{"id":28855271,"url":"https://github.com/chriso345/golumn","last_synced_at":"2025-10-10T18:11:42.704Z","repository":{"id":298841713,"uuid":"998538489","full_name":"ChrisO345/golumn","owner":"ChrisO345","description":"In-Memory DataFrames for Go","archived":false,"fork":false,"pushed_at":"2025-07-03T06:18:40.000Z","size":34,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-09-06T13:59:16.483Z","etag":null,"topics":["data-science","dataframe","golang","statistics"],"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/ChrisO345.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}},"created_at":"2025-06-08T20:26:37.000Z","updated_at":"2025-08-05T08:27:23.000Z","dependencies_parsed_at":"2025-06-13T07:36:59.900Z","dependency_job_id":"3b448e3d-5825-4ba4-80f6-364bb9aa310a","html_url":"https://github.com/ChrisO345/golumn","commit_stats":null,"previous_names":["chriso345/golumn"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ChrisO345/golumn","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChrisO345%2Fgolumn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChrisO345%2Fgolumn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChrisO345%2Fgolumn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChrisO345%2Fgolumn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ChrisO345","download_url":"https://codeload.github.com/ChrisO345/golumn/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChrisO345%2Fgolumn/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279004911,"owners_count":26083802,"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":"2025-10-10T02:00:06.843Z","response_time":62,"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-science","dataframe","golang","statistics"],"created_at":"2025-06-19T23:04:10.086Z","updated_at":"2025-10-10T18:11:42.700Z","avatar_url":"https://github.com/ChrisO345.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# golumn\n\n`golumn` is a fast, type-safe, and easy-to-use in-memory dataframe library in Go. Built for data manipulation, transformation, and analysis, it provides a simple API for creating and manipulating columnar data structures.\n\n\u003e golumn: sturdy columns for in-memory data.\n\n---\n\n## Features\n\n- Purely in-memory dataframe and series structures\n- Type-safe column operations\n- Immutable-style transformations\n- Fast, portable, and Go-native\n\n---\n\n## Installation\n\ngolumn is available on GitHub and can be installed using Go modules:\n\n```bash\ngo get github.com/chriso345/golumn\n```\n\n---\n\n## Usage\n\nHere's a quick example of how to use `golumn`:\n\n```go\npackage main\n\nimport (\n  \"fmt\"\n  \"github.com/chriso345/golumn\"\n)\n\nfunc main() {\n\t// Create a new DataFrame\n\tdf := golumn.New(\n\t\tseries.New([]string{\"Alice\", \"Bob\", \"Charlie\"}, series.String, \"Name\"),\n\t\tseries.New([]int{25, 30, 35}, series.Int, \"Age\"),\n\t)\n\n\t// Print the DataFrame\n\tfmt.Println(df)\n\n\t// Add a new column\n\tdf.Append(series.New([]string{\"New York\", \"Los Angeles\", \"Chicago\"}, series.String, \"City\"))\n\n\t// Print the updated DataFrame\n\tfmt.Println(df)\n\n\t// Filter where Age is greater than 28\n\tfiltered := df.Filter(func(row golumn.Row) bool {\n\t\treturn row.Get(\"Age\").(int) \u003e 28\n\t})\n\n\t// Print the filtered DataFrame\n\tfmt.Println(filtered)\n}\n```\n\n---\n\n## Submodules\n\n* **`golumn/series`**\n  Core implementation of the `Series` type — a one-dimensional, type-safe, columnar data structure.\n\n* **`golumn/dfio`**\n  I/O utilities for loading and saving `DataFrame`s and `Series` in formats like CSV and JSON.\n\n* **`golumn/math`** *(planned)*\n  Statistical functions and numerical operations for both `Series` and `DataFrame` types.\n\n* **`golumn/plot`** *(planned)*\n  Minimal plotting tools for visual exploration of tabular data (e.g. line charts, histograms).\n\n--- \n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchriso345%2Fgolumn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchriso345%2Fgolumn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchriso345%2Fgolumn/lists"}