This release is a pre-release and may not be stable for production use.
GoodData Pandas
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.
See DOCUMENTATION for more details.
Requirements
-
GoodData.CN installation; either running on your cloud infrastructure or the free Community Edition running on your workstation
-
Python 3.10 or newer
Installation
Run the following command to install the gooddata-pandas package on your system:
pip install gooddata-pandas
Example
Create an indexed and a not-indexed 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")
Bugs & Requests
Please use the GitHub issue tracker to submit bugs or request features.
Changelog
Consult Github releases for a released versions and list of changes.
Metadata
Release files for gooddata-pandas 1.74.1.dev3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gooddata_pandas-1.74.1.dev3.tar.gz | 575.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gooddata_pandas-1.74.1.dev3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 643.5 kB
Release files / gooddata_pandas-1.74.1.dev3.tar.gz
| Download URL | gooddata_pandas-1.74.1.dev3.tar.gz |
|---|---|
| Size | 575.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
93ecce4ffa26e3b1bc368695022d3c66c84de864f2249cbccc09084f375a5e49
|
|
BLAKE2b-256 checksum How to use checksums |
824603b8f803a57a1ced8be34e3a6c1a6fc3cb49e0d92b9d6592f39d69b17ffe
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.
Transparency logRelease files / gooddata_pandas-1.74.1.dev3-py3-none-any.whl
| Download URL | gooddata_pandas-1.74.1.dev3-py3-none-any.whl |
|---|---|
| Size | 67.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
86d4a4d282f3af100c1adc3947d740d3e36e12fef129bbcbe3bd051bc2bd351c
|
|
BLAKE2b-256 checksum How to use checksums |
73690e86fce3426eb748c2e0d242ebd4aefd040ea6bfa92cd0b1c7cf70f4c1fc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.
Transparency log