API Reference

This page contains the full documentation for each function of the Studio Data SDK. Use the sidebar to navigate to any function.

Sharing Functions Skip link to Sharing Functions

This section contains detailed reference documentation for the Data SDK's sharing functions.

To view each function with a brief description and an example, visit the sharing page.

get_permissions Skip link to get_permissions

\ Enterprise feature. Contact us to learn more.](https://location.foursquare.com/contact-us)

Get permissions for a resource stored in your Studio workspace.

Permissions include "viewer" and "editor", and can be modified for individuals (via emails) or your entire organization via the set-permissions functions.

Method Skip link to Method

PythonCLIHTTP

data_sdk.get_permissions(
  self,
  *,
  resource_type: ResourceType,
  resource_id: UUID | str,
) -> CategorizedPermissions:

fsq-data-sdk get-permissions
  --resource-type <Type of resource, map, dataset, etc.>
  --resource-id <Resource UUID>

GET https://data-api.foursquare.com/v1/permissions/map/ HTTP/1.1
GET https://data-api.foursquare.com/v1/permissions/dataset/ HTTP/1.1

Response Skip link to Response

A list of categorized permissions for both the organization and any other users (via email).

200

{
    "organization": "viewer",
    "users": [\
        {\
            "email": "user1@mail.com",\
            "permission": "editor"\
        },\
        {\
            "email": "user2@mail.com",\
            "permission": "viewer"\
        }\
    ]
}

Examples Skip link to Examples

PythonCLIHTTP

data_sdk.get_permissions(
  resource_type = "map",
  resource_id = "[UUID]"
)

fsq-data-sdk get-permissions
  --resource-type "map"
  --resource-id "[UUID]"

curl GET 'https://data-api.foursquare.com/v1/permissions/map/1l9e5c4e-2f3-4f24-19fb-7e7514b43c44' --header 'Authorization: Bearer <token>'

curl GET 'https://data-api.foursquare.com/v1/permissions/dataset/1l9e5c4e-2f3-4f24-19fb-7e7514b43c44' --header 'Authorization: Bearer <token>'

set_permissions Skip link to set_permissions

\ Enterprise feature. Contact us to learn more.](https://location.foursquare.com/contact-us)

Set permissions for a resource stored in your Studio workspace.

Permissions include "viewer" and "editor", and can be modified for individuals (via emails) or your entire organization. To remove permissions, simply omit them from set_permissions.

Method Skip link to Method

PythonCLIHTTP

data_sdk.set_permissions(
  self,
  *,
  resource_type: ResourceType,
  resource_id: UUID | str,
  permissions: CategorizedPermissions | Dict,
) -> None:

fsq-data-sdk set-permissions
  --resource-type <Type of resource, map, dataset, etc.>
  --resource-id <Resource UUID>
  --organization <viewer or editor>
  --viewer <User (email) with 'viewer' permissions>
  --editor <User (email) with 'editor' permissions>

POST https://data-api.foursquare.com/v1/permissions/ HTTP/1.1

Body Skip link to Body

The body of the request should contain the permissions encoded as a JSON blob.

--data-raw '{
    "resourceType": "map",
    "resourceId": "1l9e5c4e-2f3-4f24-19fb-7e7514b43c44",
    "permissions": {
        "users": [\
            {\
                "email": "user1@mail.com",\
                "permission": "editor"\
            }, {\
                "email": "user2@mail.com",\
                "permission": "view"\
            }\
        ]
    }
}'

Response Skip link to Response

If successful, you will receive a message indicating which records were created, updated, and removed.

200

{
    "message": "Permissions set successfully (2 created, 0 updated, 0 removed)."
}

If certain emails don't have a Studio account associated with them, they will be skipped.

200

{
    "message": "Permissions set successfully (4 created, 2 updated, 1 removed). Some emails were skipped because there is no account associated with them: invalid@email.com."
}

Collaborators can create an account by visiting https://studio.foursquare.com/. If you are looking to collaborate with coworkers, we recommend you invite them into your organization.

Examples Skip link to Examples

PythonCLIHTTP

data_sdk.set_permissions(
    resource_type = "map",
    resource_id = "[UUID]"
    permissions = {
      "organization": "viewer",
      "users": [\
        {\
          "email": "one@mail.com",\
          "permission": "editor"\
        },\
        {\
          "email": "three@mail.com",\
          "permission": "viewer\
        }\
      ]
    }
  )

fsq-data-sdk set-permissions
  --resource-type "map"
  --resource-id "[UUID]"
  --editor one@mail.com
  --editor two@mail.com
  --viewer three@mail.com
  --viewer four@mail.com
  --editor five@mail.com

curl POST 'https://data-api.foursquare.com/v1/permissions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <token>' \
--data-raw '{
    "resourceType": "map",
    "resourceId": "1l9e5c4e-2f3-4f24-19fb-7e7514b43c44",
    "permissions": {
        "users": [\
            {\
                "email": "user1@mail.com",\
                "permission": "editor"\
            }, {\
                "email": "user2@mail.com",\
                "permission": "view"\
            }\
        ]
    }
}'

Map Functions Skip link to Map Functions

This section contains detailed reference documentation covering the Data SDK's map functions.

To view each function with a brief description and an example, visit the map functions documentation.

create_map Skip link to create_map

Create a map record from JSON, including the map configuration and list of associated datasets.

Method Skip link to Method

PythonCLIHTTP

data_sdk.create_map(
      name: Optional[str] = None,
      description:  Optional[str] = None,
      map_state: Optional[MapState] = None,
      datasets: Optional[Iterable[Union[Dataset, UUID, str0]]])

fsq-data-sdk create-map \
      --name <name> \
      --description <description> \
      --map-state <path> \
      --dataset-ids <uuid1>,<uuid2>

POST https://data-api.foursquare.com/v1/maps/ HTTP/1.1

Body Skip link to Body

The body of the request should be the JSON data for the map record you want to create. All properties are optional, and unknown properties or those which cannot be updated will be ignored. In order to refer to datasets in the map state, they must be included in the datasets list, which can be either a list of dataset UUIDs or a list of objects in the form {"id": "string"}.

