在 Python 中的两个列表之间执行匹配

Question

I have two Tables. I want to compare two columns and want to get matches row counts and row numbers. How can I get the expected result using Python. df1:

Name	Score	Year
Pat	82	1990
Chris	38	1993
Pat	92	1994
Noris	88	1997
Mit	62	1999
Chen	58	1996

df2:

Applicant
Pat
Chris
Meet

Expected result

Applicant	Match (Y/N)	Matched Row reference	Count
Pat	Y	1,3	2
Chris	Y	2	1
Meet	N	NA	0

Answer 1

Approach based on Pandas outer merge

• Outer merge of df1 & df2 creates all required rows
• Group by Applicant so we can aggregate count
• Use a function to aggregate and produce values for desired output rows
• To keep index of df1 after merge, use method from to keep index after merge

Code

import pandas as pd
import numpy as np

def process(df1, df2):
    ' Overall function for generating desired output '
    
    def create_result(df, columns = ["Match (Y/N)", "Matched Row reference", "Count"]):
        '''
            Creates the desired columns of df2

            Input:
                df      - Dataframe from groupby
                columns - column names for df2
            Output:
                Pandas Series corresponding to row in df2
        '''
        cnt = df['Name'].count()   # Number of items in group
        if cnt > 0:
            # Convert index to comma delimited list, numbered from 1 (i.e. int(x) + 1)
            indexes = ','.join(str(int(x) + 1) for x in df.index.to_list())
        else:
            indexes = "NA"   # empty dataframe

        lst = ["Y" if cnt > 0 else 'N', 
                indexes,
                df.shape[0] if cnt > 0 else 0]

        return pd.Series(lst, index = columns)

    # Merge df1 with df2 but
    # add method from [to keep index after merge](https://stackoverflow.com/questions/11976503/how-to-keep-index-when-using-pandas-merge/11982843#11982843)
    # to have the index of df1 in the merge result
    return (df1
            .reset_index()
            .merge(df2, left_on = "Name", right_on = 'Applicant', how = "outer")
            .set_index('index')
            .groupby(['Applicant'])
            .apply(lambda grp_df: create_result(grp_df)))
        

Usage

from io import StringIO

s = '''Name Score   Year
Pat 82  1990
Chris   38  1993
Pat 92  1994
Noris   88  1997
Mit 62  1999
Chen    58  1996'''

df1 = pd.read_csv(StringIO(s), sep = '\t', engine = 'python')

s = '''Applicant
Pat
Chris
Meet'''

df2 = pd.read_csv(StringIO(s), sep = '\t', engine = 'python')

from pprint import pprint as pp
pp(process(df1, df2))            # process and pretty print result

Output

                   Match (Y/N) Matched Row reference  Count
Applicant                                         
Chris               Y                     2           1
Meet                N                    NA           0
Pat                 Y                   1,3           2

Answer 2

I would use numpy and pandas for this. Because I belive that Pandas is the great libraries for dealing with huge data. Although you do not great number of data, I would still recommend you to use pandas.

For information about pandas https://pandas.pydata.org/

You are able to create list file with pandas

data = {'Name': ListForName, 
    'Score': ListForScore, 
    'Year': ListForScore}

For more information about creating a list. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html

I would use a basic for loop for matching. For example.

match = 0
for i in range(0, FirstList):
    for j in range(0, SecondList):
        if(FirstList['Colunm'].iloc[i] == SecondList['Colunm'].iloc[j)):
             match += 1