validate data stored in CSV, PRN, ODS or Excel files

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  • License: GNU Lesser General Public License v3 (LGPLv3), GNU Library or Lesser General Public License (LGPL) (LGPL-3.0-only)
  • Author: Thomas Aglassinger
  • Tags check, csv, excel, ods, validate
  • Requires: Python <4, >=3.9
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PyPI Documentation Build Status 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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