.. 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
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: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.
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