{"id":31925254,"url":"https://github.com/donghquinn/gopandas","last_synced_at":"2025-10-14T00:48:10.884Z","repository":{"id":318502050,"uuid":"1070050167","full_name":"donghquinn/gopandas","owner":"donghquinn","description":"gopandas","archived":false,"fork":false,"pushed_at":"2025-10-07T14:36:06.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-07T16:15:50.758Z","etag":null,"topics":["data","go","golang"],"latest_commit_sha":null,"homepage":"","language":"Go","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/donghquinn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-05T06:44:37.000Z","updated_at":"2025-10-07T14:36:01.000Z","dependencies_parsed_at":"2025-10-07T16:18:32.916Z","dependency_job_id":"a94ffeb9-f0a2-4cd5-81f4-b0dd29aa0f6d","html_url":"https://github.com/donghquinn/gopandas","commit_stats":null,"previous_names":["donghquinn/gopandas"],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/donghquinn/gopandas","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/donghquinn%2Fgopandas","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/donghquinn%2Fgopandas/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/donghquinn%2Fgopandas/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/donghquinn%2Fgopandas/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/donghquinn","download_url":"https://codeload.github.com/donghquinn/gopandas/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/donghquinn%2Fgopandas/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279017348,"owners_count":26086052,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-13T02:00:06.723Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","go","golang"],"created_at":"2025-10-14T00:48:05.196Z","updated_at":"2025-10-14T00:48:10.876Z","avatar_url":"https://github.com/donghquinn.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# gopandas\n\nA Go library for data manipulation and analysis, inspired by Python's pandas library. Provides DataFrame and Series data structures with essential data processing capabilities, all implemented without external dependencies.\n\n## Features\n\n- **DataFrame and Series** - Core data structures for handling structured data\n- **CSV Support** - Read and write CSV files with automatic type inference\n- **Excel Support** - Read Excel files (.xlsx) without external dependencies\n- **Data Operations** - Filter, select, sort, and group data\n- **Statistical Functions** - Calculate sum, mean, count, and more\n- **Zero Dependencies** - Pure Go implementation\n\n## Installation\n\n```bash\ngo get github.com/donghquinn/gopandas\n```\n\n## Quick Start\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n    gopandas \"github.com/donghquinn/gopandas\"\n)\n\nfunc main() {\n    // Read CSV file\n    df, err := gopandas.ReadCSV(\"data.csv\")\n    if err != nil {\n        log.Fatal(err)\n    }\n\n    // Display basic info\n    rows, cols := df.Shape()\n    fmt.Printf(\"Shape: (%d, %d)\\n\", rows, cols)\n    fmt.Printf(\"Columns: %v\\n\", df.Columns())\n    \n    // Show first 5 rows\n    fmt.Print(df.Head(5))\n}\n```\n\n## Core Data Structures\n\n### DataFrame\n\nA 2-dimensional labeled data structure with columns of potentially different types.\n\n```go\n// Create a new DataFrame\ndf := gopandas.NewDataFrame([]string{\"name\", \"age\", \"city\"})\n\n// Add rows\ndf.AddRow([]interface{}{\"Alice\", 25, \"New York\"})\ndf.AddRow([]interface{}{\"Bob\", 30, \"London\"})\n\n// Get shape\nrows, cols := df.Shape()\n\n// Get column names\ncolumns := df.Columns()\n\n// Display first n rows\nhead := df.Head(3)\n```\n\n### Series\n\nA 1-dimensional labeled array capable of holding any data type.\n\n```go\n// Create a new Series\ndata := []interface{}{1, 2, 3, 4, 5}\nseries := gopandas.NewSeries(\"numbers\", data)\n\n// Statistical operations\nsum, _ := series.Sum()      // 15.0\nmean, _ := series.Mean()    // 3.0\ncount := series.Count()     // 5\n```\n\n## File I/O\n\n### CSV Operations\n\n```go\n// Read CSV with default options (header=true, delimiter=',')\ndf, err := gopandas.ReadCSV(\"data.csv\")\n\n// Read CSV with custom options\ndf, err := gopandas.ReadCSV(\"data.csv\", \n    gopandas.WithHeader(false),\n    gopandas.WithDelimiter(';'))\n\n// Write to CSV\nerr = df.ToCSV(\"output.csv\")\n\n// Write CSV with custom options\nerr = df.ToCSV(\"output.csv\",\n    gopandas.WithHeader(true),\n    gopandas.WithDelimiter(','))\n```\n\n### Excel Operations\n\n```go\n// Read Excel file (first sheet)\ndf, err := gopandas.ReadExcel(\"data.xlsx\")\n\n// Read specific sheet\ndf, err := gopandas.ReadExcel(\"data.xlsx\", \"Sheet2\")\n```\n\n## Data Manipulation\n\n### Filtering\n\n```go\n// Filter rows based on condition\nfiltered := df.Filter(func(row []interface{}) bool {\n    age := row[1].