Skip to main content

SmileDB

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

ko-fi

Install

pip install SmileDB

Usage:

To use the SmileDB class, you would typically instantiate an object of the class and provide the path or name of the SQLite database file as a parameter. For example:

db = SmileDB("mydatabase.db")

You can then call the table() method on the db object to create a new table in the database:

my_table = db.table("my_table")

The table() method returns a Table object (my_table in this example) that can be used to perform operations on the created table.

Insert or add new record

db = SmileDB('database.db')

tasks = db.table('tasks')

tasks.Insert(
    data = {
        'name': 'This is task name 2', 
        'description': 'This task description 2',
        'coins': 50,
        'workers': 80
    }
)

Insert list of data

list_data = [
    {'name': 'test 1', 'description': 'test 1', 'coins': 50, 'workers': 10},
    {'name': 'test 2', 'description': 'test 2', 'coins': 500, 'workers': 20},
    {'name': 'test 3', 'description': 'test 3', 'coins': 100, 'workers': 50},
    {'name': 'test 4', 'description': 'test 4', 'coins': 5000, 'workers': 80}
]

tasks.InsertMany(data_list=list_data)

If you want to make a login system, SmileDB makes it easy for you.

Register a new user

db = SmileDB('database.db')

users = db.table('users')

users.Register(data = {'email': 'test@example.com', 'password': '2020'})

Note

By default, the function 'Register' makes a hash for any password automatically. If you want to keep it as plain text, make sure that the value of  'password_hash' argument is False

db = SmileDB('database.db')

users = db.table('users')

users.Register(
    data = {'email': 'test666@example.com', 'password': '2020'},
    password_hash = False
)

Login

db = SmileDB('database.db')

users = db.table('users')


result = users.LogIn(
    data = {'email': 'test666@example.com', 'password': '2020'},
)

print(result)

Result

{'_index': 3, 'email': 'test666@example.com', 'password': '2020', 'uuid': '2626a962-d5d7-49fa-98f1-53d4bd722ee9', 'created_at': '2024-07-11 11:10:46.319231'}

Get all records

print(tasks.GetALL())

Get records by id

print(tasks.GetByID(uuid='e927c787-bf9a-4ec7-b575-2efacd90728e'))

Get records by any column like name

print(tasks.GetBy(name = 'This is task name'))

You can use also FindBy or FilterBy function it will give you the same result

Get one record by any column like workers in this example

tasks = db.table('tasks')
print(tasks.FindOne(workers = 80))

Result

{'_index': 4, 'name': 'test 4', 'description': 'test 4', 'coins': 5000, 
'workers': 80, 'uuid': '1fad0ed8-fbbc-4fc9-8e46-3f6c355dbf84', 'created_at': '2024-07-11 10:42:26.818831'}

Filtring records by any column

use larger_than, less_than, not, equal, range

print(tasks.Filter(filter_keys = {'coins': {'less_than': 50}}))

example of how to use range filter

result = tasks.Filter(filter_keys={'workers': {'range': [20, 80]}})
print(result)

Result

{'range': [
    {'_index': 2, 'name': 'test 2', 'description': 'test 2', 'coins': 500, 'workers': 20, 'uuid': 'ef0b06ce-1abd-46db-836c-166b57cc57e7', 'created_at': '2024-07-11 10:42:26.599986'},

    {'_index': 3, 'name': 'test 3', 'description': 'test 3', 'coins': 100, 'workers': 50, 'uuid': '5fc3ddd0-4316-4de2-bdf7-c9536d487526', 'created_at': '2024-07-11 10:42:26.709459'}, 
    
    {'_index': 4, 'name': 'test 4', 'description': 'test 4', 'coins': 5000, 'workers': 80, 'uuid': '1fad0ed8-fbbc-4fc9-8e46-3f6c355dbf84', 'created_at': '2024-07-11 10:42:26.818831'}
]
}

Update record by uuid

result = tasks.Update(
    uuid='31c37a83-02db-4e8d-9d62-124888626892',
    data = {'coins': 1}
)

print(result)

Result

{'_index': 5, 'name': 'This is task name 2', 'description': 'This task description 2', 'coins': 1, 'workers': 80, 'uuid': '31c37a83-02db-4e8d-9d62-124888626892', 'created_at': '2024-07-11 10:24:26.020235'}

Update many by name or any other column name

result = tasks.UpdateMany(
    data = {'coins': 1000},
    name = 'This is task name 2'
)

print(result)

Result

[
    {'_index': 2, 'name': 'This is task name 2', 'description': 'This task description 2', 'coins': 1000, 'workers': 80, 'uuid': 'e927c787-bf9a-4ec7-b575-2efacd90728e', 'created_at': '2024-07-11 10:09:48.163728'}, 

    {'_index': 3, 'name': 'This is task name 2', 'description': 'This task description 2', 'coins': 1000, 'workers': 80, 'uuid': '9d74c2ea-b943-4aa9-bd06-52ba1beecc5b', 'created_at': '2024-07-11 10:23:49.426697'}, 
    
    {'_index': 4, 'name': 'This is task name 2', 'description': 'This task description 2', 'coins': 1000, 'workers': 80, 'uuid': '93ed7665-f6e3-40be-ab3b-8d609eaf1896', 'created_at': '2024-07-11 10:24:16.574957'}, 
    
    {'_index': 5, 'name': 'This is task name 2', 'description': 'This task description 2', 'coins': 1000, 'workers': 80, 'uuid': '31c37a83-02db-4e8d-9d62-124888626892', 'created_at': '2024-07-11 
    10:24:26.020235'
    }
]

Delete record by uuid

tasks.Delete(uuid='b3bd4856-28f2-4d24-aaca-c22724d2e0a1')

Delete records by name or any other column name

tasks.DeleteMany(
    name = 'This is task name 2'
)

Credits

sqlite3:

The sqlite3 library is part of the Python Standard Library, which means it is included with Python itself. You can import and use the sqlite3 module directly in your Python code without needing to install any additional

bcrypt:

Modern password hashing for your software and your servers

Metadata

Release files for SmileDB 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for SmileDB 0.2.0
File Interpreter ABI Platform
SmileDB-0.2.0-py3-none-any.whl Python 3 none any Details

Release files / SmileDB-0.2.0-py3-none-any.whl

Download URL SmileDB-0.2.0-py3-none-any.whl
Size 6.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
79438852705336519841ef5d1cd8d0e65f575243453935ba970607c295eaf9a9
BLAKE2b-256 checksum
How to use checksums
86d32257cf3e320b3b3368e97a333cc7ff16a43e5c4f6756617a9df242929f8d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.0.0 CPython/3.12.1

Release history Release notifications | RSS feed

This release

0.2.0 This release

1 release file

0.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page