Aggregate Time Series Count

async AsyncCogniteClient.time_series.aggregate_count(
advanced_filter: Filter | dict[str, Any] | None = None,
filter: TimeSeriesFilter | dict[str, Any] | None = None,
) → int

Count of time series matching the specified filters and search.

Parameters:

  • advanced_filter (Filter | dict[str, Any] | None) – The filter to narrow down the time series to count.

  • filter (TimeSeriesFilter | dict[str, Any] | None) – The filter to narrow down time series to count requiring exact match.

Returns:

The number of time series matching the specified filters and search.

Return type:

int

Note

State time series are never returned by this method as they are a Data Modeling-only feature (the API leaves them out by default). Aggregation of state time series must go through the generic Data Modeling endpoint instead: client.data_modeling.instances.aggregate.

Examples:

Count the number of time series in your CDF project:

>>> from cognite.client import CogniteClient, AsyncCogniteClient
>>> client = CogniteClient()
>>> # async_client = AsyncCogniteClient()  # another option
>>> count = client.time_series.aggregate_count()

Count the number of numeric time series in your CDF project:

>>> from cognite.client.data_classes import filters
>>> from cognite.client.data_classes.time_series import TimeSeriesProperty
>>> is_numeric = filters.Equals(TimeSeriesProperty.is_string, False)
>>> count = client.time_series.aggregate_count(advanced_filter=is_numeric)