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嗨，我有以下两个文件，我想在这两个文件之间找到匹配项。Test1.csv 中的每条记录最多可以匹配 Test2.csv 中的一条记录，但 Test1.csv 中的多条记录可以匹配 Test2.csv 中的同一条记录。我匹配名称和 domainWithExtension 列。

测试1.csv

测试2.csv

这是代码：

import csv
import re
import logging
import optparse

import dedupe
from unidecode import unidecode

def preProcess(column):
  column = unidecode(column)
  column = re.sub('\n', ' ', column)
  column = re.sub('-', '', column)
  column = re.sub('/', ' ', column)
  column = re.sub("'", '', column)
  column = re.sub(",", '', column)
  column = re.sub(":", ' ', column)
  column = re.sub('  +', ' ', column)
  column = column.strip().strip('"').strip("'").lower().strip()
  if not column:
      column = None
  return column

def readData(filename):
  """
  Read in our data from a CSV file and create a dictionary of records,
  where the key is a unique record ID.
  """

  data_d = {}
  with open(filename,encoding='utf-8') as f:
      reader = csv.DictReader(f)
      for i, row in enumerate(reader):
          clean_row = dict([(k, preProcess(v)) for (k, v) in row.items()])
          data_d[filename + str(i)] = dict(clean_row)

  return data_d



if __name__ == '__main__':

  optp = optparse.OptionParser()
  optp.add_option('-v', '--verbose', dest='verbose', action='count',
                  help='Increase verbosity (specify multiple times for more)'
                  )
  (opts, args) = optp.parse_args()
  log_level = logging.WARNING
  if opts.verbose:
      if opts.verbose == 1:
          log_level = logging.INFO
      elif opts.verbose >= 2:
          log_level = logging.DEBUG
  logging.getLogger().setLevel(log_level)

  output_file = 'data_matching_output.csv'
  settings_file = 'data_matching_learned_settings'
  training_file = 'data_matching_training.json'

  left_file = 'Test1.csv'
  right_file = 'Test2.csv'

  print('importing data ...')
  data_1 = readData(left_file)
  data_2 = readData(right_file)

  if os.path.exists(settings_file):
      print('reading from', settings_file)
      with open(settings_file, 'rb') as sf:
          linker = dedupe.StaticRecordLink(sf)

  else:

      fields = [
                  {'field' : 'name', 'type': 'String', 'has missing': True},
                  {'field' : 'domainWithExtension', 'type': 'String', 'has missing': True},
              ]


      linker = dedupe.RecordLink(fields)

      if os.path.exists(training_file):
          print('reading labeled examples from ', training_file)
          with open(training_file) as tf:
              linker.prepare_training(data_1,
                                      data_2,
                                      training_file=tf,
                                      sample_size=15000)
      else:
          linker.prepare_training(data_1, data_2, sample_size=15000)

          print('starting active labeling...')

          dedupe.console_label(linker)

          linker.train()

          with open(training_file, 'w') as tf:
              linker.write_training(tf)

          with open(settings_file, 'wb') as sf:
              linker.write_settings(sf)

  print('clustering...')
  linked_records = linker.join(data_1, data_2, 0.5, constraint='many-to-one')
  print(linked_records)
  print('# duplicate sets', len(linked_records))

  cluster_membership = {}
  for cluster_id, (cluster, score) in enumerate(linked_records):
      for record_id in cluster:
          cluster_membership[record_id] = {'Cluster ID': cluster_id,
                                           'Link Score': score}
  print(cluster_membership)
  with open(output_file, 'w',encoding = "utf-8") as f:

      header_unwritten = True

      for fileno, filename in enumerate((left_file, right_file)):
          with open(filename,encoding = "utf-8") as f_input:
              reader = csv.DictReader(f_input)

              if header_unwritten:

                  fieldnames = (['Cluster ID', 'Link Score', 'source file'] +
                                reader.fieldnames)

                  writer = csv.DictWriter(f, fieldnames=fieldnames)
                  writer.writeheader()

                  header_unwritten = False

              for row_id, row in enumerate(reader):

                  record_id = filename + str(row_id)
                  cluster_details = cluster_membership.get(record_id, {})
                  row['source file'] = fileno
                  row.update(cluster_details)

                  writer.writerow(row)

这有效并给出以下结果：

输出

“Boxaround”的记录在 Test1.csv 中出现两次。因此，我希望这两个记录都与 Test2.csv 中的“Boxaround”记录匹配，并且输出中应该具有相同的集群 ID，但是输出中的集群 ID 4 有两条记录，另一条的集群 ID 为 0，用于“环绕”。我希望所有三个“Boxaround”记录都具有相同的集群 ID 4。我怎样才能做到这一点？请帮忙。

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