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a Python package for DeepEye:Towards automatic Data Visualization API

Project description

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/
      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:
    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'])
    
    1. 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()
      # 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()
      path = "file.csv" # the path where the file located
      dp.from_csv(path)
      
  3. Visualization
    # choose one from three
    dp.learning_to_rank()
    dp.partial_order()
    dp.diversified_ranking()
    
  4. Output
    # 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()
    

Project details


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This version

0.2

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