Introduction

DBPlus is a interface layer between the several python database interfaces and your program. It makes the SQL access from your program database-agnostic meaning the same code can run unmodified on several databases. All you need to change is the database URL. Of course if you use specific SQL that will only work on a certain database DBPlus can not change this.

Installation

The latest stable release from pypi: pip install dbplus

From github: Clone the repository using git and issue "pip install ."

Please note that DBPlus requires you to install the clients and their pre-req's:

• DB2: ibm_db
• SQLite: builtin into python (no client required)
• MySQL: Mysql Connector
• Oracle: CX_Oracle
• Postgresql: psycopg2

Documentation : https://klaasbrant.github.io/DBPlus/

Example

from dbplus import Database

# Examples of database urls

#db = Database('SQLite:///test.db')  # driver included in python
#db = Database('Postgres://<user>:<password>@127.0.0.1:5432/dvdrental') # requires psycopg2
#db = Database('MySQL://<user>:<password>@127.0.0.1:3306/test') # requires Mysql Connector
#db = Database('Oracle://<user>:<password>@127.0.0.1:1521/xe') # requires CX_Oracle

db = Database('DB2://db2demo:[email protected]:50000/sample') # requires ibm_db

# Using named variables in query

rows = db.query('select * from klaas.emp where edlevel=:edlevel and workdept=:wd',edlevel=18,wd='A00')
print(rows,'\n')
print('rows[1]={}\n'.format(rows[1]))
df=rows.as_DataFrame()
print('csv to stdout, check the many options with dataframes!  \n',df.to_csv())

# Full transaction support

with db.transaction():
    # DELETE
    num = db.execute('DELETE FROM klaas.texample')
    print('Rows deleted from klaas.texample={} \n'.format(num))
    # INSERT
    for i in range(1, 11):
        db.execute('INSERT INTO klaas.texample VALUES (?,?)', i, i)
    # UPDATE
    num = db.execute('UPDATE klaas.texample SET col2 = col2+100  WHERE col1 > ?', 5)
    print ('Rows updated in klaas.texample={} \n'.format(num))

# transaction is now commited

print(db.query('select * from klaas.texample'))

Output from example above:

empno	firstnme	midinit	lastname	workdept	phoneno	hiredate	job	edlevel	sex	birthdate	salary	bonus	comm
000010	CHRISTINE	I	HAAS	A00	3978	1995-01-01	PRES	18	F	1963-08-24	152750.00	1000.00	4220.00
200010	DIAN	J	HEMMINGER	A00	3978	1995-01-01	SALESREP	18	F	1973-08-14	46500.00	1000.00	4220.00

rows[1]=<Record {"empno": "200010", "firstnme": "DIAN", "midinit": "J", "lastname": "HEMMINGER", "workdept": "A00", "phoneno": "3978", "hiredate": "1995-01-01", "job": "SALESREP", "edlevel": 18, "sex": "F", "birthdate": "1973-08-14", "salary": "46500.00", "bonus": "1000.00", "comm": "4220.00"}>

csv to stdout, check the many options with dataframes! ,birthdate,bonus,comm,edlevel,empno,firstnme,hiredate,job,lastname,midinit,phoneno,salary,sex,workdept 0,1963-08-24,1000.00,4220.00,18,000010,CHRISTINE,1995-01-01,PRES ,HAAS,I,3978,152750.00,F,A00 1,1973-08-14,1000.00,4220.00,18,200010,DIAN,1995-01-01,SALESREP,HEMMINGER,J,3978,46500.00,F,A00

Rows deleted from klaas.texample=10

Rows updated in klaas.texample=5

col1	col2
1	1
2	2
3	3
4	4
5	5
6	106
7	107
8	108
9	109
10	110

What's next?

• Add tests / bug fixing
• Add more documentation / examples
• more cool stuff and of course your suggestions are welcome