Shape · Time series

Resample

Rolls a time series up into regular buckets, from minutes to years, with a total or other summary per bucket. Empty buckets can be left out, carried forward or set to zero.

Also known as: Time bucket, daily totals

How it works

Pick the Timestamp column and a Frequency (Day by default); each row is assigned to the bucket its timestamp is truncated to. Each aggregation row names a column and a function: Sum, Count, Average, Min, Max, Median, Std dev, First or Last, where First and Last take the value at the earliest and latest timestamp in the bucket. The alias defaults to the column name. Fill gaps decides what happens to periods between the first and last bucket that have no rows: None leaves them out, Zero adds them with 0, and Forward fill adds them with the last known value. The bucket column keeps the timestamp column's name unless you set Output timestamp name.

Inputs and outputs

Settings

{
  timestampColumn: string
  frequency: "minute"|"hour"|"day"|"week"|"month"|"quarter"|"year"
  aggregations: [{ column: string, func: "sum"|"mean"|"median"|"std"|"count"|"min"|"max"|"first"|"last", alias?: string }]
  fillGaps: "none"|"ffill"|"zero"
  outputTimestampName?: string
}

Example

Sensor readings every few minutes become one row per day: timestamp column read_at, frequency Day, aggregations Average of temp and Count of temp with alias readings, gaps set to Zero so a day with no readings still appears.

More in Time series

• Shift / Lag — Copies a value from N rows earlier or later onto each row, so you can compare a row with the one before it. For example, last month's sales next to this month's.
• Diff / Pct Change — Compares each row with the one N periods earlier and writes the difference or percent change. For example, month-over-month growth per store.
• Rolling Window — Adds moving statistics over the last N rows: sum, average, standard deviation, min, max, count or median. For example, a 7-day moving average.
• Exponential Weighted Mean — Smooths a number column so recent rows count more than older ones. Gives a weighted mean, standard deviation or variance.
• Forecast — Forecasts the next periods of a measure from a dated series, one forecast per group, with a lower and upper band. It can also backtest the recent past to show how accurate the forecast would have been.
• Sessionize — Groups events into sessions: a new session starts when the gap since the last event is longer than you set, such as 30 minutes.