这是我发现在数据库中工作最好/最容易的东西。使用Python内置的数据库sqlite3。

cat import_1.csv 

Address,Price
101 Main Street,"£50,000"
102 Main Street,"£100,000"

cat import_2.csv 
Address,Price
101 Main Street,"£55,000"
102 Main Street,"£100,000"
103 Main Street,"£200,000"


import csv
import sqlite3

con_sqlite = sqlite3.connect(":memory:")
cur_sqlite = con_sqlite.cursor()
cur_sqlite.execute("create table import_1 (address varchar, price varchar)")
cur_sqlite.execute("create table import_2 (address varchar, price varchar)")

with open("import_1.csv") as csv_1:
    reader = csv.reader(csv_1)
    next(reader)
    for row in reader:
       cur_sqlite.execute("insert into import_1 values(?, ?)", row)

with open("import_2.csv") as csv_2:
    reader = csv.reader(csv_2)
    next(reader)
    for row in reader:
       cur_sqlite.execute("insert into import_2 values(?, ?)", row)

with open('daily-diff.csv', 'w') as out_file:
    cur_sqlite.execute("select i1.address, i1.price AS old_price, i2.price AS new_price from import_1 AS i1 join import_2 AS i2 on i1.address = i2.address where i1.price != i2.price")
    diff_list = cur_sqlite.fetchall()
    writer = csv.writer(out_file)
    writer.writerow(["Address", "Old Price", "New Price"])
    writer.writerows(diff_list)

cat daily-diff.csv 

Address,Old Price,New Price
101 Main Street,"£50,000","£55,000"

下面是一个工作示例，说明如何仅使用标准库工具来完成此操作：

import csv
from pathlib import Path
from typing import Any, TypeAlias

AnyDict: TypeAlias = dict[str, Any]

FIRST_COLUMN_NAME = "Address"

def load_dict_from_csv(file_path: Path, **reader_kwargs: Any) -> AnyDict:
    """
    Reads CSV file and returns contents as a dictionary.

    Keys are the contents of the first column,
    values are lists of contents in the remaining columns.
    """
    with file_path.open("r") as file:
        reader = csv.reader(file, **reader_kwargs)
        return {row[0]: row[1:] for row in reader}

def dict_intersect_diff(dict_1: AnyDict, dict_2: AnyDict) -> AnyDict:
    """
    Returns the difference of two dictionaries as a new dictionary.

    Only keeps identical keys. (Discards keys not present in both.)
    """
    common_keys = tuple(key for key in dict_1.keys() if key in dict_2.keys())
    return {
        key: (dict_1[key], dict_2[key])
        for key in common_keys
        if dict_1[key] != dict_2[key]
    }

def write_dict_diff_to_csv(
    dict_diff: AnyDict,
    title_row: list[str],
    file_path: Path,
    **writer_kwargs: Any,
) -> None:
    """
    Writes a "difference dictionary" to a csv file.
    
    Creates two columns for each column in the title row,
    one for the "old" values and one for the "new" values.
    """
    diff_title = [title_row[0]]
    for col in title_row[1:]:
        diff_title.append(f"{col} old")
        diff_title.append(f"{col} new")
    with file_path.open("w") as file:
        writer = csv.writer(file, **writer_kwargs)
        writer.writerow(diff_title)
        for key, (values_old, values_new) in dict_diff.items():
            writer.writerow([key] + values_old + values_new)

def main() -> None:
    file_path_old = Path("01.csv")
    file_path_new = Path("02.csv")
    file_path_diff = Path("diff.csv")
    dict_old = load_dict_from_csv(file_path_old)
    dict_new = load_dict_from_csv(file_path_new)
    title_row = [FIRST_COLUMN_NAME] + dict_old[FIRST_COLUMN_NAME]
    dict_diff = dict_intersect_diff(dict_old, dict_new)
    write_dict_diff_to_csv(dict_diff, title_row, file_path_diff)

if __name__ == '__main__':
    main()

使用示例CSV输入文件运行此命令会得到所需的结果。

注：这假定第一列具有唯一值（即，在一个CSV文件中没有两行具有相同的“地址”）。

这也适用于其他列（“价格”除外）。
如果您要“大规模”地使用Pandas（如评论中所建议的那样），那么使用特定的工具来实现这一目的可能是明智的。但是对于这个简单的用例，像上面的脚本这样的东西应该是可以的。
希望这对你有帮助。