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2 changes: 2 additions & 0 deletions .github/styles/config/vocabularies/docs/accept.txt
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Expand Up @@ -64,3 +64,5 @@ bool
boolean
(?i)modis
reprojected
(?i)geospatial
(?i)cartesian
2 changes: 2 additions & 0 deletions api-reference/python/tilebox.datasets/Collection.find.mdx
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Expand Up @@ -42,6 +42,8 @@ data = collection.find(


# check if a datapoint exists
from tilebox.datasets.sync.dataset import NotFoundError

try:
collection.find(
"0186d6b6-66cc-fcfd-91df-bbbff72499c3",
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415 changes: 415 additions & 0 deletions datasets/geometries.mdx

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8 changes: 7 additions & 1 deletion datasets/ingest.mdx
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Expand Up @@ -148,7 +148,7 @@ Measurements: [2025-03-28T11:44:23.000 UTC, 2025-03-28T11:45:19.000 UTC] (2 data
#### xarray Dataset

[`xarray.Dataset`](/sdks/python/xarray) is the default format in which Tilebox Datasets returns data when
[querying data](/datasets/query) from a collection.
[querying data](/datasets/query/querying-data) from a collection.
Tilebox also supports it as input for ingestion. The example below shows how to construct an `xarray.Dataset`
from scratch, that matches the schema of the `MyCustomDataset` dataset and can then be ingested into it.
To learn more about `xarray.Dataset`, visit Tilebox dedicated [Xarray documentation page](/sdks/python/xarray).
Expand Down Expand Up @@ -397,3 +397,9 @@ Through the usage of `xarray` and `pandas` you can also easily ingest existing d
formats, such as CSV, [Parquet](https://parquet.apache.org/), [Feather](https://arrow.apache.org/docs/python/feather.html) and more.

Check out the [Ingestion from common file formats](/guides/datasets/ingest-format) guide for examples of how to achieve this.

## Geometries

Ingesting Geometries can traditionally be a bit tricky, especially when working with geometries that cross the antimeridian or cover a pole.
Tilebox is designed to take away most of the friction involved in this, but it's still recommended to follow the [best practices for handling geometries](/datasets/geometries).

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