使用另一个系列过滤 Pandas 数据框
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Filter Pandas dataframe with another series
提问by ChrisArmstrong
I have Pandas Series we'll call approved_fields which I'd like to use to filter a df by:
我有 Pandas 系列，我们将其称为 allowed_fields，我想用它来过滤 df：
approved_field(['Field1','Field2','Field3')]
df
Field
0 Field1
1 Field4
2 Field2
3 Field5
4 Field2
After applying the approved_field filter, the resulting df should look like:
应用approved_field 过滤器后，生成的df 应如下所示：
Field
0 Field1
1 Field2
2 Field2
Thanks!
谢谢！
回答by DSM
You can use isinand boolean indexing:
您可以使用isin布尔索引：
>>> import pandas as pd
>>> df = pd.DataFrame({"Field": "Field1 Field4 Field2 Field5 Field2".split()})
>>> approved_fields = "Field1", "Field2", "Field3"
>>> df['Field'].isin(approved_fields)
0 True
1 False
2 True
3 False
4 True
Name: Field, dtype: bool
>>> df[df['Field'].isin(approved_fields)]
Field
0 Field1
2 Field2
4 Field2
回答by Jeff
Note that you indices in your expected solution are off
请注意，您在预期解决方案中的索引已关闭
In [16]: approved_field = ['Field1','Field2','Field3']
In [17]: df = DataFrame(dict(Field = ['Field1','Field4','Field2','Field5','Field2']))
In [18]: df
Out[18]:
Field
0 Field1
1 Field4
2 Field2
3 Field5
4 Field2
In [19]: df[df.Field.isin(approved_field)]
Out[19]:
Field
0 Field1
2 Field2
4 Field2
