semvec/pypi-packages huggingface dataset:
pythOyO 0.1.1
A fun and whimsical UyU programming language written in Python
Requires Python: >=3.6

<h1 align="center"><a href="https://youtube.com/@rajdeepchatterjee-pb5jw?si=LAEg_mWzrHpJcrp6">pythOyO</a></h1>


[![](https://github.com/RajChat-hub/pythOyO/blob/main/asyets/imyages/pythOyO_500px_Logo3.png)](https://youtube.com/@rajdeepchatterjee-pb5jw?si=LAEg_mWzrHpJcrp6)

<p align="center">
  <b>pythOyO is an uyu programming language written in Python. UyU.</b>
</p>

<p align="center">
  <a href="https://marketplace.visualstudio.com/items?itemName=Haddle.pyoyo">Get PythOyO LangUyUage support for VSCode</a>
</p>
<p align="center">
  <a href="https://pypi.org/project/PythOyO/">PythOyO on PyPi</a><br>
</p>

```bash
pip install PythOyO
```

<h2 align="center">Uyusage</h2>

<h4 align="left">Cruyueate a nuyu file - <code>test.pyoyo</code></h4>

```py
pyint("Heyyo Woyrld")
```

<h4 align="left">Ruyun</h4>

Either run from the interpreter directly:
```sh
python/python3 pythoyo.py test.pyoyo
```

Or run in an interactive shell:
```sh
python shÿell.py
(｡･ÿ･｡)ﾉ★ > ryun("test.pyoyo")
```

<h4 align="left">Ouyutpuyut</h4>

```
Heyyo Woyrld
```

<h2 align="center">Docuyumentation</h2>

<h3 align="center">General</h3>

<p align="center"><code>python shÿell.py</code> opens the pythOyO shell. Running <code>ryun("test.pyoyo")</code> from the shell execuyutes code from the file <code>test.pyoyo</code>.</p>


<h3 align="center">Variables</h3>
<p align="center">Variables can be decluyuared using the keyword <code>pyease</code>.</p>

```py
pyease baka = 100
pyease tehe = "tyo"
pyease chan = 25
pyease baka = baka + 1
pyease tehe = 500
pyease chan = chan * 2

pyint(tehe)
pyint(baka)
pyint(chan)
```

<h4 align="left">Ouyutpuyut</h4>

```
500
101
50
```

<h3 align="center">Conditionals</h3>
<p align="center">IF《condition》THYEN《expression》EYIF《condition》THYEN《expression》</p>

```py
pyease tehe = 501

IF tehe == 502 THYEN pyease chan = "tehe is 502!" EYIF tehe == 501 THYEN pyease chan = "
is 501!" EYSE pyease chan = "tehe is 500!"

pyint(chan)
```

<h4 align="left">Ouyutpuyut</h4>

```
tehe is 501!
```

<h3 align="center">Loops</h3>

```py
FOR i = 0 TO 5 THYEN
	pyint("UyU")
END
```

<h4 align="left">Ouyutpuyut</h4>

```
UyU
UyU
UyU
UyU
UyU
```

<h3 align="center">Functions</h3>

```py
FYUNCTION oyofy(pyefix) -> pyefix + "OyO"

pyint(oyofy("This is pyth"))
```

<h4 align="left">Ouyutpuyut</h4>

```
This is pythOyO
```

<h3 align="center">Clear</h3>
<p align="center">You can clear the shell with the cyear command</p>

```
(｡･ÿ･｡)ﾉ★ > cyear
```


<h2 align="center">Contribuyuting</h2>
<p align="center">All contribuyutions are welcome, just make a puyull request!</p>

</br>

<h2 align="center">Testing</h2>
<p align="center">Unit tests are available through the unittest module. New test files must start with `test*.py` to be detected.</p>

```sh
python -m unittest
```

---

> Pythoyo logo [credit](https://www.pngegg.com/)


