您可以使用panda和groupby：

import pandas as pd
import json

df = pd.read_csv("your_file.csv", keep_default_na=False)
res = {}
for a1, df_gp in df.groupby('A1'):
    res[a1] = df_gp.drop(columns='A1').to_dict(orient='records')
print(json.dumps(res, indent=4))

输出：

{
    "NA": [
        {
            "sDate": "01/01/2023",
            "eDate": "01/05/2023",
            "A2": 2
        },
        {
            "sDate": "02/01/2023",
            "eDate": "02/05/2023",
            "A2": 5
        }
    ],
    "NE": [
        {
            "sDate": "01/01/2023",
            "eDate": "01/05/2023",
            "A2": 3
        },
        {
            "sDate": "02/01/2023",
            "eDate": "02/05/2023",
            "A2": 6
        }
    ],
    "SE": [
        {
            "sDate": "01/01/2023",
            "eDate": "01/05/2023",
            "A2": 4
        },
        {
            "sDate": "02/01/2023",
            "eDate": "02/05/2023",
            "A2": 7
        }
    ]
}

defaultdict对于此类处理非常有用

def build_structure(reader: typing.List[dict]) -> dict:
    structure = collections.defaultdict(list)
    for d in reader:
        structure[d["A1"]].append(
            {"sDate": d["sDate"], "eDate": d["eDate"], "a2": d["A2"]}
        )
    return structure

1.读取文件并创建文件的csv.DictReader，然后将其转换为列表，
1.将list发送到build_structure函数。
1.你有一个defaultdict，你可以用json来dumps它。

import collections
import csv
import json
import typing

def read_csv(file_name: str) -> list:
    with open(file_name, "r") as f:
        reader = csv.DictReader(f)
        return list(reader)

def build_structure(reader: typing.List[dict]) -> dict:
    structure = collections.defaultdict(list)
    for d in reader:
        structure[d["A1"]].append(
            {"sDate": d["sDate"], "eDate": d["eDate"], "a2": d["A2"]}
        )
    return structure

data = read_csv("data.csv")
structure = build_structure(data)
print(json.dumps(structure))
# {'NA': [{'a2': '2', 'eDate': '01/05/2023', 'sDate': '01/01/2023'},
#         {'a2': '5', 'eDate': '02/05/2023', 'sDate': '02/01/2023'}],
#  'NE': [{'a2': '3', 'eDate': '01/05/2023', 'sDate': '01/01/2023'},
#         {'a2': '6', 'eDate': '02/05/2023', 'sDate': '02/01/2023'}],
#  'SE': [{'a2': '4', 'eDate': '01/05/2023', 'sDate': '01/01/2023'},
#         {'a2': '7', 'eDate': '02/05/2023', 'sDate': '02/01/2023'}]}