Documentation

Welcome to the GPandas documentation. GPandas is a high-performance data manipulation and analysis library written in Go, inspired by Python's popular pandas library.

Architecture Overview

GPandas uses a columnar architecture for efficient data operations:

Quick Start

Install GPandas using go get:

go get github.com/apoplexi24/gpandas

Minimal Example

package main

import (
    "fmt"
    "github.com/apoplexi24/gpandas"
)

func main() {
    gp := gpandas.GoPandas{}
    
    // Load data from CSV
    df, err := gp.Read_csv("data.csv")
    if err != nil {
        panic(err)
    }
    
    // Display the DataFrame
    fmt.Println(df.String())
}

Requirements

Requirement	Version
Go	1.18 or above
Architecture	Any (amd64, arm64)

GPandas requires Go version 1.18 or above due to its use of generics.

Core Features

Data Loading

Feature	Function	Description
CSV Files	`Read_csv()`	Load CSV files with concurrent parsing
JSON	`Read_json()`, `ToJSON()`	Read and write records-oriented JSON
Excel	`Read_excel()`, `ToExcel()`	Read and write .xlsx spreadsheets
Parquet	`Read_parquet()`, `ToParquet()`	Read and write Parquet files
SQL Databases	`Read_sql()`	Query SQL Server, PostgreSQL, and more
Google BigQuery	`From_gbq()`	Query BigQuery tables directly
In-Memory	`DataFrame()`	Create DataFrames from Go data structures

DataFrame Operations

Feature	Methods	Description
Column Selection	`Select()`, `SelectCol()`	Extract specific columns
Renaming	`Rename()`	Rename columns while preserving order
Adding Columns	`Assign()`, `AssignFunc()`, `Insert()`	Add, compute, or insert columns
Filtering	`Filter()`, `Where()`	Subset rows by comparison or predicate
Membership & Range	`Isin()`, `Between()`	Subset rows by a set of values or a value range
Top N	`Nlargest()`, `Nsmallest()`	Take the n largest or smallest rows without a full sort
Transformation	`Apply()`, `Map()`, `ApplyRow()`	Transform values and derive columns
Arithmetic	`Add()`, `Sub()`, `Mul()`, `Div()`, `AddScalar()`	Element-wise arithmetic on columns and scalars
Comparison	`Gt()`, `Lt()`, `Eq()`, `GtScalar()`	Element-wise comparisons producing boolean columns
String Methods	`Str().Lower()`, `Contains()`, `Len()`	Vectorized string operations
Missing Data	`FillNA()`, `DropNA()`, `IsNA()`	Detect, fill, and drop null values
Deduplication	`Unique()`, `Duplicated()`, `DropDuplicates()`	Find distinct values and remove duplicates
Type Casting	`AsType()`, `DTypes()`, `Info()`	Convert column types and inspect structure
Statistics	`Describe()`, `Mean()`, `ValueCounts()`	Summarize and aggregate numeric data
Correlation	`Corr()`, `Cov()`	Pairwise correlation and covariance
Sampling	`Sample()`, `Pipe()`	Random sampling and method chaining
Grouping	`GroupBy()`, `Agg()`	Group rows and aggregate
Window	`Rolling()`, `Shift()`, `CumSum()`	Moving and cumulative operations
Reshaping	`Stack()`, `Unstack()`, `PivotTable()`, `Melt()`	Convert between wide and long
DateTime	`ToDatetime()`, `Dt()`	Parse dates and extract components
Categorical	`AsCategorical()`, `Categories()`	Memory-efficient repeated strings
Merging	`Merge()`, `MergeOn()`	Join DataFrames on one or more keys
Display	`String()`	Pretty-print DataFrame as table
Export	`ToCSV()`	Export to CSV file or string
Plotting	`PlotBar()`, `PlotScatter()`, `PlotHistogram()`, `PlotHeatmap()`	Generate interactive charts

Indexing

Type	Accessor	Description
Label-based	`Loc()`	Access by row labels and column names
Position-based	`ILoc()`	Access by integer positions
Index Management	`SetIndex()`, `ResetIndex()`	Custom row labels

Documentation Guide

Explore the documentation to learn more about GPandas capabilities:

Getting Started

Installation & Setup - Install GPandas and run your first program

Loading Data

Loading CSV Files - Read CSV files into DataFrames
JSON, Excel & Parquet I/O - Read and write JSON, Excel, and Parquet files
SQL Integration - Connect to databases and BigQuery

Working with DataFrames

Creating DataFrames - Build DataFrames from scratch
DataFrame Operations - Select, rename, display, and export
Adding Columns - Add, compute, and insert columns
Filtering Data - Subset rows by comparison or predicate
Membership, Range & Top-N - Subset rows by value set, range, or the n largest and smallest values
Transforming Columns - Apply and map functions over columns
Arithmetic & Comparison - Element-wise arithmetic and comparison across columns and scalars
String Methods - Vectorized string operations on columns
Handling Missing Data - Detect, fill, and drop null values
Unique Values & Deduplication - Distinct values and duplicate removal
Type Casting & Inspection - Convert column types and inspect structure
Summary Statistics - Describe and aggregate numeric data
Correlation, Sampling & Pipe - Correlation, covariance, sampling, and chaining
Grouping & Aggregation - Group rows and aggregate
Window Functions - Rolling, shift, and cumulative operations
DateTime - Parse dates and extract components
Categorical Data - Memory-efficient repeated-string columns
Sorting Data - Order rows by values or index labels
Pivot and Melt - Reshape data between wide and long formats
Stacking & MultiIndex - Stack/unstack and composite indexes
Merging Data - Join and combine DataFrames (one or more keys)
Plotting Charts - Visualize data with bar, line, pie, scatter, histogram, and heatmap charts

Indexing & Selection

Label-based Indexing (Loc) - Access data by labels
Position-based Indexing (iLoc) - Access data by position

Core Types

Series - The fundamental column type

Performance Highlights

GPandas is designed for speed:

Columnar Storage: Efficient memory layout for analytical queries
Concurrent CSV Parsing: Multi-core utilization for large files
Zero-Copy Operations: Minimal data copying where possible
Thread-Safe Series: RWMutex protection for concurrent access

Documentation

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