semvec/pypi-packages huggingface dataset:
graphinate 0.0.4a0
Graphinate. Data to Graphs.
Requires Python: >=3.9
Keywords: declarative,graph

# Graphinate. Data to Graphs.

> ⚠️ **Under Development!** This library is alpha-quality ⚠️

[![Tests](https://github.com/erivlis/graphinate/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/erivlis/graphinate/actions/workflows/tests.yml)
[![Supported Versions](https://img.shields.io/pypi/pyversions/graphinate.svg)](https://pypi.org/project/graphinate)
[![Contributors](https://img.shields.io/github/contributors/erivlis/graphinate.svg)](https://github.com/erivlis/graphinate/graphs/contributors)
## Gallery

![d3_graph_ast](https://github.com/erivlis/graphinate/assets/9897520/9e7e1ed2-3a5c-41fe-8c5f-999da4b741ff)
![repo_graph](https://github.com/erivlis/graphinate/assets/9897520/9c044bbe-1f21-41b8-b879-95b8362ad48d)
![AST 3D Force animation](https://github.com/erivlis/graphinate/assets/9897520/2e9a53b1-5686-4683-a0e4-fbffa850a27b)


## Install

Graphinate is available on PyPI:

```console
python -m pip install graphinate 
```
Graphinate officially supports Python 3.9+.

## Examples

- GitHub
  - Repository
  - Followers
- Code AST (Python)

## API

### By Convention

### By Configuration

## Build

```console
python -m build
```

## Test

```console
 python -m pytest ./tests --cov=./src --cov-branch --cov-report=xml --junitxml=test_results.xml
```
scikit-gbm 0.2.1
scikit-learn compatible tools to work with GBM models
Requires Python: >=3.7

# scikit-gbm

[![Documentation Status](https://readthedocs.org/projects/scikit-gbm/badge/?version=latest)](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/scikit-gbm.svg)](https://badge.fury.io/py/scikit-gbm)

scikit-learn compatible tools to work with GBM models

## Installation

```
pip install scikit-gbm

# or 

pip install git+https://github.com/krzjoa/scikit-gbm.git
```

## Usage

Fo the moment, you can find the following tools in the library:

* `GBMFeaturizer`
* `GBMDiscretizer`
* `trees_to_dataframe`
* `AXIL`

Take a look at the [documentation](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest) to learn more.
A simple example, how to use `GBMFeaturizer` in a classification task.

```python

# Classification
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

from skgbm.preprocessing import GBMFeaturizer
from xgboost import XGBClassifier

X, y = make_classification()
X_train, X_test, y_train, y_test = train_test_split(X, y)

pipeline = \
    Pipeline([
        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),
        ('logistic_regression', LogisticRegression())
    ])

# Try also:
# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),
# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),
# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),

# Predictions for the test set
pipeline_pred = pipeline.predict(X_test)
```
easyebay 0.1.1
A library for scraping items from eBay
Requires Python: >=3.8,<4.0

<div style="text-align: center">

# Easy eBay

*A library for scraping items from eBay*

</div>

## To begin with

Here is a sample code

##### Example 1

```python
import easyebay

result = easyebay.search(keyword="4090",
                         pageNum="1",
                         maxPrice="1000",
                         minPrice="500",
                         conditions=[easyebay.condition.used])

parsed = easyebay.parseSearch(result)

for item in parsed:
    print(item.title, item.price, item.shipping, item.url)
```

Here's a sample output

```text
YASKAWA CDBR-4090B B... $989.99 None https://www.ebay...
LOT OF 5 Motorola RS... $769.69 None https://www.ebay...
MELLTRONICS 222-4090... $750.00 None https://www.ebay...
...
```

`easyebay.search` returns the html, and easyebay.parseSearch returns `easyebay.item` object. This is the definition of
item object

```python
class item():
    title: str
    condition: str
    price: str
    seller: list
    shipping: str
    returns: str
    sold: str
    url: str
    image: str
```
xethhung12-minio 0.0.4
A small program for downloading and uploading file to minio storage
Requires Python: >=3.7