Response Skip link to Response

Updated map record

JSON

{
  "id": "string",
  "name": "string",
  "createdAt": "2020-11-03T21:27:14.000Z",
  "updatedAt": "2020-11-13T01:44:07.000Z",
  "description": "string",
  "privacy": "private",
  "permission": "editor",
  "latestState": {
    "id": "string",
    "data": MapConfig
  },
  "datasets": [\
    {\
      "id": "string",\
      "name": "string",\
      "createdAt": "2020-11-10T18:09:39.000Z",\
      "updatedAt": "2020-11-10T18:09:39.000Z",\
      "privacy": "private",\
      "permission": "editor",\
      "isValid": true\
    }\
  ]
}

Example Skip link to Example

PythonCLIHTTP

unfolded_map = data_sdk.create_map(
      name="map name",
      description="map description",
      map_state={"id": "<uuid>", "data": {...}},
      datasets=['<uuid1>', '<uuid2>'])

fsq-data-sdk create-map \
      --name "map name" \
      --description "map description" \
      --map-state /path/to/map-state.json \
      --dataset-ids <uuid1>,<uuid2>

curl -X POST https://data-api.foursquare.com/v1/maps/ \
  -H 'Authorization: Bearer <token>' \
  -H 'Content-Type: application/json' \
  --data-binary '@/path/to/my_map.json'

copy_map Skip link to copy_map

Creates a copy of an existing map, duplicating its layers and map state.

The user must choose whether to copy the target map's datasets or point to them as a data source.

Method Skip link to Method

PythonCLIHTTP

data_sdk.copy_map(
      map: Union[Map, str, UUID],
      copy_datasets: bool,
      name: Optional[str] = None
      ) -> Map:

fsq-data-sdk clone-map \
      --map-id <map-id> \
      --clone-datasets/--no--clone--datasets \
      --name <name>

curl --request POST \
     --url https://data-api.foursquare.com/v1/maps/map_id/copy \
     --header 'Authorization: Bearer <Token>' \
     --header 'accept: application/json' \
     --header 'content-type: application/json' \
     --data '
{
  "copyDatasets": true,
  "name": "map_copy"
}
'

Body Skip link to Body

Specify copyDatasets option boolean to toggle whether to also copy datasets during the map copy operation. You can also provide a name for the new copy of the map with the name field.

Positional Parameters Skip link to Positional Parameters

Parameter Type Description
map Map, string, or UUID The map record to copy. Can be a Map object representing a created map or a string/UUID id pointing to an existing map.

Keyword Parameters Skip link to Keyword Parameters

Parameter Type Description
copy_datasets bool Required. If true, copy all underlying datasets of the target map.
name string The name to give the copied map. Default: "Copy of {source_map_name}"

Response Skip link to Response

JSON

Examples Skip link to Examples

PythonCLIHTTP

unfolded_map = data_sdk.copy_map(
      map = "<uuid>",
      copy_datasets = True,
      name = "My Copied Map Example")

fsq-data-sdk clone-map \
      --map-id "<uuid>" \
      --clone-datasets/--no--clone--datasets \
      --name "My Cloned Map Example"

curl -X POST https://data-api.foursquare.com/v1/maps/<uuid>/clone \
    -H 'Authorization: Bearer <token>

replace_dataset Skip link to replace_dataset

Replace a dataset on a map, updating the visualization with the data from the new dataset.

By default, this function expects a dataset with an identical schema and will error if the new dataset is not compatible with the old one. To override the error, set force = True. To use strict type checking, set strict=True

Method Skip link to Method

PythonCLIHTTP

data_sdk.replace_dataset(
        map: Union[Map, str, uuid.UUID],
        dataset_to_replace: Union[Dataset, str, uuid.UUID],
        dataset_to_use: Union[Dataset, str, uuid.UUID],
        force: bool = False
        strict: bool = False
        ) ‑> Map

fsq-data-sdk replace-dataset \
      --map-id, \
      --dataset-to-replace-id \
      --dataset-to-use-id
      --force
      --strict

POST https://data-api.foursquare.com/v1/maps/<uuid>/datasets/replace HTTP/1.1

Positional Parameters Skip link to Positional Parameters

Parameter Type Description
map Map, string, or UUID Required. The map record containing the dataset to replace. Can be a Map object representing a created map or a string/UUID id pointing to an existing map.
dataset_to_replace Dataset, string, or UUID Required. The dataset to replace. Can be a Dataset object representing a dataset or a string/UUID id pointing to an existing dataset.
dataset_to_use Dataset, string, or UUID Required. The new dataset to use in the replace operation. Can be a Dataset object representing a dataset or a string/UUID id pointing to an existing dataset.

Keyword Parameters Skip link to Keyword Parameters

Parameter Type Description
force bool If True, force the dataset replacement operation, overriding any errors caused by mismatched schemas. Default: False
strict bool If True, use strict typechecking and throw an error if fields don't have the same type. Default: False

Response Skip link to Response

The Map object that was operated on.

JSON

Examples Skip link to Examples

PythonCLIHTTP

data_sdk.replace_dataset(
      map_id = "38bbed5-eb0e-4c65-8bcc-cc173dc497qb",
      dataset_to_replace = "750dfn07-f8b9-4d37-b698-bacd1d8e6156",
      dataset_to_use =  "c9ff8f3e-8821-4k68-b7fc-94cb95fe65e2"
)

curl -X POST https://data-api.foursquare.com/v1/maps/<uuid>/datasets/replace \
    -H 'Authorization: Bearer <token>' \

curl --request POST \
     --url https://data-api.foursquare.com/v1/maps/<uuid>/datasets/replace \
     --header 'Authorization: Bearer <Token>' \
     --header 'accept: application/json' \
     --header 'content-type: application/json' \
     --data '
{
  "datasetToReplaceId": "dataset_1",
  "datasetToUseId": "dataset_2",
  "force": false,
  "strict": false
}
'

get_map_by_id Skip link to get_map_by_id

Get a map record by id.

PythonCLIHTTP

unfolded_map = data_sdk.get_map_by_id(uuid: str) -> Map

fsq-data-sdk get-map <uuid>

GET https://data-api.foursquare.com/v1/maps/<uuid> HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
id string The UUID of the map record to get.

Response Skip link to Response

Map record, including the full map state and a list of associated datasets. The map state (the configuration of the map styles, layers, etc) is omitted in the sample record below due to its complexity.

