Pavan.DataAnalysis.NET 1.0.0

DataAnalysis.NET

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.

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 DataAnalysis.NET via NuGet Package Manager:

Install-Package DataAnalysis.NET

Or via .NET CLI:

dotnet add package DataAnalysis.NET

Quick Start

using 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

Excel Support

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.

Happy Data Analyzing with DataAnalysis.NET!