Merge pull request #15 from JustinFrizzell/sqlconnector-methods

Sqlconnector methods

README.md

@@ -12,77 +12,79 @@
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
 </p>
 
-**SQLconnect** is a Python package designed to simplify the process of connecting to SQL databases. It uses a `sqlconnect.yaml` file for database configuration and a `sqlconnect.env` file for secure credentials management. SQLconnect supports executing SQL from `.sql` files or Python strings, and retrieving data into pandas DataFrames. This package is particularly useful for data analysts and developers who need a straightforward way to interact with SQL databases.
+**SQLconnect** is a Python package designed to provide a straightforward way to interact with SQL databases (Postgres, Microsoft SQL Server, Oracle ect.). It enables direct population of DataFrames from .sql files. A `sqlconnect.yaml` file is used for database configuration and a `sqlconnect.env` file for secure credentials management.
 
 ## Features
 
-- Allows easy configuration and management of database connections using a `sqlconnect.yaml` file.
+- Turn SQL queries into DataFrames in as few as 3 lines of code
 
-- Supports the use of a `sqlconnect.env` file for secure storage of database credentials, enhancing security.
+- Easy management of multiple database connections using a configuration file
 
-- Leverages SQLAlchemy for database connections, providing a robust and flexible framework for SQL operations.
+- Secure storage of database credentials using environment variables
 
-- Enables executing SQL queries and directly retrieving the results into pandas DataFrames, facilitating easy data manipulation and analysis.
+- Execute SQL queries and commands directly from .sql files or from a string
 
-- Capable of handling multiple database connections, allowing users to switch between different databases as needed.
+- Integration with SQLAlchemy & Pandas providing robust and flexible SQL operations
 
-- Provides functionality to execute SQL queries either by reading from an external file or directly from a string.
+```python
+import sqlconnect as sc
 
-## Installation
+connection = sc.Sqlconnector("My_Database") # Set up a database connection based on sqlconnect.yaml
 
-```bash
-pip install sqlconnect
+df = connection.sql_to_df("path/to/sql_query.sql") # Assign the results of a query to a DataFrame
+
+print(df.describe()) # Explore the DataFrame with Pandas
 ```
 
+
 ## Configuration
 
 To use SQLconnect, create a `sqlconnect.yaml` file in the root of your project (or in your home directory) with the following example structure:
 
 ```yaml
 connections:
-  Database_PROD:
-    sqlalchemy_driver: 'mssql+pyodbc'
-    odbc_driver: 'SQL+Server'
-    server: 'prod-server.database.com'
-    database: 'ProdDB'
-    username: '${DB_PROD_USERNAME}' # References DB_PROD_USERNAME in sqlconnect.env
-    password: '${DB_PROD_PASSWORD}' # References DB_PROD_PASSWORD in sqlconnect.env
-    options:
-      - 'Trusted_Connection=No'
+  My_Database:
+    dialect: 'mssql'
+    dbapi: 'pyodbc'
+    host: 'prod-server.example.com'
+    database: 'master'
+    username: '${MSSQL_USERNAME}' # References MSSQL_USERNAME in sqlconnect.env
+    password: '${MSSQL_PASSWORD}' # References MSSQL_PASSWORD in sqlconnect.env
+    options: 
+      driver: 'ODBC Driver 17 for SQL Server'
+
+  A_Postgres_Database:
+    dialect: 'postgresql'
+    dbapi: 'psycopg2'
+    host: 'dbserver123.company.com'
+    database: 'postgres'
+    username: '${POSTGRES_USERNAME}'
+    password: '${POSTGRES_PASSWORD}'      
 ```
 
 Also create a `sqlconnect.env` file in the root of your project (or in your home directory) with the following example structure:
 
