semvec/pypi-packages huggingface dataset:
datadock 0.1.3
Datadock is a PySpark-based data interoperability library. It automatically detects schemas from heterogeneous files (CSV, JSON, Parquet), groups them by structural similarity, and performs standardized batch reads. Designed for pipelines handling non-uniform large-scale data, enabling robust integration and reuse in distributed environments.
Requires Python: <4.0,>=3.10

# Datadock

**Datadock** is a Python library built on top of PySpark, designed to simplify **data interoperability** between files of different formats and schemas in modern data engineering pipelines.

It automatically detects schemas from CSV, JSON and Parquet files, groups structurally similar files, and allows standardized reading of all grouped files into a single Spark DataFrame — even in highly heterogeneous datasets.


## ✨ Key Features

- 🚀 **Automatic parsing** of multiple file formats: `.csv`, `.json`, `.parquet`
- 🧠 **Schema-based file grouping** by structural similarity
- 📊 **Auto-selection of dominant schemas**
- 🛠️ **Unified read** across similar files into a single PySpark DataFrame
- 🔍 **Schema insight** for diagnostics and inspection


## 🔧 Installation

```bash
pip install datadock
```


## 🗂️ Expected Input Structure

Place your data files (CSV, JSON or Parquet) inside a single folder. The library will automatically detect supported files and organize them by schema similarity.

```bash
/data/input/
├── sales_2020.csv
├── sales_2021.csv
├── products.json
├── archive.parquet
├── log.parquet
```


## 🧪 Usage Example

```python
from datadock import scan_schema, get_schema_info, read_data

path = "/path/to/your/data"

# Logs schema groups detected
scan_schema(path)

# Retrieves schema metadata
info = get_schema_info(path)
print(info)

# Loads all files from schema group 1
df = read_data(path, schema_id=1, logs=True)
df.show()
```


## 📌 Public API

### `scan_schema`
Logs the identified schema groups found in the specified folder.


### `get_schema_info`
Returns a list of dictionaries containing:
- `schema_id`: ID of the schema group
- `file_count`: number of files in the group
- `column_count`: number of columns in the schema
- `files`: list of file names in the group


### `read_data`
Reads and merges all files that share the same schema.  
If `schema_id` is not specified, the group with the most columns will be selected.


## ✅ Requirements

- Python 3.10+
- PySpark


## 📚 Motivation

In real-world data engineering workflows, it's common to deal with files that represent the same data domain but have slight structural variations — such as missing columns, different orders, or evolving schemas.  
**Datadock** automates the process of grouping, inspecting, and reading these files reliably, allowing you to build pipelines that are schema-aware, scalable, and format-agnostic.


## 📄 License

This project is licensed under the **MIT License**.
simple-blogger 0.2.15
A simple blogger library
Requires Python: >=3.10.6
Keywords: python blog ai

# **PythonSimpleBlogger library (simple_blogger)** #

This is simple library to make simple blog project with Python. 
The library is distributed under the MIT license and can be downloaded and used by anyone.

----------

## How to install ##

To install, you can use the command:

    pip3 install simple_blogger

Or download the repository from [GitHub](https://github.com/athenova/simple_blogger)

----------

## Initialization ##

Just start with a simple code. 

```python

blogger = simple_blogger.CommonBlogger(PUT-YOUR-TELEGRAM-PRIVATE-CHAT-ID-IN-HERE)
blogger.init_project()

```

It initalizes folder structure of your own blog project in the working directory.

### Blog topics ###

Find project ideas json-file in `files/ideas` folder. Fill it with topics and categories of your blog.

### Creating tasks ###

Call `push`.

```python

blogger.push()

```

It creates tasks json-file in `files` folder with dates of publications and prompts to AI that generate image and text for topics. 

### Adding Tasks ###

Put any json-file in `files/ideas` folder. It has to be idea-structured. You can put as many idea-files in idea folder as you want.

Call `revert` to put unhandled tasks back in backlog.

```python

blogger.revert()

```

Call `push` again. Now all backloged tasks and new ideas are in progress.

### Publication Review ###

Call `review` to send tommorow's publication to your private telegram channel.

```python

blogger.review()

```

### Publication ###

Call `send` to send today's publication to your public telegram channel.

```python

blogger.send()

```

**Note:** call `review` before `send` or call `send` with `image_gen`=`True`(*to produce image*) and `text_gen`=`True`(*to produce text*).
  
### Error handling ###

If something goes wrong method sends Exception text to your private telegram channel. 

### Default parameters ###

Library uses working directory name as project name and production telegram channel name by default.

