Metadata-Version: 2.1
Name: sqlDataframe
Version: 1.0
Summary: This is an idea to further simplify the process of fetching/storing data into structured databases using pandas
Home-page: https://github.com/sehan10/SQL_Pandas.git
Author: Arsal Rahim | Sehan Ahmed Farooqui
Author-email: arsalrahim1994@gmail.com
License: MIT
Download-URL: https://github.com/sehan10/SQL_Pandas/archive/0.1.tar.gz
Description: # sqlDataframe
        
        SQL To Pandas is a tool you can use to connect with your database, somme of the basic Functionalities are:
        
          - Can convert your database table into pandas dataframe
          - You can insert a pandas dataframe as a new table into your database
        
        Usage pattern is different for both linux and windows.
        
        # Linux
        #### Quickstart
        Installing dependencies first:
        ```sh
        $ import pandas as pd
        $ from sqlDataframe import sqlCred, read_sql, to_sql
        ```
        Establishing connection and importing data from mssql server into Pandas dataframe:
        ```sh
        $ creds = sqlCred(database='database_name', username='username', password='pass', OS='Linux')
        $ dataframe = read_sql(creds,'table_name')
        ```
        Establishing connection and importing data Pandas dataframe into mssql server:
        ```sh
        $ creds = to_sql(database='database_name', username='username', password='pass', OS='Linux')
        $ to_sql(creds, dataframe, 'table_name')
        ```
        Once  connection is establised, you can use **creds** for both **read_sql** and **to_sql**.
        
        # Windows
        #### Quickstart
        Installing dependencies first:
        ```sh
        $ import pandas as pd
        $ from sqltopandas import sqlCred, to_sql, read_sql
        ```
        
        Establishing connection and importing data from mssql server into Pandas dataframe:
        ```sh
        $ creds = sqlCred('database='database_name', server='server_name', OS='Windows')
        $ dataframe = read_sql(creds,'table_name')
        ```
        
        Establishing connection and importing data Pandas dataframe into mssql server:
        ```sh
        $ creds = sqlCred('database='database_name', server='server_name', OS='Windows')
        $ to_sql(creds, dataframe, 'table_name')
        ```
        
        Once  connection is establised, you can use **creds** for both **read_sql** and **to_sql**.
        **Note:** This is only accessible using Microsoft Sql Server windows authentication.
        
        # Upcommings
        Its a start of our journey and every journey begins with some simple steps. So we have enlisted some more features to be the part of **sqltopandas** in future updates. 
        
          - Integration with multiple database platforms
          - Load specific data into dataframe from databasse by using custom queries
          - Server name will automatically be fetched from the system (Windows only)
          - Will provide access through sql server authentication (windows only) 
        
        # Limitations
        Yes we do have some limitations and we are working over it:
        
        * Support with sql server authentication is not available right now (windows only)
        * Only SELECT and INSERT querrying is working at the backend.
        * User's custom queries will be entetained.
        * Sql server instance name have to be provided as a parameter to build connection, in future we will fetch it by ourself.
        
Keywords: Pandas,Dataframe,Structured databases,SQL query
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
