pydqkit 1.0.0

A developer-first Python toolkit for data quality profiling, validation, and interactive HTML reports.

python data quality kit

pydqkit is a lightweight, developer-first Python toolkit for data quality profiling, data validation, sanity check and interactive HTML visualization.

It helps data engineers and analysts quickly understand the structure, completeness, and patterns of tabular datasets, without requiring any external platforms or services.

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Features

• Column-level data profiling
  • Null and non-null statistics
  • Distinct and duplicate counts
  • Length analysis (minimum, maximum, average)
• Pattern discovery for string, datetime, and boolean columns
• Type inference (numeric, string, boolean, datetime)
• Interactive, self-contained HTML profiling reports
• Designed for exploratory analysis and debugging workflows

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Installation

pip install pydqkit

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Quick Start

import pandas as pd
from pydqkit.profiling import profile_dataframe
from pydqkit.viz import profile_to_html

df = pd.DataFrame({
    "id": ["AB123456", "CD654321", None, "EF000001"],
    "age": [25, 30, None, 40],
    "score": [88.5, 92.0, 79.5, 85.0],
    "date": ["2025-01-01", "2025-01-02", None, "2025-01-04"],
    "flag": [True, True, False, True],
})

profile = profile_dataframe(df, dataset_name="demo")

html = profile_to_html(profile)
with open("profile_report.html", "w", encoding="utf-8") as f:
    f.write(html)

Open profile_report.html in your browser to explore the interactive profiling report.

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What the Profiling Report Shows

For each column, the report includes:

Completeness

• Proportion of non-null and null values (visual bar and percentages)

Value Statistics

• Distinct count and duplicate count

Type Information

• Inferred profile type and suggested logical type

Length Metrics

• Minimum length (reported as 0 if missing values exist)
• Maximum and average length

Pattern Summary

• Common structural patterns inferred from values

Top Values

• Most frequent values with counts and percentages

The report is designed to be readable at a glance while still exposing enough detail for debugging and validation.

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Design Philosophy

pydqkit is intentionally:

Developer-first

Optimized for notebooks, scripts, and local inspection.

Platform-independent

No cloud services, no accounts, no metadata dependencies.

Explainable

Metrics and visual elements are designed to be interpretable rather than opaque.

The project follows conventions commonly used in enterprise data quality tools, but is implemented as a standalone Python library.

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License

This project is released under the MIT License.
See the LICENSE file for details.

The author reserves the right to relicense future versions.

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Disclaimer

This project is an independent open-source toolkit and is not affiliated with, endorsed by, or associated with Informatica or any other commercial data quality platform.

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Contributing

Contributions are welcome.

By submitting a pull request, you agree that your contributions will be licensed under the same license as this project.

See CONTRIBUTING.md for details.

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Roadmap (Tentative)

• Rule-based data quality checks
• Column-level and cell-level validation
• Support for multiple rule definitions:
  • Regular expressions
  • SQL-based rules
  • Plain-language rule specifications
• Data engineering quality checks (schema, type, and pipeline sanity)
• Machine learning data sanity checks (distribution drift, outliers)
• Column comparison across datasets
• Cell level data quality check
• Export profiling reports to multiple formats (HTML, PDF, Excel)
• Configurable thresholds and validation summaries

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Author

Niki Zheng, Luqun Li