db-connector-kr 0.1.5

A Python library for connecting to various databases.

db-connector-kr

db-connector-kr is a Python library designed to simplify database interactions across multiple DBMS platforms. It provides a consistent interface for executing queries, retrieving results as pandas DataFrames, and inserting data efficiently.

Supported DBMS

The following DBMS platforms are considered:

• PostgreSQL (Supported)
• Snowflake (Supported)
• MySQL (Supported)
• Microsoft SQL Server (MSSQL) (Planned)
• Oracle (Planned)

Currently, only PostgreSQL, Snowflake, and MySQL are fully supported. MSSQL and Oracle support are planned for future updates.

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Installation

Install db-connector-kr via pip (once published to PyPI):

pip install db-connector-kr

Optional Dependencies

PostgreSQL: To use PostgreSQL, install the additional dependency:

pip install db-connector-kr[postgres]

!PostgreSQL

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Features

1. Database Connection

• Automatically handles database connection initialization and validation.

2. Query Execution

• Retrieve results as pandas DataFrames (read_to_df).
• Execute DML queries such as INSERT, UPDATE, or DELETE (execution_query).

3. DataFrame Insertion

• Insert pandas DataFrames into database tables efficiently (insert_df)

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Usage

1. Initialization

from db_connector.db_connector import DBConnector

# Initialize the connector for PostgreSQL
config = {
    "dbms": "pg",
    "user": "your_username",
    "pw": "your_password",
    "dbname": "your_database",
    "host": "localhost",
    "port": 5432
}
# Initialize the connector for maria, mysql
config = {
    "dbms": "mysql",
    "user": "test_user",
    "pw": "test_password",
    "dbname": "test_db",
    "host": "localhost",
    "port": 3306,
}

# Initialize the connector for snowflake
config = {
    "dbms": "sf",
    "user": "test_user",
    "pw": "test_password",
    "dbname": "test_db",
    "account": "test_account",
    "warehouse": "test_warehouse",
}

connector = DBConnector(**config)

2. Retrieve Data as DataFrame

query = "SELECT * FROM your_table;"
df = connector.read_to_df(query)
print(df.head())

3. Execute DML Queries

query = "INSERT INTO your_table (id, name) VALUES (1, 'Alice');"
connector.execution_query(query)

4. Insert DataFrame into a Table

import pandas as pd

# Example DataFrame
data = {"id": [1, 2], "name": ["Alice", "Bob"]}
df = pd.DataFrame(data)

# Insert into the table
connector.insert_df(df, table="your_table")

5. Close Connection

connector.close()

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Planned Features

• Support for Microsoft SQL Server (MSSQL).
• Support for Oracle.
• Enhanced error handling and logging.

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Contributing

Contributions are welcome! If you would like to contribute, please:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request with detailed information about your changes.

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License

This project is licensed under the MIT License.

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Contact

For any inquiries or issues, please create an issue in the repository or contact the maintainer directly.