fxdb 0.1.0

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fxdb - Fast Execute in Database

fxdb - A lightweight, single-file library for easy and efficient database interactions.

Features

• Single file, no external dependencies.
• Streamlined operations for inserting, updating, and querying data.
• Simple integration with dictionaries and dataframes.
• Built-in backup functionality for data security.
• Focused on core database operations (CRUD).

Installation

Install fxdb via pip:

pip install fxdb

Quick Start

1. Initialize a Database Instance

You can initialize the database by specifying the file path. If the file does not exist, it will be automatically created:

from fxdb import fxdb

# Example 1: Default database file
db = fxdb('data')

# Example 2: Custom database file
db = fxdb('data.sqlite3')

# Example 3: Database in a specific directory
db = fxdb('path/to/data/demo.db')

2. Backup Functionality

Ensure your data is secure with automatic backups:

db.backup(backup_dir="database_backup", keep_num=5)

• backup_dir: Directory to store backups.
• keep_num: Maximum number of backups to retain.

3. Table Operations

Interact with tables seamlessly. Use db['table_name'] to access or create a table:

table = db['test_table']

Automatic Table Creation

• When inserting data, fxdb automatically creates the table based on the full set of keys provided in your data.
• All keys across your dataset are analyzed to define the table structure, ensuring consistency and avoiding key mismatches.

4.Insert Data

Insert records into the table:

# Insert a single record
table.insert(name="Jack", age=25)

# Insert multiple records from a dictionary
table.insert_from_dict([
    {"name": "Bob", "age": 25, "city": "Los Angeles"},
    {"name": "Charlie", "age": 35, "city": "Chicago"}
])

# Insert data from a DataFrame
table.insert_from_df(data)

5.Upsert Data

Update or insert records if they already exist:

# Upsert from a dictionary
table.upsert_from_dict(data, keys=["name"])

# Upsert from a DataFrame
table.upsert_from_df(data, keys=["name","gender"])

6.Query Data

Retrieve data with minimal effort:

# Fetch all records
records = table.all()

# Select records matching specific criteria
jack = table.select(name="Jack")

7.Delete Data

Remove data from the table:

# Delete records matching specific criteria
table.delete(name="Jack")

# Truncate all data in the table
table.truncate()

# Drop the entire table
table.drop()

License

This project is licensed under the MIT License.