pandas get_close_匹配返回空值

Question

我正在制定一项要求，共有2个CSV，如下所示-

CSV1.csv

   Short Description                                                    Category
    Device is DOWN!                                                      Server Down
    CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization
    CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization
    CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization
    CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization
    Device Performance Alerts was triggered on Physical memory           Memory Utilization
    Device Performance Alerts was triggered on Physical memory           Memory Utilization
    Device Performance Alerts was triggered on Physical memory           Memory Utilization
    Disk Space Is Lowon ;E:                                              Disk Space Utilization
    Disk Space Is Lowon;C:                                               Disk Space Utilization
    Network Interface Down                                               Interface Down
    Active Directory                                                     

和reference.csv

Category                         Complexity
Server Down                      Simple
Network Interface down           Complex
Drive Cleanup Windows            Medium
CPU Utilization                  Medium
Memory Utilization               Medium
Disk Space Utilization Unix      Simple
Windows Service Restart          Medium
UNIX Service Restart             Medium
Web Tomcat Instance Restart      Simple

Expected Output

Short Description                                                    Category                    Complexity
Device is DOWN!                                                      Server Down                 Simple
CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization             Medium
CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization             Medium
CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization             Medium
CPU Warning Monitoron  XSSXSXSXSXSX.com                              CPU Utilization             Medium
Device Performance Alerts was triggered on Physical memory           Memory Utilization          Medium
Device Performance Alerts was triggered on Physical memory           Memory Utilization          Medium
Device Performance Alerts was triggered on Physical memory           Memory Utilization          Medium
Disk Space Is Lowon ;E:                                              Disk Space Utilization      Medium
Disk Space Is Lowon;C:                                               Disk Space Utilization      Medium
Network Interface Down                                               Interface Down              Complex

我尝试了下面的代码-但是在输出数据框中我可以看到空白[]不确定我缺少了什么。在“输出复杂性”列中，我可以看到每行只有[]。我试图得到精确的匹配，但我需要得到所有可能的组合，所以我使用get_close_匹配。在下面的代码中，如何传递dataframe中的可能性参数，我没有找到传递可能性的方法

我尝试了一些其他的可能性，比如精确，但并没有给出预期的结果，因为我正在寻找所有可能的组合，同时比较列和字符串

import pandas as pd
import difflib
df1 = pd.read_csv('csv1.csv')
df1 = df1.fillna('')
df2 = pd.read_csv('reference.csv')
my_dict = dict(zip(df2['Category'].values, df2['Complexity'].values))
def match_key(key, default_value):
    if not key:
        return default_value
    for d_key in my_dict.keys():
        if key in d_key or d_key in key:
            return my_dict[d_key]

    return default_value
df1['Complexity'] = df1['Category'].apply(lambda x: difflib.get_close_matches(x, list(my_dict.keys(), n=1)))
df1 = df1.explode('Complexity')
df1['Complexity'] = df1['Complexity'].map(my_dict)
print(df1)

Answer 1

^{}期望第一个参数是“word”，在您的例子中，x，第二个参数是“可能性”。您已将其作为空字符串提供。这就是为什么你的函数不起作用，它试图匹配一个基本上没有任何内容的单词

my_dict包含作为键的有效选项，因此我们可以将它们用作“可能性”列表

# Use n=1, so only tries to get 1 match
df1['Complexity'] = df1['Category'].apply(lambda x: difflib.get_close_matches(x, list(my_dict.keys()), n=1))
# The output of get_close_matches is a list, we use explode to convert it to a string
df1 = df1.explode('Complexity')
# We can now apply our map, to the *Complexity* column, 
# which is technically the best match *Category*, via get_close_matches
df1['Complexity'] = df1['Complexity'].map(my_dict)

原始错误答案

但是，与其继续使用difflib，我认为您可以改变您的方法。您想将my_dict应用于df1的Category列。这通常被称为应用mappandas已通过^{}准备好此实现

df1['Complexity'] = df1['Category'].map(my_dict)