semvec/pypi-packages huggingface dataset:
pymgclient 1.5.2
Memgraph database adapter for Python language
Requires Python: >=3.9

# pymgclient - Memgraph database adapter for Python language

pymgclient is a [Memgraph](https://memgraph.com) database adapter for Python
programming language compliant with the DB-API 2.0 specification described by
PEP 249.

mgclient module is the current implementation of the adapter. It is implemented
in C as a wrapper around [mgclient](https://github.com/memgraph/mgclient), the
official Memgraph client library. As a C extension, it is only compatible with
the CPython implementation of the Python programming language.

pymgclient only works with Python 3.

Check out the documentation if you need help with
[installation](https://memgraph.github.io/pymgclient/introduction.html#installation)
or if you want to
[build](https://memgraph.github.io/pymgclient/introduction.html#install-from-source)
pymgclient for yourself!
## Documentation

Online documentation can be found on [GitHub
pages](https://memgraph.github.io/pymgclient/).

You can also build a local version of the documentation by running `make` from
the `docs` directory. You will need [Sphinx](http://www.sphinx-doc.org/)
installed in order to do that.

## Code sample

Here is an example of an interactive session showing some of the basic
commands:

```python
>>> import mgclient

# Make a connection to the database
>>> conn = mgclient.connect(host='127.0.0.1', port=7687)

# Create a cursor for query execution
>>> cursor = conn.cursor()

# Execute a query
>>> cursor.execute("""
        CREATE (n:Person {name: 'John'})-[e:KNOWS]->
               (m:Person {name: 'Steve'})
        RETURN n, e, m
    """)

# Fetch one row of query results
>>> row = cursor.fetchone()

>>> print(row[0])
(:Person {'name': 'John'})

>>> print(row[1])
[:KNOWS]

>>> print(row[2])
(:Person {'name': 'Steve'})

# Make database changes persistent
>>> conn.commit()
```
robust-mixed-dist 0.1.20
Compute statistical robust distances for mixed data.
Requires Python: >=3.7

# robust-mixed-dist

Data scientists address real-world problems using multivariate and heterogeneous
datasets, characterized by multiple variables of different natures. Selecting a suitable
distance function between units is crucial, as many statistical techniques and machine
learning algorithms depend on this concept. Traditional distances, such as Euclidean
or Manhattan, are unsuitable for mixed-type data, and although Gower distance was
designed to handle this kind of data, it may lead to suboptimal results in the presence
of outlying units or underlying correlation structure.

In the paper ***Grané , Aurea; Scielzo-Ortiz, Fabio. “On generalized Gower distance for mixed-type data: extensive simulation study and new software tools”. SORT-Statistics and Operations Research Transactions, pp. 213-44, doi:10.57645/20.8080.02.28.*** robust distances for mixed-type data are defined and explored, namely **robust generalized Gower** and **robust related metric scaling**. In addition,  the new Python package `robust-mixed-dist` is developed, which enables to
compute these robust proposals as well as classical ones.

The package is located in Python Package Index (PyPI), the standard repository of packages for the Python programming language: https://pypi.org/project/robust_mixed_dist/

- **Package documentation:** https://fabioscielzoortiz.github.io/robust-mixed-dist-docu/intro.html

- **Paper link:** https://raco.cat/index.php/SORT/article/view/9900373
dcolor-drawiks 0.1.0
dcolor by drawiks
Requires Python: >=3.7

<div align="center">
    <h1>🎨 dcolor</h1>
    <img height="20" alt="Python 3.7+" src="https://img.shields.io/badge/python-3.7+-blue">
    <img height="20" alt="License MIT" src="https://img.shields.io/badge/license-MIT-green">
    <img height="20" alt="Status" src="https://img.shields.io/badge/status-stable-brightgreen">
    <p><strong>dcolor</strong> — цветной вывод в терминал с поддержкой hex/rgb</p>
    <blockquote>(─‿‿─)</blockquote>
</div>

---

```
     ____
    / __ \ ______ ____   / /____  _____
   / / / // ____// __ \ / // __ \/ ___/
  / /_/ // /____/ /_/ // // /_/ / /
 /_____/ \____/ \____//_/ \____/_/

```

## **📦 установка**

```bash
pip install dcolor-drawiks
```

---

## **📑 быстрый старт**

```python
from dcolor import color

print(color("привет!", "#ff0000"))
print(color("привет!", (255, 0, 0)))
print(color("привет!", "red"))
print(color("привет!", "#ff0000", "bold", "underline"))
```

