---
id: date-spacy
version: "0.0.1"
license: unclear
license_treatment: permissive
maintenance: abandoned
---

# date-spacy — A spaCy extension for enhanced date and number entity recognition and extraction as structured data.

License: permissive · Maintenance: abandoned · Downloads: 89.2K/mo

## What it is and what it does

Date spaCy is a lightweight spaCy pipeline component that finds date expressions in text and converts them to structured datetime objects. It works by applying regex patterns to locate date strings, then passes them to dateparser for parsing into standardized datetime format. The parsed result is attached to each matched entity via a custom `._.date` extension, making it accessible during spaCy's standard entity iteration.

The component is designed to integrate into existing spaCy pipelines (ideally before the NER model) or to run on a blank model. Its main limitation is that dates without an explicit year default to the current year. Since the package is abandoned with no releases after August 2023, it is best suited for projects with stable, unchanging requirements and no expectation of upstream maintenance.

Use it for:

- Extract and normalize dates from unstructured text in documents or emails for downstream processing.
- Build a temporal information extraction pipeline as part of a larger spaCy NLP workflow.
- Convert informal date expressions (e.g., 'twelfth of October') into machine-readable datetime objects for database storage.
- Augment spaCy's default entity recognition with specialized date handling before applying other NER models.

## Worth the install?

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

Adds a spaCy pipeline component that identifies date entities in text using regex and converts them to structured datetime objects via dateparser, storing results in a custom token extension.

Yes, if your date extraction needs are simple and stable. The package is low-friction to install and has no known vulnerabilities. However, it is abandoned (last updated 2023-08-24) with no ongoing maintenance, so install only if the fixed functionality is sufficient and you do not expect bug fixes or compatibility updates with future spacy or dateparser versions.

## Install

pip install date-spacy
uv add date-spacy
poetry add date-spacy

## Installing date-spacy

Before you install: Low friction install with only two runtime dependencies (spacy and dateparser). However, the package is abandoned—last release was 2023-08-24 with no updates since, and the repository shows no recent activity. Use only if the fixed functionality meets your needs without expecting maintenance or bug fixes.

License in practice: Licensed under MIT (permissive), so you can use, modify, and distribute the package freely with minimal restrictions.

Quickstart:

pip install date-spacy

import spacy
from date_spacy import find_dates

nlp = spacy.blank('en')
nlp.add_pipe('find_dates')
doc = nlp("Meeting on 25th August 2023")
for ent in doc.ents:
    if ent.label_ == "DATE":
        print(ent._.date)

Requires spacy and dateparser to be installed; assumes current year if no year is provided in the date text.

Verify before relying:

- Whether the regex-based approach handles all date formats you need (e.g., ISO, locale-specific, ambiguous formats).
- Compatibility with recent spacy versions (package last released 2023-08-24).
- Performance characteristics on large document volumes or complex date patterns.

## Package facts

- License: not declared (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: abandoned
- Downloads: 89.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spacy date entity extraction, nlp date parsing pipeline, extract dates from text, spacy custom components, dateparser integration, temporal entity recognition, date normalization nlp, nlp, date-extraction, abandoned

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