DQ for Python API

Python client for dataquality.pl

Python library which allows to use http://dataquality.pl in the easy way.

Free software: Apache Software License 2.0
Documentation: https://dq-batch-client.readthedocs.io.

Features

Full API client
Automatic encoding file conversion

Credits

This package was created by Algolytics dev team.

Installation

Requirements

For the full functionality Python 3 is required.

Stable release

To install Python client for dataquality.pl, run this command in your terminal:

$ pip install dq-batch-client

This is the preferred method to install Python client for dataquality.pl, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources

The sources for Python client for dataquality.pl can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/Algolytics/dq_batch_client

Or download the tarball:

$ curl  -OL https://github.com/Algolytics/dq_batch_client/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

Usage

To use Python client for dataquality.pl in a project:

from dq import DQClient, JobConfig


dq = DQClient('https://app.dataquality.pl', user='<USER_EMAIL>', token='<API_TOKEN>')

API token can be obtain on the page “Moje konto”.

Account

Check account status:

account = dq.account_status()

print(account.email)          # user email
print(account.balance)        # account balance
print(account.total_records)  # processed records

Jobs

List jobs

jobs = dq.list_jobs()

for job in jobs:
    print(job.id)                # job id
    print(job.name)              # human readable job name
    print(job.status)            # job status
    print(job.start_date)        # job start date
    print(job.end_date)          # job end date
    print(job.source_records)    # how many records were applied
    print(job.processed_records) # how many records were processed
    print(job.price)             # price for processed records

Create new job

input_data = '''"ID","ADRES"
6876,"34-404, PYZÓWKA, PODHALAŃSKA 100"
'''

job_config = JobConfig('my job')
job_config.input_format(field_separator=',', text_delimiter='"')
job_config.input_column(0, name='ID', function='PRZEPISZ')
job_config.input_column(1, name='ADRES', function='DANE_OGOLNE')
job_config.module_std(address=1)
job_config.extend(gus=True,
                  geocode=True,
                  teryt=False,
                  building_info=False,
                  diagnostic=False,
                  area_characteristic=False,
                  financial_scoring=False)

job = dq.submit_job(job_config, input_data=input_data)                                         # with data in a variable

job = dq.submit_job(job_config, input_file='my_file.csv')                                      # with data inside file

print(job.id)
print(job.name)
print(job.status)
...

Create new deduplication job

input_data = '''unikalne_id;imie_i_nazwisko;kod_pocztowy;miejscowosc;adres;email;tel;CrmContactNumber;data
1;Jan Kowalski;37-611;Cieszanów ;Dachnów 189;abc@wp.pl;605936000;abc123;2017-11-08 12:00:00.000
2;Adam Mickiewicz Longchamps de Berier;66-400;Gorzów Wlkp.;Widok 24;qqq@ft.com;48602567000;a2b2c2;2017-11-08 12:00:00.000
3;Barbara Łęcka;76-200;Słupsk;Banacha 7;bb@gazeta.pl;79174000;emc2;2017-11-08 12:00:00.000
4;KAROL NOWAK;22-122;LEŚNIOWICE;RAKOLUPY DU—E 55;kn@ll.pp;0;f112358;2017-11-08 12:00:00.000
5;Anna Maria Jopek;34-722;Podwilk;Podwilk 464;amj@gmail.com;606394000;eipi10;2017-11-08 12:00:00.000
6;Mariusz Robert;37-611;Cieszanów ;Dachnów 189;abc@wp.pl;605936000;abc123;2017-11-08 12:00:00.000
'''

job_config = JobConfig('pr2')
job_config.input_format(field_separator=';', text_delimiter='"')
job_config.input_column(0, name='unikalne_id', function='ID_REKORDU')
job_config.input_column(1, name='imie_i_nazwisko', function='IMIE_I_NAZWISKO')
job_config.input_column(2, name='kod_pocztowy', function='KOD_POCZTOWY')
job_config.input_column(3, name='miejscowosc', function='MIEJSCOWOSC')
job_config.input_column(4, name='adres', function='ULICA_NUMER_DOMU_I_MIESZKANIA')
job_config.input_column(5, name='email', function='EMAIL1')
job_config.input_column(6, name='tel', function='TELEFON1')
job_config.input_column(7, name='CrmContactNumber', function='PRZEPISZ')
job_config.input_column(8, name='data', function='CZAS_AKTUALIZACJI')
job_config.deduplication(on=True)
job_config.module_std(address=True, names=True, contact=True)
job_config.extend(gus=True, geocode=True, diagnostic=True)

job = dq.submit_job(job_config, input_data=input_data)

print(job)
...

