Time Series Resampling and Gap Handling
Align irregular data to regular intervals.
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Help me resample time series data. Description of the data (timestamps, frequency, time zone, gaps): [DATA_DESCRIPTION]. Target frequency and purpose: [TARGET_FREQUENCY]. Explain the decisions: which aggregation suits each column (sum, mean, last), how to treat gaps (leave missing, forward fill, interpolate) and why, and how time zones and daylight saving affect results. Provide pandas code for resampling, reindexing to a full calendar, flagging imputed values with a boolean column, and checks that totals match before and after. Include a table of aggregation choices by column type and a list of three traps, such as resampling after filling. Suggest a small test dataset to verify the code. Add a short worked example with a ten-row invented series that has a gap, showing the input, the resampled output under two gap strategies, and what each does to the total. Include a decision aid on whether the gap is real zero activity or missing data, and the questions to ask the data owner to find out.
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