Table Service

The gooddata_sdk.table service allows you to consume analytics in typical tabular format. The service allows free-form computations and computations of data for GoodData.CN Insights.

Entity methods

The gooddata_sdk.table supports the following entity API calls:

• for_insight(workspace_id: str, insight: Insight)

  Returns ExecutionTable.

  Retrieve data as an ExecutionTable from the given insight.

• for_items(workspace_id: str, items: list[Union[Attribute, Metric]], filters: Optional[list[Filter]] = None)

  Returns ExecutionTable.

  Retrieve data as an ExecutionTable from the given list of attributes/metrics, and filters.

Example usage:

Get tabular data for an insight defined on your GoodData.CN server:

from gooddata_sdk import GoodDataSdk

# GoodData.CN host in the form of uri eg. "http://localhost:3000"
host = "http://localhost:3000"
# GoodData.CN user token
token = "some_user_token"
sdk = GoodDataSdk.create(host, token)

workspace_id = "demo"
insight_id = "some_insight_id_in_demo_workspace"

# Reads insight from workspace
insight = sdk.insights.get_insight(workspace_id, insight_id)

# Triggers computation for the insight. the result will be returned in a tabular form
table = sdk.tables.for_insight(workspace_id, insight)

# This is how you can read data row-by-row and do something with it
for row in table.read_all():
    print(row)

# An example of data printed for insight top_10_products
# {'781952e728204dcf923142910cc22ae2': 'Biolid', 'fe513cef1c6244a5ac21c5f49c56b108': 'Outdoor', '77dc71bbac92412bac5f94284a5919df': 34697.71}
# {'781952e728204dcf923142910cc22ae2': 'ChalkTalk', 'fe513cef1c6244a5ac21c5f49c56b108': 'Home', '77dc71bbac92412bac5f94284a5919df': 17657.35}
# {'781952e728204dcf923142910cc22ae2': 'Elentrix', 'fe513cef1c6244a5ac21c5f49c56b108': 'Outdoor', '77dc71bbac92412bac5f94284a5919df': 27662.09}
# {'781952e728204dcf923142910cc22ae2': 'Integres', 'fe513cef1c6244a5ac21c5f49c56b108': 'Outdoor', '77dc71bbac92412bac5f94284a5919df': 47766.74}
# {'781952e728204dcf923142910cc22ae2': 'Magnemo', 'fe513cef1c6244a5ac21c5f49c56b108': 'Electronics', '77dc71bbac92412bac5f94284a5919df': 44026.52}
# {'781952e728204dcf923142910cc22ae2': 'Neptide', 'fe513cef1c6244a5ac21c5f49c56b108': 'Outdoor', '77dc71bbac92412bac5f94284a5919df': 99440.44}
# {'781952e728204dcf923142910cc22ae2': 'Optique', 'fe513cef1c6244a5ac21c5f49c56b108': 'Home', '77dc71bbac92412bac5f94284a5919df': 40307.76}
# {'781952e728204dcf923142910cc22ae2': 'PortaCode', 'fe513cef1c6244a5ac21c5f49c56b108': 'Electronics', '77dc71bbac92412bac5f94284a5919df': 18841.17}
# {'781952e728204dcf923142910cc22ae2': 'Slacks', 'fe513cef1c6244a5ac21c5f49c56b108': 'Clothing', '77dc71bbac92412bac5f94284a5919df': 18469.15}
# {'781952e728204dcf923142910cc22ae2': 'T-Shirt', 'fe513cef1c6244a5ac21c5f49c56b108': 'Clothing', '77dc71bbac92412bac5f94284a5919df': 17937.49}