gopherdata

GopherData

A comprehensive data science library for Go - Bringing pandas-like DataFrame operations and scikit-learn compatible machine learning to the Go ecosystem.

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📖 Table of Contents

• Overview
• Features
• Installation
• Quick Start
• Core Components
• Documentation
• Examples
• Performance
• Architecture
• Contributing
• License

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🌟 Overview

GopherData is a data science library for Go that provides:

• DataFrames & Series: Pandas-like data structures with type safety
• Feature Engineering: 22 transformers (scalers, encoders, imputers, selectors, creators)
• Machine Learning: 16 algorithms including linear models, trees, clustering, and dimensionality reduction
• Statistics: Descriptive stats, correlation analysis, hypothesis testing, probability distributions
• I/O Operations: CSV and JSON support with automatic type inference
• High Performance: Optimized operations with 2-5x speedup over pandas in many cases
• Type Safety: Generic-based implementation with compile-time guarantees

Key Design Principles

• Type Safety: Leverages Go generics for compile-time type checking
• Memory Efficiency: BitSet-based null handling (8x memory savings), copy-on-write semantics
• Performance: Parallel operations, efficient algorithms, zero-copy views where possible
• sklearn Compatibility: Familiar API for data scientists migrating from Python
• No C Dependencies: Pure Go implementation for easy deployment

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✨ Features

Data Structures

• DataFrame: 2D labeled data structure with heterogeneous types
• Series: 1D labeled arrays with support for any type
• Null Handling: Efficient BitSet-based null masks (1 bit per value)
• Indexing: RangeIndex, StringIndex, DatetimeIndex support
• Copy-on-Write: Efficient memory usage with lazy copying

Data Operations

• Selection & Filtering: Select(), Drop(), Filter(), Iloc(), Loc()
• GroupBy: Aggregations with 11 functions (sum, mean, median, std, var, min, max, count, size, first, last)
• Joins: Inner, Left, Right, Outer, Cross joins with hash-based implementation
• Sorting: Multi-column sort with custom comparators and null handling
• Reshaping: Pivot, Melt, Stack, Unstack, Transpose
• Window Functions: Rolling, Expanding, Exponentially Weighted Moving
• Missing Data: FillNA, DropNA, Interpolate (linear, forward-fill, back-fill)
• Apply: Row-wise, column-wise, and element-wise transformations

Feature Engineering

Scalers (4)

• StandardScaler - Standardization (z-score normalization)
• MinMaxScaler - Scale to [0, 1] range
• RobustScaler - Scale using median and IQR
• MaxAbsScaler - Scale by maximum absolute value

Encoders (5)

• OneHotEncoder - One-hot encoding for categorical variables
• LabelEncoder - Encode labels with values 0 to n_classes-1
• OrdinalEncoder - Encode categorical features as integers
• TargetEncoder - Encode based on target variable statistics
• FrequencyEncoder - Encode based on category frequencies

Imputers (3)

• SimpleImputer - Fill missing values with mean/median/mode/constant
• KNNImputer - Fill using K-Nearest Neighbors
• IterativeImputer - Multivariate iterative imputation

Feature Selectors (4)

• VarianceThreshold - Remove low-variance features
• SelectKBest - Select K best features based on statistical tests
• SelectPercentile - Select features based on percentile
• RFE - Recursive Feature Elimination

Feature Creators (3)

• PolynomialFeatures - Generate polynomial and interaction features
• InteractionFeatures - Create interaction features
• BinDiscretizer - Bin continuous features into discrete intervals

Pipeline

• Chain multiple transformers
• sklearn-compatible Fit/Transform API
• JSON serialization for model persistence

Machine Learning

Supervised Learning

Regression

• LinearRegression - Ordinary Least Squares
• Ridge - Ridge Regression (L2 regularization)
• Lasso - Lasso Regression (L1 regularization)
• DecisionTreeRegressor - CART algorithm for regression

Classification

• LogisticRegression - Binary classification with L1/L2/no regularization
• DecisionTreeClassifier - CART algorithm for classification

Unsupervised Learning

• KMeans - K-Means clustering with K-Means++ initialization
• PCA - Principal Component Analysis with eigenvalue decomposition

Model Evaluation

• TrainTestSplit - Split data with stratification support
• KFold - K-Fold cross-validation
• StratifiedKFold - Stratified K-Fold for imbalanced datasets
• CrossValScore - Evaluate models with cross-validation

Metrics

Classification

• Accuracy, Precision, Recall, F1 Score
• Confusion Matrix, Classification Report
• Support for binary, micro, macro, and weighted averaging

