Dividing Time Intervals With Multiple Index Into Hourly Buckets In Python

here is the code for the sample data set I have data={'ID':[4,4,4,4,22,22,23,25,29], 'Zone':[32,34,21,34,27,29,32,75,9], 'checkin_datetime':['04-01-2019 13:07','04-01-2019

Solution 1:

Not sure if this is efficient, but should work.

import pandas as pd
from datetime import timedelta

def group_into_hourly_buckets(df):
    df['duration'] = df['checkout_datetime'] - df['checkin_datetime']
    grouped_data = []
    for idx, row in df.iterrows():
        dur = row['duration'].seconds//60
        start_time = row['checkin_datetime']
        hours_ = 0
        while dur > 0:
            _data = {}
            _data['Checked_in_hour'] = start_time.floor('H') + timedelta(hours=hours_)
            time_spent_in_window = min(dur, 60)
            if (hours_ == 0):
                time_spent_in_window = min(time_spent_in_window, ((start_time.ceil('H') - start_time).seconds)//60)
            _data['checked_in_minutes'] = time_spent_in_window
            _data['ID'] = row['ID']
            _data['Zone'] = row['Zone']
            dur -= time_spent_in_window
            hours_ += 1
            grouped_data.append(_data)
    return pd.DataFrame(grouped_data)