道，但不敢相信我已经徘徊了一个小时仍然可以找到答案，所以你在这里：
df = pd.DataFrame({"cats":["a","b"], "vals":[1,2]})
df.cats = df.cats.astype("category")
df
My problem is how to select the row that its "cats" columns's category is "a". I know that df.loc[df.cats == "a"]will work but it's based on equality on element. Is there a way to select based on levels of category?
我的问题是如何选择其“cats”列的类别为“a”的行。我知道这df.loc[df.cats == "a"]会起作用，但它基于元素的相等性。有没有办法根据类别级别进行选择？
回答by Mike Müller
This works:
这有效：
df.cats[df.cats=='a']
UPDATE
更新
The question was updated. New solution:
问题已更新。新解决方案：
df[df.cats.cat.categories == ['a']]
回答by Michael P.
You can query the categorical list using df.cats.cat.categorieswhich prints output as
您可以使用df.cats.cat.categorieswhich 打印输出来查询分类列表
Index(['a', 'b'], dtype='object')
For this case, to select a row with category of 'a'which is df.cats.cat.categories['0'], you just use:
对于这种情况，选择与A类排'a'它df.cats.cat.categories['0']，你只需使用：
df[df.cats == df.cats.cat.categories[0]]
回答by Aru
df[df.cats.cat.categories == df.cats.cat.categories[0]]
