semvec/pypi-packages huggingface dataset:
pyrule34 1.1.0
Asynchronous wrapper for rule34.xxx.
Requires Python: >=3.5
Keywords: async rule34,rule34 api,wrapper

PyRule34 ![GitHub](https://img.shields.io/github/license/Hypick122/pyrule34)
=============================
### <strong><a href="https://github.com/Hypick122/pyrule34/blob/master/.github/README_RU.md"> Р СѓСЃСЃРєРёР№ </a> | English </strong>
This is a lightweight asynchronous library for the [rule34](rule34.xxx) API

Install [![Downloads](https://pepy.tech/badge/pyrule34)](https://pepy.tech/project/pyrule34)
-------------
The library requires Python 3.5 or higher
- Latest version on PyPI: [`1.1.0`](https://pypi.org/project/pyrule34/1.1.0/)
```sh
pip install -U pyrule34
```

Usage
-------------
Importing required libraries
```python
import asyncio
from pyrule34 import AsyncRule34
```
AsyncRule34 can be used as a context manager:
```python
#example
async def main():
    async with AsyncRule34() as r34:
        get_posts = await r34.get_posts(4931536)
        
        print(get_posts[0].tags)
        
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
```

### Search
Search for posts using specified inclusion and exclusion tags.

Returns a list.

Parameters:
- `tags` - The include tags
- `exclude_tags` - The exclude tags (default: None)
- `limit` - The (maximum) limit to get posts. Maximum 1000 per one request (default: 100)
- `page_id` - The page number (default: 0)
```python
search = await r34.search(tags=["neko"], exclude_tags=["rating:general"], page_id=2, limit=1)
```
### Post
Retrieve post(s) by ID or MD5 hash.

Returns a list.

Parameters:
- `posts_id`: The list of IDs posts or post ID
- `md5`: The MD5 hash of the post (default: None)
```python
get_posts = await r34.get_posts(4931536)
```
### Comments on the post
Get comments by post ID.

Returns a list.

Parameters:
- `post_id`: The ID of the post
```python
get_post_comments = await r34.get_post_comments(4153825)
```
### Random post
Get a random post.

Returns a dict.
```python
get_random_post = await r34.get_random_post()
```
### Pool
Get Pool by ID.

Returns a list.

Parameters:
- `cid`: Pool ID
- `offset`: offset (default: 0) | 1 page - 45
```python
get_pool = await r34.get_pool(29619)
```
### User Favorites
Get user's favorites by ID.

Returns a list.

Parameters:
- `user_id`: user ID
- `offset`: offset (default: 0) | 1 page - 50
```python
favorite = await r34.users.favorites(2993217)
```
### Top 100 characters and tags
Get the top 100 characters or tags.

Returns a list.
```python
top_character = await r34.top_characters()
top_tags = await r34.top_tags()
```
### Top users
Get the top 10 best users by tags, comments, etc.

Returns a list.
```python
taggers = await r34.stats.taggers()
favorites = await r34.stats.favorites()
commenters = await r34.stats.commenters()
forum_posters = await r34.stats.forum_posters()
image_posters = await r34.stats.image_posters()
note_editors = await r34.stats.note_editors()
```
sparkplug 1.23.0rc2
An AMQP message consumer daemon

# Sparkplug

[![Build Status](https://www.travis-ci.org/freshbooks/sparkplug.svg?branch=master)](https://www.travis-ci.org/freshbooks/sparkplug)
[![PyPi version](https://img.shields.io/pypi/v/sparkplug.svg)](https://pypi.org/project/sparkplug/)

Sparkplug is a lightweight AMQP message consumer. It allows you to specify queue configuration and consumer entry points using INI files

## Testing
run tests with `nosetests`

## Releasing

Prerequisite: You have an SSH config file with your username, and email located at `~/.gitconfig`. It is needed by the script that bumps the release version.

```VERSION_BUMP_TYPE=minor PYPI_USER=some_user PYPI_PASS=some_password make release```

## Note

Version 1.11.5, 1.11.6, 1.12.0 are Python 2.7 and 3.x compatible.
Version 1.13.0 and above is not compatible with Python <= 3.4
test_run_ultinode 0.0.13
Multinode's Python client
Requires Python: >=3.8.1,<4.0.0

# Multinode's Python client

## Generating missing client code
For the code to work you need to generate control plane client. Ensure its up-to-date
schema is in `../api-schemas/control-plane.json` and run generate client script:
```commandline
bash scripts/generate_client.sh
```

## Running CLI
You can run any of the CLI commands using the `poetry run multinode` prefix. For example:
```commandline
poetry run multinode --help
```

### Deploying your first project
To deploy your first project in the development mode, first run:
```commandline
poetry run multinode login
```

You will be asked for an API key.

Once successfully logged in, create a `test.py` file with the following content:
```python
from multinode import Multinode

mn = Multinode()


@mn.job()
def a():
    print("Hello, Multinode A!")