Available column functions:

addresses
- KOD_POCZTOWY
- MIEJSCOWOSC
- ULICA_NUMER_DOMU_I_MIESZKANIA
- ULICA
- NUMER_DOMU
- NUMER_MIESZKANIA
- NUMER_DOMU_I_MIESZKANIA
- WOJEWODZTWO
- POWIAT
- GMINA
names
- IMIE
- NAZWISKO
- NAZWA_PODMIOTU
- IMIE_I_NAZWISKO
people/companies
- PESEL
- NIP
- REGON
contact
- EMAIL1
- EMAIL2
- TELEFON1
- TELEFON2
dates
- DATA_URODZENIA
- CZAS_AKTUALIZACJI
mixed
- DANE_OGOLNE
id
- ID_REKORDU
others
- PRZEPISZ
- POMIN

To process input columns, you must enable the corresponding module. Method module_std is used to set active modules:

address
names
contact
id_numbers

For address module to be started it is necessary to ensure at least one column with the role listed below:

DANE_OGOLNE
KOD_POCZTOWY
MIEJSCOWOSC

Analogously for other modules:

names require one of
- DANE_OGOLNE
- IMIE
- NAZWISKO
- IMIE_I_NAZWISKO
- NAZWA_PODMIOTU
contact
- DANE_OGOLNE
- EMAIL1
- EMAIL2
- TELEFON1
- TELEFON2
id
- DANE_OGOLNE
- PESEL
- NIP
- REGON

Check job state

state = dq.job_state('3f14e25e-9f6d-41ff-a4cb-942743a37b73')  # input parameter: job id

print(state)                                                  # 'WAITING' or 'FINISHED'

Cancel job

dq.cancel_job('3f14e25e-9f6d-41ff-a4cb-942743a37b73')  # input parameter: job id

Retrieve job report

report = dq.job_report('3f14e25e-9f6d-41ff-a4cb-942743a37b73')  # input parameter: job id

print(report.quality_issues)
print(report.quality_names)
print(report.results)

Save job results

dq.job_results('3f14e25e-9f6d-41ff-a4cb-942743a37b73', 'output.csv')

Delete job and its results

dq.delete_job('3f14e25e-9f6d-41ff-a4cb-942743a37b73')  # input parameter: job id

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/Algolytics/dq_batch_client/issues.

If you are reporting a bug, please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation

Python client for dataquality.pl could always use more documentation, whether as part of the official Python client for dataquality.pl docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/Algolytics/dq_batch_client/issues.

If you are proposing a feature:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here’s how to set up dq-batch-client for local development.

Fork the dq_batch_client repo on GitHub.

Clone your fork locally:

$ git clone git@github.com:your_name_here/dq_batch_client.git

Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
```
$ mkvirtualenv dq_batch_client
$ cd dq_batch_client/
$ python setup.py develop
```
Create a branch for local development:
```
$ git checkout -b name-of-your-bugfix-or-feature
```
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
```
$ flake8 dq tests
$ python setup.py test or pytest
$ tox
```
To get flake8 and tox, just pip install them into your virtualenv.

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
The pull request should work for Python versions 3.6 - 3.13, and for PyPy. Check https://github.com/Algolytics/dq_batch_client/pulls and make sure that the tests pass for all supported Python versions.

Tips

To run a subset of tests:

$ pytest tests.test_dq_batch_client

Credits Algolytics Team

Mateusz Białek <mateusz.bialek@algolytics.pl>
Łukasz Szpak <lukasz.szpak@algolytics.pl>

History

0.6.0 (2024-12-11)

RENAME PACKAGE NAME TO dq-batch-client
Update dependencies: minimal version of Python is now 3.6
Add financial scoring option to JobConfig
Update documentation

0.5.0 (2018-07-13)

0.4.0 (2018-07-10)

0.3.0 (2018-07-04)

0.2.0 (2018-03-05)

0.1.0 (2016-10-19)

First release on PyPI.

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Last updated 5 months ago