JSON

{
  "id": "string",
  "name": "string",
  "createdAt": "2020-11-03T21:27:14.000Z",
  "updatedAt": "2020-11-13T01:44:07.000Z",
  "description": "string",
  "privacy": "private",
  "permission": "editor",
  "latestState": {
    ...
  },
  "datasets": [\
    {\
      "id": "string",\
      "name": "string",\
      "createdAt": "2020-11-10T18:09:39.000Z",\
      "updatedAt": "2020-11-10T18:09:39.000Z",\
      "privacy": "private",\
      "permission": "editor",\
      "isValid": true\
    }\
  ]
}

Example Skip link to Example

PythonCLIHTTP

unfolded_map = data_sdk.get_map_by_id("<uuid>")

fsq-data-sdk get-map <uuid>

curl -X GET https://data-api.foursquare.com/v1/maps/<uuid> \
  -H 'Authorization: Bearer <token>'

update_map Skip link to update_map

Update a map record, including the latest state and list of associated datasets.

PythonCLIHTTP

update_map(
      map_id: Union[Map, UUID, str],
      name: Optional[str] = None,
      description: Optional[str] = None,
      map_state: Optional[MapState] = None,
      datasets: Optional[Iterable[Union[Dataset, UUID, str]]] = None) -> Map:

fsq-data-sdk update-map \
      --map-id <uuid> \
      --name <name> \
      --description <description> \
      --map-state <path> \
      --dataset-ids <uuid1>,<uuid2>

PUT https://data-api.foursquare.com/v1/maps/{id} HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
id string The UUID of the map record to update.
name string A new name for the map.
description string A new description for the map.
map_state MapState The latest MapState of the Studio map object.
datasets Dataset list A list of Dataset objects associated with the map.

Body Skip link to Body

The body of the request should be the JSON data for the map record you want to update. All properties are optional, and unknown properties or those which cannot be manually updated will be ignored. In order to refer to datasets in the map state, they must be included in the datasets list, which can be either a list of dataset UUIDs or a list of objects in the form {"id": <uuid>}.

Response Skip link to Response

Updated map record

JSON

Example Skip link to Example

PythonCLIHTTP

data_sdk.update_map(
    map_id = map.id,
    map_state = {
        "id": map.latest_state.id,
        "data": map.latest_state.data
    }
)

fsq-data-sdk update-map \
      --map-id "map-uuid" \
      --name "new name" \
      --description "new description" \
      --map-state map-state.json \
      --dataset-ids <uuid1>,<uuid2>

curl -X PUT https://data-api.foursquare.com/v1/maps/<uuid> \
    -H 'Authorization: Bearer <token>' \
    -H 'Content-Type: application/json' \
    --data-binary '@/path/to/my_map.json'

delete_map Skip link to delete_map

Delete a map record by id. This will not delete datasets associated with the map.

PythonCLIHTTP

data_sdk.delete_map(uuid: str) -> None

fsq-data-sdk delete-map <uuid>

DELETE https://data-api.foursquare.com/v1/maps/<uuid> HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
id string The UUID of the map record to delete.

Response Skip link to Response

A message indicating if deletion was successful.

JSON

{
  "message": "string"
}

Example Skip link to Example

PythonCLIHTTP

# List maps on account
maps = data_sdk.list_maps()
# Select map, then delete
map_to_delete = maps[0]
data_sdk.delete_map(map)

fsq-data-sdk delete-map <uuid>

curl -X GET https://data-api.foursquare.com/v1/maps/<uuid> \
  -H 'Authorization: Bearer <token>'

list_maps Skip link to list_maps

Get all map records for the authenticated user.

PythonCLIHTTP

maps = data_sdk.list_maps()

fsq-data-sdk list-maps

GET https://data-api.foursquare.com/v1/maps HTTP/1.1

Get all map records for the organization of the authenticated user.

PythonCLIHTTP

org_maps = data_sdk.list_maps(organization=True)

fsq-data-sdk list-maps --organization

GET https://data-api.foursquare.com/v1/maps/for-organization HTTP/1.1

Named Parameters Skip link to Named Parameters

Parameter Type Description
organization boolean If True, list map records for organization of authenticated user.

Response Skip link to Response

List of map records.

JSON

{
  "items": [\
    {\
      "id": "string",\
      "name": "string",\
      "createdAt": "2020-11-03T21:27:14.000Z",\
      "updatedAt": "2020-11-13T01:44:07.000Z",\
      "description": "",\
      "privacy": "private",\
      "permission": "editor"\
    }\
  ]
}

For non-enterprise users, organization=True will cause the request to fail with:

403: Insufficient permission level to perform this action

Example Skip link to Example

PythonCLIHTTP

maps = data_sdk.list_maps()

fsq-data-sdk list-maps

curl -X https://data-api.foursquare.com/v1/maps -H 'Authorization: Bearer <token>'

Data Functions Skip link to Data Functions

This section contains detailed reference documentation covering the Data SDK's dataset functions.

To view each function with a brief description and an example, visit the data functions documentation.

upload_file Skip link to upload_file

Create a dataset from a data upload.

PythonCLIHTTP

# upload a file
data_sdk.upload_file(
  self,
  file: Union[BinaryIO, str, Path],
  name: Optional[str] = None,
  *,
  dataset: Optional[Union[Dataset, str, UUID]] = None,
  media_type: Optional[Union[str, MediaType]] = None,
  description: Optional[str] = None,
  chunk_size: int = 256 * 1024,
  progress: bool = False,
) -> Dataset:
# upload pandas DataFrame or geopandas GeoDataFrame
data_sdk.upload_dataframe(
  self,
  df: Union["pd.DataFrame", "gpd.GeoDataFrame"],
  name: Optional[str] = None,
  index: bool = True,
  **kwargs: Any,
) -> Dataset:

fsq-data-sdk upload-file --name <name> --desc <description> --media-type <media_type> <path>

POST https://data-api.foursquare.com/v1/datasets/data?name={name}&description={description}
HTTP/1.1

Parameters Skip link to Parameters

HTTP API Skip link to HTTP API

Parameter Type Description
name string Name of the dataset to create.
description string Optional. Description of the dataset to create.
Headers Skip link to Headers
Header Description
Content-Type Required. MIME type of data you are uploading, e.g. text/csv or application/json
Body Skip link to Body

The body of the request should be the binary data you want to upload, in a format matching the supplied Content-Type.

