B. Сложная подкрутка

import os
from itertools import groupby

import pandas as pd
import kagglehub
import numpy as np


path = kagglehub.dataset_download("ersany/online-retail-dataset")
df = pd.read_excel(os.path.join(path, "Online Retail.xlsx"))

def double_price(group_data):
    group = group_data.copy()
    group['UnitPrice'] = group['UnitPrice'] * 2
    group['Country'] = group['Country'] + '_bad_times'

    return group


def decrease_price(group_data):
    group = group_data.copy()
    price_mean = group['UnitPrice'].mean()
    price_min = group['UnitPrice'].min()
    group['UnitPrice'] = np.maximum(price_min, group['UnitPrice'] - 0.15 * price_mean)
    group['Country'] = group['Country'] + '_great_times'

    return group


def change_price(group):
    double = double_price(group)
    decrease = decrease_price(group)
    group = pd.concat([double, decrease], axis=0, ignore_index=True)

    return group

def task2(df):
    df = df.groupby(df['Country'], group_keys=False).apply(change_price)

    return df

df = task2(df)
print(df.head())