pydbmanager 0.1.6

A Python package for seamless SQL Server database management, supporting secure connections, query execution, batch fetching, caching, and result exporting.

📌 PyDBManager - SQL Server Database Manager

Welcome to PyDBManager – a Python package for managing SQL Server connections and queries easily and efficiently! 🌟

This guide will help you:

• ✅ Install PyDBManager
• ✅ Set up your .env file for SQL or Windows Authentication
• ✅ Perform SQL operations using Python
• ✅ Create tables, insert, update, and bulk load DataFrames
• ✅ Save query results and use batch fetching

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1. Install PyDBManager

Run the following command to install PyDBManager:

pip install pydbmanager

If installation is successful, continue to the next step!

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2. Create .env File to Store Database Credentials

To avoid hardcoding credentials, create a .env file in your project directory.

Steps

1. Create a .env file in your project root.
2. Add the following credentials (update as needed):

   DB_SERVER=localhost
   DB_DATABASE=your_database_name
   DB_DRIVER=ODBC Driver 17 for SQL Server
   
   # For SQL Authentication
   DB_USERNAME=your_username
   DB_PASSWORD=your_password
   DB_AUTH_MODE=sql
   
   # For Windows Authentication
   # DB_AUTH_MODE=windows
   

3. Ensure .env is ignored by Git (Add .env to .gitignore).

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3. Verify .env File

Run this script to check if the values are loaded correctly:

import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

print("\u2705 Database Configuration Loaded:")
print(f"SERVER: {os.getenv('DB_SERVER')}")
print(f"DATABASE: {os.getenv('DB_DATABASE')}")
print(f"AUTH MODE: {os.getenv('DB_AUTH_MODE')}")
print(f"USERNAME: {os.getenv('DB_USERNAME')}")
print(f"PASSWORD: {'*' * len(os.getenv('DB_PASSWORD')) if os.getenv('DB_PASSWORD') else 'Not Set'}")
print(f"DRIVER: {os.getenv('DB_DRIVER')}")

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4. Connect to the Database (SQL or Windows Authentication)

from pydbmanager.connection import DatabaseConnection

# Initialize and test database connection
db = DatabaseConnection()
conn = db.create_connection()

if conn:
    print("\u2705 Connection Successful!")
    db.close_connection()
else:
    print("\u274c Connection Failed!")

---

5. Perform SQL Operations

🔹 Query Data as DataFrame

from pydbmanager.operations import DatabaseOperations

db_ops = DatabaseOperations()
df = db_ops.query_data("SELECT * FROM users", batch_size=5)
print(df)

🔹 Insert a New Record

insert_query = """
INSERT INTO users (name, email, age, gender, phone_number, address, city, country)
VALUES ('John Doe', '[e-mail]', 29, 'Male', '123-456-7890', '123 Elm St', 'New York', 'USA')
"""
db_ops.execute_query(insert_query)

🔹 Update a Record

update_query = """
UPDATE users SET age = 30 WHERE email = '[e-mail]'
"""
db_ops.execute_query(update_query)

🔹 Delete a Record

delete_query = """
DELETE FROM users WHERE email = '[e-mail]'
"""
db_ops.execute_query(delete_query)

🔹 Create Table

create_table_sql = """
IF NOT EXISTS (
    SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_NAME = 'users'
)
BEGIN
    CREATE TABLE users (
        id INT IDENTITY(1,1) PRIMARY KEY,
        name VARCHAR(100),
        email VARCHAR(100),
        age INT,
        gender VARCHAR(10),
        phone_number VARCHAR(20),
        address VARCHAR(255),
        city VARCHAR(100),
        country VARCHAR(100)
    );
END
"""
db_ops.create_table(create_table_sql)

🔹 Insert a DataFrame to SQL

import pandas as pd

# Example DataFrame
df_users = pd.DataFrame([
    {
        'name': 'Jane Smith',
        'email': '[e-mail]',
        'age': 32,
        'gender': 'Female',
        'phone_number': '555-555-5555',
        'address': '456 Oak Ave',
        'city': 'Chicago',
        'country': 'USA'
    }
])

db_ops.insert_dataframe(df_users, 'users')

🔹 Update SQL Table with DataFrame

Note: key_columns should include the column(s) used to uniquely identify each row (like id or email). These are used in the SQL WHERE clause to apply updates only to matching rows.

# Assume df_users contains updated user data
# Example update: change age for a known email

df_users_update = pd.DataFrame([
    {
        'email': '[e-mail]',
        'age': 33  # updated age
    }
])

db_ops.update_table_with_dataframe(df_users_update, 'users', key_columns=['email'])

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6. Save Query Results to File

# Save to CSV
results_df.to_csv("output.csv", index=False)
print("\u2705 Data saved to output.csv")

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7. Closing the Connection

db_ops.close()
print("\u2705 Database connection closed.")

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✅ Congratulations! 🎉

You’ve successfully used PyDBManager to:

• Connect to SQL Server using SQL or Windows authentication
• Run queries and commands
• Work with DataFrames and tables
• Create, update, and insert into SQL Server tables
• Save data to files and close connections cleanly

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Contributing & License

I welcome contributions! Feel free to submit issues and pull requests. 💪

This project is MIT Licensed — you are free to modify and distribute it as needed. 🏆

🔥 Happy Coding!