Python Pandas模糊合并/匹配重复

Question

我目前有2个数据帧,1个用于捐赠者,1个用于筹款.理想情况下,我想要找到的是,如果有任何筹款人也捐赠,如果是的话,将一些信息复制到我的募捐人数据集(捐赠者姓名,电子邮件和他们的第一次捐赠).我的数据存在问题1)我需要通过姓名和电子邮件进行匹配,但用户可能会略有不同的名称(来自Kat和Kathy).2)捐赠者和筹款人的名称重复.2a)有了捐赠者,我可以获得独特的名字/电子邮件组合,因为我只关心第一个捐赠日期2b)虽然我需要保留两行而不会丢失数据,如日期.

我现在的示例代码:

import pandas as pd
import datetime
from fuzzywuzzy import fuzz
import difflib 

donors = pd.DataFrame({"name": pd.Series(["John Doe","John Doe","Tom Smith","Jane Doe","Jane Doe","Kat test"]), "Email": pd.Series(['a@a.ca','a@a.ca','b@b.ca','c@c.ca','something@a.ca','d@d.ca']),"Date": (["27/03/2013  10:00:00 AM","1/03/2013  10:39:00 AM","2/03/2013  10:39:00 AM","3/03/2013  10:39:00 AM","4/03/2013  10:39:00 AM","27/03/2013  10:39:00 AM"])})
fundraisers = pd.DataFrame({"name": pd.Series(["John Doe","John Doe","Kathy test","Tes Ester", "Jane Doe"]),"Email": pd.Series(['a@a.ca','a@a.ca','d@d.ca','asdf@asdf.ca','something@a.ca']),"Date": pd.Series(["2/03/2013  10:39:00 AM","27/03/2013  11:39:00 AM","3/03/2013  10:39:00 AM","4/03/2013  10:40:00 AM","27/03/2013  10:39:00 AM"])})
donors["Date"] = pd.to_datetime(donors["Date"], dayfirst=True)
fundraisers["Date"] = pd.to_datetime(donors["Date"], dayfirst=True)
donors["code"] = donors.apply(lambda row: str(row['name'])+' '+str(row['Email']), axis=1)
idx = donors.groupby('code')["Date"].transform(min) == donors['Date']
donors = donors[idx].reset_index().drop('index',1)

因此,这给了我每个捐赠者的第一次捐赠(假设任何具有完全相同名称和电子邮件的人都是同一个人).

理想情况下,我希望我的筹款人数据集看起来像:

Date                Email       name        Donor Name  Donor Email Donor Date
2013-03-27 10:00:00     a@a.ca      John Doe    John Doe    a@a.ca      2013-03-27 10:00:00 
2013-01-03 10:39:00     a@a.ca      John Doe    John Doe    a@a.ca      2013-03-27 10:00:00 
2013-02-03 10:39:00     d@d.ca      Kathy test  Kat test    d@d.ca      2013-03-27 10:39:00 
2013-03-03 10:39:00     asdf@asdf.ca    Tes Ester   
2013-04-03 10:39:00     something@a.ca  Jane Doe    Jane Doe    something@a.ca  2013-04-03 10:39:00

我尝试了这个帖子:是否有可能与python pandas进行模糊匹配合并？但继续让索引超出范围错误(猜测它不喜欢筹款活动中的重复名称):(那么任何想法如何匹配/合并这些数据集？

用for循环做它(它工作但速度很慢,我觉得必须有更好的方法)

fundraisers["donor name"] = ""
fundraisers["donor email"] = ""
fundraisers["donor date"] = ""
for donindex in range(len(donors.index)):
    max = 75
    for funindex in range(len(fundraisers.index)):
        aname = donors["name"][donindex]
        comp = fundraisers["name"][funindex]
        ratio = fuzz.ratio(aname, comp)
        if ratio > max:
            if (donors["Email"][donindex] == fundraisers["Email"][funindex]):
                ratio *= 2
            max = ratio
            fundraisers["donor name"][funindex] = aname
            fundraisers["donor email"][funindex] = donors["Email"][donindex]
            fundraisers["donor date"][funindex] = donors["Date"][donindex]

Answer 1

这是更多的Pythonic（在我看来），工作（在你的示例中）代码，没有显式循环：

def get_donors(row):
    d = donors.apply(lambda x: fuzz.ratio(x['name'], row['name']) * 2 if row['Email'] == x['Email'] else 1, axis=1)
    d = d[d >= 75]
    if len(d) == 0:
        v = ['']*3
    else:
        v = donors.ix[d.idxmax(), ['name','Email','Date']].values
    return pd.Series(v, index=['donor name', 'donor email', 'donor date'])

pd.concat((fundraisers, fundraisers.apply(get_donors, axis=1)), axis=1)

输出：

                 Date           Email        name donor name     donor email           donor date
0 2013-03-27 10:00:00          a@a.ca    John Doe   John Doe          a@a.ca  2013-03-01 10:39:00
1 2013-03-01 10:39:00          a@a.ca    John Doe   John Doe          a@a.ca  2013-03-01 10:39:00
2 2013-03-02 10:39:00          d@d.ca  Kathy test   Kat test          d@d.ca  2013-03-27 10:39:00
3 2013-03-03 10:39:00    asdf@asdf.ca   Tes Ester                                                
4 2013-03-04 10:39:00  something@a.ca    Jane Doe   Jane Doe  something@a.ca  2013-03-04 10:39:00