如何按交易次数（非时间）分组聚合Pandas行情数据？

原始数据 #

open     high     low       close    Volume
ctime                                                   
2022-11-07 01:00:00.012000+01:00  20900.0  20900.0  20900.0  20900.0     209.00
2022-11-07 01:00:00.019000+01:00  20900.1  20900.1  20900.1  20900.1    1254.00
2022-11-07 01:00:00.111000+01:00  20900.0  20900.0  20900.0  20900.0   11390.50
2022-11-07 01:00:00.188000+01:00  20900.1  20900.1  20900.1  20900.1      20.90
2022-11-07 01:00:00.194000+01:00  20900.0  20900.0  20900.0  20900.0    4995.10
2022-11-07 01:00:00.203000+01:00  20900.0  20900.0  20900.0  20900.0     209.00
2022-11-07 01:00:00.217000+01:00  20900.0  20900.0  20900.0  20900.0  239827.50
2022-11-07 01:00:00.237000+01:00  20900.0  20900.0  20900.0  20900.0      20.90
2022-11-07 01:00:03.028000+01:00  20900.0  20900.0  20900.0  20900.0      20.90
2022-11-07 01:00:03.743000+01:00  20900.0  20900.0  20900.0  20900.0      41.80

聚合需求 #

按每5笔交易为一组进行聚合，规则：

• open：取组内第一行值
• close：取组内第五行值
• high：取组内最大值
• low：取组内最小值
• Volume：取组内总和

实现代码 #

import pandas as pd
import numpy as np

# 假设数据已加载为DataFrame df
# 生成分组标识：每5行一组
df['group_id'] = np.arange(len(df)) // 5

# 执行聚合
aggregated_df = df.groupby('group_id').agg(
    open=('open', 'first'),
    high=('high', 'max'),
    low=('low', 'min'),
    close=('close', 'last'),  # 每组刚好5行，last对应第5行值
    Volume=('Volume', 'sum')
).reset_index(drop=True)

print(aggregated_df)

聚合结果 #

open     high     low    close    Volume
0  20900.0  20900.1  20900.0  20900.0   17869.5
1  20900.0  20900.0  20900.0  20900.0  240120.1

内容的提问来源于stack exchange，提问作者xb1t2