Python 使用difflib SequenceMatcher比率在Pandas中合并

我试图找出是否有一种方法可以基于difflib SequenceMatcher比率在Pandas中对字符串进行模糊合并。基本上，我有两个数据帧，如下所示：

df_a
company    address        merged
Apple     PO Box 3435       1

df_b
company     address
Apple Inc   PO Box 343

df_c = pd.merge(df_a, df_b, how = 'left', on = (difflib.SequenceMatcher(None, df_a['company'], df_b['company']).ratio() > .6) and (difflib.SequenceMatcher(None, df_a['address'], df_b['address']).ratio() > .6)

我想这样合并：

df_a
company    address        merged
Apple     PO Box 3435       1

df_b
company     address
Apple Inc   PO Box 343

df_c = pd.merge(df_a, df_b, how = 'left', on = (difflib.SequenceMatcher(None, df_a['company'], df_b['company']).ratio() > .6) and (difflib.SequenceMatcher(None, df_a['address'], df_b['address']).ratio() > .6)

有几个帖子接近我要找的，但没有一个能满足我的需要。

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关于如何使用difflib进行这种模糊合并有什么建议吗？

一些可能有效的方法：测试所有列值组合的部分匹配。如果存在匹配项，则为df_b分配一个键以进行合并

df_a['merge_comp'] = df_a['company'] # we will use these as the merge keys
df_a['merge_addr'] = df_a['address']

for comp_a, addr_a in df_a[['company','address']].values:
    for ixb, (comp_b, addr_b) in enumerate(df_b[['company','address']].values)
        if difflib.SequenceMatcher(None,comp_a,comp_b).ratio() > .6:
            df_b.ix[ixb,'merge_comp'] = comp_a # creates a merge key in df_b
        if difflib.SequenceMatcher(None,addr_a, addr_b).ratio() > .6:
            df_b.ix[ixb,'merge_addr'] = addr_a # creates a merge key in df_b

现在你可以合并了

merged_df = pandas.merge(df_a,df_b,on=['merge_addr','merge_comp'],how='inner')

可能有助于：