合并 CSV 文件中类似字符串的值

Question

所以我有一个充满交易的 CSV 文件，其中一列是供应商名称，另一列是交易金额。目标是根据交易总数找到顶级供应商。那部分很简单，我有这样的代码：

with open('Transactions.csv') as Vendor_Data:
    file_reader = csv.reader(Vendor_Data, delimiter=',')
    vendor_dict = {}
    next(file_reader)
    for row in file_reader:
        if row[3] not in vendor_dict:
            vendor_dict[row[3]] = [0, 0]
            vendor_dict[row[3]][1] += round(float(row[1]), 2)
        else:
            vendor_dict[row[3]][0] += 1
            vendor_dict[row[3]][1] += round(float(row[1]), 2)

问题是，许多条目中同一供应商的拼写略有不同（"Delta Airlines" v. "Delta Air"）。在遍历 CSV 文件时检测这些相似字符串名称（例如，使用 Fuzzywuzzy）并合并交易实例和金额的最佳方法是什么？

Answer 1

import csv

from fuzzywuzzy import fuzz

with open('Transactions.csv') as Vendor_Data:
    file_reader = csv.reader(Vendor_Data, delimiter=',')
    vendor_dict = {}
    next(file_reader)  # skipping a header?
    for row in file_reader:

        # we can't use the dictionary directly (e.g. "key in vendor_dict")
        # because we want to do a similarity search.
        csv_name = row[3]
        for vendor_name, vendor_values in vendor_dict.iteritems():

            # this is *a* way to do it. You may want to use different scores
            # or even a different comparison
            if fuzz.token_set_ratio(csv_name, vendor_name) > 80:
                vendor_values[0] += 1
                vendor_values[1] += round(float(row[1]), 2)
                break
        else:
            # we didn't find anything similar enough, so create an entry
            vendor_values = [0, 0]
            vendor_values[1] += round(float(row[1]), 2)

        vendor_dict[csv_name] = vendor_values

Answer 2

读取pandas中的csv文件。然后为 fuzzywuzzy 百分比匹配添加一个新列。

创建一个关于哪个百分比应被视为相同字符串的阈值，然后通过使用 isin() 方法进行过滤然后添加交易金额的列值来进行计算。

将其循环到整个 DataFrame，您将获得所需的结果。

合并 CSV 文件中类似字符串的值

Combining values from Similar Strings in CSV File

python

fuzzywuzzy