---

## **🧩 возможности**

- 🎨 **hex цвета** — `"#ff0000"` или `"#f00"`
- 🌈 **rgb цвета** — `(255, 0, 0)`
- 🏷️ **именованные цвета** — `"red"`, `"cyan"`, `"orange"`...
- ✨ **стили** — `bold`, `italic`, `underline`, `strike`, `dim`, `blink`
- 🚫 **без зависимостей** — только stdlib

---

## **📖 использование**

### hex цвет

```python
from dcolor import color

print(color("ошибка", "#ff0000"))
print(color("успех", "#00ff00"))
print(color("инфо", "#3b82f6"))
```

короткий hex тоже работает:
```python
print(color("текст", "#f00"))  # то же что и #ff0000
```

### rgb цвет

```python
print(color("текст", (255, 165, 0)))
```

### именованные цвета

```python
print(color("текст", "red"))
print(color("текст", "cyan"))
print(color("текст", "orange"))
```

доступные имена: `black`, `red`, `green`, `yellow`, `blue`, `magenta`, `cyan`, `white`, `grey`, `orange`, `pink`, `purple`, `brown`, `lime`, `teal`

### стили

```python
print(color("текст", "red", "bold"))
print(color("текст", "#ff0000", "bold", "underline"))
print(color("текст", "cyan", "italic", "strike"))
```

доступные стили: `bold`, `dim`, `italic`, `underline`, `blink`, `strike`

### только стиль (без цвета)

```python
print(color("текст", None, "bold"))
print(color("текст", None, "underline", "italic"))
```

### убрать ansi коды

```python
from dcolor import strip

raw = color("привет", "#ff0000", "bold")
clean = strip(raw)  # "привет"
```

---

## **💡 примеры**

```python
from dcolor import color

print(color("[ERROR]",   "#ff4444", "bold"), "что-то пошло не так")
print(color("[SUCCESS]", "#44ff44", "bold"), "всё хорошо")
print(color("[WARN]",    "#ffaa00", "bold"), "осторожно")
print(color("[INFO]",    "#888888"),         "просто инфо")
```

### использование в dlogger

```python
from dlogger import logger
from dcolor import color

logger.info(color("платёж получен", "#44ff44", "bold"))
logger.error(color("соединение потеряно", "#ff4444"))
```

---

## **📜 лицензия**
[MIT](LICENSE)
npms 0.1.5
Unified Python package for data science - pandas, numpy, matplotlib, and scikit-learn under one import.
Requires Python: >=3.8
Keywords: data science, pandas, numpy, matplotlib, scikit-learn, unified, import, machine learning

# npms 📦

[![PyPI version](https://badge.fury.io/py/npms.svg)](https://pypi.org/project/npms/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Platform](https://img.shields.io/badge/platform-Windows%20%7C%20macOS%20%7C%20Linux-lightgrey.svg)](https://pypi.org/project/npms/)

Unified Python package for data science — **pandas, numpy, matplotlib, and scikit-learn** under one single import.

---

## Install

```bash
pip install npms
```

---

## Usage

```python
import npms as ns

# Pandas
df = ns.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df)

# NumPy
arr = ns.array([1, 2, 3, 4, 5])
print(ns.mean(arr))

# Matplotlib
ns.plot([1, 2, 3], [1, 4, 9])
ns.xlabel('X')
ns.ylabel('Y')
ns.show()

# Scikit-learn
from npms import linear_model
model = linear_model.LinearRegression()
```

---

## What's included

| Library | Purpose |
|---|---|
| [pandas](https://pandas.pydata.org/) | Data manipulation and analysis |
| [numpy](https://numpy.org/) | Numerical computing |
| [matplotlib](https://matplotlib.org/) | Data visualization |
| [scikit-learn](https://scikit-learn.org/) | Machine learning |

---

## Features

- ✅ Single import for all major data science libraries
- ✅ Simplified namespace — use `ns.` for everything
- ✅ Auto-installs all dependencies
- ✅ Works on Windows, macOS, Linux

---

## License

[MIT](LICENSE) © 2026 tokitahmidtoufa
wheezy.caching 3.2.1
A lightweight caching library
Requires Python: >=3.10
Keywords: caching, dependency, memory, null, memcache, memcached, pylibmc

