semvec/pypi-packages huggingface dataset:
surfcity 0.1.7
A family of Python clients for Secure Scuttlebutt
License: MIT

SurfCity is a family of Python clients for Secure Scuttlebut. The
default terminal UI is using the colorful Urwid widgets. There is also
a pure TTY version. The Kivy version of the UI is included to show
that the internal APIs can also be used also with full GUI widgets but
is functionally incomplete.
deepeye-pack 0.2
a Python package for DeepEye:Towards automatic Data Visualization API
License: MIT

# deepeye_pack

## Update - 2018/10/22 v0.0.2
1. read_csv_handld_changedate revise to pandas method
2. both the import methods require **Mandatory** table_info to specify the table 
3. mysql_handle now change to pandas dataframe verison
4. from_mysql also changes to para-method, pass port/user/db... instead of query and MySQLdb conn


## Description
1. This is a Python package for DeepEye API,can easily visualize data without too much effort. And provide with really simple usage
2. the DeepEye system: https://github.com/TsinghuaDatabaseGroup/DeepEye/tree/master/APIs_Deepeye

## Installation
1. Python 2.7
2. MySQL 5.7
3. Packages
    1. mysqldb binary packages for windows: link1:https://www.lfd.uci.edu/~gohlke/pythonlibs/ <br>link2:https://sourceforge.net/projects/mysql-python/
        - Download 'MySQL-python' and choose the right version for it 
        - Install the .whl by wheel install
        - there is a back up version in this repository under 'mysqldb' folder 
    2. numpy(latest version)
    3. pandas(latest version) above ver 0.23.0

## Usage
1. Initial
    1. example code:
    ```py
    import deepeye_pack

    #create a deepeye_pack class that wraps everything
    dp = deepeye_pack.deepeye('demo') # the name here doesnt actually matter

    # then user needs to input table info
    # as in table_info(table_name,column_names,column_types)
    dp.table_info('electricity',['city','date','electricity(kWh)'],['varchar','date','float'])
    ```
    2. the column_types that supported by deepeye_pack are specified as below:
        1. numerical: `int`, `float`, `double`
        2. temporal: `date`, `datetime`, `year`
        3. categorical: `char`, `varchar`
2. Import
    1. from_mysql()
        ```py
        # call the from_mysql() function
        dp.from_mysql(host='localhost',port=3306,user='root',passwd='ppww',db='deepeye', query='SELECT * FROM `table_name`')

        ```
    2. from_csv()
        ```py
        path = "file.csv" # the path where the file located
        dp.from_csv(path)
        ```
3. Visualization
    ```py
    # choose one from three
    dp.learning_to_rank()
    dp.partial_order()
    dp.diversified_ranking()
    ```
4. Output
    ```py
    # can use several different methods at the same time
    dp.to_print_out()
    dp.to_single_json()
    dp.to_multiple_jsons()
    dp.to_multiple_htmls()
    dp.to_single_html()
    ```