@mn.job()
def b():
    print("Hello, Multinode B!")
```

and run

```commandline
poetry run multinode deploy test.py --project-name my-first-project
```
AutoCleanPy 0.0.1
This is a python package useful for the automated data cleaning operations.
Requires Python: >=3.6

This module is about automated data cleaning of datasets for machine learning and deep learning applications. The initial version 

comprises of data cleaning operations with regards to the task of classification. Futher releases will incorporate method for 

regression and other A.I tasks. 

# Features
The module deals with the following errors in the dataset automatically:-

    * Duplicate rows and columns

    * Non-numerical datatypes

    * Missing data

    * Different ranges of data

The module also returns a summary descirption of the operations that are performed on the dataset during the process of data cleaning.

The module is designed in such a way that it handles errors and exceptions. Any errors while performing an operation doesnt affect the 

performance of the other operation.

## Handling duplicates

The module will automatically remove the duplicate rows and columns in the dataset.

## Non-numerical datatypes

The module will automatically convert the non-numerical datatypes to numerical using labelencoder.

## Missing data

The module will remove the rows that have null values, only if the dataset is very large. Or else, it will perform imputation using 

statistical measures or machine learning models. If the method is specified by the user, the particular one will be executed, or else, the 

default imputation method (standard deviation).

## Different ranges of data

The module will deal with this issue based on the type of classification/regression model the user wishes to perform. Generally models

that use gradient descent like linear regression, neural nets and distance based algorithms like

KMeans and KNN require standardisation technique, whereas other algorithms like SVM, naive bayes require normalization technique and 

algorithms like decision tree, random forest, bagging and boosting algorithms require no scaling at all.

# SYNTAX

from AutoDataCleaner import AutoDataClean

data_cleaning = AutoDataClean.DataCleaner(data, algo=None,method=None,target_name='Outcome',imputer='knn',k_neighbors=5)

## To get the data

data=data_cleaning.data

## To get the description of the operations performed

print(data_cleaning.descriptions)


## Options for the argument algo

'SVM', 'ANN', 'KNN', 'Naive Bayes', 'KMeans', 'LDA' ,'QDA', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'xgboost', 'Gradient 

Boosting', 'adaboost'

## Options for the argument method

'bfill', 'ffill', 'mean', 'median', 'mode', 'variance', 'std'

## Options for the argument imputer

'simple', 'knn'
test-run-multinode 0.0.12
Multinode's Python client
Requires Python: >=3.8.1,<4.0.0

# Multinode's Python client

## Generating missing client code
For the code to work you need to generate control plane client. Ensure its up-to-date
schema is in `../api-schemas/control-plane.json` and run generate client script:
```commandline
bash scripts/generate_client.sh
```

## Running CLI
You can run any of the CLI commands using the `poetry run multinode` prefix. For example:
```commandline
poetry run multinode --help
```

### Deploying your first project
To deploy your first project in the development mode, first run:
```commandline
poetry run multinode login
```

You will be asked for an API key.

Once successfully logged in, create a `test.py` file with the following content:
```python
from multinode import Multinode

mn = Multinode()


@mn.job()
def a():
    print("Hello, Multinode A!")


@mn.job()
def b():
    print("Hello, Multinode B!")
```

and run

```commandline
poetry run multinode deploy test.py --project-name my-first-project
```
jaraco.envs 2.6.0
Classes for orchestrating Python (virtual) environments
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.envs.svg
   :target: https://pypi.org/project/jaraco.envs

.. image:: https://img.shields.io/pypi/pyversions/jaraco.envs.svg

.. image:: https://github.com/jaraco/jaraco.envs/workflows/tests/badge.svg
   :target: https://github.com/jaraco/jaraco.envs/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: Black

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2023-informational
   :target: https://blog.jaraco.com/skeleton

Classes for orchestrating Python (virtual) environments.