semvec/pypi-packages huggingface dataset:
data-drift-detection 1.0.0
Data Drift Detection - MLOps project, Reichman University 2022
Requires Python: >=3.8.12

Implementation of a Machine Learning Operations pipeline, which consists of: model training, data drift detection & data generation to generate data drift.
Databases being used are: 
- https://archive.ics.uci.edu/ml/datasets/bank+marketing
- https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)
django-model-to-excel-csv 0.1.2
Simple Excel and CSV file Generator
Requires Python: >=3.6

Simple Excel and CSV File Generator From Django Model.

Required Packages:

    pip install pandas

Usage :

Django Models to Excel File:

    from django_model_to_excel_csv import generate_excel_file

    def func(request):
        queryset = User.objects.all().values()
        # queryset will be your model data
        # file_path : 'your absloute path' or it will store in your project Base Directory
        # file_name accepts string : 'string' 
        # sheet_name accepts string : 'string'
        # index and header : Boolean
        generate_excel = generate_excel_file.all_to_excel(queryset, file_path, file_name, sheet_name, index, header)


Django Models to CSV File:

    from django_model_to_excel_csv import generate_csv_file

    def func(request):
        queryset = User.objects.all().values()
        # queryset will be your model data
        # file_path : 'your absloute path' or it will store in your project Base Directory
        # file_name accepts string : 'string' 
        # sheet_name accepts string : 'string'
        # index and header : Boolean
        generate_excel = generate_excel_file.all_to_excel(queryset, file_path, file_name, sheet_name, index, header)
csv-to-db-converter-saatwikmehta 0.1.0
This package is useful for someone who wants to make changes inside his CSV files.It creates a local server that provides the functionality of file upload to dbReading the data inside the db table created using the name of the file and applying CRUD operations as well.
Requires Python: >=3.7

# CSV_TO_DB_CONVERTER
It takes a CSV file as input and converts 
and save the data into a default set DB (fileupload).
The table name will consist of the name of the file user is uploading.
After a successful upload user can perform CRUD operations on the file data using a localhost server.

## Installation
pip install csvtodb-converter-saatwikmehta


## License 
© 2022 Saatwik Mehta

This repository is licensed under the MIT license. See LICENSE for details.
friendly-data 0.3.3
A frictionless data package implementation for energy system data.

Frictionless Energy data - Friendly data
========================================
|unittests| |coverage| |docs|

A frictionless_ data package implementation for energy data;
maintained by the `SENTINEL collaboration`_.

This package provides a Python API and CLI utilities to read/write and
manage Energy (systems) data as a frictionless data package.  The
Python API includes easy conversion to and from standard Python data
structures common in data analysis; e.g. ``pandas.DataFrame``,
``xarray.DataArray``, ``xarray.Dataset``, etc.

It introduces energy modelling terminology by relying on an external
(but loosely coupled) registry_ that collates commonly used variables
and associated metadata.  If someone outside of the energy modelling
community wants to make use this package, it should be relatively
striaghtforward to point to a different registry while continuing to
use the same workflow.  Read more in the documentation_.

.. _frictionless:
   https://frictionlessdata.io/

.. _`SENTINEL collaboration`:
   https://sentinel.energy/

.. _registry:
   https://github.com/sentinel-energy/friendly_data_registry

.. _documentation:
   https://sentinel-energy.github.io/friendly_data/

.. |unittests| image:: https://github.com/sentinel-energy/friendly_data/workflows/Unit%20tests/badge.svg
   :target: https://github.com/sentinel-energy/friendly_data/actions?query=workflow%3A%22Unit+tests%22

.. |coverage| image:: https://codecov.io/gh/sentinel-energy/friendly_data/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/sentinel-energy/friendly_data

.. |docs| image:: https://github.com/sentinel-energy/friendly_data/workflows/Publish%20docs/badge.svg
  :target: https://github.com/sentinel-energy/friendly_data/actions?query=workflow%3A%22Publish+docs%22


Installation
------------

You can install (or update) the package with ``pip``::

  $ pip install [-U] friendly-data
    
As the registry is coupled loosely, so that it can be updated much
faster.  This will accomodate to an evolving data vocabulary of the
community.  So it is recommended to update the registry regularly, eventhough you might not want to update the main package.  You can do this with::

  $ pip install -U friendly-data-registry

If your workflow also includes converting your data packages into IAMC
format, you will need the optional dependencies; which can be
installed at any time like this (leaving out the `-U` will not upgrade
the main package)::

  $ pip install [-U] friendly-data[extras]