这里有一个例子 groupby （无下划线）：

df = pd.DataFrame({"id":["a","b","a","b","c","c"], "timestamp":[1,2,3,4,5,6]})

为您的应用程序创建一个grouper列 timestamp .

df["my interval"] = (df["timestamp"] > 3 )&  (df["timestamp"] <5)
"you need some _data_ columns, i.e. those which you do not use for grouping"
df["dummy"] = 1 
df.groupby(["id", "my interval"]).agg("count")["dummy"]

或者您可以同时使用：

df["something that I need"] = df["my interval"] & (df["id"] == "b")
df.groupby(["something that I need"]).agg("count")["dummy"]

您可能还希望应用整数除法来生成时间间隔：

df = pd.DataFrame({"id":["a","b","a","b","c","c"], "timestamp":[1,2,13,14,25,26], "sales": [0,4,2,3,6,7]})
epoch = 10
df["my interval"] = epoch* (df["timestamp"] // epoch)
df.groupby(["my interval"]).agg(sum)["sales"]

编辑：

你的例子：

import pandas as pd
A = "A"
B = "B"
df = pd.DataFrame({"id":[A,A,A,B,B], "timestamp":[5,7,3,9,5]})
df["dummy"] = 1

解决方案：

grouper = (df["id"] == A) & (4 < df["timestamp"] ) & ( df["timestamp"] < 11)
df.groupby( grouper ).agg(sum)["dummy"]

或更好：

df[grouper]["dummy"].sum()