2系列/df.列之间的模糊查找 [英] fuzzy lookup between 2 series/df.columns

问题描述

基于此链接，我试图进行模糊查找:

based on this link I was trying to do a fuzzy lookup : Apply fuzzy matching across a dataframe column and save results in a new column between 2 dfs:

import pandas as pd
df1 = pd.DataFrame(data={'Brand_var':['Johnny Walker','Guiness','Smirnoff','Vat 69','Tanqueray']})
df2 = pd.DataFrame(data={'Product':['J.Walker Blue Label 12 CC','J.Morgan Blue Walker','Giness blue 150 CC','tqry qiuyur qtre','v69 g nesscom ui123']})

我有2个dfs df1和df2，需要通过模糊查找/其他合适的方法进行映射.

I have 2 dfs df1 and df2 which needs to be mapped via a fuzzy lookup/any other method which suits.

以下是我正在使用的代码:

Below is the code I am using:

from fuzzywuzzy import fuzz
from fuzzywuzzy import process
compare = pd.MultiIndex.from_product([df1['Brand_var'],
                                      df2['Product']]).to_series()
def metrics(tup):
    return pd.Series([fuzz.ratio(*tup),
                      fuzz.token_sort_ratio(*tup)],
                     ['ratio', 'token'])
compare.apply(metrics)
df = compare.apply(metrics).unstack().idxmax().unstack(0)
print(df)

下面是我的输出:

                             ratio       token
----------------------------------------------------------
Giness blue 150 CC         Guiness      Guiness
J.Morgan Blue Walker       Johnny Walker Johnny Walker 
J.Walker Blue Label 12 CC  Johnny Walker Johnny Walker 
tqry qiuyur qtre           Tanqueray     Tanqueray
v69 g nesscom ui123        Guiness       Guiness

预期输出:

                             ratio       token
----------------------------------------------------------
Giness blue 150 CC          Guiness       Guiness
J.Morgan Blue Walker        None          None
J.Walker Blue Label 12 CC   Johnny Walker Johnny Walker 
tqry qiuyur qtre            Tanqueray     Tanqueray
v69 g nesscom ui123         Vat 69        Vat 69

有什么建议可以得到我想要的输出的更好的方法(不使用模糊模糊也可以)?

Any suggestions what could be a better approach(not using fuzzy wuzzy is also fine) to get my desired output?

先谢谢您. :)

推荐答案

以下带有规则的代码将为您提供预期的输出:

The below code with rules will give you expected output:

import pandas as pd
from fuzzywuzzy import fuzz
df1 = pd.DataFrame(data={'Brand_var':['Johnny Walker','Guiness','Smirnoff','Vat 69','Tanqueray']})
df2 = pd.DataFrame(data={'Product':['J.Walker Blue Label 12 CC','J.Morgan Blue Walker','Giness blue 150 CC','tqry qiuyur qtre','v69 g nesscom ui123']})

Guiness_Beer = ["Giness","Guiness","Gines"]
Johnny_Walker = ["J.Walker","J.walker"]
Tanqueray     =["tqry","Tanqueray","tquery"]
Vat = ["69","Vat69","Vat 69"]

matched_names = []

for row in df1.index:
    brand_name = df2.get_value(row,"Product")
    Rule_Guiness = any(word in brand_name for word in Guiness_Beer)
    Rule_Johnny_Walker = any(word in brand_name for word in Johnny_Walker)
    Rule_Tanqueray = any(word in brand_name for word in Tanqueray)
    Rule_Vat = any(word in brand_name for word in Vat)
    if Rule_Guiness:
        matched_names.append([brand_name,"Guiness"])
    elif Rule_Johnny_Walker:
        matched_names.append([brand_name,"Johnny Walker"])
    elif Rule_Tanqueray:
        matched_names.append([brand_name,"Tanqueray"])
    elif Rule_Vat:
        matched_names.append([brand_name,"Vat 69"])
    else:
        matched_names.append([brand_name,"None"])


df = pd.DataFrame(columns=['Product', 'Brand'], data=matched_names)

您可以对此进行更多修改，例如可以通过 excel 配置 Guiness_beer 之类的所有词典，并且以后不必触摸代码您要添加/减去/修改任何关键字.

You can do more modifications in this like all the dictionaries like Guiness_beer etc. can be configured through excel and you don't have to touch the code if in future you want to add/subtract/modify any keyword.

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