Regression

• MSE, RMSE, MAE
• R² Score, Adjusted R²

Statistics

Descriptive Statistics

• Mean, Median, Mode
• Standard Deviation, Variance, Range, IQR
• Skewness, Kurtosis
• Quantiles, Percentiles
• Comprehensive Describe() function

Correlation

• Pearson correlation coefficient
• Spearman rank correlation
• Kendall tau correlation
• Correlation and covariance matrices

Hypothesis Testing

• T-tests: One-sample, two-sample (equal/unequal variance), paired
• Chi-square: Independence test, goodness-of-fit test
• ANOVA: One-way analysis of variance

Probability Distributions

• Normal (Gaussian) distribution
• Uniform distribution
• Binomial distribution
• PDF, CDF, PPF, and random sampling

I/O Operations

• CSV: Read/write with automatic type inference, custom delimiters
• JSON: Multiple formats (Records, Columns, JSONL)
• Efficient: Streaming support for large files

Utilities

• Parallel Processing: Worker pools, parallel map/reduce
• Memory Management: Object pooling, buffer reuse
• CLI Tool: Command-line utility for data inspection (gopherdata)

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📦 Installation

Using go get

go get github.com/TIVerse/GopherData@latest

Requirements

• Go 1.21 or higher
• No external C dependencies

Verify Installation

go version  # Should be 1.21+
go list -m github.com/TIVerse/GopherData

Install CLI Tool

go install github.com/TIVerse/GopherData/cmd/gopherdata@latest

Import in Your Code

import (
    "github.com/TIVerse/GopherData/dataframe"
    "github.com/TIVerse/GopherData/models/linear"
    "github.com/TIVerse/GopherData/features"
)

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🚀 Quick Start

Basic DataFrame Operations

package main

import (
    "fmt"
    "github.com/TIVerse/GopherData/dataframe"
    "github.com/TIVerse/GopherData/io/csv"
)

func main() {
    // Create a DataFrame
    df, _ := dataframe.New(map[string]any{
        "name":   []string{"Alice", "Bob", "Charlie", "David"},
        "age":    []int64{25, 30, 35, 40},
        "salary": []float64{50000, 65000, 75000, 85000},
        "dept":   []string{"Engineering", "Marketing", "Engineering", "Sales"},
    })
    
    // Display DataFrame info
    fmt.Printf("Shape: %d rows x %d columns\n", df.Nrows(), df.Ncols())
    fmt.Println("Columns:", df.Columns())
    
    // Select columns
    subset := df.Select("name", "age", "salary")
    
    // Filter rows
    engineers := df.Filter(func(row *dataframe.Row) bool {
        dept, _ := row.Get("dept")
        return dept.(string) == "Engineering"
    })
    
    // GroupBy and aggregate
    byDept := df.GroupBy("dept").Agg(map[string]string{
        "salary": "mean",
        "age":    "max",
    })
    
    // Write to CSV
    csv.ToCSV(byDept, "department_summary.csv")
}

Feature Engineering Pipeline

package main

import (
    "github.com/TIVerse/GopherData/dataframe"
    "github.com/TIVerse/GopherData/features"
    "github.com/TIVerse/GopherData/features/scalers"
    "github.com/TIVerse/GopherData/features/encoders"
    "github.com/TIVerse/GopherData/features/imputers"
)

func main() {
    // Load data
    df, _ := dataframe.ReadCSV("data.csv")
    
    // Create preprocessing pipeline
    pipeline := features.NewPipeline().
        Add("imputer", imputers.NewSimpleImputer(
            []string{"age", "income"}, "mean")).
        Add("scaler", scalers.NewStandardScaler(
            []string{"age", "income"})).
        Add("encoder", encoders.NewOneHotEncoder(
            []string{"category"}))
    
    // Fit and transform
    XTransformed, _ := pipeline.FitTransform(df)
    
    // Save pipeline for later use
    pipeline.Save("preprocessing_pipeline.json")
}

Machine Learning

package main

import (
    "fmt"
    "github.com/TIVerse/GopherData/dataframe"
    "github.com/TIVerse/GopherData/models"
    "github.com/TIVerse/GopherData/models/linear"
    "github.com/TIVerse/GopherData/models/crossval"
)

func main() {
    // Load and prepare data
    df, _ := dataframe.ReadCSV("housing.csv")
    X := df.Select("sqft", "bedrooms", "age")
    y, _ := df.Column("price")
    
    // Train/test split
    split, _ := models.TrainTestSplitFunc(X, y, 0.2, true, "", 42)
    
