import pandas as pd

 

data = pd.read_csv('/datasets/visits.csv', sep='\t')

 

# фильтруем слишком быстрые и медленные заезды и АЗС

data['too_fast'] = data['time_spent'] < 60

data['too_slow'] = data['time_spent'] > 1000

too_fast_stat = data.pivot_table(index='id', values='too_fast')

good_ids = too_fast_stat.query('too_fast < 0.5')

good_data = data.query('id in @good_ids.index')

good_data = good_data.query('60 <= time_spent <= 1000')

 

# считаем данные по отдельным АЗС и по сетям

station_stat = data.pivot_table(index='id', values='time_spent', aggfunc='median')

good_stations_stat = good_data.pivot_table(index='id', values='time_spent', aggfunc='median')

stat = data.pivot_table(index='name', values='time_spent')

good_stat = good_data.pivot_table(index='name', values='time_spent', aggfunc='median')

stat['good_time_spent'] = good_stat['time_spent']

 

id_name = good_data.pivot_table(index='id', values='name', aggfunc=['first', 'count'])

id_name.columns = ['name', 'count']

station_stat_full = id_name.join(good_stations_stat)

 

 

station_stat_multi = good_data.pivot_table(index=['id'], values=['time_spent', 'too_fast', 'too_slow'], aggfunc=['mean'])

 

print(station_stat_multi.head())