# 📊 Data Analyzer

![npm](https://img.shields.io/npm/v/@private.ankit047/data-analyzer) ![npm](https://img.shields.io/npm/dt/@private.ankit047/data-analyzer)

## 📖 Overview

**Data Analyzer** is a comprehensive Node.js package designed for seamless data management and analysis. With a focus on ease of use and efficiency, it provides essential utilities for parsing CSV files, cleaning data, performing in-depth analysis, and visualizing results. This package is perfect for data scientists, analysts, and developers looking to enhance their data workflows!

## 🚀 Features

- **📥 CSV Parsing**: Effortlessly parse CSV files into JavaScript objects.
- **🧹 Data Cleaning**: Remove duplicates, fill missing values, and ensure data integrity.
- **📊 Data Analysis**: Perform various statistical analyses including averages, totals, and custom calculations.
- **📈 Data Visualization**: Create dynamic plots and graphs to visualize your data clearly.
- **📝 Documentation**: Comprehensive documentation with examples for easy integration.

## 🔗 Installation

To install the package, run the following command in your terminal:

```bash
npm install @private.ankit047/data-analyzer

🛠️ Usage

Here's a quick example to get you started with Data Analyzer. The example covers the entire workflow from parsing a CSV file to visualizing the analysis results.

Step 1: Parsing a CSV File

First, you need to parse a CSV file into JavaScript objects. This can be done using the parseCSV function.

const { parseCSV } = require('@private.ankit047/data-analyzer');

// Parse a CSV file
async function loadData() {
    const data = await parseCSV('path/to/your/file.csv');
    console.log(data);
}

loadData();

Step 2: Cleaning the Data

Once you have the data parsed, you may want to clean it to ensure it's ready for analysis. The cleanData function provides options for removing duplicates and filling in missing values.

const { cleanData } = require('@private.ankit047/data-analyzer');

async function cleanAndPrepareData() {
    let data = await parseCSV('path/to/your/file.csv');
    const cleanedData = cleanData(data, { removeDuplicates: true, fillMissing: 0 });
    console.log(cleanedData);
}

cleanAndPrepareData();

Step 3: Analyzing the Data

With cleaned data, you can now perform analysis using the analyzeData function. This function can compute various statistics such as averages, sums, and more.

const { analyzeData } = require('@private.ankit047/data-analyzer');

async function analyzeCleanedData() {
    let data = await parseCSV('path/to/your/file.csv');
    const cleanedData = cleanData(data, { removeDuplicates: true, fillMissing: 0 });
    const analysisResults = analyzeData(cleanedData);
    console.log(analysisResults);
}

analyzeCleanedData();

Step 4: Visualizing the Results

Finally, you can visualize the analysis results using the plotData function. This allows you to create charts and graphs to better understand the data.

const { plotData } = require('@private.ankit047/data-analyzer');

async function visualizeAnalysis() {
    let data = await parseCSV('path/to/your/file.csv');
    const cleanedData = cleanData(data, { removeDuplicates: true, fillMissing: 0 });
    const analysisResults = analyzeData(cleanedData);
    plotData(analysisResults);
}

visualizeAnalysis();

📜 API Reference

1. parseCSV(filePath: string): Promise<Object[]>

• Description: Parses the specified CSV file and returns an array of objects.
• Parameters:
  • filePath: The path to the CSV file (must be a valid file path).
• Returns: A promise that resolves to an array of parsed objects.
• Example:

  const data = await parseCSV('data/sample.csv');

2. cleanData(data: Object[], options: Object): Object[]

• Description: Cleans the input data based on the specified options.
• Parameters:
  • data: An array of objects to be cleaned.
  • options: An object specifying cleaning options:
    • removeDuplicates (Boolean): Remove duplicate entries (default: false).
    • fillMissing (Any): Fill missing values with the specified value.
• Returns: An array of cleaned objects.
• Example:

  const cleanedData = cleanData(data, { removeDuplicates: true, fillMissing: 0 });

3. analyzeData(data: Object[]): Object

• Description: Performs statistical analysis on the given data.
• Parameters:
  • data: An array of objects to analyze.
• Returns: An object containing analysis results such as averages and totals.
• Example:

  const results = analyzeData(cleanedData);

4. plotData(data: Object): void

• Description: Generates a visualization based on the analysis results.
• Parameters:
  • data: The analysis results to visualize.
• Returns: None. The function renders the plot.
• Example:

  plotData(results);

🛠️ Additional Features

• Error Handling: All functions include basic error handling to manage issues like file not found or invalid data formats.
• Support for Multiple File Formats: While primarily focused on CSV, you can extend parsing to support other formats with additional functions.
• Customization Options: Many functions accept additional parameters to customize their behavior (e.g., specifying which columns to analyze).

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please feel free to create a pull request or open an issue. Before contributing, please ensure you follow the coding standards and provide tests for any new functionality.

Guidelines for Contribution:

1. Fork the repository and create your branch from main.
2. Make your changes and ensure all tests pass.
3. Commit your changes with a clear message.
4. Push to your fork and submit a pull request.

📧 Contact

For any inquiries, feel free to reach out:

• Name: Ankit
• Email: [email protected]
• GitHub: @imankii01

Happy analyzing! 🎉

### Key Enhancements

1. **Expanded Usage Examples**: Provided step-by-step examples for parsing, cleaning, analyzing, and visualizing data.
2. **Detailed API Reference**: Each function is explained with parameters, return types, and example usage to ensure clarity.
3. **Additional Features**: Mentioned error handling, support for multiple file formats, and customization options.
4. **Contributing Guidelines**: Clear steps for how others can contribute to your project.
5. **Professional Formatting**: Used headings, bullet points, and code blocks for easy readability.