导入包：

import pandas as pd

读取CSV文件：

abc_df = pd.read_csv("abc.csv")
xyz_df = pd.read_csv("xyz.csv")

基于uid合并这些 Dataframe ：

merged_df = pd.merge(xyz_df, abc_df, on="uid", how="left")

定义检查状态的函数：

def status(row):
    if pd.isna(row["isDisabled"]):
        return "Does not exist in ABC"
    elif row["status"] == row["isDisabled"]:
        return "NOCHANGE"
    else:
        return "Disabled in ABC"

将status()函数应用于merged_df的每一行：

merged_df["abc_status"] = merged_df.apply(status, axis=1)

输出 Dataframe ：

output_df = merged_df[["uid", "status", "abc_status"]]

将output_df保存为CSV文件：

output_df.to_csv("output.csv", index=False)

output.csv文件：

user.0,active,NOCHANGE
user.1,active,Disabled in ABC
user.2,active,NOCHANGE
user.5,active,Disabled in ABC
user.7,active,Does not exist in ABC

您可以使用csv模块完成此操作，方法是首先阅读ABC读入dict，将status键入uid：

import csv

abc_status_map: dict[str, str] = {}

with open("abc.csv", newline="") as f:
    reader = csv.reader(f)
    next(reader)  # discard header

    for row in reader:
        abc_status_map[row[0]] = row[1]

abc状态Map如下所示：

{
    "user.0": "active",
    "user.1": "Disabled",
    "user.2": "active",
    "user.3": "Disabled",
    "user.4": "active",
    "user.5": "Disabled",
    "user.6": "active",
    "user.8": "active",
}

您的示例输出没有涵盖XYZ中禁用但ABC中激活的情况。我继续编写代码，以解决这种可能性：

def get_abc_status(uid: str, xyz_status: str) -> str:
    abc_status = abc_status_map.get(uid)

    if abc_status is None:
        return "Does not exist in ABC"

    if abc_status == xyz_status:
        return "NOCHANGE"

    if xyz_status == "active":
        return "Disabled in ABC"

    return "Enabled in ABC"

并修改了XYZ CSV来测试它：

| uid    | status   |
|--------|----------|
| user.0 | active   |
| user.1 | active   |
| user.2 | Disabled |  ← let's see what happens
| user.5 | active   |
| user.7 | active   |

创建最终行的列表，以将计算行追加到其中。使用输出标头初始化该列表：

final_rows = [["uid", "status", "abc_status"]]

然后读取XYZ，比较状态，追加计算出的最后几行，最后写入输出：

with open("xyz.csv", newline="") as f:
    reader = csv.reader(f)
    next(reader)

    for row in reader:
        uid = row[0]
        xyz_status = row[1]
        abc_status = get_abc_status(uid, xyz_status)

        final_rows.append([uid, xyz_status, abc_status])

with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerows(final_rows)

这就给了我：

| uid    | status   | abc_status            |
| ------ | -------- | --------------------- |
| user.0 | active   | NOCHANGE              |
| user.1 | active   | Disabled in ABC       |
| user.2 | Disabled | Enabled in ABC        |  ← looks correct
| user.5 | active   | Disabled in ABC       |
| user.7 | active   | Does not exist in ABC |