import pandas as pd

data = pd.read_csv("/datasets/visits.csv", sep="\\t")
data['local_time'] = (
pd.to_datetime(data['date_time'], format='%Y-%m-%dT%H:%M:%S')
+ pd.Timedelta(hours=3)
)
data['date_hour'] = data['local_time'].dt.round('1H')
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')

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

name_stat = data.pivot_table(index='name', values='time_spent')
good_name_stat = good_data.pivot_table(index='name', values='time_spent', aggfunc='median')
name_stat['good_time_spent'] = good_name_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_station_stat)

good_stat2 = (
station_stat_full
.query('count > 30')
.pivot_table(index='name', values='time_spent', aggfunc=['median', 'count'])
)
good_stat2.columns = ['median_time', 'stations']
final_stat = stat.join(good_stat2)
(final_stat
.dropna(subset=['median_time'])
.sort_values('median_time')
.plot(y='median_time', kind='bar', figsize=(10, 5), grid=True)
)