semvec/pypi-packages huggingface dataset:
leetcode-python-api-wrapper 1.0.0
Python wrapper for Alfa LeetCode API
Requires Python: >=3.7


# Leetcode Python Wrapper Python Library

A Python wrapper for the Alfa LeetCode API, providing endpoints to retrieve user profiles, badges, solved problems, and more.

## Features
- Fetch user profile, badges, solved problems, and contest details.
- Query problems with filters (tags, difficulty).
- Retrieve trending discussions and daily questions.

## Installation

Install using pip:

```bash
pip install pyleetcode
```

## Usage

```python
from LeetcodeWrapper import leetcode_Wrapper

api = leetcode_Wrapper("test_user")

# Get profile details
profile = api.get_profile()
print(profile)
```

## License
This project is licensed under the MIT License - see the LICENSE file for details.
piano-analytics-api 1.0.0
Python implementation for the Piano Anaytics Reporting API (formerly ‘At Internet’)
Requires Python: >=3.9
Keywords: at internet, piano analytics

# Piano analytics (formerly ‘AT Internet’) Python client

This library enables you to get queries from the Piano Analytics Reporting API v3.
This is a third-party library.
A subscription to Piano Analytics is required.

## Requirements ##
* [Python 3.9](https://www.python.org/downloads/) or higher.

## Installation ##

You can use **Pip** or **Download the Release**

### Pip

The preferred method is via [the Python package index](https://pypi.org/).

```sh
pip3 install piano-analytics-api
```

## Usage example

1. Create an API key in your [Piano Analytics account](https://analytics.piano.io/profile/#/apikeys).
2. Get the access key and secret key from the API key.
3. Find the site ID’s in [Piano Analytics access management](https://analytics.piano.io/access-management/#/sites).
   Select a site on the page and copy the id from the address bar.

```python
import piano_analytics_api.period as period
import piano_analytics_api.pfilter as pfilter
from piano_analytics_api import Client, Request

site_id = 614694
access_key = ""
secret_key = ""

# Create API connection
client = Client(access_key, secret_key)

# Get page titles and number of visits for each page,
# where the page title is not empty and domain is example.com,
# ordered by the number of visits from high to low.
request = Request(
    client=client,
    sites=[site_id],
    columns=["page", "m_visits"],
    period=period.today(),
    sort=["-m_visits"],
    property_filter=pfilter.ListAnd(
        pfilter.IsEmpty("page", False),
        pfilter.Contains("domain", ["example.com", "www.example.com"])
    )
)

# All results
i = 0
for item in request.get_result_rows():
    print(item)
    i += 1

# Number of results
print(f"rowcount: {request.get_rowcount()}")

# Cumulative metrics for all resulting rows
print(f"total: {request.get_total()}")
```
keras_efficient_kan 0.1.10
Keras Efficient KAN
Requires Python: <3.13,>=3.9

# Keras Efficient Kan 

Keras package reproducting the pytorch [efficient_kan implementation](https://github.com/Blealtan/efficient-kan)

Package is available on pypi [here](https://pypi.org/project/keras-efficient-kan/)

It implements KANLinear based on [Kolmogorov-Arnold Network](https://arxiv.org/abs/2404.19756) that can be used as a drop-in replacement for Dense layers in Keras models.

It supports array with more than 2 dimensions, applying the same transformation to all the dimensions except the last one.


Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
edureporter 2.1.0
Пакет инструментов для построения отчетных систем
Requires Python: >=3.9

# edureporter

Пакет инструментов для построения отчетных систем.

## Установка

```bash

  $ pip install edureporter

```

## Тестирование

Для запуска тестов используется tox.

Установка tox:

```bash

$ pip install tox

```

Запуск тестов из директории пакета:

```bash

$ tox

```
BoaHancock 1.0.0
This is tools to make starter template python for create moduls or library
Requires Python: >=3.6

# boaHancock
![Logo](.github/boa.jpg)

This is project to generate starter template for create moduls or library in python

## Fitur

- Membuat struktur folder dasar untuk proyek Python, termasuk folder `src` dan `test`.
- Menghasilkan file `LICENSE` berdasarkan jenis lisensi yang dipilih.
- Menghasilkan file `README.md` dengan template dasar.
- Membuat file `setup.py` yang telah dikonfigurasi untuk pengemasan proyek Python.
- Menghasilkan file `.gitignore` untuk mengabaikan file yang tidak perlu.
- Membuat file `__init__.py` di dalam folder `src` untuk modul Python.
- Menyediakan berbagai pilihan lisensi seperti MIT, Apache, BSD-3-Clause, dan lainnya.

## Langkah Instalasi
```bash
pip install boaHancock --break-system-packages
```
```
boa start
```