pydatafaker 0.1.2

A python package to create fake data with relationships between tables.

PyDataFaker is a python package to create fake data with relationships between tables. Creating fake data can be useful for many different applications such as creating product demos or testing software.

Python already has a great package for creating fake data called Faker https://faker.readthedocs.io/en/master/. Faker is great for creating individual fake units of data, but it can be time consuming to create more complicated fake data that is actually related to one another.

Imagine you are developing a new enterprise resource planning (ERP) software to challenge SAP. You may need to create some fake data to test your application. You will need an invoice table, a vendor listing, purchase order table, and more. PyDataFaker allows your to quickly create these tables and generates relationships between them!

PyDataFaker is currently under development. At this time it is possible to create the following entities:

• Business: create a fake business with common ERP like tables
• School: create a fake school

More entities are currently being developed. If you have any ideas of additional entities that should be included please submit an issue here: https://github.com/SamEdwardes/pydatafaker/issues.

Table of contents

• Installation
• Documentation
• Usage
• Contributing
• Credits

Installation

pip install pydatafaker

Documentation

Documentation can be found at https://pydatafaker.readthedocs.io/en/latest/index.html. The package is distributed through PyPi at https://pypi.org/project/pydatafaker/

Usage

Business

The business module allows you to create fake business data. Calling business.create_business() will return a dictionary of related tables.

import pandas as pd
from pydatafaker import business
biz =  business.create_business()
biz.keys()

dict_keys(['vendor_table', 'po_table', 'invoice_summary_table', 'invoice_line_item_table', 'employee_table', 'contract_table', 'rate_sheet_table', 'timesheet_table'])

Each value inside the dictionary contains a Pandas DataFrame.

biz['invoice_summary_table']

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	invoice_id	amount	invoice_date	po_id	vendor_id
0	inv_00001	59157	2011-01-20	po_00001	vendor_00001
1	inv_00002	87796	2007-09-06	po_00002	vendor_00002
2	inv_00003	57963	2000-03-06	po_00003	vendor_00003
3	inv_00004	59409	2001-03-31	po_00004	vendor_00004
4	inv_00005	86614	2002-01-12	po_00005	vendor_00005
...	...	...	...	...	...
445	inv_00446	83316	2012-09-02	po_00087	vendor_00087
446	inv_00447	45707	2008-07-10	po_00101	vendor_00098
447	inv_00448	111932	2002-09-26	po_00158	vendor_00012
448	inv_00449	35104	2012-09-21	po_00133	vendor_00075
449	inv_00450	15397	2015-12-15	po_00054	vendor_00054

450 rows × 5 columns

Tables can be joined together to add additional details.

invoice_summary = biz['invoice_summary_table']
vendors = biz['vendor_table']
pd.merge(invoice_summary, vendors, how='left', on='vendor_id')

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	invoice_id	amount	invoice_date	po_id	vendor_id	vendor_name	vendor_description	address	phone	email
0	inv_00001	59157	2011-01-20	po_00001	vendor_00001	Smith-Scott	Front-line multimedia emulation	75343 Harper Corners Suite 581\nJuanberg, AK 0...	[phone]x129	[e-mail]
1	inv_00002	87796	2007-09-06	po_00002	vendor_00002	Walker-Morgan	Cross-platform radical solution	941 Susan Isle\nThorntonberg, KS 82841	[phone]x3991	[e-mail]
2	inv_00003	57963	2000-03-06	po_00003	vendor_00003	Noble and Sons	Configurable demand-driven emulation	1442 Jason Rapid Apt. 409\nEast Jade, RI 44983	477-214-2021x973	[e-mail]
3	inv_00004	59409	2001-03-31	po_00004	vendor_00004	Baker, Walker and Davenport	Focused analyzing synergy	89120 Kimberly Extensions\nSouth Annettetown, ...	[phone]x290	[e-mail]
4	inv_00005	86614	2002-01-12	po_00005	vendor_00005	Patterson LLC	Profound maximized productivity	880 Bryan Tunnel Apt. 542\nKaylabury, AK 50221	586-422-7311x0127	[e-mail]
...	...	...	...	...	...	...	...	...	...	...
445	inv_00446	83316	2012-09-02	po_00087	vendor_00087	Wagner-Gutierrez	Multi-lateral motivating projection	8771 Roger Road Suite 781\nDanielton, ID 88428	001-023-820-3050x78454	[e-mail]
446	inv_00447	45707	2008-07-10	po_00101	vendor_00098	Simmons-Leonard	Focused reciprocal secured line	9010 Ashley Mountains\nMarthaton, VT 68298	391-162-6024	[e-mail]
447	inv_00448	111932	2002-09-26	po_00158	vendor_00012	Welch LLC	Versatile methodical interface	4016 Brianna Road\nPort Andrealand, AR 22214	[phone]x172	[e-mail]
448	inv_00449	35104	2012-09-21	po_00133	vendor_00075	Franklin-Bennett	Digitized holistic methodology	68125 Vega Plains Apt. 062\nEast Emily, OK 80097	001-979-468-2358x530	[e-mail]
449	inv_00450	15397	2015-12-15	po_00054	vendor_00054	Barton-Oneill	Mandatory 4thgeneration hierarchy	107 Julie Passage Suite 904\nSouth George, OH ...	[phone]x41615	[e-mail]

450 rows × 10 columns

School

import pandas as pd
from pydatafaker import school
skool =  school.create_school()
skool.keys()
skool['student_table']

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	student_id	name	grade	teacher_id
0	student_0001	Tyler Campbell	1	teacher_0007
1	student_0003	Melissa Coleman	1	teacher_0010
2	student_0011	Crystal Church	1	teacher_0014
3	student_0017	Paul Gray	1	teacher_0007
4	student_0023	Joshua Morales	1	teacher_0010
...	...	...	...	...
31	student_0258	Nicole Hoffman	7	teacher_0015
32	student_0261	Joseph Lewis	7	teacher_0009
33	student_0294	Susan Jacobs	7	teacher_0015
34	student_0299	Mark Whitehead	7	teacher_0009
35	student_0300	Melissa Sosa	7	teacher_0015

300 rows × 4 columns

Contributing

Please see docs/source/contributing.rst.

Credits

Developed by:

• Sam Edwardes

Logo:

• Icon made by Freepik from www.flaticon.com
• Front from fontmeme.com/retro-fonts/
• Logo generated using logomakr.com