PYTHON 将数据分类写入CSV

我获取一组股票代码后，我需要写入CSV文件，麻烦给写一下

# coding=utf-8
from __future__ import print_function, absolute_import
from gm.api import *

data = history(symbol='SHSE.601601', frequency='1d', start_time='2022-01-26',  end_time='2022-01-28', fields='open, close, low, high, eob', adjust=ADJUST_PREV, df=True)
print(data)

获取到的数据输出的结果是

我想把它写入CSV，
直接用data.to_csv('601601.csv') 有问题，
估计要调用pands输出csv，这里不会
日期转换麻烦，可以不用转换

import  datetime
from pandas import  DataFrame, concat
# 任何其他时区时间转化成北京时间（UTC+08:00）或其他区时间
def india_to_local(india_time_str,india_format = '%Y-%m-%d %H:%M:%S+08:00'): 
    india_dt = datetime.datetime.strptime(india_time_str, india_format)
    local_dt = india_dt + datetime.timedelta(hours=2, minutes=30)
    local_format = "%Y/%m/%d"   # "%Y-%m-%d %H:%M:%S"
    time_str = local_dt.strftime(local_format)
    return time_str

# india_time_str = '2022-01-26 00:00:00+08:00'
# time_str = india_to_local(india_time_str)
# print(time_str)

# ////  构造数据  start ///////
Columns=['open', 'close', 'low', 'high', 'eob']
data = DataFrame(columns=Columns)
# 27.2 27.22 26.92 27.31 2022-01-26 00:00:00+08:00
# 27.1 26.90 26.89 27.16 2022-01-27 00:00:00+08:00
# 27.0 26.26 26.23 27.04 2022-01-28 00:00:00+08:00
data.loc[0] =  [27.2, 27.22, 26.92, 27.31, '2022-01-26 00:00:00+08:00']
data.loc[1] =  [27.1, 26.90, 26.89, 27.16, '2022-01-27 00:00:00+08:00']
data.loc[2] =  [27.0, 26.26, 26.23, 27.04, '2022-01-28 00:00:00+08:00']
# print(data,end='\n\n')
# ////  构造数据  end ///////
# ////  转换数据  start ///////
# 修改格式
data['eob'] = data['eob'].map(lambda x: india_to_local(x))
# 修改列名
data.rename(columns={"eob":"datatime"},inplace=True)
# print(data,end='\n\n')

Columns1 = ['datatime','open','close','low','high']

# # 方法一：拼接到另一个表
# finalData = DataFrame(columns=Columns1)
# finalData = concat([finalData, data],
#                 sort=False, join_axes=[finalData.columns])
# print(finalData)

# # 方法二：交换列名顺序
finalData = data[Columns1]

# 设置数据类型为字符串
finalData = finalData.astype(str)
print(finalData)
finalData.to_excel('C:/Users/Lenovo/Desktop/1.xlsx', index=False)

调用pandas库中的to_csv("xxx.csv")