我正在尝试比较一个名称列表（复制到一个干净的文件和一个凌乱的文件中）。然后我将这些文件相互比较。我的问题是它只返回每个结果的前 1 个结果，它本身就是（每个文件中的相同记录）。我试图捕捉的是第二个结果，这将是最接近的匹配，而不是它本身。

names =  pd.read_csv('C:/Temp/messynames.txt', sep='\t')
org_names = names['VariationName'].unique()
vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)
tf_idf_matrix = vectorizer.fit_transform(org_names)

clean_org_names = pd.read_csv('C:/Temp/cleannames.txt', sep='\t')
org_name_clean = clean_org_names['StandardName'].unique()
vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams, lowercase=False)
tfidf = vectorizer.fit_transform(org_name_clean)

nbrs = NearestNeighbors(n_neighbors=3, n_jobs=-1).fit(tfidf) 
unique_org = set(names['VariationName'].values)

def getNearestN(query):
  queryTFIDF_ = vectorizer.transform(query)
  distances, indices = nbrs.kneighbors(queryTFIDF_)
  return distances, indices
distances, indices = getNearestN(unique_org)

unique_org = list(unique_org) #need to convert back to a list

matches = []
for i,j in enumerate(indices):
  temp = [round(distances[i][0],2), clean_org_names.values[j][0][0],unique_org[i]]
  matches.append(temp)

matches = pd.DataFrame(matches, columns=['Match confidence (lower is better)','Matched name','Original name'])
matches.to_csv('C:/Temp/matchednames.txt', sep='\t', encoding='utf-8', index=False, quoting=3)

对于具有以下四个名称的文件：

NOKIA
NOKIAA
NOKIA LMD
NOKIA LTD

结果如下所示：

Match confidence                         Matched name Original name
0                                 0.0    NOKIA LMD     NOKIA LMD
1                                 0.0       NOKIAA        NOKIAA
2                                 0.0        NOKIA         NOKIA
3                                 0.0    NOKIA LTD     NOKIA LTD

我正在尝试更多类似的东西：

Match confidence                         Matched name Original name
0                                 0.1    NOKIA LTD     NOKIA LMD
1                                 0.1        NOKIA        NOKIAA