Python模糊字符串匹配作为相关样式表/矩阵 [英] Python fuzzy string matching as correlation style table/matrix

问题描述

我有一个包含 x 个字符串名称及其关联 ID 的文件.基本上是两列数据.

I have a file with x number of string names and their associated IDs. Essentially two columns of data.

我想要的是一个格式为 x x x 的相关样式表(将相关数据同时作为 x 轴和 y 轴)，但我想要模糊模糊库的函数模糊，而不是相关性.ratio(x,y) 作为使用字符串名称作为输入的输出.基本上针对每个条目运行每个条目.

What I would like, is a correlation style table with the format x by x (having the data in question both as the x-axis and y axis), but instead of correlation, I would like the fuzzywuzzy library's function fuzz.ratio(x,y) as the output using the string names as input. Essentially running every entry against every entry.

这就是我的想法.只是为了表明我的意图:

This is sort of what I had in mind. Just to show my intent:

import pandas as pd
from fuzzywuzzy import fuzz

df = pd.read_csv('random_data_file.csv')

df = df[['ID','String']]
df['String_Dup'] = df['String'] #creating duplicate of data in question
df = df.set_index('ID')

df = df.groupby('ID')[['String','String_Dup']].apply(fuzz.ratio())

但显然这种方法目前对我不起作用.任何帮助表示赞赏.不一定是pandas，只是我比较熟悉的一个环境.

But clearly this approach is not working for me at the moment. Any help appreciated. It doesn't have to be pandas, it is just an environment I am relatively more familiar with.

我希望我的问题措辞清晰，真的，任何意见都值得赞赏，

I hope my issue is clearly worded, and really, any input is appreciated,

推荐答案

使用 pandas 的 crosstab 函数，后跟一个列式apply 计算模糊.这比我的第一个答案要优雅得多.

Use pandas' crosstab function, followed by a column-wise apply to compute the fuzz. This is considerably more elegant than my first answer.

import pandas as pd
from fuzzywuzzy import fuzz

# Create sample data frame.
df = pd.DataFrame([(1, 'abracadabra'), (2,'abc'), (3,'cadra'), (4, 'brabra')],
                  columns=['id', 'strings'])
# Create the cartesian product between the strings column with itself.
ct = pd.crosstab(df['strings'], df['strings'])
# Note: for pandas versions <0.22, the two series must have different names.
# In case you observe a "Level XX not found" error, the following may help:
# ct = pd.crosstab(df['strings'].rename(), df['strings'].rename())

# Apply the fuzz (column-wise). Argument col has type pd.Series.
ct = ct.apply(lambda col: [fuzz.ratio(col.name, x) for x in col.index])

# This results in the following:
#       strings      abc  abracadabra  brabra  cadra
#       strings
#       abc          100           43      44     25
#       abracadabra   43          100      71     62
#       brabra        44           71     100     55
#       cadra         25           62      55    100

为简单起见，我省略了您问题中建议的 groupby 操作.如果需要在组上应用模糊字符串匹配，只需创建一个单独的函数:

For simplicity, I omitted the groupby operation as suggested in your question. In case need want to apply the fuzzy string matching on groups, simply create a separate function:

def cross_fuzz(df):
    ct = pd.crosstab(df['strings'], df['strings'])
    ct = ct.apply(lambda col: [fuzz.ratio(col.name, x) for x in col.index])
    return ct

df.groupby('id').apply(cross_fuzz)

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