# This project is not maintained anymore. Please go for

- [xethhung12_minio_common](https://github.com/xh-dev-py/xethhung_minio_common)
- [xethhung12_minio_download_file](https://github.com/xh-dev-py/xethhung12_minio_download_file)
- [xethhung12_minio_upload_file](https://github.com/xh-dev-py/xethhung12_minio_upload_file)

---

# Build and deploy
```shell
rm -fr dist/*
python -m build
python twine upload dist/* -u __token__ -p {token}
```

# Behind Proxy 
if the client is connecting to internet though a company proxy server, use the environment variable(`http_proxy`) to setup the proxy. \
e.g. `http_proxy=http://127.0.0.1:8080`


# Usage of upload
```python
python -m xethhung12_minio_upload_file \
    --url {url} \
    --access-key {access-key} \
    --secret-key {secret-key} \
    --bucket {bucket} \
    --local-file {local file} \
    --remote-file {remote file}
```

# Usage  of download
```python
python -m xethhung12_minio_download_file \
    --url {url} \
    --access-key {access-key} \
    --secret-key {secret-key} \
    --bucket {bucket} \
    --local-file {local file} \
    --remote-file {remote file}
```
freq-mob 0.2.2
Monotonic Optimal Binning for Frequency Models

#### Introduction

To mimic the py\_mob package (https://pypi.org/project/py-mob) for binary outcomes, the freq\_mob is a collection of python functions that would generate the monotonic binning and perform the variable transformation for frequency outcomes such that the Pearson correlation between the transformed $X$ and $Log(Y)$ is equal to 1. In case of frequency count models with $Log()$ link function, the transformation is derived as $F(x)_i = Log \frac{\sum_i Y / \sum_i Exposure}{\sum Y / \sum Exposure}$ in the training sample, where $Exposure$ is the number of cases and $i$ refers to the $ith$ bin groupped by $x$ values.  


Should you have any question or suggestion about the freq\_mob package, please feel free to drop me a line. 

#### Core Functions

```
freq_mob
  |-- qtl_bin()  : An iterative discretization based on quantiles of X.  
  |-- cnt_bin()  : A revised iterative discretization for records with Y > 0.
  |-- iso_bin()  : A discretization algorthm driven by the isotonic regression between X and Y. 
  |-- rng_bin()  : A revised iterative discretization based on the value range of X.  
  |-- kmn_bin()  : A discretization algorthm based on the kmeans clustering of X.  
  |-- gbm_bin()  : A discretization algorthm based on the gradient boosting machine.  
  |-- view_bin() : Displays the binning outcome in a tabular form. 
  |-- cal_newx() : Applies the variable transformation to a numeric vector based on the binning outcome.
  `-- mi_score() : Calculates the mutual information score between X and Y.
```

###  Authors

[WenSui Liu](mailto:liuwensui@gmail.com) is a seasoned data scientist with 15-year experience in the financial service industry. 

[Joyce Liu](mailto:joyce.jl.liu@gmail.com) is a college student majoring in Mathematics with a strong passion for data science.
kabelwerk 0.1.2
The Kabelwerk SDK for Python.
Keywords: kabelwerk

|logo|_

|Kabelwerk|_ is a fast and secure chat as a service for digital health
applications. This is the Kabelwerk SDK for Python.


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

The SDK is available at the `Cheese Shop`_:

.. code:: sh

    # usually inside a virtual environment
    pip install kabelwerk

Releases follow semantic versioning. Make sure to check the `CHANGELOG.rst`_
whenever upgrading to a newer version.


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

Documentation — including API reference and code examples — can be found at
`docs.kabelwerk.io`_.


Contributing
============

Please refer to `CONTRIBUTING.rst`_ if you have found a bug or if you want to
setup the project locally.


Licence
=======

Licensed under the `MIT`_ licence.