# wheezy.caching

[![tests](https://github.com/akornatskyy/wheezy.caching/actions/workflows/tests.yml/badge.svg)](https://github.com/akornatskyy/wheezy.caching/actions/workflows/tests.yml)
[![Coverage Status](https://coveralls.io/repos/github/akornatskyy/wheezy.caching/badge.svg?branch=master)](https://coveralls.io/github/akornatskyy/wheezy.caching?branch=master)
[![Documentation Status](https://readthedocs.org/projects/wheezycaching/badge/?version=latest)](https://wheezycaching.readthedocs.io/en/latest/?badge=latest)
[![pypi version](https://badge.fury.io/py/wheezy.caching.svg)](https://badge.fury.io/py/wheezy.caching)

[wheezy.caching](https://pypi.org/project/wheezy.caching/) is a
[python](http://www.python.org) package written in pure Python code. It
is a lightweight caching library that provides integration with:

- [python-memcached](https://pypi.org/project/python-memcached/) -
  Pure Python [memcached](http://memcached.org) client.
- [pylibmc](https://pypi.org/project/pylibmc/) - Quick and small
  [memcached](http://memcached.org) client for Python written in C.

It introduces idea of *cache dependency* (effectively invalidate
dependent cache items) and other cache related algorithms.

It is optimized for performance, well tested and documented.

Resources:

- [source code](https://github.com/akornatskyy/wheezy.caching)
  and [issues](https://github.com/akornatskyy/wheezy.caching/issues)
  tracker are available on
  [github](https://github.com/akornatskyy/wheezy.caching)
- [documentation](https://wheezycaching.readthedocs.io/en/latest/)

## Install

[wheezy.caching](https://pypi.org/project/wheezy.caching/) requires
[python](http://www.python.org) version 3.10+. It is independent of operating
system. You can install it from
[pypi](https://pypi.org/project/wheezy.caching/) site:

```sh
pip install -U wheezy.caching
pip install -U wheezy.caching[pylibmc]
pip install -U wheezy.caching[python-memcached]
```

If you run into any issue or have comments, go ahead and add on
[github](https://github.com/akornatskyy/wheezy.caching).
liturgical-calendar 0.5.6
Library to determine liturgical dates and colours for the Anglican Church of England
Requires Python: <4.0,>=3.11

# Liturgical Calendar

This Python module will return the name, season, week number and liturgical
colour for any day in the Gregorian calendar, according to the Anglican
tradition of the Church of England.

This module's algorithm is a direct port to Python of
[`DateTime::Calendar::Liturgical::Christian`](https://github.com/gitpan/DateTime-Calendar-Liturgical-Christian),
which was originally written in Perl and loaded with the calendar of the Episcopal
Church of the USA. It has now been fed with data from the Church of England's
[Calendar of saints](https://en.wikipedia.org/wiki/Calendar_of_saints_(Church_of_England))
and substantially modified to suit the Anglican calendar.

The output of this module is compared against the
[Church of England Lectionary](https://www.chpublishing.co.uk/features/lectionary),
which is taken to be the canonical source.

## Installation

This library is [published on PyPI](https://pypi.org/project/liturgical-calendar/).

```console
pip install liturgical-calendar
```

## Usage, as a command

Once installed, this can be run at the command line. Currently it prints
an object with various attributes. This portion of the module needs
improvement, although it is probably more useful as a library.

Specify the date in YYYY-MM-DD format, or leave blank to return info
for today.

```console
# Get info for today
$ liturgical_calendar
name : 
prec : 1
season : Lent
season_url : https://en.wikipedia.org/wiki/Lent
week : Lent 1
date : 2025-03-13
colour : purple
colourcode : #664fa6
ember : 0

# Get info for an arbitrary date
$ liturgical_calendar 2023-01-25
name : The Conversion of Paul
url : https://en.wikipedia.org/wiki/Conversion_of_Paul
prec : 7
type : Festival
type_url : https://en.wikipedia.org/wiki/Festival_(Church_of_England)
season : Epiphany
season_url : https://en.wikipedia.org/wiki/Epiphany_season
week : Epiphany 3
date : 2023-01-25
colour : white
colourcode : #fffff6
ember : 0
```

## Usage, as a library

```py
# Get info for today
dayinfo = liturgical_calendar()

# Get info for an arbitrary date
# Date can be expressed as a string in YYYY-MM-DD format, a Datetime object, or a Date object
dayinfo = liturgical_calendar('YYYY-MM-DD')

# Access the attributes individually
print(dayinfo['colour'])
```

## Development

This Python project is managed with Poetry. For local testing without installing, run

```console
poetry run liturgical_calendar
```

## Issues

If you find bugs (either in the code or in the calendar data), please
[create an issue on GitHub](https://github.com/liturgical-app/liturgical-calendar/issues).

Pull requests are always welcome, either to address bugs or add new features.