pydateinfer

Python library to infer date format from examples. This is an actively maintained fork of the original dateinfer library by Jeffery Starr. It maintains python 2/3 compatibility and will be released as pydateinfer. Pull requests and issues welcome.

Table of Contents

• Problem Statement
• Installation
• Usage

Problem Statement

Imagine that you are given a large collection of documents and, as part of the extraction process, extract date information and store it in a normalized format. If the documents follow a single schema, the ideal approach is to craft a date parsing string for the schema. However, if the documents follow different schemas or if the contents are noisy (e.g. date fields were hand-populated), the development can become onerous.

This library makes a "best guess" on the proper date parsing string (datetime.strptime) based on examples in the file.

Installation

git clone https://github.com/nedap/dateinfer.git
cd dateinfer
pip install .

Usage

>>> import dateinfer
>>> dateinfer.infer(['Mon Jan 13 09:52:52 MST 2014', 'Tue Jan 21 15:30:00 EST 2014'])
'%a %b %d %H:%M:%S %Z %Y'
>>>

Give dateinfer.infer a list of example date strings. infer returns a datetime.strftime/strptime-compliant date format string for its "best guess" of a format string that will correctly parse the majority of the examples.

Development

Use the following to install the package locally for development purposes:

# create empty virtual environment
virtualenv venv --python=python3.7
source venv/bin/activate
# install dateinfer in editable mode
pip install -e .
# install development dependencies
pip install -r requirements.txt

You can run unit tests as follows:

python -m unittest dateinfer/tests.py