gooddata-pandas

GoodData to pandas adapters

This package contains a thin layer that utilizes gooddata-sdk and allows you to conveniently create pandas series and data frames from the computations done against semantic model in your GoodData.CN workspace.

Examples

Here are a couple of introductory examples how to create indexed and not-indexed series and data frames.

The examples show the simplest possible way to specify labels, facts or metrics to use in your series or data frames. The factory functions allow for greater flexibility on the inputs - you can also use the objects that are normally input to the compute model - Attribute, different types of Measures and also specify filters to apply during the computation on the server.

Series

from gooddata_pandas import GoodPandas

# initialize the adapter to work on top of GD.CN host and use the provided authentication token
gp = GoodPandas(host=HOST, token=TOKEN)

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")

Data Frames

from gooddata_pandas import GoodPandas

# initialize the adapter to work on top of GD.CN host and use the provided authentication token
gp = GoodPandas(host=HOST, token=TOKEN)

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
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'
))

# creates data frame based on the contents of the insight. if the insight contains labels and measures, the data
# frame will contain index or hierarchical index.
insight_df = frames.for_insight('insight_id')

# creates 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'
))