> ## Documentation Index > Fetch the complete documentation index at: https://docs.tilebox.com/llms.txt > Use this file to discover all available pages before exploring further. # Querying data > Access and filter data stored in your datasets using time-based and spatial queries, with built-in support for pagination and progress tracking. Tilebox offers a powerful and flexible querying API to access and filter data from your datasets. When querying, you can [filter by time](/datasets/query/filter-by-time) and for [Spatio-temporal datasets](/datasets/types/spatiotemporal) optionally also [filter by a location in the form of a geometry](/datasets/query/filter-by-location). ## Running a query You can query data from either a specific collection of a dataset, from a selected set of collections of a dataset, or from all collections of a dataset at once. Below is a simple example showcasing those options by querying Sentinel-2 data for April 2025 over the state of Colorado. ```python Python theme={"system"} from shapely import Polygon from tilebox.datasets import Client area = Polygon( # area roughly covering the state of Colorado ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)), ) client = Client() sentinel2_msi = client.dataset("open_data.copernicus.sentinel2_msi") # query data from a specific collection collection = sentinel2_msi.collection("S2A_S2MSI2A") data = collection.query( temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) # query data from a selected set of collections data = sentinel2_msi.query( collections=["S2A_S2MSI2A", "S2B_S2MSI2A", "S2C_S2MSI2A"], temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) # query data from all collections in the dataset data = sentinel2_msi.query( # omit the collections argument to query all collections temporal_extent=("2025-04-01", "2025-05-01"), spatial_extent=area, show_progress=True, ) ``` ```go Go theme={"system"} startDate := time.Date(2025, 4, 1, 0, 0, 0, 0, time.UTC) endDate := time.Date(2025, 5, 1, 0, 0, 0, 0, time.UTC) timeInterval := query.NewTimeInterval(startDate, endDate) area := orb.Polygon{ // area roughly covering the state of Colorado {{-109.05, 41.00}, {-109.045, 37.0}, {-102.05, 37.0}, {-102.05, 41.00}, {-109.05, 41.00}}, } collectionA, err := client.Collections.Get(ctx, dataset.ID, "S2A_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } collectionB, err := client.Collections.Get(ctx, dataset.ID, "S2B_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } collectionC, err := client.Collections.Get(ctx, dataset.ID, "S2C_S2MSI2A") if err != nil { log.Fatalf("Failed to get collection: %v", err) } // query data from a specific collection var fromCollection []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &fromCollection, datasets.WithCollectionIDs(collectionA.ID), datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } // query data from a selected set of collections var datapoints []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapoints, datasets.WithCollectionIDs(collectionA.ID, collectionB.ID, collectionC.ID), datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } // query data from all collections in the dataset var datapointsFromAllCollections []*v1.Sentinel2Msi err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapointsFromAllCollections, datasets.WithTemporalExtent(timeInterval), datasets.WithSpatialExtent(area), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } ``` To learn more about how to specify filters to narrow down the query results, check out the following sections about filtering by time, by geometry or by datapoint ID. Learn how to run queries for datapoints within a given temporal extent. Learn how to filter your query results by a certain geographical extent. Find individual datapoints by their unique ID. ## Automatic pagination Querying large datasets can result in a large number of data points. For those cases Tilebox automatically handles pagination for you by sending paginated requests to the server. When using the python SDK in an interactive notebook environment, you can additionally also display a progress bar to keep track of the progress of the query by setting the `show_progress` parameter to `True`. ## Skipping data fields Sometimes, only the ID or timestamp associated with a datapoint is required. In those cases, you can speed up querying by skipping downloading of all dataset fields except of the `time`, the `id` and the `ingestion_time` by setting the `skip_data` parameter to `True`. For example, when checking how many datapoints exist in a given time interval, you can use `skip_data=True` to avoid loading the data fields. This works the same way on both collection-level and dataset-level queries. ```python Python theme={"system"} interval = ("2023-01-01", "2023-02-01") data = collection.query(temporal_extent=interval, skip_data=True) print(f"Found {data.sizes['time']} data points.") ``` ```go Go theme={"system"} startDate := time.Date(2023, time.January, 1, 0, 0, 0, 0, time.UTC) endDate := time.Date(2023, time.February, 1, 0, 0, 0, 0, time.UTC) interval := query.NewTimeInterval(startDate, endDate) var datapoints []*v1.Sentinel1Sar err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapoints, datasets.WithCollectionIDs(collection.ID), datasets.WithTemporalExtent(interval), datasets.WithSkipData(), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } log.Printf("Queried %d datapoints", len(datapoints)) log.Printf("First datapoint time: %s", datapoints[0].GetTime().AsTime()) ``` Output ```plaintext Python theme={"system"} Size: 160B Dimensions: (time: 1) Coordinates: ingestion_time (time) datetime64[ns] 8B 2024-08-01T08:53:08.450499 id (time) ## Empty response Query will not raise an error if no data points are found for the specified query, instead an empty result is returned. In python, this is an empty `xarray.Dataset` object. In Go, an empty slice of datapoints. To check for an empty response, you can coerce the result to a boolean or check the length of the slice. ```python Python theme={"system"} timestamp_with_no_data_points = "1997-02-06 10:21:00:000" data = collection.query(temporal_extent=timestamp_with_no_data_points) if not data: print("No data points found") ``` ```go Go theme={"system"} timeWithNoDatapoints := query.NewPointInTime(time.Date(1997, time.February, 6, 10, 21, 0, 0, time.UTC)) var datapoints []*v1.Sentinel1Sar err = client.Datapoints.QueryInto(ctx, dataset.ID, &datapoints, datasets.WithCollectionIDs(collection.ID), datasets.WithTemporalExtent(timeWithNoDatapoints), ) if err != nil { log.Fatalf("Failed to query datapoints: %v", err) } if len(datapoints) == 0 { log.Println("No data points found") } ``` Output ```plaintext Python theme={"system"} No data points found ``` ```plaintext Go theme={"system"} No data points found ```