SmileyDB3 0.3.7

SmileyDB3 is a library built on sqlite3 to make working with databases easier

SmileyDB3

SmileyDB3 simplifies database interactions by providing an intuitive and feature-rich interface for SQLite3. Whether you're working with single records, multiple records, or advanced filtering, SmileyDB3 makes your database operations effortless.

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Features

• Dynamic Schema Creation: Automatically generates table schema based on provided data types.
• Record Management: Insert single or multiple records, update, fetch, and delete with ease.
• Filtering Capabilities: Apply advanced filters such as greater_than, less_than, between, and more.
• Export Data: Export table data to a JSON file for external use.
• Indexing: Add indexes to columns for optimized query performance.
• Security: Built-in sanitization to protect against SQL injection and XSS attacks.

Installation

Install SmileyDB3 using pip:

pip install SmileyDB3

Quick Start Guide

1. Create and Initialize a Table

import sqlite3
from smileydb3 import Table

# Connect to SQLite database
db = sqlite3.connect('my_database.db')
cursor = db.cursor()

# Initialize a table
users = Table('users', db, cursor)

# Define and insert data
data = {'name': 'Alice', 'age': 25, 'email': '[e-mail]'}
users.Insert(data)

2. Insert Multiple Records

data_list = [
    {'name': 'Bob', 'age': 30, 'email': '[e-mail]'},
    {'name': 'Charlie', 'age': 28, 'email': '[e-mail]'}
]
users.InsertMany(data_list)

3. Fetch All Records

all_users = users.GetALL()
print(all_users)

4. Filter Records

filters = {'age': {'greater_equal': 25, 'less_than': 30}}
filtered_users = users.Filter(filters)
print(filtered_users)

5. Export to JSON

users.ExportToJSON('users_data.json')

6. Update a Record

uuid = 'some-uuid'
updated_data = {'name': 'Alice Smith'}
users.Update(uuid, updated_data)

7. Delete a Record

users.Delete(uuid)

Additional Features

• Dynamic UUID and Timestamp: Automatically adds uuid and created_at fields to each record.
• Pandas Integration: Seamlessly use Pandas for data analysis by converting fetched data into DataFrames (easily extendable).
• Logging: Comprehensive logging for debugging and monitoring.

Contributing

We welcome contributions! If you have ideas, suggestions, or bug reports, feel free to open an issue or create a pull request on GitHub.

License

This project is licensed under the MIT License.