semvec/pypi-packages huggingface dataset:
pyfurby 0.1
Controlling the various parts of the Furby with Python and a Pi Zero
License: MIT

# PyFurby
Control a Raspberry Pi Zero enpowered furby via a served Jupyter notebook or via a restless API.

The victim, looking scared...

[![fear](images/fear.thumbnail.jpg)<br/>(click to enlarge)](images/fear.JPG)

## Module details and functionality

See [API notes](API.md)

The objective is to make the furby easy to control and customise.
Say in a Furby served Jupyter notebook within the local network:
    
    from pyfurby import Furby
    furby = Furby()
    furby.wait_until_squeezed()
    furby.say('Get your filthy hands off me')
    furby.wait_until_moved()
    furby.say('Put me down you degenerate')
    
It contains several methods, ranging from the comical
    
    furby.flutter() # it flutters its eyelids
    
to the useful 
   
    furby.recite_ip()
    
And can be set commands remotely:

    furby.restless()
    

## Background

The idea and the majority of the setup came form [a blog post](https://howchoo.com/g/otewzwmwnzb/amazon-echo-furby-using-raspberry-pi-furlexa)
However, there were lots of problems and differences.
Namely

* the code here is different
* the components are squeezed in differently
* gyroscope

## Project
This project can be divided into:

* [hardware](hardware.md)
* [software](software.md)

On the software side, I was disappointed that Alexa not on an Echo cannot play music.
However, making it serve a jupyter notebook and having a nice API (`pyfurby`) mean that it's rather fun.
As a result it is not a Furbexa, but a pyfurby. What do you reckon Furby?

    from pyfurby import Furby
    furby = Furby()
    furby.say('I rather have my fur back on, you creep!')

In fact, I am going to find someone who can sew to make it a lab coat so it can give instructions at open days.
midtest 0.1.0
test package
License: MIT
Requires Python: >=3.9,<4.0

# data-soap

## What is it?

A CLI menu that allows users to request overall daily rating for group of websites, with other options for individual site readouts.

## Main Features

- Strips unnecessary characters from numerical data fields in pandas dataframes to ensure consistent data formatting
- Provides before and after representations of dataframes to allow for comparison

## Where to get it

Source code is hosted on: [github.com/snake-fingers/data-soap](https://github.com/Snake-Fingers/data-soap)

## Dependencies

pandas - Python package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

## Installation

[Binary Files](https://pypi.org/project/data-soap/#files)

To install data-soap from poetry, in a virtual environment:
```
poetry add data-soap
```

## License

MIT License

## Documentation

Documentation to come.

## Background

Data-Soap originated from a Code Fellows 401 Python midterm project. The project team includes Alex Angelico, Grace Choi, Robert Carter, Mason Fryberger, and Jae Choi. After working with a few painful datasets using, we wanted to create a library that allows users to more efficiently manipulate clean datasets extracted from CSVs that may have inconsistent formatting.

## Getting Help

email: mason.fryberger@gmail.com
datasoaptest 0.1.0
test description
License: MIT
Requires Python: >=3.9,<4.0

# data-soap

## What is it?

A CLI menu that allows users to request overall daily rating for group of websites, with other options for individual site readouts.

## Main Features

- Strips unnecessary characters from numerical data fields in pandas dataframes to ensure consistent data formatting
- Provides before and after representations of dataframes to allow for comparison

## Where to get it

Source code is hosted on: [github.com/snake-fingers/data-soap](https://github.com/Snake-Fingers/data-soap)

## Dependencies

pandas - Python package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

## Installation

[Binary Files](https://pypi.org/project/data-soap/#files)

To install data-soap from poetry, in a virtual environment:
```
poetry add data-soap
```

## License

MIT License

## Documentation

Documentation to come.

## Background

Data-Soap originated from a Code Fellows 401 Python midterm project. The project team includes Alex Angelico, Grace Choi, Robert Carter, Mason Fryberger, and Jae Choi. After working with a few painful datasets using, we wanted to create a library that allows users to more efficiently manipulate clean datasets extracted from CSVs that may have inconsistent formatting.

## Getting Help

email: mason.fryberger@gmail.com
cpca 0.5.5
Chinese Province, City and Area Recognition Utilities
License: MIT
Keywords: Simplified Chinese,Chinese geographic information,Chinese province city and area recognition and map

chinese_province_city_area_mapper
==================================

chinese_province_city_area_mapper：一个用于识别简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块

举个例子::

    ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
            ↓ 转换
   |省    |市   |区    |地址                 |
   |上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
   |福建省|泉州市|洛江区|万安塘西工业区        |

chinese_province_city_area_mapper: built to be recognize Chinese province,city and area in simplified Chinese string, it can automaticall map area to city
and map city to province.

for example::

    ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
            ↓ transform
   |省    |市   |区    |地址                 |
   |上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
   |福建省|泉州市|洛江区|万安塘西工业区        |

完整文档见该模块的Github，
GitHub: `https://github.com/DQinYuan/chinese_province_city_area_mapper <https://github.com/DQinYuan/chinese_province_city_area_mapper>`_


安装说明
========
代码目前仅仅支持python3
    pip install cpca

Get Started
============

本模块中最主要的方法是cpca.transform,
该方法可以输入任意的可迭代类型（如list，pandas的Series类型等），
然后将其转换为一个DataFrame，下面演示一个最为简单的使用方法::

    location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "北京朝阳区北苑华贸城"]
    import cpca
    df = cpca.transform(location_str)
    df

输出的结果为::

       省     市    区          地址              adcode
    0 上海市 上海市  徐汇区     虹漕路461号58号楼5楼  310104
    1 福建省 泉州市  洛江区     万安塘西工业区        350504
    2 北京市 市辖区  朝阳区     北苑华贸城           110105

如果还想知道更多的细节，请访问该
模块的github地址 `https://github.com/DQinYuan/chinese_province_city_area_mapper <https://github.com/DQinYuan/chinese_province_city_area_mapper>`_，
在那里我写了更多的细节