semvec/pypi-packages huggingface dataset:
web3-wrapped-contract 0.0.16
web3_wrapped_contract
Requires Python: >=3.4

# web3_wrapped_contract

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## Description

web3_wrapped_contract

## Install

~~~~bash
pip install web3_wrapped_contract
# or
pip3 install web3_wrapped_contract
~~~~

## Usage

~~~~python
import web3_wrapped_contract
~~~~

## Dependencies

[eth-account](https://pypi.org/project/eth-account), [setuptools](https://pypi.org/project/setuptools), [web3](https://pypi.org/project/web3)
math-problem-generator 0.0.3
A package for generating math problems
Requires Python: >=3.8

# Python package math_problem_generator
A python package for generating simple math problems. The package is only for
generating the problems. There is no support for presentation or user interaction.

# Examples
How to use the package.

```python
# Import the package
from math_problem_generator import generator

# Generate 2 simple addition problems with
# 4 numbers between 1 and 10 to be added
p = generator.simple_problems(
        "add",no_of_problems=2,min_number=1,max_number=10,numbers=4
    )

# Print the math problems
print(p)
```

The code above should print something like this:
```
[
    {'type': 'simple', 'operator': 'add', 'numbers': [5, 2, 3, 6], 'solution': 16, 'users_answer': None},
    {'type': 'simple', 'operator': 'add', 'numbers': [6, 10, 10, 5], 'solution': 31, 'users_answer': None}]
]
```

# Development

Setup virtual environment
1. `python3 -m venv .venv`
2. `source .venv/bin/activate`
3. `pip3 install -r requirements-dev.txt`
4. `pip install -e src/`
5. `pre-commit install`

Leave virtual environment (when done)
1. `deactivate`
rpm-ostree-gui 0.0.2
GUI application for managing RPMs via rpm-ostree.
Requires Python: >=3.6

# RPM OSTree GUI

GUI application for managing RPMs via rpm-ostree.

# Installation

Note: For search to work you will need to install python-hawkey.

From PyPi:
```
pip3 install rpm-ostree-gui
```

From source:
```
git clone https://github.com/epiccakeking/rpm-ostree-gui
cd rpm-ostree-gui
pip3 install .
```
DiffPriv 2.0.0b2
A Differential Privacy Package
Requires Python: >= 3.8.1

**This is an alpha release, meaning it might not be stable. We suggest you install the latest stable build.**

![DiffPriv Logo](https://docs.google.com/drawings/d/e/2PACX-1vQ8A92uJpy4g09GFYxayNQXOvtl0wmXXkYFiteDFSaXVcfdbcm835wc_IjjlKHlM94rjdsM7H1Szzjq/pub?w=600)

[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Quantalabs/DiffPriv/Build?style=flat-square)](https://github.com/actions/workflows/test.yml)
[![PyPI Version](https://shields.mitmproxy.org/pypi/v/DIffPriv.svg)](https://pypi.org/project/DiffPriv)
[![DeepSource](https://deepsource.io/gh/Quantalabs/DiffPriv.svg/?label=active+issues&show_trend=true)](https://deepsource.io/gh/Quantalabs/DiffPriv/?ref=repository-badge)
[![codecov](https://codecov.io/gh/Quantalabs/DiffPriv/branch/master/graph/badge.svg?token=wCz4qTJxEO)](https://codecov.io/gh/Quantalabs/DiffPriv)
[![License](https://img.shields.io/badge/License-GNU%20GPL%20v3.0-green)](https://github.com/Quantalabs/DiffPriv/blob/master/LICENSE)
[![Downloads](https://static.pepy.tech/personalized-badge/diffpriv?period=total&units=none&left_color=black&right_color=orange&left_text=PyPI%20Downloads)](https://pepy.tech/project/diffpriv)
[![Downloads](https://pepy.tech/badge/diffpriv/month)](https://pepy.tech/project/diffpriv/month)
[![Downloads](https://pepy.tech/badge/diffpriv/week)](https://pepy.tech/project/diffpriv/week) 
[![Chat on GitHub Discussions](https://img.shields.io/badge/Chat-on%20Discussions-green)](https://github.com/Quantalabs/DiffPriv/discussions)
![Conda](https://img.shields.io/conda/dn/conda-forge/diffpriv?label=Anaconda%20Downloads)


> The truth is more important than ever—let's make sure easy privacy protection is available.

Differential privacy should be simple. Now that data defines our world, we need to look at the cost of privacy. Let's make protecting privacy easy.

<br><br>

## What is differential privacy?

Differential privacy allows for data to be preserved while making sure that attackers cannot gain access to an individual's data. Even if you publish summary statistics (like average age of participants, unlabeled addresses of participants, etc.), attackers can gain access to *individual* data (like age of *each* participant, *labeled* addresses of participants, etc.). In order to achieve this, differential privacy slightly changes the actual dataset to make sure that any uncovered data will not give away personal information. See below for how to get started!

<br>

## Downloading DiffPriv
To download, open up your command prompt and type
```sh
    pip install DiffPriv==v2.0.0b2
```
or from the source repo:
```sh
    git clone https://github.com/Quantalabs/DiffPriv
    cd diffpriv
    git switch v2.0.0-beta2
    python setup.py install
```
### Conda Envioronment

You can install it from conda through the command:
```sh
conda install -c conda-forge/label/diffpriv_dev diffpriv
```

### Docs

Once installed, check out the docs at https://quantalabs.github.io/DiffPriv/v2a3/