npm package: @private.ankit047/data-analyzer
Description: A powerful data management and analysis package for Node.js.
Author: Ankit
Latest version: 1.0.1

```markdown
# 📊 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.

```javascript
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.

```javascript
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.

```javascript
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.

```javascript
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**:
  ```javascript
  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**:
  ```javascript
  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**:
  ```javascript
  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**:
  ```javascript
  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](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**: [private.ankit047@gmail.com](mailto:private.ankit047@gmail.com)
- **GitHub**: [@imankii01](https://github.com/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.