Python Query Language PQL

PQL is a python wrapper of the sql sintax based in SQLalchemy and pandas, this code introduces a new syntax to use in your code when calling a database table.

Requirements

You only need a distribution of python3 installed.

⚙️Installation:

You can install the requirements (preferably in an environment) using:

pip install PythonQueryLanguage

Basic Usage:

PQL is managed by a class called SQLManager, to instanciated you will need to pass your connection strings in a dictionary and the enviroment you are willoing to use

connection_dict = {'test_env': 'connection string from engine', 'prod_env': 'mssql+pyodbc://ur@prod.com/url2?driver=ODBC+Driver+17+for+SQL+Server' } env = 'test_env' pql = SQLManager(connection_dict,env)

Once you have instanciated the class you can use it with his convenient functions that will wrap SQL expresions.

pql.select_all('tableA','id','myid') = SELECT * FROM tableA WHERE id = 'myid

PQL supports searchs in arrays:

pql.select_all('tableA','id',['myid','myid2']) = SELECT * FROM tableA WHERE id IN ('myid','myid2')

PQL cast the data in a inteligent manner:

pql.select_all('tableA',['id','name'],['myid','myName']) = SELECT * FROM tableA WHERE id = 'myid' AND myName = 'myid2'

PQL accepts adiotional arguments:

pql.select('column','tableA',['id','name'],['myid','myName'],'OR') = SELECT column FROM tableA WHERE id = 'myid' OR myName = 'myid2'

PQL accepts direct evaluation or raw sql expressions (thougt only recommended in edge cases)

pql.query("SELECT * FROM tableA WHERE id = 'myid") = SELECT * FROM tableA WHERE id = 'myid'

PQL accepst function and Store procedure evaluations.

Multy enviroment usage.

PQL is built to support working with different db environments at the same time, this multi enviroment work can be done in different ways:

• 1. Instanciate the PQLmanager with different enviroments and run them.
• 2. Changing the enviroment of the class with the change_enviroment method.
• 3. Using scoped functions.

Example of scoped functions:

You are working in a database test enviroment but you need to extract some data from the production enviroment without changing the enviroment of the PQLmanager Then you can query the table at producion using a scope:

pql.select_all('TableA',env='prod')

This function will run in the production environment and retrieve the information from it without changing your global environment.

IUD

PQL supports Insert Update Delete Operations, all these operations are based in pandas dataframes.

Interactive.

PQL is thought to be used in a jupyter notebook as well as used in real code. PQL contains different functionalities that allows the user to know what query will be executed in the database and confirmation security.