Python Skip link to Python

Use upload_file for uploading data files.

upload_file Skip link to [object Object]
Positional Arguments Skip link to Positional Arguments
Argument Type Description
file string of a path, or file object Path or file object to use for uploading data.
name string Optional. Name of the dataset to create.
Keyword Arguments Skip link to Keyword Arguments
Argument Type Description
dataset Dataset, str, UUID Optional. If provided, dataset whose data should be updated. Otherwise, creates a new dataset.
media_type string or MediaType Optional. File type (e.g. MediaType.CSV or text/csv). By default, tries to infer media type from file name.
description string Optional. Description of the dataset to create.
chunk_size int Optional. Number of bytes to upload at a time. Used for progressbar. Default: 256 * 1024
progress bool Optional. When true, display a progress bar.

Example Skip link to Example

PythonCLIHTTP

# upload a file
data_sdk.upload_file(
    file='new_file.csv',
    name='Dataset name',
    media_type=MediaType.CSV,
    description='Dataset description')
# upload pandas or geopandas dataframe
data_sdk.upload_dataframe(
    dataframe,
    name='Dataset name',
    description='Dataset description')

fsq-data-sdk upload-file \
  --name "Dataset name" \
  --desc "Dataset description" \
  --media-type text/csv \
  new_file.csv

curl -X POST https://data-api.foursquare.com/v1/datasets/data?name=My+Dataset \
-H 'Authorization: Bearer <token>' \
-H 'Content-Type: text/csv' \
--data-binary '@/path/to/my_dataset.csv'

upload_dataframe Skip link to upload_dataframe

You can upload pandas/geopandas dataframes directly to the Studio cloud, creating a new dataset.

Argument Type Description
df pandas.DataFrame or geopandas.DataFrame Either a Pandas DataFrame or a GeoPandas GeoDataFrame to upload to Studio.
name string Name of dataset record. Required if creating a new dataset record (instead of updating an existing dataset record).
index boolean Optional. If True, include row names in output. Default: True

Response Skip link to Response

Created dataset record

JSON

{
  "id": "string",
  "name": "string",
  "createdAt": "2021-02-04T00:17:38.652Z",
  "updatedAt": "2021-02-04T00:17:38.652Z",
  "description": "string",
  "isValid": true
}

Example Skip link to Example

Python

data_sdk.upload_dataframe(
    dataframe,
    name='Dataset name',
    description='Dataset description')

create_external_dataset Skip link to create_external_dataset

Create an external dataset record referencing a dataset by URL. External datasets will be loaded from source every time, and will not be stored in our system. If the URL references a cloud storage object, e.g. with the s3:// or gcs:// protocol, and that URL requires authentication, you can include a data connector id referencing a connector with appropriate privileges to read that object.

Note that this feature is in beta and may not work for all datasets.

Method Skip link to Method

PythonCLIHTTP

create_external_dataset(
  self,
  *,
  name: str,
  description: str | None = None,
  source: str,
  connector: DataConnector | UUID | str | None = None,
) -> Dataset:

fsq-data-sdk create-external-dataset \
  --source <Source URL of the dataset> \
  --name <Name of the new dataset> \
  --description <Description of the new dataset> \
  --connector-id <Id of optional Data Connector to use>

POST https://data-api.foursquare.com/v1/datasets/ HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
source string Required. The source URL of the dataset.
name string Required. THe name of the dataset.
description string Description for the dataset record.
connector string ID of an (optional) associated data connector, for cloud storage URLs.

Body Skip link to Body

External dataset parameters encoded as a JSON blob.

--data '{
    "name": "My S3 Dataset",
    "type": "externally-hosted",
    "metadata": {
      "source": "s3://my-bucket/path/to/data.parquet"
    },
  "dataConnectionId": "<SOME_ID>"
}'

Response Skip link to Response

New dataset record.

Examples Skip link to Examples

PythonCLIHTTP

data_sdk.create_external_dataset(
  name = "test-external-dataset",
  description = "my external dataset",
  source = "https://s3data.example.com/data-source",
  connector = "<data-connector-uuid>"
)

fsq-data-sdk create-external-dataset \
  --source "test-external-dataset" \
  --name "my external dataset", \
  --description "https://s3data.example.com/data-source" \
  --connector-id "<data-connector-uuid>"

curl POST 'https://data-api.foursquare.com/catalog/v1/datasets' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <TOKEN>' \
--data
'{
    "name": "My S3 Dataset",
    "type": "externally-hosted",
    "metadata": {
      "source": "s3://my-bucket/path/to/data.parquet"
    },
  "dataConnectionId": "<SOME_ID>"
}'

create_query_dataset Skip link to create_query_dataset

\ enterprise feature. contact us to learn more.](https://location.foursquare.com/contact-us)

Create a dataset from a query.

PythonCLIHTTP

create_query_dataset(
        self,
        connector: DataConnector | UUID | str,
        query: str,
        name: str,
        description: str | None = None,
) -> Dataset:

fsq-data-sdk create-query-dataset --connector-id <connector uuid> --query <SQL query to use> --name <name for the new queried dataset> --description <description of the queried dataset>

POST https://data-api.foursquare.com/v1/datasets/data-query HTTP/1.1

Parameters Skip link to Parameters

Parameter Description
connector Required. The data connector to use, or its UUID.
query Required. The SQL query.
name Name of the dataset record.
description Description for the dataset record.

Response Skip link to Response

The newly created dataset object.

Examples Skip link to Examples

PythonCLIHTTP

create_query_dataset(query_dataset.id, "select * from table;", "query-dataset", "sample-description")

POST https://data-api.foursquare.com/v1/datasets/data-query HTTP/1.1

execute_query Skip link to execute_query

\ enterprise feature. contact us to learn more.](https://location.foursquare.com/contact-us)

Execute a query against a data connector, returning a dataframe with the results of the query, or None of the output was written to a file.

PythonCLIHTTP

 execute_query(
    self,
    connector: DataConnector | UUID | str,
    query: str,
    output_file: str | None = None,
    output_format: QueryOutputType | str | None = None,
) -> pd.DataFrame | None:

fsq-data-sdk execute-query --connector-id <connector uuid> --query <SQL query to use>

POST https://data-api.foursquare.com/v1/query/gateway/data-queries HTTP/1.1

Parameters Skip link to Parameters

Parameter Description
connector Required. The data connector to use, or its UUID.
query Required. The SQL query.
output_file The path to write the query output to.
output_format The format in which to write the output.

