{"id":50504320,"url":"https://github.com/ivankmk/dfaudit","last_synced_at":"2026-06-02T14:30:43.288Z","repository":{"id":356943921,"uuid":"1230103516","full_name":"ivankmk/dfaudit","owner":"ivankmk","description":"Audit your pandas DataFrame before you trust it - missing values, soft missing, cardinality and top categories in one call.","archived":false,"fork":false,"pushed_at":"2026-05-10T14:31:52.000Z","size":528,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-05-10T16:29:44.838Z","etag":null,"topics":["data-audit","data-governance","data-profiling","data-quality","eda","exploratory-data-analysis","matplotlib","missing-data","pandas"],"latest_commit_sha":null,"homepage":"","language":"Python","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/ivankmk.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":"2026-05-05T17:21:29.000Z","updated_at":"2026-05-10T14:31:55.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/ivankmk/dfaudit","commit_stats":null,"previous_names":["ivankmk/dfaudit"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/ivankmk/dfaudit","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ivankmk%2Fdfaudit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ivankmk%2Fdfaudit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ivankmk%2Fdfaudit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ivankmk%2Fdfaudit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ivankmk","download_url":"https://codeload.github.com/ivankmk/dfaudit/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ivankmk%2Fdfaudit/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33827062,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-02T02:00:07.132Z","response_time":109,"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-audit","data-governance","data-profiling","data-quality","eda","exploratory-data-analysis","matplotlib","missing-data","pandas"],"created_at":"2026-06-02T14:30:42.292Z","updated_at":"2026-06-02T14:30:43.281Z","avatar_url":"https://github.com/ivankmk.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"docs/assets/logo.png\" alt=\"dfaudit logo\" width=\"160\"/\u003e\n  \u003ch1\u003edfaudit\u003c/h1\u003e\n  \u003cp\u003e\u003cstrong\u003eKnow if you can trust your data before you use it.\u003c/strong\u003e\u003c/p\u003e\n  \u003cp\u003eA fast, modern data audit tool for pandas DataFrames -- built for analysts who need to assess data reliability, not just explore it.\u003c/p\u003e\n\n  [![PyPI version](https://img.shields.io/pypi/v/dfaudit.svg)](https://pypi.org/project/dfaudit/)\n  [![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/)\n  [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n\u003c/div\u003e\n\n---\n\n## What is dfaudit?\n\n**dfaudit** is a lightweight Python package for auditing pandas DataFrames. Before you clean, model, or report -- you need to know whether your data is reliable. dfaudit gives you that answer fast.\n\nThink of it as an auditor's first pass: one call to understand how complete, consistent, and trustworthy your data actually is. Missing values, cardinality, dominant categories -- the things that tell you if the data holds up.\n\n## Install\n\n```bash\npip install dfaudit\n```\n\n## Quickstart\n\n```python\nimport pandas as pd\nimport dfaudit as dfa\n\ndf = pd.read_csv(\"https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv\")\n\ndfa.overview(df)\ndfa.missing_matrix(df)\ndfa.unique_matrix(df)\n```\n\n## What you get\n\n### `overview(df)` - Column-level data quality table\n\nA styled summary table covering every column at once: dtypes, missing counts and percentages, unique value counts, mode, and top-3 categories - all color coded so problems jump out instantly.\n\n![overview table](docs/assets/screenshot_overview.png)\n\n**missing** — standard `NaN` / `None` / `NaT` values pandas considers null.\n\n**soft_missing** — values that are technically non-null but meaningless in practice: empty strings, placeholder text, and numeric sentinel codes commonly used in legacy systems or exports. dfaudit flags these automatically:\n\n- Strings: `\"\"`, `\"n/a\"`, `\"na\"`, `\"null\"`, `\"none\"`, `\"nan\"`, `\"nil\"`, `\"-\"`, `\"--\"`, `\"?\"`, `\"unknown\"`, `\"missing\"`, `\"not available\"`, `\"not applicable\"`\n- Numbers: `-999`, `-9999`, `9999`, `99999`\n\nMissing percentage cells are highlighted red for high missingness, blue for high cardinality.\n\n### `missing_matrix(df)` - Row-level missingness visualization\n\nA matrix plot that shows *where* your data is missing across every row, with a bar chart of missing percentages per column on the right. Spot correlated missingness, systematic gaps, and data loading issues that column-level stats miss entirely.\n\n![missing matrix](docs/assets/screenshot_missing_matrix.png)\n\n```python\ndfa.missing_matrix(df, style=\"vivid\")\n```\n\n### `unique_matrix(df)` - Row-level unique value visualization\n\nA matrix plot that shows the distribution of unique values across every row and column. Low-cardinality columns (≤ 10 unique values) get a distinct color per value — so a binary column like `Sex` renders in two colors, a 3-class column like `Pclass` in three. High-cardinality columns (`Name`, `Ticket`, `PassengerId`) appear in a uniform gray, making it immediately obvious which columns are categorical vs. continuous or near-unique identifiers. A bar chart on the right shows the exact unique value count per column.\n\n![unique matrix](docs/assets/screenshot_unique_matrix.png)\n\n```python\ndfa.unique_matrix(df)\ndfa.unique_matrix(df, style=\"vivid\", max_colors=8)\n```\n\n## Why I built this\n\nI work with a lot of different data on a daily basis. For years I carried the same audit snippets from notebook to notebook -- copy, paste, tweak, repeat. At some point I decided to stop doing that and package the most common data audit tasks into something reusable, with visuals I actually want to look at.\n\ndfaudit is what I reach for now at the start of every new dataset. I hope it saves you the same time it saves me.\n\nIf you work with modern data and want to contribute -- please do, pull requests are very welcome.\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  Made by \u003ca href=\"https://github.com/ivankmk\"\u003eIvan Kumeyko\u003c/a\u003e \u0026nbsp;·\u0026nbsp; MIT License\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivankmk%2Fdfaudit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fivankmk%2Fdfaudit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivankmk%2Fdfaudit/lists"}