Pandas: Bin dates into 30 minute intervals and calculate averages
Converting to datetime and using pandas.Grouper + Offset Aliases:
df['date'] = pd.to_datetime(df.date)df.groupby(pd.Grouper(key='date', freq='30min')).mean().dropna() speeddate 2018-09-20 01:30:00 47.0400002018-09-20 07:30:00 26.3114292018-09-20 10:30:00 39.9475002018-09-20 11:00:00 32.298000
Since your date column isn't really a date, it's probably more sensible to convert it to a timedelta that way you don't have a date attached to it.
Then, you can use dt.floor to group into 30 minute bins.
import pandas as pddf['date'] = pd.to_timedelta(df.date)df.groupby(df.date.dt.floor('30min')).mean()Output:
speeddate 01:30:00 47.04000007:30:00 26.31142910:30:00 39.94750011:00:00 32.298000