GoodData Pandas Documentation

GoodData Pandas contains a thin layer that utilizes GoodData Python SDK and allows you to conveniently create pandas series and data frames from the computations done against semantic model in your GoodData.CN workspace.

Installation

Requirements

Python 3.7 or newer
GoodData.CN installation; either running on your cloud infrastructure or the free Community Edition running on your workstation

Installation

Run the following command to install the gooddata-pandas package on your system:

pip install gooddata-pandas

Examples

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

Series

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")

Data Frames

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'
    )
)

# 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'
    )
)

API

`gooddata_pandas`
`gooddata_sdk`	The gooddata-sdk package aims to provide clean and convenient Python APIs to interact with GoodData.CN.