本文介绍了相当于SQL non-equi JOIN的Pandas的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
所以我有2个要合并在一起的数据框.
So I've got 2 data-frames I'd like to merge together.
我正在合并3列,其中2个是简单的联接.
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,类似这样:
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'])
不确定此处使用正确的语法.
Not sure what the right syntax is here.
如果是SQL,对我来说将很容易.
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)
有帮助吗?
推荐答案
熊猫merge
仅支持等联接.您需要添加第二步来过滤结果,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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