Usage¶

Installation¶

To Install please run:

pip intsall pydatafaker

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["vendor_table"]
biz["employee_table"]
biz["po_table"]
biz["invoice_summary_table"]
biz["invoice_line_item_table"]

	invoice_id	invoice_line_id	amount	description
0	inv_00001	line_item_000000001	4404	0-7299-7353-0
1	inv_00001	line_item_000002781	1233	0-8184-1802-8
2	inv_00001	line_item_000004837	5056	1-4642-5447-8
3	inv_00001	line_item_000004797	5253	1-175-64411-0
4	inv_00001	line_item_000000947	1792	1-956222-62-6
...	...	...	...	...
5445	inv_00450	line_item_000000707	4198	0-603-62234-8
5446	inv_00450	line_item_000003163	4131	1-06-101759-1
5447	inv_00450	line_item_000003874	5193	0-482-36167-0
5448	inv_00450	line_item_000005441	4189	1-03-258645-1
5449	inv_00450	line_item_000000915	3398	0-03-148841-2

5450 rows × 4 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')

	invoice_id	amount	invoice_date	po_id	vendor_id	vendor_name	vendor_description	address	phone	email
0	inv_00001	50384	2014-01-22	po_00001	vendor_00001	Duran LLC	Multi-lateral bottom-line attitude	756 Emma Loop\nNew Randallton, MA 34074	001-403-947-8923x710	lreed@example.net
1	inv_00002	41242	2001-08-07	po_00002	vendor_00002	Mcconnell, Cook and Jacobs	Multi-channeled 4thgeneration access	3908 Moore Ferry Suite 731\nRileystad, OH 66846	7967687131	hscott@example.org
2	inv_00003	93168	2018-08-03	po_00003	vendor_00003	Fowler PLC	De-engineered analyzing matrix	3744 Sarah Islands Apt. 917\nThompsonhaven, AK...	881.131.6277x81348	brandon73@example.com
3	inv_00004	62921	2003-07-08	po_00004	vendor_00004	Washington, Jimenez and Melendez	Horizontal 24/7 flexibility	872 Ware Terrace\nLake Sarafort, NY 25369	964.601.5818	sramirez@example.org
4	inv_00005	72479	2000-10-01	po_00005	vendor_00005	Johnson-Martinez	Upgradable modular middleware	2928 Dalton Station Apt. 170\nNorth Theresahav...	(100)611-9164x12103	danieljeanette@example.org
...	...	...	...	...	...	...	...	...	...	...
445	inv_00446	63753	2014-08-19	po_00059	vendor_00059	Romero, Miller and Cruz	Configurable optimizing instruction set	5751 Brian Green\nSouth Nicoleside, OR 95098	751.189.4118x40072	lbrewer@example.org
446	inv_00447	114723	2018-06-03	po_00032	vendor_00032	Hill LLC	Stand-alone regional intranet	1052 Benjamin Spurs\nPort Carl, FL 75681	(460)310-2789x04620	michelle35@example.net
447	inv_00448	45416	2016-03-05	po_00134	vendor_00047	Harris-Lyons	Virtual value-added archive	751 Paul Square\nNorth Raymondview, AL 26626	+1-211-463-2487x4474	terri31@example.net
448	inv_00449	58418	2018-09-27	po_00052	vendor_00052	Thompson-Young	Secured 3rdgeneration archive	957 Sharon Lakes Suite 644\nSouth Victoriaside...	0474775509	edward24@example.net
449	inv_00450	59770	2018-08-31	po_00101	vendor_00041	Wood, Mason and Lopez	Visionary explicit software	73688 Daugherty Coves\nPort Pamela, UT 76207	574.997.1700	stevenlowery@example.com

450 rows × 10 columns

School¶

The school module allows you to generate fake school data

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

	student_id	test_score	date
0	student_0009	0.824952	2020-12-27
1	student_0009	0.651192	2021-02-15
2	student_0009	0.884640	2021-01-25
3	student_0009	0.862511	2021-04-03
4	student_0009	1.000000	2021-01-18
...	...	...	...
2995	student_0300	0.762023	2021-06-02
2996	student_0300	0.820461	2020-12-10
2997	student_0300	0.703434	2021-07-25
2998	student_0300	0.714107	2021-04-14
2999	student_0300	0.832456	2020-10-30

3000 rows × 3 columns