### Environment variables ###

Library uses `dall-e-3` model to generate images and `deepseek-chat` to generate texts by default and sends publications to telegram channels.
It needs following environment variables:
- BLOGGER_BOT_TOKEN
- OPENAI_API_KEY
- DEEPSEEK_API_KEY

Yandex generators needs following environment variables:
- YC_API_KEY
- YC_FOLDER_ID


## From the developer ##

> There are(or will be) more examples of using this library in sibling repos on [GitHub](https://github.com/athenova/simple_blogger)
dbt-fabric-samdebruyn 1.10.3
A Microsoft Fabric Synapse Data Warehouse adapter plugin for dbt
Requires Python: <3.14,>=3.11

# dbt-fabric-samdebruyn

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/sdebruyn/dbt-fabric/forked-version/assets/dbt-signature_tm_light.png">
  <img alt="dbt logo" src="https://raw.githubusercontent.com/sdebruyn/dbt-fabric/forked-version/assets/dbt-signature_tm.png">
</picture>
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/sdebruyn/dbt-fabric/forked-version/assets/fabric.png">
  <img alt="Fabric logo" src="https://raw.githubusercontent.com/sdebruyn/dbt-fabric/forked-version/assets/fabric.png">
</picture>

This is a maintained and extended fork of the [dbt-fabric](https://github.com/microsoft/dbt-fabric) adapter. This fork has [additional features and bugfixes](https://dbt-fabric.debruyn.dev/feature-comparison/) compared to the original adapter.

The adapter was [originally developed by the community](https://github.com/microsoft/dbt-fabric/graphs/contributors) and later adopted by Microsoft.
Given Microsoft's limited investments in the adapter, this fork aims to continue its development and maintenance.

[![PyPI - Version](https://img.shields.io/pypi/v/dbt-fabric-samdebruyn)](https://pypi.org/project/dbt-fabric-samdebruyn/)

## Documentation

A website with all documentation with regards to using dbt with Microsoft Fabric can be found at [http://dbt-fabric.debruyn.dev/](http://dbt-fabric.debruyn.dev/).

## Drop-in replacement

This adapter is a drop-in replacement for the original `dbt-fabric` adapter. To start using this adapter, all you have to do is a `pip uninstall dbt-fabric` and a `pip install dbt-fabric-samdebruyn`.

## Code of Conduct

Everyone interacting in this project's codebases, issues, discussions, and related Slack channels is expected to follow the [dbt Code of Conduct](https://docs.getdbt.com/community/resources/code-of-conduct).

## Acknowledgements

Special thanks to:

* [Jacob Mastel](https://github.com/jacobm001): for his initial work on building dbt-sqlserver.
* [Mikael Ene](https://github.com/mikaelene): for his initial work and continued maintenance on the dbt-sqlserver adapter.
* [Anders Swanson](https://github.com/dataders): for his continued maintenance of the dbt-sqlserver adapter and the creation of the dbt-synapse adapter. And for his work at [dbt Labs](https://www.getdbt.com/).
* [dbt Labs](https://www.getdbt.com/): for their continued support of the dbt open source ecosystem.
* the Microsoft Fabric product team, for their support and contributions to the dbt-fabric adapter.
* every other contributor to dbt-sqlserver, dbt-synapse, and dbt-fabric.
csu 1.21.4
Clean Slate Utils - bunch of utility code, mostly Django/DRF specific.
Requires Python: >=3.14

