semvec/pypi-packages huggingface dataset:
treeschema 1.0.8
A python client that simplifies interactions with the Tree Schema REST API.
Keywords: data lineage,metadata

===========================
 Tree Schema Python Client
===========================

:Version: 1.0.8
:Documentation: https://developer.treeschema.com/python-client
:Download: https://pypi.org/project/treeschema
:Source: https://github.com/tree-schema/tree-schema-python
:Keywords: data lineage, metadata


Your ETL/ELT processes are diverse, your pipelines are serverless - the Tree Schema Python client aims to provide you the flexibility to integrate into any ecosystem!

What You Can Do
---------------

The **Tree Schema** library is a simple interface that enables data creators and data users to manage their Tree Schema `Data Catalog <https://treeschema.com>`_ as code. The Tree Schema library enables you to:

- Manage `data lineage <https://treeschema.com/blog/data-lineage-api/>`_ as code
- Capture the values of your fields and their definitions
- Explore your catalog in the same notebook as your data analysis

Here are a few quick snippets:

Manage Your Data Lineage as Code
--------------------------------
.. sourcecode:: python

   # Import Tree Schema
   from treeschema import TreeSchema
   ts = TreeSchema('<your email>', '<your secret key>')

   # Define the source and target fields 
   src_field = ts.data_store('Kafka Cluster').schema('dvc.user:v1').field('user_id')
   tgt_field = ts.data_store('Kafka Cluster ').schema('usr.clickstream:v1').field('user_id')
   link = (src_field, tgt_field)

   # Select your transformation
   t = ts.transformation('Kafka Pub-Sub')

   # Create the data lineage
   t.create_links(link)

The complexity of your transformation is only limited by your own imagination. Data lineage can be explored within the Tree Schema GUI
once created, a slightly more complex example may look like:

.. image:: ./docs/imgs/links_example.png
    :width: 500
 

Create Definitions for Field Values
-----------------------------------
.. sourcecode:: python

   # Import Tree Schema
   from treeschema import TreeSchema
   ts = TreeSchema('<your email>', '<your secret key>')

   # Select the field to add sample values to
   src_field = ts.data_store('Kafka Cluster').schema('dvc.user:v1').field('user_status')
   
   # Define the value and it's definition 
   sample_value = {
      'field_value': '02',
      'description': 'User has converted from lead to customer'
   }
   field.field_value(sample_value)

How to Get Started
------------------
Make sure you sign up for Tree Schema - it's completely free to sign up with no card required - and follow the instructions for `how to get your secret key <https://help.treeschema.com/api/api_access.html>`_ .

Check out all of the `Examples <https://developer.treeschema.com/python-client/examples/examples.html>`_ in the documentation to see all of the library features and to explore more in-depth and complex use-cases.

Have Questions or Need Help?
----------------------------
Shoot an email to **developer@treeschema.com** to get help integrating Tree Schema.
brac 0.0.3
Business rules as code
Requires Python: >=3.6

# What is this?

This is a humble attempt to change the world by getting rid of the boring stuff that is both required and unrelated to the awesome idea you are trying to build by following the good enough principle. This project aims to save 90% of the time you would expend on 90% of the things you need to do to launch your startup.

What you get out of the box:
auth, database, api, admin dashboard, pages, event handling.

Extend:
If your idea needs more, extend this.

# Core + Faces + Events

## Core

data, config

## Faces

- api
- admin
- pages

## Events

stuff done in core, generates events

## Search

- ?
py-countreg 0.0.4
Regression Models for Count Outcomes

#### Introduction

The package py\_countreg is a collection of functions to estimate various regression models for count outcomes.

It is an ongoing project. More functionalities will come later.

#### Core Functions

```
Count Outcome Regressions
  |
  |-- Equi-Dispersion (Baseline)
  |     |
  |     `-- stdpoisson() : Standard Poisson 
  |
  |-- Over-Dispersion
  |     |
  |     `-- negbinom2()  : Negative Binomial (NB-2)
  |
  |-- Over- and Under-Dispersions
  |     |
  |     |-- genpoisson() : Generalized Poisson
  |     |
  |     `-- compoisson() : Conway-Maxwell Poisson 
  |
  |-- Zero-Inflation
  |     |
  |     |-- hdlpoisson() : Hurdle Poisson 
  |     |
  |     |-- hdgpoisson() : Hurdle Generalized Poisson 
  |     |
  |     |-- hdlnegbin2() : Hurdle Negative Binomial (NB-2)
  |     |
  |     |-- zifpoisson() : Zero-Inflated Poisson 
  |     |
  |     |-- zigpoisson() : Zero-Inflated Generalized Poisson 
  |     |
  |     `-- zifnegbin2() : Zero-Inflated Negative Binomial (NB-2)
  |
  `-- Zero-Truncation
        |
        |-- ztrpoisson() : Zero-Truncated Poisson
        |
        |-- ztgpoisson() : Zero-Truncated Generalized Poisson
        |
        |-- ztcpoisson() : Zero-Truncated Conway-Maxwell Poisson
        |
        `-- ztrnegbin2() : Zero-Truncated Negative Binomial (NB-2)
```

#### Reference

WenSui Liu and Jimmy Cela (2008), Count Data Models in SAS, Proceedings SAS Global Forum 2008, paper 371-2008.