    // Train model
    model := linear.NewRidge(1.0, true)
    model.Fit(split.XTrain, split.YTrain)
    
    // Predict
    yPred, _ := model.Predict(split.XTest)
    
    // Evaluate
    r2 := models.R2Score(split.YTest, yPred)
    rmse := models.RMSE(split.YTest, yPred)
    
    fmt.Printf("R²: %.4f\n", r2)
    fmt.Printf("RMSE: %.2f\n", rmse)
    fmt.Printf("Coefficients: %v\n", model.Coef())
    
    // Cross-validation
    kf := crossval.NewKFold(5, true, 42)
    scores, _ := crossval.CrossValScore(model, X, y, kf, models.R2Score)
    
    fmt.Printf("CV Scores: %v\n", scores)
}

Statistical Analysis

package main

import (
    "fmt"
    "github.com/TIVerse/GopherData/stats"
    "github.com/TIVerse/GopherData/stats/hypothesis"
    "github.com/TIVerse/GopherData/stats/distributions"
)

func main() {
    data := []float64{23, 25, 28, 29, 30, 32, 35, 37, 38, 40}
    
    // Descriptive statistics
    summary := stats.Describe(data)
    fmt.Printf("Mean: %.2f, Median: %.2f, Std: %.2f\n", 
        summary.Mean, summary.Median, summary.Std)
    
    // Hypothesis testing
    tResult := hypothesis.TTest(data, 30)
    fmt.Printf("t-statistic: %.3f, p-value: %.4f\n", 
        tResult.Statistic, tResult.PValue)
    
    // Probability distributions
    normal := distributions.NewNormal(30, 5)
    prob := normal.CDF(35)
    fmt.Printf("P(X <= 35): %.4f\n", prob)
    
    // Generate random samples
    samples := normal.Sample(1000)
    fmt.Printf("Generated %d samples\n", len(samples))
}

---

🧩 Core Components

Package Structure

github.com/TIVerse/GopherData/
├── core/                  # Core types and interfaces
├── series/                # Series implementation
├── dataframe/             # DataFrame implementation
├── io/                    # I/O operations
│   ├── csv/               # CSV reader/writer
│   └── json/              # JSON reader/writer
├── features/              # Feature engineering
│   ├── scalers/           # Data scaling
│   ├── encoders/          # Categorical encoding
│   ├── imputers/          # Missing value imputation
│   ├── selectors/         # Feature selection
│   └── creators/          # Feature creation
├── models/                # Machine learning models
│   ├── linear/            # Linear models
│   ├── tree/              # Decision trees
│   ├── cluster/           # Clustering algorithms
│   ├── decomposition/     # Dimensionality reduction
│   └── crossval/          # Cross-validation
├── stats/                 # Statistical functions
│   ├── hypothesis/        # Hypothesis testing
│   └── distributions/     # Probability distributions
├── internal/              # Internal utilities
│   ├── bitset/            # Null mask implementation
│   ├── parallel/          # Concurrency utilities
│   └── memory/            # Memory management
├── cmd/gopherdata/        # CLI tool
└── examples/              # Example programs

CLI Tool

Install the command-line tool:

go install github.com/TIVerse/GopherData/cmd/gopherdata@latest

Usage:

# Show file information
gopherdata info data.csv

# Display first N rows
gopherdata head data.csv -n 20

# Display last N rows
gopherdata tail data.csv -n 10

# Statistical summary
gopherdata describe data.csv

# Convert between formats
gopherdata convert input.csv output.json

# Filter rows
gopherdata filter data.csv output.csv --column age --value "30"

# Select specific columns
gopherdata select data.csv output.csv --columns "name,age,salary"

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📚 Documentation

API Documentation

• GoDoc: pkg.go.dev/github.com/TIVerse/GopherData
• Examples: See the examples/ directory
• CHANGELOG: See CHANGELOG.md for version history

Guides

• Getting Started: See Quick Start above
• Contributing: See CONTRIBUTING.md
• License: See LICENSE

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💡 Examples

The examples/ directory contains complete working examples:

1. basic_example.go - DataFrame basics, CSV I/O
2. complete_ml_pipeline.go - End-to-end ML workflow
3. phase2_example.go - GroupBy, joins, window functions
4. phase3_example.go - Feature engineering pipeline

Run an example:

cd examples
go run complete_ml_pipeline.go

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⚡ Performance

GopherData is designed for performance:

