Resampling or backfilling for a Pandas MultiIndex (to higher freq)

I would like to modify a dataframe of hourly stock prices that has a datetime column with hourly frequencies and missing values. Below is a min example:

date_times =['2020-12-30 14:30:00+00:00',   '2022-03-20 20:00:00+00:00' ]
prices =[25.60, 21.40   ]
stock_names =['AAPL',   'MSFT'  ]
df = pd.DataFrame({'date_time':date_times, 'stock_name':prices, 'price':stock_names})
df.date_time = pd.to_datetime(df.date_time, utc = True )

I would like to bfill or resample this in such a way that it becomes and hourly data for each stock and for the missing values uses the next available one.

Whatever the easiest way is. Maybe recreate a new dataframe with this index and merge? Not sure what the easiest solution is.

source https://stackoverflow.com/questions/71982865/resampling-or-backfilling-for-a-pandas-multiindex-to-higher-freq

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