Desbordante is an open-source data profiler specifically focused on complex patterns — such as numerical association rules, differential dependencies, denial constraints, and more. The pip package lets you discover and validate patterns, inspect where they fail, and combine discovered patterns with other Python libraries (including machine learning ones) to build ad‑hoc data‑quality workflows. These patterns can help with data deduplication, schema matching, anomaly detection, data understanding, hypothesis generation, and more. You can also extract and enforce complex integrity constraints.
Key features:
Can discover and validate complex patterns in data
Supports tabular, transactional and graph data types
High-performance: the core is implemented in C++ with the emphasis on speed
Provides explanations: it can pinpoint why a given pattern fails
Each supported pattern comes with usage examples
What's the difference between this Python project and similar ones?
Traditional profilers (e.g., YData Profiling) focus on basic stats — min/max, NULL counts, distinct values, correlations — and generally don’t support complex patterns. Desbordante is the only tool aimed specifically at discovering those complex patterns.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/2797
**Author:** [@chernishev](https://github.com/chernishev)
**Created:** 11/12/2025
**Status:** ✅ Merged
**Merged:** 11/20/2025
**Merged by:** [@vinta](https://github.com/vinta)
**Base:** `master` ← **Head:** `chernishev-awesome-python`
---
### 📝 Commits (1)
- [`18d6892`](https://github.com/vinta/awesome-python/commit/18d6892ccb7dad22b655bb9b219ef5fd1f0d9f79) Add Desbordante
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
[Desbordante](https://github.com/desbordante/desbordante-core) is an open-source data profiler specifically focused on complex patterns — such as [numerical association rules](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Numerical_Association_Rules.ipynb), [differential dependencies](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Differential_Dependencies.ipynb), [denial constraints](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Denial_Constraints.ipynb), and more. The pip [package](https://pypi.org/project/desbordante/) lets you discover and validate patterns, inspect where they fail, and combine discovered patterns with other Python libraries (including machine learning ones) to build ad‑hoc data‑quality workflows. These patterns can help with data deduplication, schema matching, anomaly detection, data understanding, hypothesis generation, and more. You can also extract and enforce complex integrity constraints.
Key features:
* Can discover and validate complex patterns in data
* Supports tabular, transactional and graph data types
* High-performance: the core is implemented in C++ with the emphasis on speed
* Provides explanations: it can pinpoint why a given pattern fails
* Each supported pattern comes with usage examples
What's the difference between this Python project and similar ones?
Traditional profilers (e.g., [YData Profiling](https://docs.profiling.ydata.ai/latest/)) focus on basic stats — min/max, NULL counts, distinct values, correlations — and generally don’t support complex patterns. Desbordante is the only tool aimed specifically at discovering those complex patterns.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/2797
Author: @chernishev
Created: 11/12/2025
Status: ✅ Merged
Merged: 11/20/2025
Merged by: @vinta
Base:
master← Head:chernishev-awesome-python📝 Commits (1)
18d6892Add Desbordante📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
Desbordante is an open-source data profiler specifically focused on complex patterns — such as numerical association rules, differential dependencies, denial constraints, and more. The pip package lets you discover and validate patterns, inspect where they fail, and combine discovered patterns with other Python libraries (including machine learning ones) to build ad‑hoc data‑quality workflows. These patterns can help with data deduplication, schema matching, anomaly detection, data understanding, hypothesis generation, and more. You can also extract and enforce complex integrity constraints.
Key features:
What's the difference between this Python project and similar ones?
Traditional profilers (e.g., YData Profiling) focus on basic stats — min/max, NULL counts, distinct values, correlations — and generally don’t support complex patterns. Desbordante is the only tool aimed specifically at discovering those complex patterns.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.