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Data science friendly ORM combining Python

Project description

dbhydra

Data science friendly ORM (Object Relational Mapping) library combining Python, Pandas, and various SQL dialects For full documentation see official documentation

Installation

Use the package manager pip to install dbhydra.

pip install dbhydra

Usage

import dbhydra.dbhydra_core as dh
db1=dh.db()

table1 = dh.Table(db1,"test",["test1","test2","test3","test4"],["int","int","int","int"])
#table1.drop()
#table1.create()
#rows=[[1,2,3,4],[5,4,7,9]]
#table1.insert(rows)

list1=table1.select("SELECT * FROM test")
print(list1)

#list2=table1.select_all()
#print(list2)

#table1.drop()

table1.export_to_xlsx()

tables=db1.get_all_tables()
table_dict=db1.generate_table_dict()
print(tables)

columns=table_dict['test'].get_all_columns()
types=table_dict['test'].get_all_types()
print(columns,types)

table_test=dh.Table.init_all_columns(db1,"test")

print(table_test.columns)

table2 = dh.Table(db1,"test_new",["id","test2"],["int","nvarchar(20)"])
#table2.create()

Current scope

Aims: Easy integration with Pandas, SQL SERVER/MySQL database, and exports/imports to/from excel/CSV format

Done: Table functions (Create, Drop, Select, Update, Insert, and Delete) should be working fine

Todo: Group by, Order by, Where, Linking of FK, Customizable PK,...

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

Project details


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