---
id: dataproperty
version: "1.1.1"
license: MIT License
license_treatment: permissive
maintenance: active
---

# DataProperty — Python library for extract property from data.

License: permissive · Maintenance: active · Downloads: 13.6M/mo

## What it is and what it does

DataProperty is a Python library that analyzes individual values and tabular data to extract structural and formatting properties. For a single value, it determines its type (integer, float, string, boolean, datetime, NaN, infinity) and computes metadata like display width, alignment, digit counts, and decimal places. For matrices, it can extract properties for each cell or aggregate properties by column, returning objects that describe the characteristics of each column's data.

The library is designed for data inspection and formatting tasks—useful when you need to understand how data should be displayed, validate type consistency across columns, or prepare metadata for table rendering. It handles multi-byte strings, special numeric values, and mixed-type columns, making it practical for data cleaning, validation, and presentation workflows.

Use it for:

- Inspect and classify the type and formatting properties of individual values before processing or display.
- Analyze tabular data to determine column types and optimal display widths for formatted output.
- Validate data consistency by extracting type information from each cell in a matrix.
- Generate metadata for table rendering systems that need alignment, width, and type hints.
- Detect and handle edge cases like NaN, infinity, and mixed-type columns in data validation pipelines.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extracts and classifies properties from individual data values and matrices—type, alignment, width, digit counts, and formatting metadata for integers, floats, strings, booleans, datetimes, and special values like NaN and infinity.

Yes. The package is actively maintained, has no known vulnerabilities, installs with minimal friction, and solves a specific problem—extracting data properties for inspection and formatting. It's appropriate for data processing, validation, and table rendering tasks. The MIT license poses no restrictions.

## Install

pip install dataproperty
uv add dataproperty
poetry add dataproperty

## Installing DataProperty

Before you install: Low friction: pure Python wheel with only two runtime dependencies (mbstrdecoder, typepy). Active maintenance with a recent release (97 days ago) and current commit history.

License in practice: MIT License permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal obligations.

Quickstart:

pip install DataProperty

from dataproperty import DataProperty
result = DataProperty(-1.1)
print(result)  # data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, ...

Requires Python 3.9 or later.

Verify before relying:

- Performance characteristics when processing large matrices or high-cardinality datasets.
- Whether the package handles edge cases like very large numbers or unusual string encodings beyond the documented examples.

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 13.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

data type detection and property extraction, analyze value properties and formatting, data matrix column analysis, extract data type metadata, inspect numeric and string properties, data-inspection, type-detection, tabular-data

[View on SkillFed](https://skillfed.io/packages/dataproperty) · [View on PyPI](https://pypi.org/project/dataproperty/)