Registry / data / probablepeople

probablepeople

library0.5.6pypypi✓ verified 85d ago

Probable People (version 0.5.6) is a Python library for parsing romanized names and company names using advanced Natural Language Processing (NLP) methods. Developed by DataMade, it focuses on segmenting and labeling components of person and company strings into standardized fields. Releases are infrequent but it is actively maintained.

data ai-ml serialization

pip install probablepeople

github ↗package ↗

INSTALL

IMPORT

SIG · PROBABLEPEOPLE

probablepeople

datapythonv0.5.6

Install

2.5s avg

Import

—

Disk

32MB

Pass rate

8/ 10

Env Coverage8 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.6 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ timeout

py 3.11

✓ —

✓ 2.4s

py 3.12

✓ —

✓ 2.25s

py 3.13

✓ —

✓ 2.4s

py 3.9

✓ —

✓ 2.8s

32MB installed

● package 32MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

parse

✓ from probablepeople import parse

✗ from probablepeople import parse_person

Demonstrates how to use `parse_person` and `parse_company` to segment and label components of given strings, returning a dictionary of parsed parts and a type classification (e.g., 'person', 'company').

from probablepeople import parse_person, parse_company # Example for parsing a person's name name = "Mr. John A. Doe Jr." parsed_name, name_type = parse_person(name) print(f"Parsed Name: {parsed_name}\nName Type: {name_type}") # Example for parsing a company name company = "Google Inc." parsed_company, company_type = parse_company(company) print(f"Parsed Company: {parsed_company}\nCompany Type: {company_type}")

Debug

Known issues

gotchaParsing models may not be 100% accurate, especially with highly ambiguous, culturally specific, or non-romanized names/company structures.

fix

Always review parsed results, especially for critical applications. Consider combining with manual review or fuzzy matching techniques for validation.

affects: All versions

gotchaProcessing large datasets string-by-string can be computationally intensive and slow, as each parsing operation involves loading and running NLP models.

fix

For high-throughput requirements, explore batch processing, parallelization, or consider using optimized commercial APIs if performance is a bottleneck.

affects: All versions

gotchaHeavy dependencies like `scikit-learn` and `python-crfsuite` can lead to a larger installation footprint and potential version conflicts with other libraries in the same environment.

fix

Use virtual environments (e.g., `venv`, `conda`) to isolate `probablepeople` and its dependencies from other projects. Pin specific versions if conflicts arise.

affects: All versions

Upgrade

Version history

0.5.6latest on PyPI · released Oct 29, 2024

Audit

Dependencies

numpyrequiredRequired for numerical operations and data structures.

scipyrequiredRequired for scientific computing and algorithms.

scikit-learnrequiredMachine learning algorithms for parsing models (requires >=0.23).

python-crfsuiterequiredConditional Random Fields for sequence labeling (requires >=0.9.7).

dedupe-variable-rtreerequiredUsed for efficient spatial indexing in clustering (requires >=0.9.1).

futurerequiredCompatibility layer for Python 2/3, though library now targets Python 3.9+.

Agent activity

11 hits · last 30 days

node

Resources

githubhttps://github.com/datamade/probablepeople ↗

packagehttps://pypi.org/project/probablepeople/ ↗