semvec/pypi-packages huggingface dataset:
kindlenotes2md 0.1.2
Convert your kindle notes to markdown format
Requires Python: >=3.8,<4.0

# kindlenotes2md

Command line utility to convert your Kindle highlights from html to markdown.


## Installation

```
pip install kindlenotes2md
```

## Usage

From the Kindle app, you have the option to export the highlights that you've made in the book.
The easiest way to get access to these are to email them to yourself.
These exported highlights are in html format, so are a bit tricky to use in other tools.

Run this tool with the `.html` file as a command line argument.

```
kindlenotes2md INPUTFILENAME
```

Here's the documentation for the tool you get by running `--help`.

```
Usage: kindlenotes2md [OPTIONS] INPUTFILENAME

Options:
  -o, --outfilename TEXT
  --help                  Show this message and exit.
```

You have the option to specify a filename with `-o` or `--outfilename`.
This will write the markdown output to file, rather than stdout.
distributions-kareem 1.1
A small package built for the Data Science Nanodegree program of Udacity for Gaussian and Binomial Distributions

[![LinkedIn][linkedin-shield]][linkedin-url]
<!-- TABLE OF CONTENTS -->
<details open="open">
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#about-the-project">About The Project</a>
    </li>
    <li>
      <a href="#getting-started">Getting Started</a>
      <ul>
        <li><a href="#installation">Installation</a></li>
      </ul>
    </li>
    <li><a href="#license">License</a></li>
    <li><a href="#contact">Contact</a></li>
    <li><a href="#acknowledgements">Acknowledgements</a></li>
  </ol>
</details>



<!-- ABOUT THE PROJECT -->
## About The Project

This project was built for the Udacity Data Science Nanodegree Program.

<!-- GETTING STARTED -->
## Getting Started

This is an example of how you may give instructions on setting up your project locally.
To get a local copy up and running follow these simple example steps.

### Installation
   ```pip install distributions-kareem```


<!-- LICENSE -->
## License

Distributed under the MIT License. See `LICENSE` for more information.

<!-- CONTACT -->
## Contact

Kareem Ayyad- [@kareem_ayyad](https://twitter.com/kareem_ayyad) - kareem@ayyad.net

Project Link: [https://github.com/kareemayyad/PyPi-Package-Gaussian-Distribution](https://github.com/kareemayyad/PyPi-Package-Gaussian-Distribution)

<!-- ACKNOWLEDGEMENTS -->
## Acknowledgements
* Some of the code used was provided by Andrew Paster, Udacity Instructor, as well as the detailed instructions for creating this project.

Thanks!

[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/kareemayyad/
async-helper 0.0.3
A bridge between the asynchronous and synchronous world
Requires Python: >=3.6

# async-helper

*async-helper by [Sebastian Steins](https://seb.st/)*

Licence: MIT

## Usage
```
import time, random
from async_helper import first_parallel_result

def time_consuming_function(i):
    print("I consume a lot of time")
    time.sleep(random.randint(1, 10))
    return i

candidates = [
    lambda: time_consuming_function(1),
    lambda: time_consuming_function(2),
]

best_effort_result = first_parallel_result(candidates)

print(best_effort_result)
```

## Giving preference

```
import time, random
from async_helper import first_parallel_result

def time_consuming_function(i):
    print("I consume a lot of time")
    time.sleep(random.randint(1, 10))
    return i

candidates = [
    0, lambda: time_consuming_function(1),  # This callable will start immediately.
    1, lambda: time_consuming_function(2),  # This callable will start with `1` sec delay.
]

best_effort_result = first_parallel_result(candidates)

print(best_effort_result)
```

___

© 2021 [Sebastian Steins](https://seb.st/)
async-helper-sebst 0.0.2
A bridge between the asynchronous and synchronous world
Requires Python: >=3.6

# async-helper

*async-helper by [Sebastian Steins](https://seb.st/)*

Licence: MIT

## Usage
```
import time, random
from async_helper import first_parallel_result

def time_consuming_function(i):
    print("I consume a lot of time")
    time.sleep(random.randint(1, 10))
    return i

candidates = [
    lambda: time_consuming_function(1),
    lambda: time_consuming_function(2),
]

best_effort_result = first_parallel_result(candidates)

print(best_effort_result)
```

## Giving preference

```
import time, random
from async_helper import first_parallel_result

def time_consuming_function(i):
    print("I consume a lot of time")
    time.sleep(random.randint(1, 10))
    return i

candidates = [
    0, lambda: time_consuming_function(1),  # This callable will start immediately.
    1, lambda: time_consuming_function(2),  # This callable will start with `1` sec delay.
]

best_effort_result = first_parallel_result(candidates)

print(best_effort_result)
```

___

© 2021 [Sebastian Steins](https://seb.st/)
miax 0.0.4
use it to make sure to have your values your useing are corect
Requires Python: >=3.9.1
Keywords: geting,counting,words

this package will let you use a minv and a maxv value and put a numv if that numv is less then the minv get the minv but if is biger then maxv you get maxv else you get the numv if you need help you get it here this is how to use it miax.miax(Example minv Value, Example maxv Value, Example)