> Thank youyu @Haddle for making VSCode language support for PythOyO

> Thank youyu @Tulip-0333 for helping with putting PythOyO up on PyPi

<h5 align="left">uyu</h5>
countest 0.1.5
CountEst is a module containing the implementation of count-based estimators, i.e., supervised learning algorithms that make predictions based on the frequency or count of specific events, categories, or values within a dataset.
Requires Python: >=3.9
Keywords: supervised learning, machine learning, count-based classifier

# CountEst

CountEst is a Python module containing the implementation of count-based estimators, i.e., supervised learning algorithms that make predictions based on the frequency or count of specific events, categories, or values within a dataset. These estimators operate under the assumption that the distribution of counts or frequencies provides valuable information for making predictions or inferences. The classifier is inspired by "Vastu Moolak Ganit" as described in a recent Indian philosophy [Madhyasth Darshan](https://madhyasth-darshan.info) by Late Shri A Nagraj.

Currently, we have implemented count-based classifier for categorical data. It operates by learning the distribution of category counts (i.e. the number of occurrences or frequency of each distinct category within a specific feature) within each class during the training phase. It then predicts class probabilities for unseen data based on the observed counts of categories within each feature, employing a voting mechanism to handle ties. 

## Installation

The package can be installed using `pip`:

```
pip install countest
```

## Dependencies

The installation dependency requirements are:

- Python (>= 3.9)
- NumPy (>= 1.5)
- Pandas (>= 1.1.5)
- Scikit-Learn (>=0.20.0)

These dependencies are automatically installed using the pip commands above.

## Examples

Here we show an example using the CategoricalCBC (i.e. count-based classifier for categorical data):

```
from countest.classifiers import CategoricalCBC
import numpy as np

X = np.random.choice(range(0,10),(100,6))
y = np.random.choice([0, 1], size=100)

estimator = CategoricalCBC()
estimator.fit(X, y)

print(estimator.predict([3, 6, 7, 2, 3, 4]))
```

## Changelog

See the [changelog](https://github.com/sanskriti-02/CountEst/blob/master/CHANGELOG.md) for a history of notable changes to CountEst.

## Development

This project is currently in its early stages. We're actively building and shaping it, and we welcome contributions from everyone, regardless of experience level. If you're interested in getting involved, we encourage you to explore the project and see where you can contribute! For specific contribution guidelines, visit our [GitHub page](https://github.com/sanskriti-02/CountEst).
nebius-connect 0.1.0
Nebius AI connector for Apache Spark™
Requires Python: >=3.8

# Nebius AI connector for Apache Spark™

The [Managed Service for Apache Spark](https://nebius.ai/services/managed-spark), a [Nebius AI](https://nebius.ai/) service, offers access to _sessions:_ managed environments that can handle multiple independent ad-hoc computations at the same time. 

With this connector, you can connect to your Managed Spark sessions using [Spark Connect](https://spark.apache.org/docs/latest/spark-connect-overview.html) and process data with Spark APIs from your machine.

## Installing

```bash
pip install nebius-connect
```

## Example

```py
from pyspark.sql.connect.session import SparkSession
from nebius.spark.connect import create_channel_builder

nebius_spark_cb = create_channel_builder(
    'spsession-example123.nebius.cloud:443',
    password='my-password'
)

spark = SparkSession \
    .builder \
    .channelBuilder(nebius_spark_cb) \
    .getOrCreate()

columns = ["id","name"]
data = [(1,"Sarah"),(2,"Maria")]
df = spark.createDataFrame(data).toDF(*columns)
df.show()
spark.stop()
```

## License

Copyright 2024 Nebius B.V.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

_Apache and [Apache Spark](http://spark.apache.org/) are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries._
rpa-test 0.2.1
test yingdao rpa installation python package
Requires Python: >=3.7

# rpa_test_zhang

[![PyPI - Version](https://img.shields.io/pypi/v/rpa-test-zhang.svg)](https://pypi.org/project/rpa-test-zhang)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/rpa-test-zhang.svg)](https://pypi.org/project/rpa-test-zhang)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install rpa-test-zhang
```

## License

`rpa-test-zhang` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.