semvec/pypi-packages huggingface dataset:
tgedr-dataops-ext 0.0.3
data operations related code - extended
Requires Python: >=3.11

# tgedr-dataops-ext

![Coverage](./coverage.svg)
[![PyPI](https://img.shields.io/pypi/v/tgedr-dataops-ext)](https://pypi.org/project/tgedr-dataops-ext/)


data operations related code - extended

## motivation
*dataops-ext* is a library with tested and used code aligning on some standards regarding code structure and quality and to avoid reinventing the wheel. It builds on top of *dataops-abs* and *dataops* providing distributed processing features based on pyspark.

## installation
        `pip install tgedr-dataops-ext`

## package namespaces and its contents

#### commons
- __Dataset__: immutable class to wrap up a dataframe along with metadata ([example](tests/tgedr_dataops_ext/commons/test_dataset.py))
- __Metadata__: immutable class depicting dataset metadata ([example](tests/tgedr_dataops_ext/commons/test_metadata.py))
- __UtilsSpark__: utility class to work with spark, mostly helping on creating a session ([example](tests/tgedr_dataops_ext/commons/test_utils_spark.py))

#### quality
- __PysparkValidation__ : __GreatExpectationsValidation__ implementation to validate pyspark dataframes with Great Expectations library ([example](tests/tgedr_dataops_ext/quality/test_pyspark_validation.py))

#### source

- __DeltaTableSource__: abstract __Source__ class used to read delta lake format datasets returning a pandas dataframe" ([example](tests/tgedr_dataops_ext/source/test_delta_table_source.py))
- __LocalDeltaTable__: __Source__ class used to read delta lake format datasets from local fs with python only, pyspark not needed, returning a pandas dataframe ([example](tests/tgedr_dataops_ext/source/test_local_delta_table.py))
- __S3DeltaTable__: __Source__ class used to read delta lake format datasets from s3 bucket with python only, pyspark not needed, returning a pandas dataframe ([example](tests/tgedr_dataops_ext/source/test_s3_delta_table.py))


#### store
- __SparkDeltaStore__ : __Store__ implementation for pyspark distributed processing with delta table format ([example](tests/tgedr_dataops_ext/store/test_spark_delta.py))



## development
- main requirements:
  - _uv_  
  - _bash_
- Clone the repository like this:

  ``` bash
  git clone git@github.com:tgedr/dataops-ext
  ```
- cd into the folder: `cd dataops-ext`
- install requirements: `./helper.sh reqs`
lldap-py 0.2.0
Python tool for managing LLDAP servers
Requires Python: >=3.8

# lldap-py

Python client library for managing LLDAP servers  [lldap/lldap](https://github.com/lldap/lldap)

## Usage

This package provides a Python interface to interact with LLDAP servers for user and group management. The idea is that it would be used in an onboarding/offboarding automation script and make similar automation tasks easier.

### Quickstart
1. Install the package.
2. Create a `LLDAPManager` with your server URL and credentials.
3. Use the user and group helpers to perform actions.

```python
from lldap import LLDAPManager

manager = LLDAPManager(
	http_url="http://localhost:17170",
	username="admin",
	password="your-password",
	# Needed for LDAP password changes
	base_dn="dc=example,dc=com",
	ldap_server="ldap://localhost:3890",
)

users = manager.list_users()
groups = manager.list_groups()

user_id = "jdoe"
manager.create_user(
	user_id,
	"jdoe@example.com",
	"Jane Doe",
	"Jane",
	"Doe",
)

group_id = manager.get_group_id("BasicUserGroup")
if group_id is not None:
	manager.add_user_to_group(user_id, group_id)

# Requires base_dn and ldap_server
manager.set_password(user_id, "TempPassw0rd!")

manager.close()
```

Notes:
- You can authenticate with `token` or `refresh_token` instead of `username` and `password`.
- Set `verify_ssl=False` if you are connecting to a test server with self-signed certs.