(int)\n    return age \u003e= 30\n})\n```\n\n### Column Selection\n\n```go\n// Select specific columns\nsubset, err := df.Select(\"name\", \"age\")\n```\n\n### Sorting\n\n```go\n// Sort by column (ascending)\nsorted, err := df.Sort(\"age\", true)\n\n// Sort by column (descending)\nsorted, err := df.Sort(\"salary\", false)\n```\n\n### Grouping\n\n```go\n// Group by column\ngroups, err := df.GroupBy(\"department\")\n\n// Iterate through groups\nfor key, group := range groups {\n    fmt.Printf(\"Group %v:\\n\", key)\n    fmt.Print(group)\n}\n```\n\n### Column Operations\n\n```go\n// Get a column as Series\nageColumn, err := df.GetColumn(\"age\")\n\n// Calculate statistics\navgAge, err := ageColumn.Mean()\ntotalAge, err := ageColumn.Sum()\ncount := ageColumn.Count()\n```\n\n## Complete Example\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n    gopandas \"github.com/donghquinn/gopandas\"\n)\n\nfunc main() {\n    // Create sample data\n    df := gopandas.NewDataFrame([]string{\"name\", \"age\", \"department\", \"salary\"})\n    df.AddRow([]interface{}{\"Alice\", 25, \"Engineering\", 70000})\n    df.AddRow([]interface{}{\"Bob\", 30, \"Sales\", 50000})\n    df.AddRow([]interface{}{\"Charlie\", 35, \"Engineering\", 80000})\n    df.AddRow([]interface{}{\"Diana\", 28, \"Marketing\", 55000})\n\n    // Display basic information\n    rows, cols := df.Shape()\n    fmt.Printf(\"Dataset shape: (%d, %d)\\n\", rows, cols)\n    fmt.Print(df)\n\n    // Filter engineering employees\n    engineers := df.Filter(func(row []interface{}) bool {\n        return row[2].(string) == \"Engineering\"\n    })\n    fmt.Println(\"\\nEngineering employees:\")\n    fmt.Print(engineers)\n\n    // Calculate average salary\n    salaryColumn, _ := df.GetColumn(\"salary\")\n    avgSalary, _ := salaryColumn.Mean()\n    fmt.Printf(\"\\nAverage salary: $%.2f\\n\", avgSalary)\n\n    // Group by department\n    groups, _ := df.GroupBy(\"department\")\n    fmt.Println(\"\\nEmployees by department:\")\n    for dept, group := range groups {\n        rows, _ := group.Shape()\n        fmt.Printf(\"%s: %d employees\\n\", dept, rows)\n    }\n\n    // Save to CSV\n    err := df.ToCSV(\"employees.csv\")\n    if err != nil {\n        log.Fatal(err)\n    }\n    fmt.Println(\"Data saved to employees.csv\")\n}\n```\n\n## API Reference\n\n### DataFrame Methods\n\n- `NewDataFrame(columns []string) *DataFrame` - Create new DataFrame\n- `Shape() (int, int)` - Get number of rows and columns\n- `Columns() []string` - Get column names\n- `Head(n int) *DataFrame` - Get first n rows\n- `AddRow(row []interface{}) error` - Add a new row\n- `GetColumn(name string) (*Series, error)` - Get column as Series\n- `Filter(predicate func([]interface{}) bool) *DataFrame` - Filter rows\n- `Select(columns ...string) (*DataFrame, error)` - Select columns\n- `Sort(column string, ascending bool) (*DataFrame, error)` - Sort by column\n- `GroupBy(column string) (map[interface{}]*DataFrame, error)` - Group by column\n- `ToCSV(filename string, options ...CSVOption) error` - Write to CSV\n\n### Series Methods\n\n- `NewSeries(name string, data []interface{}) *Series` - Create new Series\n- `Sum() (interface{}, error)` - Calculate sum\n- `Mean() (float64, error)` - Calculate mean\n- `Count() int` - Count non-null values\n\n### File I/O Functions\n\n- `ReadCSV(filename string, options ...CSVOption) (*DataFrame, error)` - Read CSV\n- `ReadExcel(filename string, sheetName ...string) (*DataFrame, error)` - Read Excel\n\n### CSV Options\n\n- `WithHeader(hasHeader bool)` - Set header option\n- `WithDelimiter(delimiter rune)` - Set delimiter\n\n## Testing\n\nRun tests:\n\n```bash\ngo test\n```\n\nRun example:\n\n```bash\ncd example\ngo run main.go\n```\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Contributing\n\nContributions are welcome! 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