========
Overview
========


.. list-table::
    :stub-columns: 1

    * - tests
      - |github-actions| |coveralls| |codecov| |scrutinizer| |codacy|
    * - package
      - |version| |wheel| |supported-versions| |supported-implementations| |commits-since|
.. |github-actions| image:: https://github.com/ionelmc/python-csu/actions/workflows/github-actions.yml/badge.svg
    :alt: GitHub Actions Build Status
    :target: https://github.com/ionelmc/python-csu/actions
.. |coveralls| image:: https://coveralls.io/repos/github/ionelmc/python-csu/badge.svg?branch=main
    :alt: Coverage Status
    :target: https://coveralls.io/github/ionelmc/python-csu?branch=main
.. |codecov| image:: https://codecov.io/gh/ionelmc/python-csu/branch/main/graphs/badge.svg?branch=main
    :alt: Coverage Status
    :target: https://app.codecov.io/github/ionelmc/python-csu
.. |codacy| image:: https://img.shields.io/codacy/grade/[Get ID from https://app.codacy.com/gh/ionelmc/python-csu/settings].svg
    :target: https://www.codacy.com/app/ionelmc/python-csu
    :alt: Codacy Code Quality Status
.. |version| image:: https://img.shields.io/pypi/v/csu.svg
    :alt: PyPI Package latest release
    :target: https://pypi.org/project/csu
.. |wheel| image:: https://img.shields.io/pypi/wheel/csu.svg
    :alt: PyPI Wheel
    :target: https://pypi.org/project/csu
.. |supported-versions| image:: https://img.shields.io/pypi/pyversions/csu.svg
    :alt: Supported versions
    :target: https://pypi.org/project/csu
.. |supported-implementations| image:: https://img.shields.io/pypi/implementation/csu.svg
    :alt: Supported implementations
    :target: https://pypi.org/project/csu
.. |commits-since| image:: https://img.shields.io/github/commits-since/ionelmc/python-csu/v1.21.4.svg
    :alt: Commits since latest release
    :target: https://github.com/ionelmc/python-csu/compare/v1.21.4...main
.. |scrutinizer| image:: https://img.shields.io/scrutinizer/quality/g/ionelmc/python-csu/main.svg
    :alt: Scrutinizer Status
    :target: https://scrutinizer-ci.com/g/ionelmc/python-csu/


Clean Slate Utils - bunch of utility code, mostly Django/DRF specific.

* Free software: BSD 2-Clause License

Installation
============

::

    pip install csu

You can also install the in-development version with::

    pip install https://github.com/ionelmc/python-csu/archive/main.zip


Documentation
=============

None.

Development
===========

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox


Changelog
=========

0.0.0 (2024-03-24)
------------------

* First release on PyPI.
lanscape 3.1.0a1
A python based local network scanner
Requires Python: >=3.10
Keywords: network, scanner, lan, local, python

# LANscape

A local network scanner with a built-in web UI. Discover devices, open ports, and running services on your network.

```sh
pip install lanscape
python -m lanscape
```

PyPi Stats: 

![Version](https://img.shields.io/pypi/v/lanscape)
![Monthly Downloads](https://img.shields.io/pypi/dm/lanscape)

Latest release: 

![Stable](https://img.shields.io/github/v/tag/mdennis281/LANScape?filter=releases%2F*&label=Stable)
![RC](https://img.shields.io/github/v/tag/mdennis281/LANScape?filter=pre-releases%2F*rc*&label=RC)
![Beta](https://img.shields.io/github/v/tag/mdennis281/LANScape?filter=pre-releases%2F*b*&label=Beta)

Health: 

![pytest](https://img.shields.io/github/actions/workflow/status/mdennis281/LANscape/test.yml?branch=main&label=pytest) 
![packaging](https://img.shields.io/github/actions/workflow/status/mdennis281/LANscape/test-package.yml?label=packaging) 
![pylint](https://img.shields.io/github/actions/workflow/status/mdennis281/LANscape/pylint.yml?branch=main&label=pylint)

---

![LANscape UI](docs/img/lanscape-ui-main.jpg)

<details>
<summary>More screenshots</summary>

![Scan Configuration](docs/img/lanscape-config.png)

![Port Detail](docs/img/lanscape-port-detail.jpg)

</details>

---

## Flags

| Flag | Description |
|------|-------------|
| `--port <number>` | Port for the web UI (default: auto) |
| `--ws-port <number>` | Port for the WebSocket server (default: 8766) |
| `--ws-server` | Start WebSocket server only (no UI) |
| `--persistent` | Don't auto-shutdown when the browser tab closes |
| `--logfile <path>` | Write log output to a file |
| `--loglevel <level>` | Set log level: DEBUG, INFO, WARNING, ERROR, CRITICAL (default: INFO) |
| `--debug` | Shorthand for `--loglevel DEBUG` |

```sh
python -m lanscape
python -m lanscape --port 8080
python -m lanscape --debug --persistent
python -m lanscape --logfile /tmp/lanscape.log --loglevel WARNING
python -m lanscape --ws-server --ws-port 9000
```

## Troubleshooting

### MAC Address / Manufacturer is inaccurate or unknown
LANscape does an ARP lookup to determine MAC addresses. This can require
elevated permissions to get accurate results — try running your shell as admin.

### Scan accuracy seems low
The scanner uses a combination of ARP, ICMP, and port probing to find devices.
If results aren't great out of the box:

- Tweak the scan configuration preset (accessible from the gear icon)
- Set up ARP lookup properly — see [ARP issues](./docs/arp-issues.md)
- Open an issue if something still seems off

### Something else
Feel free to [submit an issue](https://github.com/mdennis281/LANscape/issues) with details about what you're seeing.