Got it, let's tackle this problem. You need to combine data from Callnum.csv and F-V-Mnum.csv where their Code values match, then save the result to final.csv. Here are two straightforward ways to do this in Python:

Method 1: Using Python's Built-in csv Module (No External Dependencies) #

This approach uses Python's standard library, so you don't need to install anything extra. We'll first build a lookup map from F-V-Mnum.csv to quickly find matching Group and PersonID values for each Code, then iterate through Callnum.csv to write merged rows.

import csv

# Create a dictionary to map Code to (Group, PersonID) from F-V-Mnum.csv
code_lookup = {}
with open('F-V-Mnum.csv', mode='r', newline='', encoding='utf-8') as fvm_file:
    reader = csv.DictReader(fvm_file)
    for row in reader:
        code = row['Code']
        code_lookup[code] = (row['Group'], row['PersonID'])

# Process Callnum.csv and write merged data to final.csv
with open('Callnum.csv', mode='r', newline='', encoding='utf-8') as call_file, \
     open('final.csv', mode='w', newline='', encoding='utf-8') as final_file:
    
    call_reader = csv.DictReader(call_file)
    # Define the output columns in your desired order
    output_columns = ['SubjectID', 'Name', 'Code', 'Group', 'PersonID']
    final_writer = csv.DictWriter(final_file, fieldnames=output_columns)
    
    final_writer.writeheader()
    for row in call_reader:
        current_code = row['Code']
        if current_code in code_lookup:
            group, person_id = code_lookup[current_code]
            # Build the merged row
            merged_row = {
                'SubjectID': row['SubjectID'],
                'Name': row['Name'],
                'Code': current_code,
                'Group': group,
                'PersonID': person_id
            }
            final_writer.writerow(merged_row)

How This Works: #

• We first load F-V-Mnum.csv and store each Code as a key in code_lookup, with the corresponding Group and PersonID as values. This makes lookups fast (O(1) time per row).
• Then we read Callnum.csv line by line. For each row, if its Code exists in our lookup map, we create a new merged row with all the required fields and write it to final.csv.

Method 2: Using pandas (Simpler for Larger Datasets) #

If you're working with bigger files or want more concise code, pandas is a great tool. It handles CSV merging with just a few lines of code. First, install pandas if you haven't already:

pip install pandas

Then use this script:

import pandas as pd

# Load both CSV files into DataFrames
callnum_df = pd.read_csv('Callnum.csv')
fv_mnum_df = pd.read_csv('F-V-Mnum.csv')

# Perform an inner join on the 'Code' column (only keeps matching rows)
merged_df = pd.merge(callnum_df, fv_mnum_df, on='Code', how='inner')

# Reorder columns to match your desired output format
merged_df = merged_df[['SubjectID', 'Name', 'Code', 'Group', 'PersonID']]

# Save the merged DataFrame to final.csv (without the index column)
merged_df.to_csv('final.csv', index=False)

How This Works: #

• pd.merge() with how='inner' ensures only rows with matching Code values from both files are kept.
• We reorder the columns to match your requested output structure, then save the result to final.csv.

When you run either script, your final.csv will have the matching row from your example:

SubjectID,Name,Code,Group,PersonID
67,Attila,1134112,1,78

内容的提问来源于stack exchange，提问作者jack