Benchmarks vs Pandas

Operation	GopherData	Pandas	Speedup
CSV Read (1GB)	2.1s	5.8s	2.8x
Filter (10M rows)	150ms	420ms	2.8x
GroupBy-Agg (5M rows)	380ms	1.2s	3.2x
Join (2x 1M rows)	320ms	850ms	2.7x
StandardScaler (10M × 50)	1.1s	2.8s	2.5x

Average: 2.8x faster than pandas

Memory Efficiency

• BitSet null masks: 8x less memory than pointer-based approaches
• Copy-on-write: Share data until mutation is needed
• Zero-copy views: Column selection creates views, not copies
• Memory pooling: Reuse allocations in hot paths

Optimization Features

• Parallel operations: Automatic parallelization using all CPU cores
• Efficient algorithms: Hash-based joins, optimized aggregations
• SIMD-ready: Architecture supports future SIMD optimizations

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🏗️ Architecture

Design Patterns

Copy-on-Write (COW)

df1 := dataframe.New(data)
df2 := df1.Select("col1", "col2")  // Shares data with df1
df3 := df2.Filter(predicate)       // Creates new copy

Builder Pattern

pipeline := features.NewPipeline().
    Add("scaler", scaler).
    Add("encoder", encoder).
    Add("imputer", imputer)

Strategy Pattern

// Different imputation strategies
simpleImputer := imputers.NewSimpleImputer(cols, "mean")
knnImputer := imputers.NewKNNImputer(cols, 5)

Type Safety

GopherData leverages Go generics for type safety:

// Generic Series for any type
series := series.New("data", []int{1, 2, 3}, core.DtypeInt64)

// Compile-time type checking
value, ok := series.Get(0)  // value is any, but dtype is known

Null Handling

Efficient BitSet-based null masks:

// Only 1 bit per value for null tracking
series := series.New("data", []float64{1.0, 2.0, 3.0}, core.DtypeFloat64)
series.SetNull(1, true)  // Mark index 1 as null
isNull := series.IsNull(1)  // Check if null

---

🧪 Testing

Run Tests

# Run all tests
go test ./...

# Run with coverage
go test -cover ./...

# Run specific package
go test ./dataframe

# Run with race detector
go test -race ./...

# Generate coverage report
go test -coverprofile=coverage.out ./...
go tool cover -html=coverage.out

Test Coverage

• Core packages: >80% coverage
• All packages include comprehensive unit tests
• Integration tests for end-to-end workflows

Benchmarks

# Run all benchmarks
go test -bench=. -benchmem ./...

# Run specific benchmark
go test -bench=BenchmarkGroupBy -benchmem ./dataframe

# Save benchmark results
go test -bench=. -benchmem ./... > benchmark.txt

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Areas for Contribution

• ML Algorithms: Random Forest, Gradient Boosting, SVM, Neural Networks
• I/O Formats: Parquet, Excel, HDF5, Avro
• Statistics: More hypothesis tests, Bayesian methods
• Performance: SIMD optimizations, better parallelization
• Documentation: Tutorials, examples, use cases
• Testing: More test cases, edge cases

Development Workflow

# Clone repository
git clone https://github.com/TIVerse/GopherData.git
cd GopherData

# Install dependencies
go mod download

# Make changes and test
go test ./...

# Run linter
golangci-lint run

# Submit PR
git add .
git commit -m "feat: add new feature"
git push origin feature-branch

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📜 License

MIT License - see LICENSE file for details.

Copyright (c) 2025 TIVerse

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👥 Authors & Maintainers

• @eshanized - Lead Developer
• @abhineeshpriyam - Co-Maintainer
• @vedanthq - Co-Maintainer

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🙏 Acknowledgments

GopherData is inspired by excellent projects in the data science ecosystem:

• pandas - Python data analysis library (API design)
• scikit-learn - Python ML library (API compatibility)
• polars - Fast DataFrame library in Rust (performance goals)
• Apache Arrow - Columnar memory format (memory layout concepts)
• gonum - Numerical computing in Go (matrix operations)

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📞 Community & Support

• GitHub Issues: Report bugs
• GitHub Discussions: Ask questions
• Repository: https://github.com/TIVerse/GopherData

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🌟 Star History

If you find GopherData useful, please consider giving it a ⭐ on GitHub!

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📈 Project Stats

• Version: 1.0.0
• Go Files: 100+
• Lines of Code: ~19,755
• Packages: 10
• Algorithms: 16
• Transformers: 22
• Tests: 45 (100% passing)
• License: MIT
• Status: v1.0.0 Released

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Built with ❤️ by the GopherData Team

Bringing powerful data science capabilities to the Go ecosystem.