semvec/pypi-packages huggingface dataset:
IN-Utility 1.0.1
A utility for text manipulation, compression, and task management.

# Your Utility Name

[![PyPI version](https://badge.fury.io/py/IN-utility.svg)](https://badge.fury.io/py/your-utility)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

A versatile command-line utility for text manipulation, compression, and task management.

## Features

- Text manipulation: Count words, lines, characters; convert to lowercase/uppercase; replace words; and more.
- Compression: Create ZIP or TAR archives with optional gzip compression.
- Todo List Manager: Add, remove, and list tasks in a todo list.

## Installation

You can install the utility using `pip`:

```bash
pip install your-utility
``` 

## Usage

### Text Manipulation Mode

```bash
your-utility text --input input.txt --words
your-utility text --input input.txt --lines
your-utility text --input input.txt --characters
your-utility text --input input.txt --lower
your-utility text --input input.txt --upper
your-utility text --input input.txt --count-word target_word
your-utility text --input input.txt --replace old_word new_word
```

### Compression Mode

```bash
your-utility compress
```

### Todo List Manager Mode

```bash
your-utility todo add "Task description"
your-utility todo remove task_index
your-utility todo list
```

## Example

- Count words in a text file:

```bash
your-utility text --input sample.txt --words
```

- Create a compressed ZIP archive:

```bash
your-utility compress
```

- Add a task to the todo list:

```bash
your-utility todo add "Complete project proposal"
```


## LICENSE

This project is licensed under the MIT License - see the LICENSE file for details
reg-stat-inference 0.1.9
Treat multicollinearity and non-significant variables from your statsmodels linear and logistic regressions
Requires Python: >=3.7,<4.0

# Regression and Statistical Inference Toolkit

Welcome to the **reg_stat_inference** toolkit!

This toolkit provides functions for treating multicollinearity and performing feature selection based on p-values in linear regression and logistic regression models. It leverages the `statsmodels` library for model analysis.

## Purpose

The purpose of this toolkit is to offer a collection of Python functions that streamline the process of dealing with multicollinearity and feature selection in regression models. It aims to simplify the analysis of complex datasets by automating tasks like variance inflation factor (VIF) calculation and p-value-based feature removal.

## reg_stat_inference

**reg_stat_inference** is a Python package that provides functions for treating multicollinearity and performing statistical inference in regression models. It offers tools to identify and address multicollinearity issues and to iteratively drop features based on p-values. This can help you build more interpretable and robust regression models.

## Installation

You can install **reg_stat_inference** using pip:

```bash
pip install reg-stat-inference
```

## Usage

To use the toolkit, import the functions in your scripts or notebooks:

```python
import pandas as pd
import statsmodels.api as sm
from reg_stat_inference import treat_regression_model

# Create sample data
X = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [4, 5, 6]})
y = pd.DataFrame({'target': [0, 1, 0]})

# Use the treat_regression_model function with OLS regression
result = treat_regression_model(X, y, threshhold_vif=5, threshold_pval=0.05, reg_type='OLS')

# Print the treated feature list and the model summary
print("Treated Features:", result.metric_list)
print(result.model.summary())
```
You can then apply these functions to your dataset to enhance the quality of your regression models.

## Git Repo

For detailed documentation and usage examples, please refer to the [GitHub repository](https://github.com/DirceuVio/reg_stat_inference).


## Contributing
Contributions are welcome! If you have suggestions, bug reports, or improvements, please open an issue or submit a pull request.
PloudosAPI 0.1.0
PloudOS API interactions like starting servers etc.
Keywords: python,ploudos,api,ploudos api,ploudos api wrapper

PloudosAPI
==========

An API Wrapper to interact with ploudos.com
-----------------------------------------------

With PloudosAPI, you can start and stop your PloudOS servers, get the
server console, run commands and do lots of other stuff.

Install the package
-------------------

To install the package, run the command ``pip install PloudosAPI``.

Get started
-----------

Log in to your PloudOS account:

::

   import PloudosAPI
   session = PloudosAPI.login("username", "password") #replace with your PloudOS username / password

Get a PloudOS server by its id:

::

   server = session.get_server("server_id")

... or by its name:

::

   servers = session.get_servers_with_name("name")
   server = servers[0]

Start the server:

::

   server.start()

.. warning::
   Keeping a server online with a Bot or other programs is against the PloudOS `Terms of Service <https://ploudos.com/rules/>`_.