.. |logo| image:: https://kabelwerk.io/images/logo_256.png
.. _logo: https://kabelwerk.io

.. |Kabelwerk| replace:: **Kabelwerk**
.. _Kabelwerk: https://kabelwerk.io

.. _`Cheese Shop`: https://pypi.org/project/kabelwerk/
.. _`CHANGELOG.rst`: https://github.com/kabelwerk/sdk-python/blob/master/CHANGELOG.rst

.. _`docs.kabelwerk.io`: https://docs.kabelwerk.io/python/

.. _`CONTRIBUTING.rst`: https://github.com/kabelwerk/sdk-python/blob/master/CONTRIBUTING.rst

.. _`MIT`: https://github.com/kabelwerk/sdk-python/blob/master/LICENSE
kon-quotes 1.0.1
K-ON Quotes API, get quotes from the K-ON Anime
Requires Python: >=3.6

# K-ON Quotes
Get quotes from the K-ON anime in the most easiest way possible<br/>
<img src="https://i.imgur.com/0qVCV8Z.jpeg" alt="Mio Akiyama" width="46%"/>

# Table of contents
* Content
* Usage
* Splitting the quote from the author
* Fixing UnicodeError whe using local file

# Content
Currently the file contains 213 quotes, however, more quotes are going to come in the future. <br/>
You can ask to add quotes by opening either an issue or a pull request

# Usage
## 0 - With python package
`pip install kon_quotes`
```py
from kon_quotes import GETquote

quote = GETquote(character_you_want) # mio, yui, azusa, tsumugi, ritsu, sawako, jun, general, all
quote_author = quote.GETauthor()
print(f"{quote} - {quote_author}")
```

## 1 - With .txt file (online)
```py
import requests, random

file_url = "https://zeyatsu.github.io/k-on-quotes/quotes.json" # No need to download the txt file.
response = requests.get(file_url)

if response.status_code == 200:
  lines = response.text.splitlines()
  random_line = random.choice(lines)
  print(random_line)
else:
  print("Error: ", response.status_code)
```
## 2 - With API (.json file (online))
```py
import random
import requests

json_url = "https://zeyatsu.github.io/k-on-quotes/quotes.json" # No need to download the json file.

response = requests.get(json_url)

if response.status_code == 200:
    data = response.json()
    random_author = random.choice(list(data["authors"].keys()))
    random_quote = random.choice(data["authors"][random_author])

    print(f"{random_quote} by {random_author}")
```

For characters replace "k-on-quotes/quotes.json" by "k-on-quotes/characters/the_character_you_want.json"

# Splitting the quote from the author
```py
quote, author = random_quote.strip().split(" / ")
print(f"Quote: {quote} by {author}")
```

# Fixing UnicodeError when using local file
If you download the quotes.txt file instead of using the url, it is possible to get a UnicodeError. In this case, you will have to add a encoding parameter </br>
```py
with open('quotes.txt', 'r', encoding='utf-8') as f:
```

# Credits
<table>
  <tr>
    <td style="align:center;">
      <a href="https://github.com/ZeyaTsu"> 
        <img src="https://avatars.githubusercontent.com/u/43354103?v=4" alt="Author: ZeyaTsu" width="100" height="100"/>
        <br/>
        <span>ZeyaTsu</span>
      </a>
    </td>
  </tr>
</table>
dmypy-ls 1.13
super fast mypy language server
Requires Python: >=3.9,<4

dmypy-ls: super fast mypy language server
=========================================

It leverages mypy.dmypy_server instead of the slow cli interface.

First run takes same time as mypy cli, but next are super fast as
mypy.dmypy_server only reload the changed file.

It supports diagnostics only.

Status
------

On works on Open and Save for now

The on Change could be implemented if the mypy FineGrainedBuildManager
uses BuildSource of the dmypy_server instead of always reread file from
disk…

Install
-------

.. code:: shell

   $ pip install --user dmypy-ls

vim-lspconfig
-------------

.. code:: lua

   lua << EOF
   require("lspconfig.configs")["dmypyls"] = {
       default_config = {
           cmd = { 'dmypy-ls' },
           filetypes = { 'python' },
           root_dir = lspconfig.util.root_pattern('pyproject.toml', 'setup.py', 'setup.cfg', 'requirements.txt', 'Pipfile'),
           single_file_support = true,
       },
   }
   require("lspconfip").dmypyls.setup({})
   EOF