Response Skip link to Response

A dataframe containing the results of the query, or None if the output was written to a file.

Example Skip link to Example

PythonCLIHTTP

 df = data_sdk.execute_query(
    example_data_connector.id,
    "select * from table;"
)

fsq-data-sdk execute-query --connector-id <connector uuid> --query <SQL query to use>

POST https://data-api.foursquare.com/v1/query/gateway/data-queries HTTP/1.1

get_dataset_by_id Skip link to get_dataset_by_id

Retrieve a dataset metadata record in JSON format.

PythonCLIHTTP

data_sdk.get_dataset_by_id(dataset: Union[Dataset, str, UUID]) -> None

fsq-data-sdk get-dataset --dataset-id <uuid>

GET https://data-api.foursquare.com/v1/datasets/<uuid> HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
dataset dataset object or string dataset object or UUID of the dataset record to retrieve.

Response Skip link to Response

Dataset record

JSON

Example Skip link to Example

PythonCLIHTTP

# List and select dataset
datasets = data_sdk.list_datasets()
dataset = datasets[0]
# Get dataset record
data_sdk.get_dataset_by_id(dataset)

fsq-data-sdk get-map <uuid>

curl -X GET https://data-api.foursquare.com/v1/datasets/<uuid> \
  -H 'Authorization: Bearer <token>'

download_dataset Skip link to download_dataset

Download data from a dataset record by id.

PythonCLIHTTP

data_sdk.download_dataset(
    dataset: Union[Dataset, str, UUID],
    output_file: Optional[Union[BinaryIO, str, Path]] = None) -> Optional[bytes]
data_sdk.download_dataframe(
    dataset: Union[Dataset, str, UUID]) -> Union[pandas.DataFrame, geopandas.GeoDataFrame]

fsq-data-sdk download-dataset --dataset-id <uuid> --output-file <path>

GET https://data-api.foursquare.com/v1/datasets/<uuid>/data HTTP/1.1

Parameters Skip link to Parameters

download_dataset Skip link to [object Object]

Parameter Type Description
dataset dataset object or string dataset object or UUID of the dataset record to download.
output_file string of a path, or file object If provided, a path or file object to write dataset's data to. Otherwise will return a bytes object with the dataset's data.

download_dataframe Skip link to [object Object]

Parameter Type Description
dataset dataset object or string dataset object or UUID of the dataset record to download.

Response Skip link to Response

download_dataset Skip link to [object Object]

If output_file was provided, returns None and writes data to the provided file. Otherwise, returns a bytes object with the dataset's data.

download_dataframe Skip link to [object Object]

Returns either a pandas DataFrame or a geopandas GeoDataFrame. If the original dataset was a CSV file, a pandas DataFrame will be returned. If it was a GeoJSON file, a geopandas GeoDataFrame will be returned.

Example Skip link to Example

PythonCLIHTTP

datasets = data_sdk.list_datasets()
dataset = datasets[0]
# download to local file
data_sdk.download_dataset(dataset, output_file='output.csv')
# download to buffer
buffer = data_sdk.download_dataset(dataset)
# download to a dataframe
df = data_sdk.download_dataframe(dataset)

fsq-data-sdk download-dataset --dataset-id <uuid> --output-file output.csv

curl -X GET https://data-api.foursquare.com/v1/datasets/<uuid> \
  -H 'Authorization: Bearer <token>'

update_dataset Skip link to update_dataset

Update a dataset record, including the underlying data, and metadata such as name or description.

PythonCLIHTTP

update_dataset(
    dataset_id: Dataset | str | UUID,
    name: str | None = None,
    description: str | None = None,
    file: BinaryIO | str | Path | None = None,
    media_type: str | MediaType | None = None,
    **kwargs: Any,
) -> Dataset:

fsq-data-sdk update-dataset \
    --dataset-id <id> \
    --media-type <media_type> \
     --file <path> \
    --name <name> \
    --description <description>

PUT https://data-api.foursquare.com/v1/datasets/{id}/data HTTP/1.1
PUT https://data-api.foursquare.com/v1/datasets/{id} HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
dataset_id string The UUID of the dataset record to update.
name string A new name for the dataset.
description string A new description for the dataset.
file string of a path, or file object The new data to use for the dataset.
media_type string The media type of the new data

Body Skip link to Body

There are two HTTP endpoints called by the function. For the PUT /datasets/{id}/data endpoint to update the dataset data, the body of the request should be the binary data you want to upload, in a format matching the supplied Content-Type header.

For the PUT datasets/{id} endpoint to update the dataset metadata, the request should be new metadata in JSON format, with the appropriate Content-Type header.

Response Skip link to Response

Updated dataset record

JSON

Example Skip link to Example

PythonCLIHTTP

data_sdk.update_dataset(
    dataset,
    name='New name'
    description='New description'
    file='new_file.csv',
    media_type=MediaType.CSV
)

fsq-data-sdk update-dataset \
    --dataset-id <id> \
    --media-type <media_type> \
     --file <path> \
    --name <name> \
    --description <description>

curl -X PUT https://data-api.foursquare.com/v1/datasets/<uuid>/data HTTP/1.1 \
    -H 'Authorization: Bearer <token>'
    -H 'Content-Type: text/csv' \
--data-binary '@/path/to/my_dataset.csv'

curl --request PUT \
     --url https://data-api.foursquare.com/v1//v1/datasets/<uuid> \
     --header 'accept: application/json' \
     --header 'content-type: application/json' \
     --data '
{
  "name": "New name",
  "description": "New description"
}

delete_dataset Skip link to delete_dataset

Delete a dataset record by id. This will also delete any data associated with the dataset.

❗️

Warning Skip link to Warning

This operation cannot be undone. If you delete a dataset currently used in one or more maps, the dataset will be removed from those maps, possibly causing them to render incorrectly.

PythonCLIHTTP

data_sdk.delete_dataset(dataset: Union[Dataset, str, UUID]) -> None

fsq-data-sdk delete-dataset --dataset-id <uuid>

DELETE https://data-api.foursquare.com/v1/datasets/<uuid> HTTP/1.1

Parameters Skip link to Parameters

Parameter Type Description
dataset string or dataset object The UUID or dataset object of the dataset to delete.

Response Skip link to Response

A message indicating if deletion was successful.