Get the server console and execute commands:
(Only works when the server is running)

::

   console = server.get_console()

   server.post_to_console("command") #replace with the command you want to run

Stop the PloudOS server:

::

   server.stop()


Get your PloudOS servers:

::

   my_servers = session.servers()

``session.servers()`` returns a dict with your PloudOS servers that looks like this:

.. code:: json

   {
       "owned" : [
           PloudosAPI.PloudosServer,
           PloudosAPI.PloudosServer,
           ...
       ],
       "shared" : [
           PloudosAPI.PloudosServer,
           PloudosAPI.PloudosServer,
           ...
       ]
   }

Other stuff
-----------

Force a server shutdown:

::

   server.force_stop()

Regenerate the serverâ€™s world:

::

   server.regenerate_world(*, seed="your_seed", hardcore=False)


Share the server with a PloudOS user:

::

   server.share_add("user")

Remove the access of a PloudOS user:

::

   server.share_remove("user")

Ban / Unban a minecraft player from the server:

::

   server.ban("player")

   server.unban("player")
signapp 1.0.1
Apk signer script

# signapp
Corss-Platform script used to sign APK files <br/>
<br />
*NOTE* that you should use this tool for debugging / educational purposes only! <br />
*NOTE* that you must accept the LICENSE of the tools listed in the [requirements](#Requirements) section

## Installation
Simply run:
> pip install signapp --upgrade && signapp_fetch_tools

Make sure to have python scripts folder in your path, <br/>
And use the correct version of pip for python3

## Usage
> signapp -a <original_apk> -o <output_apk>

## Recommended Projects
I'd like to recommend my other projects here, [`buildapp`](https://github.com/mon231/buildapp) and [`apkmod`](https://github.com/mon231/apkpatcher) <br />
[`buildapp`](https://github.com/mon231/buildapp) is used to recompile and resign an apk that was originally decompiled by [`apktool`](https://ibotpeaches.github.io/Apktool/install/) <br />
[`apkmod`](https://github.com/mon231/apkpatcher) is used to inject [frida js-script](https://frida.re/docs/javascript-api/) into an apk

## Requirements
The project uses these tools (can be fetched using `signapp_fetch_tools` after `pip install signapp`):
- android SDK tools ([download build_tools](https://dl.google.com/android/repository/build-tools_r33-windows.zip), [download platform_tools](https://dl.google.com/android/repository/platform-tools_r34.0.1-windows.zip))
    - adb (default at SDK\platform_tools, only required if `-i` flag is used)
    - zipalign (default at SDK\build_tools)
    - apksigner (default at SDK\build_tools)
ShdwDB 0.0.8
A package for databasing
Requires Python: >=3.8.2

# The ShdwDB package
This package is for databases and storage.
Use it to create tables with rows and columns.
## Functions
### `make(name, column_names, row_names)`
This creates a new database. `column_names` must be an iterable containing the names of the columns. `row_names` must be also be an iterable with the row names.
### `retrieve(name, key_saved)`
Retrive a database from the replit db storage. Give it a name and specifiy the key you saved it with.
## Database methods
+ `set(column, row, value)`: Set a value to a cell.
+ `delete_item(column, row)`: Set the cell value to None.
+ `delete_column(column)`: Delete the column.
+ `delete_row(row)`: Delete the row.
+ `get_value(column, row)`: Get the value of a cell.
+ `get_column(column)`: Get a column.
+ `get_row(row)`: Get a row(as a string).
+ `save(key_to_save)`: Save the Database to replit db.
+ `add_column(name)`: Add a column.
+ `add_row(name)`: Add a row.

Release notes:
+ removed id entirely
easy-expectations 0.1.0
A package that simplifies usage of Great Expectations tool for Data Validation.
Requires Python: >=3.9,<3.11
Keywords: Great Expectations,data quality,data validation,data contracts



A package that simplifies data validation in your pipeline by abstracting the complexity
of Great Expectations away, in addition to using AI to profile and generate tests for your data.


For more information and detailed instructions, check out the [GitHub
repository](https://github.com/Elsayed91/easy_expectations).