datagenix 0.1.1

A robust and simple library for generating synthetic datasets for ML/DL projects.

data_genix

A robust and simple library for generating synthetic datasets for machine learning and deep learning projects. Avoid the hassle of downloading and managing data files for testing and prototyping.

Installation

Clone the repository and install using pip:

git clone [https://github.com/yourusername/data_genix.git](https://github.com/yourusername/data_genix.git)
cd data_genix
pip install .

Quick Start

Generate a DataFrame with a variety of data types with a single function call.

from data_genix import DataGenerator

# Initialize the generator
generator = DataGenerator()

# Generate a dataset with 1000 rows
df = generator.generate(
    num_rows=1000,
    numerical_whole=3,
    decimal=2,
    categorical=2,
    ordinal=1,
    boolean=1,
    datetime=1,
    text=1,
    uuid=1,
    object_types=['name', 'country', 'email', 'job']
)

print(df.head())
print(df.info())

Features

• Numerical Data: Generate columns of whole numbers (integers) or decimals (floats).
• Categorical Data: Generate columns with a predefined set of unordered categories.
• Ordinal Data: Generate columns with a predefined set of ordered categories.
• Boolean Data: Generate columns of True/False values.
• Datetime Data: Generate columns with datetime objects.
• Text Data: Generate columns with random sentences.
• ID Data: Generate columns with unique identifiers (UUIDs).
• Coordinates: Generate paired latitude and longitude columns.
• Web Data: Generate columns for IP addresses, URLs, and phone numbers.
• Nested Data: Generate columns containing JSON-formatted strings.
• Object/Text Data: Leverage the power of the Faker library to generate realistic text data like names, addresses, emails, and much more.

Supported object_types

You can use any standard Faker provider method name as a string in the object_types list. Common examples include:

• name
• email
• address
• country
• city
• job
• text
• datetime
• phone_number
• company
• url
• credit_card_number