Use pandas `transform` when a grouped calculation must remain beside the rows that produced it. It supports window-function-like analysis without creating a separate summary table and joining it back. section 9.2.1
Hourly share of daily cycling
After aggregating cycling counts to one row per location, date, and hour, group by location and date and transform `Pedal_cycles` with `sum`. The resulting daily total is repeated on each hourly row, so dividing the hourly count by that total yields `hourly_pct`. Check a location's raw records to confirm both the total and percentages before using them. section 9.2.1
cycling_daily_hourly['TotalDailyCount'] = (
cycling_daily_hourly
.groupby(['Count_point_id', 'Count_date'])
['Pedal_cycles']
.transform('sum') #1
)
cycling_daily_hourly['hourly_pct'] = (
cycling_daily_hourly['Pedal_cycles']
/ cycling_daily_hourly['TotalDailyCount']
)
cycling_daily_hourly
This listing appends each group's daily total, then calculates the hourly proportion from values aligned to the hourly rows. section 9.2.1