JSON

{
  "message": "string"
}

Example Skip link to Example

PythonCLIHTTP

datasets = data_sdk.list_datasets()
dataset = datasets[0]
data_sdk.delete_dataset(dataset)

fsq-data-sdk delete-dataset --dataset-id <uuid>

curl -X DELETE https://data-api.foursquare.com/v1/datasets/<uuid> \
  -H 'Authorization: Bearer <token>'

generate_vectortile Skip link to generate_vectortile

\ Enterprise feature. Contact us to learn more.](https://location.foursquare.com/contact-us)

Create Vector Tiles by specifying a source GeoJSON (.geojson), CSV .csv or FlatGeobuf (.fgb) file, and optionally, a target dataset.

For GeoJSON and FlatGeoBuf source files geometry can be read automatically, but source latitude/longitude columns must be specified for CSV source files

Method Skip link to Method

PythonCLIHTTP

generate_vectortile(
  self,
  source: Dataset | str | UUID,
  *,
  target: Dataset | str | UUID | None = None,
  source_lat_column: str | None = None,
  source_lng_column: str | None = None,
  attributes: List[str] | None = None,
  exclude_all_attributes: bool | None = None,
  tile_size_kb: int | None = None,

) -> Dataset:

fsq-data-sdk generate-vectortile \
  --source <Source URL of the dataset> \
  --target <Optional target dataset> \
  --source-lat-column lat
  --source-lng-column lng

POST https://data-api.foursquare.com/v1/datasets/vectortile

Parameters Skip link to Parameters

Parameter Type Description
source string Required. The source URL of the dataset.
target string Optional target dataset to overwrite.
source_lat_column string Source lat column (CSV only).
source_lng_column string Source lng column (CSV only).
attributes List[string] Attributes to keep.
exclude_all_attributes bool Whether to exclude all attributes.
tile_size_kb int Maximum tile size (in kilobytes).

Body Skip link to Body

Vector tile parameters encoded as a JSON blob.

{
  "source": "source-dataset-uuid",
  "target": "target-dataset-uuid",
  "sourceLatColumn": "lat",
  "sourceLngColumn": "lng",
  "attributes": ["foo", "bar"],
  "tileSizeKb": 2000
}

Response Skip link to Response

New dataset record.

Examples Skip link to Examples

PythonCLIHTTP

data_sdk.generate_vectortile(
    source="source_dataset_uuid",
    target=None,
    source_lat_column="lat",
    source_lng_column="lng",
    attributes=["foo", "bar"].
    tile_size_kb=2000
)

fsq-data-sdk create-external-dataset \
  --source "source-dataset-uuid" \
  --target "optional-target-uuid" \
  --source-lat-column lat \
  --source-lng-column lng \
  -y foo -y bar \
  --tile-size-kb 2000

curl --request POST \
     --url https://data-api.foursquare.com/v1/datasets/vectortile \
     --header 'accept: application/json' \
     --header 'content-type: application/json' \
     --data '
{
  "source": "source-dataset-uuid",
  "target": "target-dataset-uuid",
  "sourceLatColumn": "lat",
  "sourceLngColumn": "lng",
  "attributes": ["foo", "bar"],
  "tileSizeKb": 2000
}
'

list_data_connectors Skip link to list_data_connectors

Get all data connectors for the authenticated user or organization.

PythonCLIHTTP

list_data_connectors(
        self, *, organization: bool = False
    ) -> List[DataConnector]:

fsq-data-sdk list-data-connectors

GET https://data-api.foursquare.com/v1/data-connections HTTP/1.1

Keyword Arguments Skip link to Keyword Arguments

Parameter Type Description
organization boolean If True, list data connectors for organization of authenticated user.

Response Skip link to Response

Returns a list of data connector objects associated with the user, or if specified, the organization.

Example:

[DataConnector(id="...", name="connector", description="desc", type=DataConnectorType.POSTGRES, ...)]

Example Skip link to Example

Find examples for both individual and organization requests below.

PythonCLIHTTP

# List data connectors associated with user account
data_connectors = data_sdk.list_data_connectors()
# List data connectors associated with organization
data_connectors = data_sdk.list_data_connectors(organization=True)

fsq-data-sdk list-data-connectors
fsq-data-sdk list-data-connectors --organization

GET https://data-api.foursquare.com/v1/data-connections HTTP/1.1
GET https://data-api.foursquare.com/v1/data-connections/for-organization HTTP/1.1

list_datasets Skip link to list_datasets

Get all dataset records for the authenticated user.

PythonCLIHTTP

datasets = data_sdk.list_datasets()

fsq-data-sdk list-datasets

GET https://data-api.foursquare.com/v1/datasets HTTP/1.1

Get all map records for the organization of the authenticated user.

PythonCLIHTTP

datasets = data_sdk.list_datasets(organization=True)

fsq-data-sdk list-datasets --organization

GET https://data-api.foursquare.com/v1/datasets/for-organization HTTP/1.1

Keyword Parameters Skip link to Keyword Parameters

Parameter Type Description
organization boolean If True, list dataset records for organization of authenticated user.

Response Skip link to Response

List of dataset records:

JSON

{
  "items": [\
    {\
      "id": "string",\
      "name": "string",\
      "createdAt": "2021-02-03T23:51:14.527Z",\
      "updatedAt": "2021-02-03T23:51:14.527Z",\
      "description": "string",\
      "isValid": true\
    }\
  ]
}

For non-enterprise users, organization=True will cause the request to fail with:

403: Insufficient permission level to perform this action

Example Skip link to Example

PythonCLIHTTP

datasets = data_sdk.list_datasets()

fsq-data-sdk list-datasets

curl https://data-api.foursquare.com/v1/datasets -H 'Authorization: Bearer <token>'

Hex Tile Functions Skip link to Hex Tile Functions

This section contains detailed reference documentation covering the Data SDK's Hex Tile functions.

To view each function with a brief description and an example, visit the Hex Tile functions documentation.

generate_hextile Skip link to generate_hextile

Renamed from create_hextile

Data can be processed into Hex Tiles using the Studio Data SDK.

