cutplace

.. image:: https://img.shields.io/pypi/v/cutplace :target: https://pypi.org/project/cutplace/ :alt: PyPI

.. image:: https://readthedocs.org/projects/cutplace/badge/?version=latest :target: https://cutplace.readthedocs.io/ :alt: Documentation

.. image:: https://github.com/roskakori/cutplace/actions/workflows/build.yaml/badge.svg :target: https://travis-ci.org/roskakori/cutplace :alt: Build Status

.. image:: https://coveralls.io/repos/roskakori/cutplace/badge.png?branch=master :target: https://coveralls.io/r/roskakori/cutplace?branch=master :alt: Test coverage

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black :alt: Black

Cutplace is a tool and API to validate that tabular data stored in CSV, Excel, ODS and PRN files conform to a cutplace interface definition (CID).

As an example, consider the following customers.csv file that stores data about customers::

customer_id,surname,first_name,born,gender
1,Beck,Tyler,1995-11-15,male
2,Gibson,Martin,1969-08-18,male
3,Hopkins,Chester,1982-12-19,male
4,Lopez,Tyler,1930-10-13,male
5,James,Ana,1943-08-10,female
6,Martin,Jon,1932-09-27,male
7,Knight,Carolyn,1977-05-25,female
8,Rose,Tammy,2004-01-12,female
9,Gutierrez,Reginald,2010-05-18,male
10,Phillips,Pauline,1960-11-09,female

A CID can describe such a file in an easy to read way. It consists of three sections. First, there is the general data format:

== ============== =========== .. Property Value == ============== =========== D Format Delimited D Encoding UTF-8 D Header 1 D Line delimiter LF D Item delimiter , == ============== ===========

Next there are the fields stored in the data file:

== ============= ========== ===== ====== ======== ============================== .. Name Example Empty Length Type Rule == ============= ========== ===== ====== ======== ============================== F customer_id 3798 Integer 0...99999 F surname Miller ...60 F first_name John X ...60 F date_of_birth 1978-11-27 DateTime YYYY-MM-DD F gender male X Choice female, male == ============= ========== ===== ====== ======== ==============================

Optionally you can describe conditions that must be met across the whole file:

== ======================= ======== =========== .. Description Type Rule == ======================= ======== =========== C customer must be unique IsUnique customer_id == ======================= ======== ===========

The CID can be stored in common spreadsheet formats, in particular Excel and ODS, for example cid_customers.ods.

Cutplace can validate that the data file conforms to the CID::

$ cutplace cid_customers.ods customers.csv

Now add a new line with a broken date_of_birth::

73921,Harris,Diana,04.08.1953,female

Cutplace rejects this file with the error message:

customers.csv (R12C4): cannot accept field 'date_of_birth': date must
match format YYYY-MM-DD (%Y-%m-%d) but is: '04.08.1953'

Additionally, cutplace provides an easy to use API to read and write tabular data files using a common interface without having to deal with the intrinsic of data format specific modules. To read and validate the above example::

import cutplace
import cutplace.errors

cid_path = 'cid_customers.ods'
data_path = 'customers.csv'
try:
    for row in cutplace.rows(cid_path, data_path):
        pass  # We could also do something useful with the data in ``row`` here.
except cutplace.errors.DataError as error:
    print(error)

For more information, read the documentation at http://cutplace.readthedocs.org/ or visit the project at https://github.com/roskakori/cutplace.