a Python package for DeepEye:Towards automatic Data Visualization API
Project description
deepeye_pack
Update - 2018/10/22 v0.0.2
- read_csv_handld_changedate revise to pandas method
- both the import methods require Mandatory table_info to specify the table
- mysql_handle now change to pandas dataframe verison
- from_mysql also changes to para-method, pass port/user/db... instead of query and MySQLdb conn
Description
- This is a Python package for DeepEye API,can easily visualize data without too much effort. And provide with really simple usage
- the DeepEye system: https://github.com/TsinghuaDatabaseGroup/DeepEye/tree/master/APIs_Deepeye
Installation
- Python 2.7
- MySQL 5.7
- Packages
- 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
- numpy(latest version)
- pandas(latest version) above ver 0.23.0
- mysqldb binary packages for windows: link1:https://www.lfd.uci.edu/~gohlke/pythonlibs/
Usage
- Initial
- 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'])
- the column_types that supported by deepeye_pack are specified as below:
- numerical:
int
,float
,double
- temporal:
date
,datetime
,year
- categorical:
char
,varchar
- numerical:
- Import
- 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`')
- from_csv()
path = "file.csv" # the path where the file located dp.from_csv(path)
- from_mysql()
- Visualization
# choose one from three dp.learning_to_rank() dp.partial_order() dp.diversified_ranking()
- 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()
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