PythonCLIHTTP

# hextile a file
  data_sdk.generate_hextile(
      source: Union[Dataset, str, UUID],
      *,
      target: Optional[Union[Dataset, str, UUID]] = None,
      source_hex_column: Optional[str] = None,
      source_time_column: Optional[str] = None,
      time_intervals: Optional[Sequence[Union[TimeInterval, str]]] = None,
      target_res_offset: Optional[int] = None,
      _tile_mode: Optional[Union[TileMode, str]] = None,
      output_columns: Optional[\
          Sequence[Union[dict, HexTileOutputColumnConfig]]\
      ] = None,
      _positional_indexes: Optional[bool] = None,
  ) -> Dataset

fsq-data-sdk generate-hextile
    --source str,
    --target str | None,
    --source-hex-column str | None,
    --source-lat-column str | None,
    --source-lng-column str | None,
    --source-time-column: str | None,
    --time-interval: List[str],
    --output-column List[str],
    --finest-resolution int | None

POST https://data-api.foursquare.com/internal/v1/datasets/hextile HTTP/1.1

Python Skip link to Python

You can access the Studio Data SDK with Python to process your dataset into Hex Tiles.

generate_hextile function Skip link to [object Object], function

Use the generate_hextilefunction to create Hex Tiles from a dataset.

Positional Arguments Skip link to Positional Arguments

Argument Type Description
source string Required. Dataset record or UUID of the dataset to convert to Hex Tile.

Keyword Arguments Skip link to Keyword Arguments

Argument Type Description
target string Dataset record or UUID of an existing Hex Tile dataset to overwrite.
source_hex_column string Name of the hex (h3) column in the source dataset. Hex column must contain hex indexes as string.
source_lat_column string Name of the latitude column in the source dataset.
source_lng_column string Name of the longitude column in the source dataset.
finest_resolution int Finest resolution for the data hexes within a tile (when creating a tileset from lat/lng columns).
source_time_column string Name of the time column in the source dataset, or null if non-temporal data.
time_intervals string array Array of time intervals to generate for temporal datasets. Accepted intervals: ["YEAR"], ["MONTH"], ["DAY"], ["HOUR"], ["MINUTE"], ["SECOND"].
output_columns object array Object array used to aggregate a new data column during Hex Tile generation.
output_columns.source_column string Column name in the source dataset.
output_columns.target_column string Column name in the target hex tile dataset.
output_columns.agg_method string Method to aggregate the data column with when generating coarser tile resolutions. Accepted methods: "sum", "count", "min", "max", "mean", "median", "mode". Defaults to "sum" for numeric columns.
output_columns.dtype string Data type to encode the column in the Hex Tile dataset. Example values include "float32", "uint8", "int64".
target_res_offset int Optional integer controlling the depth of the tile hierarchy.
tile_mode string Experimental. "dense", "sparse", or "auto". Defaults to "auto".
positional_indexes boolean Experimental. Enables the positional indexes encoding feature.

HTTP API Skip link to HTTP API

You can access the Studio Data API through the HTTP REST API to process your dataset into Hex Tiles.

Headers Skip link to Headers

Header Description
Content-Type Must be application/json. This header is required.

Body Skip link to Body

The body of the request should be the parameters encoded as a JSON blob.

Parameter Type Description
source string Required. Dataset record or UUID of the dataset to convert to Hex Tile.
target string Dataset record or UUID of an existing Hex Tile dataset to overwrite.
sourceHexColumn string Name of the hex (h3) column in the source dataset. Hex column must contain hex indexes as string.
sourceLatColumn string Name of the latitude column in the source dataset.
sourceLngColumn string Name of the longitude column in the source dataset.
finestResolution int Finest resolution for the data hexes within a tile (when creating a tileset from lat/lng columns)
sourceTimeColumn string Name of the time column in the source dataset, or null if non-temporal data.
timeIntervals string array Array of time intervals to generate for temporal datasets. Accepted intervals: ["YEAR"], ["MONTH"], ["DAY"], ["HOUR"], ["MINUTE"], ["SECOND"].
outputColumns object array Object array used to aggregate a new data column during Hex Tile generation.
outputColumns.sourceColumn string Column name in the source dataset.
outputColumns.targetColumn string Column name in the target hex tile dataset.
outputColumns.aggMethod string Method to aggregate the data column with when generating coarser tile resolutions. Accepted methods: "sum", "count", "min", "max", "mean", "median", "mode". Defaults to "sum" for numeric columns.
outputColumns.dtype string Data type to encode the column in the Hex Tile dataset. Example values include "float32", "uint8", "int64".
targetResOffset int Optional integer controlling the depth of the tile hierarchy.
tileMode string Experimental. "dense", "sparse", or "auto". Defaults to "auto".
positionalIndexes boolean Experimental. Enables the positional indexes encoding feature.

Response Skip link to Response

Upon completion, you will receive a response containing the metadata of your dataset.

JSON

Dataset Location Skip link to Dataset Location

Once processed, your dataset will be stored on your Studio Cloud account. You may download it using the download dataset function in the Data SDK.

Check Hex Tiling Status via API Skip link to Check Hex Tiling Status via API

You can find the status of the Hex Tile dataset in the API.

Retrieve the dataset's metadata, then find one of three codes in the dataStatus field:

Status code Description
pending The tiling process is still running.
ready The tiling process is complete and the Hex Tiles can be used.
error The tiling process has failed.

Examples Skip link to Examples

PythonCLIHTTP

# Import the HexTileOutputColumn Model from Studio Data SDK
data_sdk.generate_hextile(
    source="0b341204-1a76-4c1e-82a1-a856f28c522e",
    source_time_column = "time",
    source_lat_column = "lat",
    source_lng_column = "lon",
    finest_resolution = 9,
    time_intervals= ["HOUR"],
    output_columns= [\
      {\
        "source_column": "precip_kg/m2",\
        "target_column": "precip_sum",\
        "agg_method": "sum"\
      }\
    ]
)

fsq-data-sdk generate-hextile
  --source e32f527e-0917-40aa-955f-8d55105f9673
  --source-lat-column Latitude
  --source-lng-column Longitude
  --output-column '{"sourceColumn":"Magnitude", "targetColumn":"Avg Magnitude", "aggMethod":"mean"}'
  --output-column '{"sourceColumn":"Depth", "targetColumn":"Max Depth", "aggMethod":"max"}'
  --finest-resolution 5

curl -X POST https://data-api.foursquare.com/internal/v1/datasets/hextile \
-H 'Authorization: Bearer <token>' \
-H 'Content-Type: application/json' \
--data-raw '{
    "source": "<source_dataset_id>",
    "sourceHexColumn": "hex",
    "sourceTimeColumn": "datestr",
    "timeIntervals": ["DAY"],
    "targetResOffset": 4,
    "outputColumns": [\
        {\
        "sourceColumn": "metric",\
        "targetColumn": "metric_sum",\
        "aggMethod": "sum",\
        "dtype": "uint16"\
        }\
    ]
}

enrich Skip link to enrich

Datasets can be enriched with Hex Tiles using the Studio Data SDK.

