pandas分组排序 如何获取第二大的数据

import pandas as pd
df = pd.DataFrame([
    {"class": 1, "name": "aa", "english": 120},
    {"class": 1, "name": "bb", "english": 110},
    {"class": 1, "name": "cc", "english": 110},
    {"class": 1, "name": "dd", "english": 110},
    {"class": 2, "name": "ee", "english": 120},
    {"class": 2, "name": "ff", "english": 140},
    {"class": 2, "name": "gg", "english": 130},
    {"class": 2, "name": "hh", "english": 130},
    {"class": 3, "name": "tt", "english": 130},
    {"class": 4, "name": "xx", "english": 130},
    {"class": 4, "name": "yy", "english": 130},
    {"class": 5, "name": "zz", "english": None},
  ])

def fun(df):
  # english数据去重
  sort_set = set(df["english"].values.tolist())
  if len(sort_set)<=1:
    # 数据量小于等于1，无法取到第二大的数据
    return None
  else:
    # 取english中第二大的值
    sort_value = sorted(sort_set,reverse=True)[1]
    temp_df = df[df["english"]==sort_value]
    return temp_df
 
df = df.groupby(by=["class"]).apply(fun).reset_index(drop=True)
print(df)

  class name english
0 1 bb 110.0
1 1 cc 110.0
2 1 dd 110.0
3 2 gg 130.0
4 2 hh 130.0

import pandas as pd
data = pd.read_csv('test.csv')
df = pd.DataFrame(data)
df = df.sort_values('updateTime', ascending=False).groupby(['B','C']).first().reset_index()

import pandas as pd 
data = pd.read_csv('test.csv',header = 0)
df = pd.DataFrame(data) 
# 按日期分组，分组后对 value 列从大到小排序，取每组前十行
df = df.groupby('date', group_keys=False).apply(lambda x: x.sort_values('value', ascending=False)).groupby('date').head(10).reset_index()