python - 是否可以与 python pandas 进行模糊匹配合并？

Question

我有两个要基于列合并的 DataFrame。但是，由于拼写不同、空格数量不同、变音符号的缺失/存在，只要它们彼此相似，我希望能够合并。

任何相似性算法都可以(soundex、Levenshtein、difflib's)。

假设一个DataFrame有以下数据:

df1 = DataFrame([[1],[2],[3],[4],[5]], index=['one','two','three','four','five'], columns=['number'])

       number
one         1
two         2
three       3
four        4
five        5

df2 = DataFrame([['a'],['b'],['c'],['d'],['e']], index=['one','too','three','fours','five'], columns=['letter'])

      letter
one        a
too        b
three      c
fours      d
five       e

那我想得到结果DataFrame

       number letter
one         1      a
two         2      b
three       3      c
four        4      d
five        5      e

Answer 1

类似于@locojay 的建议，可以申请difflib的get_close_matches到 df2 的索引，然后应用 join :

In [23]: import difflib 

In [24]: difflib.get_close_matches
Out[24]: <function difflib.get_close_matches>

In [25]: df2.index = df2.index.map(lambda x: difflib.get_close_matches(x, df1.index)[0])

In [26]: df2
Out[26]: 
      letter
one        a
two        b
three      c
four       d
five       e

In [31]: df1.join(df2)
Out[31]: 
       number letter
one         1      a
two         2      b
three       3      c
four        4      d
five        5      e

.

如果这些是列，同样您可以应用到列然后 merge :

df1 = DataFrame([[1,'one'],[2,'two'],[3,'three'],[4,'four'],[5,'five']], columns=['number', 'name'])
df2 = DataFrame([['a','one'],['b','too'],['c','three'],['d','fours'],['e','five']], columns=['letter', 'name'])

df2['name'] = df2['name'].apply(lambda x: difflib.get_close_matches(x, df1['name'])[0])
df1.merge(df2)