### Installation
You can install the package via pip:

``` pip install lldap-py ```

https://pypi.org/project/lldap-py

## Requirements
- Python 3.8+
- requests
- ldap3
- toml
- click

## TODO
- Maybe improve error handling and passing of graphql errors to the user.
- Add more examples and documentation.
- Check coverage of tests
- Add support for costum user and group attributes.

## Credit
This project is heavely inspired by and uses alot of code from [Zepmann/lldap-cli](https://github.com/Zepmann/lldap-cli) and [JaidenW/LLDAP-Discord](https://github.com/JaidenW/LLDAP-Discord)


## License

MIT
checkdrift 1.0.0
One-line drift detection for ML APIs. Like Pydantic or a rate limiter, but for data drift.
Requires Python: >=3.10
Keywords: drift-detection, fastapi, machine-learning, mlops, monitoring

# checkdrift

One-line drift detection for ML APIs. Like Pydantic or a rate limiter, but for data drift.

Just add @check_drift decorator to your FastAPI endpoint:

```python
@app.post("/predict")
@check_drift(baseline="baseline.json")
async def predict(application: LoanApplication):
    return model.predict(application)
```

## Installation

```bash
pip install checkdrift
```

## What It Does

- Maintains a sliding window of recent requests
- Computes drift metrics every N requests (default: 50)
- Logs warnings when drift is detected
- Minimal impact on your endpoint response (about 1ms in my tests)

Uses PSI (Population Stability Index) and KS test - industry standards from banking.

## Baseline Format

```json
{"distributions": {"feature1": [1.0, 2.0, ...], "feature2": [...]}}
```

See [examples/lendingclub](examples/lendingclub) for a complete example with sample data.

## Options

```python
@check_drift(
    baseline="baseline.json",
    window_size=100,       # Sliding window size
    check_interval=50,     # Check every N requests
    on_drift=my_callback,  # Optional callback
)
```

## License

MIT
ome-zarr 0.13.0
Implementation of images in Zarr files.
Requires Python: >3.10

|pypi| |docs| |coverage|

===========
ome-zarr-py
===========

Tools for multi-resolution images stored in Zarr filesets, according to the `OME NGFF spec`_.

See `Documentation <https://ome-zarr.readthedocs.io/>`_ for usage information.

Documentation
-------------

Documentation will be automatically built with `readthedocs`.

It can be built locally with::

    $ pip install -r docs/requirements.txt
    $ sphinx-build -b html docs/source/ docs/build/html

Tests
-----

Tests can be run locally via `tox` with::

    $ pip install tox
    $ tox

To enable pre-commit code validation::

    $ pip install pre-commit
    $ pre-commit install

Release process
---------------

This repository uses `setuptools_scm` to manage version numbers.

Go to https://github.com/ome/ome-zarr-py/releases and click on `Draft a new release`.

Under `Choose a tag`, type the new version number (e.g. `0.1.0`) and choose
`Create a new tag on publish` from the dropdown.

Click on `Generate release notes` to create a changelog and `Publish release` to publish the release.

License
-------

Distributed under the terms of the `BSD`_ license,
"ome-zarr-py" is free and open source software

.. _`OME NGFF spec`: https://github.com/ome/ngff
.. _`BSD`: https://opensource.org/licenses/BSD-2-Clause

.. |pypi| image:: https://badge.fury.io/py/ome-zarr.svg
    :alt: PyPI project
    :target: https://badge.fury.io/py/ome-zarr

.. |docs| image:: https://readthedocs.org/projects/ome-zarr/badge/?version=stable
    :alt: Documentation Status
    :target: https://ome-zarr.readthedocs.io/en/stable/?badge=stable

.. |coverage| image:: https://codecov.io/gh/ome/ome-zarr-py/branch/master/graph/badge.svg
    :alt: Test coverage
    :target: https://codecov.io/gh/ome/ome-zarr-py