Packages

Package

Description

Root package containing main application class and core interfaces

com.leumanuel.woozydata.config

Configuration classes for MongoDB and other external services integration

com.leumanuel.woozydata.controller

Interface layer defining the core API operations for data manipulation and analysis

com.leumanuel.woozydata.model

Data models and structure definitions, including the DataFrame class for data representation

com.leumanuel.woozydata.repository

Data access layer for MongoDB and other data sources

com.leumanuel.woozydata.service

Core business logic implementing statistical analysis, regression models, probability distributions, time series analysis, and data cleaning operations

com.leumanuel.woozydata.util

Utility classes for data import/export, transformations, and common operations

# Woozydata

Woozydata is a powerful and easy-to-use Java data analysis library designed to simplify complex tasks like data cleaning, exporting, statistical analysis, and modeling. Perfect for data analysts and full stack developers, Woozydata provides a unified interface for working with tabular data, offering descriptive statistics, cleaning methods, advanced analysis, and support for multiple export formats.

🚀 Purpose of the Library

Woozydata was built to:

Make data analysis accessible and efficient with a simple and intuitive API.
Unify tasks such as cleaning, exporting, statistics, and forecasting into a single library.
Empower developers and analysts to build robust analytical solutions in Java.

🧰 Key Features

Data Loading:
- Supports CSV, Excel, JSON files, and MongoDB databases.
Descriptive Statistics:
- Calculates mean, median, standard deviation, variance, covariance, and more.
Data Cleaning:
- Removes null values and duplicates, fills missing values, normalizes, and standardizes data.
Data Export:
- Save your data in CSV, Excel, JSON, Power BI, HTML, or LaTeX formats.
Advanced Analysis:
- Supports linear regression, logistic regression, time series forecasting, and more.
Data Transformation:
- Pivot tables, melt, binary columns (dummies), and grouping.
Statistical Distributions:
- Generate normal, uniform, binomial, and Poisson distributions.
Outlier Detection and Time Series Analysis.

📦 Installation

Add the library to your Maven project:

<dependency>
    <groupId>io.github.leupesquisa</groupId>
    <artifactId>woozydata</artifactId>
    <version>1.0.0</version>
  </dependency>

🌟 Examples of Usage

1. Initialization and Data Loading

Woozydata woozydata = new Woozydata();

  // Load a JSON file or fromCsv to Load a CSV file  
  DataFrame dataFrame = woozydata.fromJson("data/sample_data.json");
  System.out.println(dataFrame);

  // Load data from a MongoDB database
  DataFrame mongoData = woozydata.fromMongo("mongodb://localhost:27017", "myDatabase", "myCollection");
  System.out.println(mongoData);

2. Descriptive Statistics

// Calculate statistics
  double mean = woozydata.mean("age");
  System.out.println("Mean of 'age' column: " + mean);

  double median = woozydata.median("salary");
  System.out.println("Median of 'salary' column: " + median);

  Map<String, Object> description = woozydata.describe("income");
  System.out.println("Full description of 'income' column: " + description);

3. Data Cleaning

// Clean the DataFrame
  DataFrame cleanedData = woozydata.clean();
  System.out.println("Cleaned DataFrame: " + cleanedData);

  // Drop null values
  DataFrame withoutNa = woozydata.dropNa();
  System.out.println("Without null values: " + withoutNa);

  // Fill missing values
  DataFrame filledData = woozydata.fillNa(0);
  System.out.println("Filled missing values: " + filledData);

4. Data Export

// Export to Excel
  woozydata.toExcel("output/cleaned_data.xlsx");
  System.out.println("Data exported to Excel.");

  // Export to JSON
  woozydata.toJson("output/data.json");
  System.out.println("Data exported to JSON.");

5. Data Transformation

// Create a pivot table
  DataFrame pivoted = woozydata.pivot("category", "year", "sales");
  System.out.println("Pivoted DataFrame: " + pivoted);

  // Melt the DataFrame
  DataFrame melted = woozydata.melt(new String[]{"id"}, new String[]{"sales", "profit"});
  System.out.println("Melted DataFrame: " + melted);

6. Advanced Analysis

// Linear Regression
  double[] regression = woozydata.linearReg("experience", "salary");
  System.out.println("Linear Regression (intercept, slope): " + regression[0] + ", " + regression[1]);

  // Time Series Forecasting
  DataFrame forecasted = woozydata.forecast("date", "sales", 12);
  System.out.println("Forecasted DataFrame: " + forecasted);

7. Outlier Detection

// Detect outliers in a column
  DataFrame outliers = woozydata.detectOutliers("sales");
  System.out.println("Detected outliers: " + outliers);

🔧 Requirements

Java 17+
Apache Commons Math 3.6.1
MongoDB Driver (optional)

📈 Performance

Operation	10K Rows	100K Rows	1M Rows
Loading Data	0.5s	2.3s	15.1s
Data Cleaning	0.2s	1.1s	8.4s
Data Analyis	0.3s	1.5s	9.2s

📊 Integration with Dashboards

You can integrate Woozydata with front-end tools like React or Angular to create interactive dashboards. Additionally, export your data to Power BI or HTML files for visual reporting.

📖 Complete Documentation

For more details, refer to the official documentation.

🛠️ Support

If you encounter issues or have questions, open an issue on GitHub or contact us via email.

Email: leu.manuel@hotmail.com
LinkedIn: Leu Manuel

📢 Contribute!

We welcome contributions! Check out the contribution guide.

Apache Commons Math team

Java Community

🌍 License

This project is licensed under the MIT License.

Woozydata 1.0.0 API (maven central repository)