这应该会得到你想要的结果：

# Create sample data
df = pd.DataFrame({'id':[1, 1, 1, 5, 5, 5, 5],
'speed':[1.2, 1.9, 1.0, 0.9, 1.3, 9.5, 0.4]
})
df

输出：
x一个一个一个一个x一个一个二个x
输出：

id   speed
0   1   1.2
4   5   1.3

我花了一个小时才弄明白，但我得到了你需要的东西。你需要REVERSE Dataframe ，并在groupbyed id中使用.cumsum()（累积和）来找到你设置的速度阈值之后的值。然后删除超过阈值的速度，以及不满足条件的行。最后，反向返回 Dataframe ：

# Create sample data
df = pd.DataFrame({'id':[1, 1, 1, 5, 5, 5, 5],
'speed':[1.2, 1.9, 1.0, 0.9, 1.3, 9.5, 0.4]
})

# Reverse the dataframe
df = df.iloc[::-1]

thre = 1.5
# Find rows with speed more than threshold
df = df.assign(ge=df.speed.ge(thre))

# Groupby and cumsum to get the rows that are after the threshold in with same id
df.insert(0, 'beforethre', df.groupby('id')['ge'].cumsum())

# Drop speed more than threshold  
df['ge'] = df['ge'].replace(True, np.nan)

# Drop rows that don't have any speed more than threshold or after threshold
df['beforethre'] = df['beforethre'].replace(0, np.nan)
df = df.dropna(axis=0).drop(['ge', 'beforethre'], axis=1)

# Reverse back the dataframe 
df = df.iloc[::-1]

# Viola!
df

输出：

id   speed
0   1   1.2
3   5   0.9
4   5   1.3