semvec/pypi-packages huggingface dataset:
wrangle-pypes 0.3
Easy, model based data wrangling.
License: MIT

# wrangle_pypes
_Easy, model based data wrangling._

<a href="https://mypy-lang.org/"><img alt="Checked with mypy" src="http://www.mypy-lang.org/static/mypy_badge.svg"></a>
<a href="https://github.com/python/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>

## Origin
This project came about thanks to having implemented this concept in a prior project ([dfb_predict](https://github.com/tim-fi/dfb_predict)) and wanting to reuse it. In said previous project it came about, because we wanted to play around with data-oriented design and figured that if we break up the transformations required during data-wrangling/munging into a simple "_AST_" where each node represents a single transformation. And to further the idea we to some inspiration from one of the [Unix philosophy](https://en.wikipedia.org/wiki/Unix_philosophy) core tenants, i.e. a single transformation should do one simple thing only, and allow for composition to implement more complex behavior.

## Example
```python
from dataclasses import dataclass
import json

from wrangle_pypes import Pipeline
from wrangle_pypes.transformations import Get, Create, Cast

@dataclass
class Point:
    x: int
    y: int


@dataclass
class Square:
    A: Point
    B: Point


pipeline = Pipline({
    Square: {
        "A": Get("A") | Create(Point),
        "A": Get("B") | Create(Point),
    },
    Point: {
        "x": Get("x") | Cast(int),
        "y": Get("y") | Cast(int),
    }
})

data = """
[
    {
        "A": {"x": 0, "y", 0},
        "B": {"x": 1, "y", 1},
    }, {
        "A": {"x": 10, "y", 10},
        "B": {"x": 11, "y", 11},
    }
]
"""

squares = list(pipeline.create_multiple(json.loads(data)))
```
videocloud 3.0.0
A command line tool that generates word clouds from YouTube video captions
License: MIT
Requires Python: >= 3.6


# VideoCloud
[![Build Status](https://travis-ci.org/paramt/videocloud.svg?branch=master)](https://travis-ci.org/paramt/videocloud)
[![License](https://img.shields.io/github/license/paramt/videocloud.svg)](https://github.com/paramt/videocloud/blob/master/LICENSE)

Generate word clouds from YouTube video captions

## Installation
```
pip install videocloud
```
*Note: A C compiler is required for the installation process.*

## Usage
```
videocloud <link to YouTube video> [options]
```

```
videocloud www.youtu.be/MHTizZ_XcUM --language=es --color=#eee
```

### Options

#### `--filepath`
Where on disk to save the generated videocloud

Default: <kbd>videocloud.png</kbd>

#### `--language`
2-letter language code of the captions

Default: <kbd>en</kbd>

#### `--color`
Hex color code of the videocloud's background color

Default: <kbd>#d1d1d1</kbd>

#### `--font`
Link to a TTF font file

Default: [Noto Sans](assets/fonts/NotoSans/NotoSans.ttf)

#### `--mask`
Link to a PNG mask file

Default: [Cloud](https://github.com/paramt/videocloud/blob/v2.5/assets/masks/cloud.png)

## Development
This project is maintained by [paramt](https://github.com/paramt) on [GitHub](https://github.com/paramt/videocloud).
fluxx 0.1.1.post1
Finite State Machines & Trait-based programming for Python 3
License: MIT
Requires Python: >3.5
Keywords: fsm,finite,state,trait,actor,observer

# fluxx - modern Finite State Machines & more
The results of my experiments with FSM's and trait-based programming based on Python 3 enums and sets.

Check the Showcase notebook in docs on https://github.com/amogorkon/fluxx for working examples and documentation.

Have fun!
- Anselm Kiefner
thonny-microbit 0.3
A plug-in which adds BBC micro:bit support for Thonny
License: MIT
Requires Python: >=3.5
Keywords: IDE education programming microbit MicroPython Thonny

This is a plug-in for Thonny which adds BBC micro:bit support (works with Thonny 3.0 and 3.1).

      Not required for Thonny 3.2 and later (same features are built-in). 

More info: 

* https://bitbucket.org/KauriRaba/thonny-microbit
* https://github.com/thonny/thonny/wiki/MicroPython
* https://thonny.org