dbsys 0.3.1

A library for managing database operations using SQLAlchemy and pandas

dbsys

dbsys is a comprehensive Python library for managing database operations using SQLAlchemy and pandas. It provides a high-level interface for common database operations, including reading, writing, creating tables, deleting tables, columns, and rows, as well as advanced features like searching, backup, and restore functionality.

Features

• Easy-to-use interface for database operations
• Support for multiple database types through SQLAlchemy
• Integration with pandas for efficient data handling
• Comprehensive error handling and custom exceptions
• Backup and restore functionality
• Advanced search capabilities
• Deduplication of data

Installation

You can install the latest version of dbsys using pip:

pip install --upgrade dbsys

For development purposes, you can install the package with extra dependencies:

pip install dbsys[dev]

This will install additional packages useful for development, such as pytest, flake8, and mypy.

Quick Start

Here's a comprehensive example of how to use dbsys:

from dbsys import DatabaseManager
import pandas as pd

# Initialize the DatabaseManager
db = DatabaseManager("sqlite:///example.db")

# Create a sample DataFrame
data = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'Alice'], 'age': [30, 25, 35, 30]})

# Create a new table and write data
db.use_table("users").create(data)

# Read the table
result = db.use_table("users").read().get_data()
print("Original data:")
print(result)

# Deduplicate the data
db.dedup(subset=['name'], keep='first')
result = db.get_data()
print("\nDeduplicated data:")
print(result)

# Update data in the table
new_data = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie'], 'age': [31, 26, 36]})
db.use_table("users").write(new_data)

# Search for users older than 30
result = db.use_table("users").search({"age": 30}, limit=2).get_data()
print("\nUsers older than 30:")
print(result)

# Delete a specific row
db.use_table("users").delete_row({"name": "Bob"})

# Backup the table
db.use_table("users").backup("users_backup.json")

# Delete the table
db.use_table("users").delete_table()

# Restore the table from backup
db.use_table("users").restore("users_backup.json")

# Verify restored data
result = db.use_table("users").read().get_data()
print("\nRestored data:")
print(result)

API Reference

DatabaseManager

The main class for interacting with the database.

Methods:

• __init__(database_url: str): Initialize the DatabaseManager with a database URL.
• use_table(table_name: str) -> DatabaseManager: Set the table to be used for subsequent operations.
• read() -> DatabaseManager: Read the entire contents of the currently selected table into memory.
• write(data: Optional[pd.DataFrame] = None) -> DatabaseManager: Write data to the currently selected table.
• create(data: pd.DataFrame) -> DatabaseManager: Create a new table with the provided data.
• delete_table() -> DatabaseManager: Delete the currently selected table.
• delete_column(column_name: str) -> DatabaseManager: Delete a specific column from the currently selected table.
• delete_row(row_identifier: Dict[str, Any]) -> DatabaseManager: Delete a specific row from the currently selected table.
• search(conditions: Union[Dict[str, Any], str], limit: Optional[int] = None, case_sensitive: bool = False) -> DatabaseManager: Search for rows in the current table that match the given conditions.
• backup(file_path: str, columns: Optional[List[str]] = None) -> DatabaseManager: Backup the current table or specified columns to a JSON file.
• restore(file_path: str, mode: str = 'replace') -> DatabaseManager: Restore data from a JSON file to the current table.
• get_data() -> Optional[pd.DataFrame]: Get the current data stored in memory.
• dedup(subset: Optional[List[str]] = None, keep: str = 'first') -> DatabaseManager: Deduplicate the current DataFrame based on specified columns.

For detailed usage of each method, please refer to the docstrings in the source code.

Error Handling

dbsys provides custom exceptions for better error handling:

• DatabaseError: Base exception for database operations.
• TableNotFoundError: Raised when a specified table is not found in the database.
• `ColumnNotFoun

dError`: Raised when a specified column is not found in the table.

• InvalidOperationError: Raised when an invalid operation is attempted.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

If you encounter any problems or have any questions, please open an issue on our GitHub repository.

About Lifsys, Inc

Lifsys, Inc is an AI company dedicated to developing innovative solutions for data management and analysis. For more information, visit www.lifsys.com.

Changelog

0.3.1

• Prepared for PyPI update
• Reviewed codebase for corrections
• Updated documentation and README
• Incremented version number

0.3.0

• Added comprehensive docstrings to all methods
• Updated README with detailed API reference and examples
• Improved error handling and type hints
• Added backup and restore functionality
• Implemented advanced search capabilities
• Added deduplication feature

0.2.5

• Prepared for PyPI update
• Reviewed codebase for corrections
• Updated documentation and README
• Incremented version number

(... previous changelog entries ...)