Pandas Overview

pip install gooddata-pandas

from gooddata_pandas import GoodPandas

# GoodData.CN host in the form of uri eg. "http://localhost:3000"
host = "http://localhost:3000"
# GoodData.CN user token
token = "some_user_token"
# initialize the adapter to work on top of GD.CN host and use the provided authentication token
gp = GoodPandas(host, token)

workspace_id = "demo"
series = gp.series(workspace_id)

# create indexed series
indexed_series = series.indexed(index_by="label/label_id", data_by="fact/measure_id")

# create non-indexed series containing just the values of measure sliced by elements of the label
non_indexed = series.not_indexed(data_by="fact/measure_id", granularity="label/label_id")

from gooddata_pandas import GoodPandas

# GoodData.CN host in the form of uri eg. "http://localhost:3000"
host = "http://localhost:3000"
# GoodData.CN user token
token = "some_user_token"
# initialize the adapter to work on top of GD.CN host and use the provided authentication token
gp = GoodPandas(host, token)

workspace_id = "demo"
frames = gp.data_frames(workspace_id)

# create indexed data frame
indexed_df = frames.indexed(
    index_by="label/label_id",
    columns=dict(
        first_label='label/first_label_id',
        second_label='label/second_label_id',
        first_metric='metric/first_metric_id',
        second_metric='fact/fact_id'
    )
)

# create data frame with hierarchical index
indexed_df = frames.indexed(
    index_by=dict(first_label='label/first_label_id', second_label='label/second_label_id'),
    columns=dict(first_metric='metric/first_metric_id', second_metric='fact/fact_id')
)

# create non-indexed data frame
non_indexed_df = frames.not_indexed(
    columns=dict(
        first_label='label/first_label_id',
        second_label='label/second_label_id',
        first_metric='metric/first_metric_id',
        second_metric='fact/fact_id'
    )
)

# create data frame based on the contents of the visualization. if the visualization contains labels and
#  measures, the data frame will contain index or hierarchical index.
df = frames.for_visualization('visualization_id')

# create data frame based on the content of the items dict. if the dict contains both labels
# and measures, the frame will contain index or hierarchical index.
df = frames.for_items(
    items=dict(
        first_label='label/first_label_id',
        second_label='label/second_label_id',
        first_metric='metric/first_metric_id',
        second_metric='fact/fact_id'
    )
)

# create data frame from custom execution definition
exec_def = ExecutionDefinition(
    attributes=[
        Attribute(local_id="region", label="region"),
        Attribute(local_id="state", label="state"),
        Attribute(local_id="product_category", label="products.category"),
    ],
    metrics=[
        SimpleMetric(local_id="price", item=ObjId(id="price", type="fact")),
        SimpleMetric(local_id="order_amount", item=ObjId(id="order_amount", type="metric")),
    ],
    filters=[],
    dimensions=[
        TableDimension(item_ids=["state", "region"]),
        TableDimension(item_ids=["product_category", "measureGroup"]),
    ],
)
df, df_metadata = frames.for_exec_def(exec_def=exec_def)

# use result ID from computation above and generate dataframe just from it
df_from_result_id, df_metadata_from_result_id = frames.for_exec_result_id(
    result_id=df_metadata.execution_response.result_id,
)