Pandas 相当于 SQL non-equi JOIN [英] Pandas equivalent of SQL non-equi JOIN

问题描述

所以我有 2 个数据框想要合并在一起.

So I've got 2 data-frames I'd like to merge together.

我正在合并 3 列，其中 2 列很容易合并.

I'm merging on 3 columns, 2 is an easy join.

joined_df = pd.merge(df1, df2, how='left', on=['name', 'city'])

我希望它使用第三列，但这将是一个比较，如下所示:

I want this to be using a third column, but it's going to be a comparison, something like this:

joined_df = pd.merge(df1, df2, how='left',
on=['name', 'city', 'df1.year' >= 'df2.year_min'])

不确定这里的正确语法是什么.

Not sure what the right syntax is here.

如果是 SQL，那对我来说很容易.

If it was SQL, it would be easy for me.

SELECT * FROM df1
JOIN df2 on (df1.name = df2.name and df1.year = df2.year and df1.year > df2.year_min)

有什么帮助吗?

推荐答案

Pandas merge 仅支持 equi-joins.您需要添加过滤结果的第二步，如下所示:

Pandas merge only supports equi-joins. You'll need to add a second step that filters the result, something like this:

joined_df = df1.merge(df2, how='left', on=['name', 'city'])
joined_df = joined_df[joined_df.year > joined_df.year_min]

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