Is Gota production ready or still experimental?

The library is actively developed but the README warns of an unstable API, making it better suited for experimental projects or internal tools rather than mission-critical production systems. Always check the latest releases for stability improvements.

How to filter data in a Gota DataFrame?

Use the Filter or FilterAggregation methods with predefined comparators like series.Eq or custom functions, as shown in the README examples for complex row subsetting. Chaining filters allows combining AND/OR logic.

Gota vs pandas: which is better for Go projects?

Gota is ideal for pure Go environments where you want to avoid Python dependencies and leverage type safety, but pandas has a more mature ecosystem with advanced features. For Go-centric data pipelines, Gota offers native integration and performance benefits.

Does Gota support missing values like NaN?

Yes, Gota DataFrames support non-measurements or NaN elements, allowing representation of missing data. Handling can be done through filters or custom operations, similar to other data frame libraries.

What are the performance trade-offs of using Gota for large datasets?

As a Go library, it benefits from compiled language performance for CPU-bound tasks, but it loads data entirely into memory, so memory usage can be high for very large datasets. Distributed processing isn't built-in.

How do I convert a Gota DataFrame to a gonum matrix?

Implement the dataframe.Matrix interface by adding At and T methods to a wrapper struct, as detailed in the README's gonum integration section. This allows seamless use with numerical computing workflows.

Open-Awesome

gota

NOASSERTIONGov0.12.0

A Go library providing DataFrames, Series, and data wrangling operations for structured data manipulation.

GitHub

3.3k stars290 forks0 contributors

What is gota?

Gota is a Go library that implements DataFrames and Series for structured data manipulation. It provides data wrangling operations like filtering, aggregation, joining, and transformation, bringing data science workflows to Go. The library handles tabular data with column type integrity and supports various data formats.

Target Audience

Go developers working with structured data, data engineers building ETL pipelines in Go, and data scientists seeking performance-oriented data manipulation tools in a compiled language.

Value Proposition

Gota offers a native Go solution for DataFrame operations without relying on Python/R bridges, providing type safety, performance, and seamless integration with the Go ecosystem and Gonum for numerical computing.

Overview

Gota: DataFrames and data wrangling in Go (Golang)

Use Cases

Best For

Building data processing pipelines in Go
Performing ETL operations on structured datasets
Implementing data analysis workflows without Python/R dependencies
Manipulating CSV/JSON data with chainable operations
Integrating data wrangling with Gonum for numerical computing
Creating type-safe data transformation tools

Not Ideal For

Production systems requiring a stable, versioned API without breaking changes
Data science projects needing extensive statistical libraries or machine learning integrations
Teams heavily reliant on Python/R ecosystems for seamless data analysis interoperability

Pros & Cons

Pros

Type-Safe DataFrames

Ensures column integrity with supported types (Int, Float, String, Bool), preventing common data type errors during manipulation, as highlighted in the DataFrame usage examples.

Chainable Operations

Supports expressive method chaining for data wrangling, allowing filters, selections, and mutations in sequence, as demonstrated in the README's chaining section.

Multiple Data Sources

Loads data from CSV, JSON, structs, maps, and records with configurable type detection, simplifying ingestion from various formats without external dependencies.

Gonum Integration

Compatible with gonum/mat matrices for numerical computing, enabling seamless workflows between data frames and mathematical operations, as noted in the integration example.

Cons

Unstable API

The README explicitly warns that the API is still in flux, posing risks for production use due to potential breaking changes and requiring frequent updates.

Limited Data Types

Only supports Int, Float, String, and Bool series, lacking native handling for dates, times, or complex types, which may require manual workarounds for real-world data.

Basic Statistical Functions

Aggregation and filtering are provided, but advanced statistical operations common in pandas or R are missing, forcing users to implement custom functions or rely on external libraries.

Open Source Alternative To

gota is an open-source alternative to the following products:

Pandas

Pandas is a fast, powerful, and flexible open-source data analysis and manipulation library for Python, built on top of NumPy.

R data.table

data.table is an R package that provides an enhanced version of data.frame with faster performance and more concise syntax for data manipulation operations.

dplyr

dplyr is an R package for data manipulation that provides a grammar of data manipulation with functions like filter, select, mutate, and summarize.

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