semvec/pypi-packages huggingface dataset:
gooddata-sdk 1.60.0
GoodData Cloud Python SDK
Requires Python: <4.0,>=3.10
Keywords: analytics, api, business, cloud, gooddata, headless, headless-bi, intelligence, layer, metrics, native, sdk, semantic, sql

# GoodData Python SDK

The `gooddata-sdk` package provides a clean and convenient Python API to interact with [GoodData](https://www.gooddata.com/).

At the moment the SDK provides services to inspect and interact with the Semantic Model and consume analytics:
* Catalog Workspaces Service
* Catalog Workspace Content Service
* Catalog Data Source Service
* Catalog User Service
* Catalog Permission Service
* Catalog Organization Service
* Visualizations Service
* Compute Service
* Table Service

See [DOCUMENTATION](https://www.gooddata.com/docs/python-sdk/1.60.0) for more details.

## Requirements

-  GoodData Cloud or GoodData.CN installation
-  Python 3.10 or newer

## Installation

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

    pip install gooddata-sdk

## Example

Compute an visualization:
```python
import gooddata_sdk

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

workspace_id = "demo"
visualization_id = "customers_trend"
# reads visualization from workspace
visualization = sdk.visualizations.get_visualization(workspace_id, visualization_id)
# triggers computation for the visualization. the result will be returned in a tabular form
table = sdk.tables.for_visualization(workspace_id, visualization)

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


## Bugs & Requests

Please use the [GitHub issue tracker](https://github.com/gooddata/gooddata-python-sdk/issues) to submit bugs
or request features.

## Changelog

See  [Github releases](https://github.com/gooddata/gooddata-python-sdk/releases) for released versions
and a list of changes.
gooddata-flexconnect 1.60.0
Build your own data source for GoodData Cloud and GoodData Cloud Native.
Requires Python: >=3.10
Keywords: analytics, business, cloud, custom functions, flight, flight rpc, gooddata, headless, headless-bi, intelligence, layer, metrics, native, rpc, semantic, sql

# GoodData FlexConnect

GoodData FlexConnect allows you to build your own data source for GoodData Cloud or Cloud Native.

FlexConnect works with a concept similar to 'table functions' that you may already know
from database technologies.

-  To build your own data source, you implement one or more FlexConnect functions. The
   functions compute and return tabular data - how they do it is completely up to you.
-  The functions are hosted and invoked inside a FlexConnect server (which is included in this package).
-  A running FlexConnect server can be added as a data source to your GoodData Cloud or GoodData Cloud Native.
-  The functions available on FlexConnect server will be mapped to data sets within GoodData's Semantic Model
   and from then on can be used during report computation.


## Getting Started using the FlexConnect

The easiest and recommended way to get started with FlexConnect is to use [the template repository](https://github.com/gooddata/gooddata-flexconnect-template).

The template repository is set up with project infrastructure and boilerplate related to testing, packaging and
running your FlexConnect functions. You can start building your own data source in under a minute.

The template also comes with extensive documentation which will guide you through all important steps and facets
of building production-ready FlexConnect functions.

If you are eager to get started, here is a short snippet to bootstrap a new FlexConnect project:

```shell
git clone https://github.com/gooddata/gooddata-flexconnect-template.git my-flexconnect
cd my-flexconnect
rm -rf .git && git init && git add . && git commit -m "Initial commit"
```
gooddata-fdw 1.60.0
GoodData Cloud Foreign Data Wrapper For PostgreSQL
Requires Python: >=3.10
Keywords: analytics, business, cloud, fdw, gooddata, headless, headless-bi, intelligence, layer, metrics, native, postgresql, semantic, sql

# GoodData Foreign Data Wrapper

This project delivers PostgreSQL foreign data wrapper extension built on top of [multicorn](https://multicorn.org/).
The extension makes [GoodData](https://www.gooddata.com/docs/cloud/) insights, computations and ad-hoc report data available in PostgreSQL as tables.
It can be selected like any other table using SQL language.

See [DOCUMENTATION](https://gooddata-fdw.readthedocs.io/en/latest/) for more details.

## Requirements

-  [GoodData](https://www.gooddata.com/docs/cloud/) installation; either running on your cloud
   infrastructure or the free Community Edition running on your workstation

-  Python 3.10 or newer

-  The GoodData Cloud Foreign Data Wrapper is tested with the latest version of multicorn (1.4.0) and PostgreSQL 12

## Installation

Refer to the [documentation](https://gooddata-fdw.readthedocs.io/en/latest/).

## Bugs & Requests

Please use the [GitHub issue tracker](https://github.com/gooddata/gooddata-python-sdk/issues) to submit bugs
or request features.

## Changelog

Consult [Github releases](https://github.com/gooddata/gooddata-python-sdk/releases) for a released versions
and list of changes.

## Limitations

FDW is suitable for small to medium insights results or scheduled reports created in the third party tools. Carefully consider use in a production environment.
gooddata-pandas 1.60.0
GoodData Cloud to pandas
Requires Python: >=3.10
Keywords: analytics, business, cloud, data, data_frame, frame, gooddata, headless, headless-bi, intelligence, layer, metrics, native, pandas, semantic, series, sql

# 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](https://www.gooddata.com/developers/cloud-native/) workspace.

See [DOCUMENTATION](https://gooddata-pandas.readthedocs.io/en/latest/) 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:

```python
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](https://github.com/gooddata/gooddata-python-sdk/issues) to submit bugs
or request features.

## Changelog

Consult [Github releases](https://github.com/gooddata/gooddata-python-sdk/releases) for a released versions
and list of changes.