 ```bash
 # This file should be kept secure and not checked into version control (add to .gitignore)
 # Production Database Credentials
-DB_PROD_USERNAME=prodUser
-DB_PROD_PASSWORD=prodPassword
+MSSQL_USERNAME=prodUsername
+MSSQL_PASSWORD=actualprodPassword
+POSTGRES_USERNAME=postgresProdUsername
+POSTGRES_PASSWORD=actualprodPassword
 ```
 
 Replace the example values with your actual database connection details.
 
-## Usage
-
-Here's a quick example to get you started:
-
-```python
-import sqlconnect as sc
+## Documentation
 
-# Set up a database connection, all configuration is handled with sqlconnect.yaml and sqlconnect.env
-connection = sc.Sqlconnector("Database_PROD")
+Full documentation for SQLconnect can be found at https://sqlconnect.readthedocs.io/
 
-# Assign the results of a query to a pandas DataFrame
-df = connection.sql_to_df("your_query.sql")
+## Installation
 
-# Explore the dataframe with pandas
-print(df.describe())
+```bash
+pip install sqlconnect
 ```
 
-## Documentation
-
-Full documentation for SQLconnect can be found at https://sqlconnect.readthedocs.io/
-
 ## License
 
 This project is licensed under the [MIT License](https://raw.githubusercontent.com/JustinFrizzell/sqlconnect/main/LICENCE).

docs/conf.py

@@ -63,7 +63,8 @@
 
 
 # The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
+pygments_style = "default"
+pygments_dark_style = "material"
 
 # We need headers to be linkable to so ask MyST-Parser to autogenerate anchor IDs for
 # headers up to and including level 3.

docs/index.rst

@@ -5,6 +5,9 @@
 .. raw:: html
 
    <div style="text-align: center;">
+      <a href="https://github.com/JustinFrizzell/sqlconnect/actions/workflows/ci.yaml">
+         <img src="https://github.com/JustinFrizzell/sqlconnect/actions/workflows/ci.yaml/badge.svg" alt="CI-testing">
+      </a>
       <a href="https://pypi.org/project/sqlconnect/">
          <img src="https://img.shields.io/pypi/v/sqlconnect" alt="PyPI">
       </a>
@@ -20,7 +23,7 @@
    </div>
    <div style="height: 20px;"></div> <!-- Spacer -->
 
-**SQLconnect** is a Python package designed to simplify the process of connecting to SQL databases. It uses a ``sqlconnect.yaml`` file for database configuration and a ``sqlconnect.env`` file for secure credentials management. SQLconnect supports executing SQL from ``.sql`` files or Python strings, and retrieving data into pandas DataFrames. This package is particularly useful for data analysts and developers who need a straightforward way to interact with SQL databases.
+**SQLconnect** is a Python package designed to provide a straightforward way to interact with SQL databases (Postgres, Microsoft SQL Server, Oracle ect.). It enables direct population of DataFrames from .sql files. A ``sqlconnect.yaml`` file is used for database configuration and a ``sqlconnect.env`` file for secure credentials management.
 
 Contents
 ========
@@ -35,7 +38,6 @@ Contents
    :caption: User Guide
 
    install
-   tutorial
    documentation
 
 .. toctree::

setup.py

@@ -3,9 +3,9 @@
 
 setuptools.setup(
     name="sqlconnect",
-    version="0.2.1",
+    version="0.3.0",
     author="Justin Frizzell",
-    description="Package to simplify connections to SQL databases.",
+    description="Simplifies connections to SQL databases for data analysts. Populate DataFrames with the results of queries directly from .sql files.",
     long_description=Path("README.md").read_text(encoding="utf-8"),
     long_description_content_type="text/markdown",
     url="https://github.com/JustinFrizzell/sqlconnect",
@@ -14,7 +14,7 @@
         "Source": "https://github.com/JustinFrizzell/sqlconnect",
     },
     packages=setuptools.find_packages(exclude=["tests", "tests.*"]),
-    install_requires=["pandas", "sqlalchemy", "pyyaml", "pyodbc", "python-dotenv"],
+    install_requires=["pandas", "sqlalchemy", "pyyaml", "python-dotenv", "pyodbc", "psycopg2-binary", "oracledb", "PyMySQL"],
     classifiers=[
         "License :: OSI Approved :: MIT License",
         "Intended Audience :: Developers",