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Uma biblioteca para gerar dados fictícios para testes

Project description

Mockbinn - Gerador de Dados Fictícios em Python

PyPI Version Python Versions License: MIT

Biblioteca Python para geração de dados fictícios estruturados, perfeita para testes, desenvolvimento e prototipagem.

Instalação

pip install mockbinn

Uso Básico

from mockbinn import Mockbinn
from mockbinn.generators import UUIDGenerator, NameGenerator, EmailGenerator, DateGenerator, BooleanGenerator

# Inicializar o Mockbin
mocker = Mockbinn()

# Configurar um modelo de dados
mocker.set_model("users", 10).set_columns("users", {
    "id": UUIDGenerator,
    "name": NameGenerator,
    "email": EmailGenerator,
    "signup_date": DateGenerator,
    "is_active": BooleanGenerator
})

# Gerar DataFrame
df = mocker.get_df_from_model("users")
print(df)

Exemplos de Uso

# Gerar usuários e pedidos relacionados
users = mocker.set_model("users", 100).set_columns("users", {
    "user_id": UUIDGenerator,
    "name": NameGenerator
}).get_df_from_model("users")

orders = mocker.set_model("orders", 500).set_columns("orders", {
    "order_id": UUIDGenerator,
    "user_id": lambda: random.choice(users['user_id']),
    "item": ItemGenerator,
    "value": lambda: NumberGenerator(10, 1000, decimal=True)
}).get_df_from_model("orders")

Customização de Geradores

# Criando geradores customizados
corporate_email = lambda: EmailGenerator(domain="empresa.com.br")
high_value = lambda: NumberGenerator(1000, 10000, decimal=True)

mocker.set_model("employees", 50).set_columns("employees", {
    "name": NameGenerator,
    "email": corporate_email,
    "salary": high_value
})

Exportando Dados

Você pode exportar os modelos gerados para CSV ou Parquet:

from mockbinn import Mockbinn
from mockbinn.generators import NameGenerator

mocker = Mockbinn()
mocker.set_model("users", 100).set_columns("users", {
    "name": NameGenerator
})

# Método alternativo
mocker.export_model(
    model_name="users",
    output_path="data/users.csv",
    format='csv',
    index=False
)

Para usar exportação Parquet, instale as dependências extras:

pip install mockbinn[parquet]
# ou
pip install pyarrow

Licença

Distribuído sob a licença MIT. Veja LICENSE para mais informações.

Contato

Nathan Rodrigo - nathan.lopes@sptech.school

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