Probable People

0.5.6 · active · verified Thu Apr 16

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.

Common errors

```
ModuleNotFoundError: No module named 'probablepeople'
```
cause The `probablepeople` library is not installed in the current Python environment.

fix Run `pip install probablepeople` to install the library.
```
TypeError: expected string or bytes-like object
```
cause The parsing functions `parse_person` or `parse_company` were called with an input that is not a string (e.g., None, int, list).

fix Ensure that the input argument to parsing functions is always a string. Handle non-string inputs by converting them (e.g., `str(value)`) or skipping them.

Warnings

gotcha Parsing 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.
gotcha Processing 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.
gotcha Heavy 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.

Install

pip install probablepeople Install stable version

Imports

parse_person
```
from probablepeople import parse_person
```

parse_company

from probablepeople import parse_company

Quickstart

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}")

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