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PyGWalker Cloud APIs

Use computation="cloud" when a live PyGWalker session should run data queries through Kanaries cloud. Use the cloud helper APIs when you need to create or open Kanaries cloud assets directly.

import pygwalker as pyg
 
walker = pyg.walk(
    df,
    spec_path="./gw_config.json",
    computation="cloud",
    kanaries_api_key="...",
)

create_cloud_dataset

create_cloud_dataset uploads a dataset and returns the cloud dataset ID.

from pygwalker.api.kanaries_cloud import create_cloud_dataset
 
dataset_id = create_cloud_dataset(
    df,
    name="sales_dataset",
    is_public=False,
    kanaries_api_key="...",
)

Signature:

create_cloud_dataset(
    dataset,
    *,
    name=None,
    is_public=False,
    kanaries_api_key="",
) -> str

dataset can be a pandas DataFrame, polars DataFrame, pyarrow Table, or database Connector.

Legacy cloud walker helpers

These helpers exist for compatibility with older cloud workflows.

from pygwalker.api.kanaries_cloud import create_cloud_walker, walk_on_cloud
 
create_cloud_walker(
    df,
    chart_name="Revenue Dashboard",
    workspace_name="Analytics",
    kanaries_api_key="...",
)
 
walk_on_cloud(
    workspace_name="Analytics",
    chart_name="Revenue Dashboard",
    kanaries_api_key="...",
)

Signatures:

create_cloud_walker(
    dataset,
    *,
    chart_name,
    workspace_name,
    field_specs=None,
    kanaries_api_key="",
) -> str
 
walk_on_cloud(workspace_name, chart_name, kanaries_api_key="")

Prefer computation="cloud" in the regular PyGWalker APIs when the goal is cloud-backed computation in a live notebook or app.

Cloud computation vs legacy flags

Use:

pyg.walk(df, computation="cloud", kanaries_api_key="...")

Do not start new code with:

pyg.walk(df, cloud_computation=True)

cloud_computation is a legacy compatibility flag and is scheduled for removal in PyGWalker 0.7.0. It also conflicts with non-auto computation values when enabled.

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