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Infers date format from examples, by using a series of pattern matching and rewriting rules to compute a "best guess" datetime.strptime format string give a list of example date strings.

Project description

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

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

Project details


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