PythonHTTP

# enrich a dataframe or existing dataset
data_sdk.enrich(
    dataset: Union[pd.DataFrame, Dataset, UUID, str],
    source_id: UUID,
    source_column: str,
    *,
    h3_column: Optional[str] = None,
    lat_column: Optional[str] = None,
    lng_column: Optional[str] = None,
    time_column: Optional[str] = None,
) -> pd.DataFrame

POST https://data-api.foursquare.com/internal/v1/query HTTP/1.1

HTTP API Skip link to HTTP API

Enrichment is provided through the Query API, which can support a range of flexible queries. The following parameters describe a simple enrichment query.

Headers Skip link to Headers

Header Description
Content-Type Must be application/json. This header is required.
Accept May be _/_, application/json, text/csv, or application/vnd.apache.arrow.file. The response dataset will have the corresponding data format (by default, text/csv).

Body Skip link to Body

The body of the request should be the parameters, encoded as a JSON blob.

Parameters Skip link to Parameters
Parameter Type Required Description
type string Yes Use enrich to select the enrich process.
sourceId string Yes The UUID of the Hex Tile dataset for enrichment.
sourceColumn string or
string array		 Yes The label of the Hex Tile column for enrichment, or an array of labels for multiple columns.
targetType string Yes Must be either "H3" or "LATLNG".
column string Yes for type H3 Column in target dataset containing H3 addresses.
latColumn string Yes for type LATLNG Column in target dataset containing latitude values.
lngColumn string Yes for type LATLNG Column in target dataset containing longitude values.
timeColumn string Yes for
temporal datasets.		 Column in target dataset containing time values in epoch timestamp or ISO-8601 format.
timeInterval string No Time interval to use for enrichment. The target time interval must be available in the Hex Tile dataset.
Accepted methods: YEAR, MONTH, DAY, and HOUR. Defaults to the finest available interval.
input string array Yes Array containing a single object describing the target dataset, in the form {"type": "dataset", "uuid": <uuid>}

Python Skip link to Python

enrich function Skip link to [object Object], function

Positional Arguments Skip link to Positional Arguments
Argument Type Required Description
dataset string Yes Pandas DataFrame, Dataset record, or UUID of the dataset to hextile.
source_id string Yes UUID of the Hex Tile dataset to use for enrichment.
source_column string Yes Label of the Hex Tile column to use for enrichment.
Keyword Arguments Skip link to Keyword Arguments
Argument Type Required Description
h3_column string Yes for H3 data. Column in target dataset with H3 addresses.
lat_column string Yes for lat/lng data. Column in target dataset with latitude values.
lng_column string Yes for lat/lng data. Column in target dataset with longitude values.
time_column string Yes for temporal data. Column in target dataset with time values in epoch timestamp or ISO-8601 format.

Response Skip link to Response

Upon completion, you will receive the enriched dataset in CSV, JSON, or Arrow format depending on the Accept header.

Examples Skip link to Examples

PythonHTTP

data_sdk.enrich(
    dataset="my-dataset-uuid",
    source_id="my-hex-tile-uuid",
    source_column="some_value",
    lat_column="lat",
    lng_column="lng",
    time_column="date",
)

curl -X POST https://data-api.foursquare.com/internal/v1/datasets/hextile \
-H 'Authorization: Bearer <token>' \
-H 'Content-Type: application/json' \
-H 'Accept: text/csv' \
--data-raw '{
    "type": "enrich",
    "input": [\
        {\
            "type": "dataset",\
            "uuid": "my-target-uuid"\
        }\
    ],
    "sourceId": "my-hex-tile-uuid",
    "sourceColumn": "some_value",
    "timeColumn": "date",
    "targetType": "LATLNG",
    "latColumn": "lat",
    "lngColumn": "lng"
}'

extract Skip link to extract

You may specify an area of Hex Tiles (represented by a GeoJSON geometry) to extract. Returns a geopandas H3 dataframe.

PythonHTTP

# extract hex tiles
  data_sdk.extract(
   self,
    source_id: Union[str, UUID],
    geojson: Dict,
    *,
    source_column: Optional[Union[str, List[str]]] = None,
    res: Optional[int] = None,
    h3_column: Optional[str] = None,
    time_column: Optional[str] = None,
    time_interval: Optional[Union[Dict, TimeInterval]] = None,
) -> pd.DataFrame:

POST https://data-api.foursquare.com/internal/v1/query HTTP/1.1
    Note: Extracting Hextiles makes use of Studio's Query API.
    Please contact us if you wish to use the Query API to extract Hex Tiles.

Python Skip link to Python

Use the tile_extractfunction to extract a region of Hex Tiles from a Hex Tile dataset.

Positional Arguments Skip link to Positional Arguments

Argument Type Description
source_id string Required. Dataset UUID of the dataset to convert to Hex Tile.
geojson dict Required. A geojson geometry of the area to extract.

Keyword Arguments Skip link to Keyword Arguments

Argument Type Description
source_column string Column in Hex Tile dataset.
res int H3 resolution of data to extract.
h3_column string Name of the output column containing H3 indexes. Default: h3_<res>
time_column string Name of the output column containing time indexes. Default: date
time_interval string array Time interval to extract. Accepted intervals: "YEAR", "MONTH", "DAY", "HOUR", "MINUTE", "SECOND".

Response Skip link to Response

Upon completion, you will receive a response containing a Pandas dataframe with the extracted dataset.

Python

# Extract Hex Tiles within a specified GeoJSON geometry
extracted_dataset = data_sdk.extract(
    source_id="<UUID>",
    geojson="
    {
      "type": "Feature",
      "geometry": {
        "type": "Polygon",
        "coordinates": [\
          [\
            [0, 0],\
            [0, 10],\
            [10, 0],\
            [0, 0]\
          ]\
        ]
      }
    };,
    source_column="hextile_column",
    res=8,
    h3_column = "h3_8",
    time_column = "year",
    time_interval = "YEAR"
)

Updated 5 months ago

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