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- import pandas as pd
- data = pd.read_csv("/datasets/visits.csv", sep="\ ")
- 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 and 60 <= time_spent <= 1000')
- 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'])
- print(id_name.head())
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