Pavan.DataAnalysis.NET 1.0.1

DataAnalysis.NET

Pavan.DataAnalysis.NET is a powerful .NET library designed for efficient data analysis and manipulation. It provides a flexible DataFrame structure and a suite of tools for reading, writing, cleaning, and analyzing data, with a focus on Excel and CSV file formats. Here is the link: https://www.nuget.org/packages/Pavan.DataAnalysis.NET

Features

• DataFrame Structure: A versatile data structure for holding and manipulating tabular data.
• File I/O:
  • Read and write CSV files
  • Read and write Excel (.xlsx) files
• Data Cleaning:
  • Replace NaN values with a specified value or column mean
  • Drop rows with NaN values
  • Filter rows based on custom predicates
• Data Analysis:
  • Column-wise operations: Sum, Mean, Variance
  • Create pivot tables with custom aggregation functions
• Performance: Utilizes SIMD (Single Instruction, Multiple Data) for efficient column summation

Installation

Install Pavan.DataAnalysis.NET via NuGet Package Manager:

Install-Package Pavan.DataAnalysis.NET

Or via .NET CLI:

dotnet add package Pavan.DataAnalysis.NET

Quick Start

Using Dependency Injection

1. First, add the following using statements to your file:

using Pavan.DataAnalysis.NET;
using Pavan.DataAnalysis.NET.Abstractions;

2. In your Program.cs or startup file, use one of the extension methods provided to register the IDataAnalysis interface with the dependency injection container:

// For singleton service (recommended for most cases)
builder.Services.AddDataAnalysis();

// Or for scoped service
// builder.Services.AddDataAnalysisScoped();

// Or for transient service
// builder.Services.AddDataAnalysisTransient();

3. Then, you can inject and use the IDataAnalysis interface in your classes:

public class MyAnalysisService
{
    private readonly IDataAnalysis _dataAnalysis;

    public MyAnalysisService(IDataAnalysis dataAnalysis)
    {
        _dataAnalysis = dataAnalysis;
    }

    public void PerformAnalysis(string filePath)
    {
        _dataAnalysis.LoadData(filePath);
        _dataAnalysis.AnalyzeData();
        _dataAnalysis.ExportResults("results.csv", "csv");
    }
}

Basic Usage

using Pavan.DataAnalysis.NET;

// Read a CSV file
var df = ExcelReader.ReadCsv("data.csv");

// Clean the data
DataCleaner.ReplaceNaNsWithMean(df);

// Perform analysis
var columnMeans = DataFrameOperations.MeanColumns(df);

// Create a pivot table
var pivotTable = DataFrameOperations.CreatePivotTable(df, 0, 1, arr => arr.Average());

// Write results back to Excel
DataFrame.WriteXlsx("results.xlsx", pivotTable);

Key Components

DataFrame

The core data structure for holding tabular data. It supports:

• Adding rows
• Retrieving columns
• Getting column names
• Printing data to console

ExcelReader

Provides methods for reading and writing CSV files:

• ReadCsv(string filePath): Read data from a CSV file into a DataFrame
• WriteCsv(string filePath, DataFrame df): Write a DataFrame to a CSV file

DataFrameOperations

Offers various data analysis operations:

• SumColumns(DataFrame df): Calculate the sum of each column
• MeanColumns(DataFrame df): Calculate the mean of each column
• VarianceColumns(DataFrame df): Calculate the variance of each column
• CreatePivotTable(DataFrame df, int groupByCol, int valueCol, Func<double[], double> aggregationFunc): Create a pivot table with custom aggregation

DataCleaner

Provides methods for cleaning and filtering data:

• ReplaceNaNs(DataFrame df, double replacementValue): Replace NaN values with a specified value
• DropNaNs(DataFrame df): Remove rows containing NaN values
• ReplaceNaNsWithMean(DataFrame df): Replace NaN values with the column mean
• FilterRows(DataFrame df, Func<double[], bool> predicate): Filter rows based on a custom predicate

IDataAnalysis Interface

The IDataAnalysis interface provides a high-level API for data analysis operations:

• LoadData(string filePath): Load data from a CSV or Excel file
• LoadData(Stream stream): Load data from a stream (not implemented in the current version)
• AnalyzeData(): Perform data analysis (implementation may vary)
• ExportResults(string filePath, string format): Export analysis results to CSV or Excel

Excel Support

Pavan.DataAnalysis.NET includes built-in support for Excel files:

• DataFrame.ReadXlsx(string filePath): Read data from an Excel file into a DataFrame
• DataFrame.WriteXlsx(string filePath, DataFrame df): Write a DataFrame to an Excel file

Use Cases

1. Financial Analysis: Process and analyze financial data from CSV or Excel files.
2. Data Cleaning: Easily handle missing values and filter datasets.
3. Statistical Analysis: Compute basic statistical measures like mean and variance.
4. Data Aggregation: Create pivot tables for summarizing large datasets.
5. Data Transformation: Filter and manipulate data using custom predicates.

Contributing

We welcome contributions! Please see our Contributing Guidelines for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

If you encounter any issues or have questions, please file an issue on the GitHub repository.

Building a Data Science Community in .NET

As C# and .NET developers, we have a unique opportunity to contribute to the growing field of data science. While languages like Python and R have traditionally dominated this space, the power and versatility of C# make it an excellent choice for data analysis and machine learning tasks.

Why C# for Data Science?

1. Performance: C# offers superior performance, crucial for handling large datasets and complex computations.
2. Type Safety: The strong typing system in C# helps catch errors early and improves code reliability.
3. Integration: Seamlessly integrate data science workflows into existing .NET applications and ecosystems.
4. Tooling: Leverage the robust IDE support and debugging tools available in the .NET ecosystem.
5. Cross-Platform: With .NET Core, run your data science applications on Windows, Linux, and macOS.

Join the Movement

By contributing to projects like DataAnalysis.NET and other data science initiatives in the .NET ecosystem, you can:

• Innovate: Bring fresh perspectives and ideas from the .NET world to data science challenges.
• Learn: Expand your skillset by applying C# knowledge to new domains.
• Collaborate: Connect with like-minded developers and data scientists, bridging the gap between software engineering and data analysis.
• Impact: Help shape the future of data science in the .NET ecosystem and create tools that empower developers worldwide.

Get Involved

• Contribute to open-source data science libraries for .NET.
• Share your experiences and learnings through blog posts, talks, or tutorials.
• Participate in data science hackathons and competitions using C#.
• Engage with the community on forums, social media, and at conferences.

Together, we can build a thriving data science community in C# and .NET, creating powerful tools and pushing the boundaries of what's possible in data analysis and machine learning.

Let's code, analyze, and innovate – the .NET way!

Happy Data Analyzing with Pavan.DataAnalysis.NET!