Healthcare
22 columns
200 rows
8 formats

Prescription Drug Purchase Trends

This dataset provides detailed, transaction-level records of prescription drug purchases across multiple pharmacies, including information on drugs, pharmacies, prescribers, and patient demographics. It is ideal for analyzing purchase trends, forecasting demand, optimizing inventory, and supporting public health research or regulatory oversight. The dataset's granularity enables deep insights into drug utilization patterns and supply chain efficiency.

last updated 22 Jul 2025
by GoMask

The brief that made it

Forecasting prescription drug demand and optimizing pharmacy inventory

Sample rows

preview · 8 of 200 rows · all 22 columns

purchase_idstring	payment_methodstring	unit_pricefloat	prescription_requiredboolean	prescriber_specialtystring	pharmacy_idstring	pharmacy_namestring	pharmacy_street_addressstring	pharmacy_citystring	pharmacy_statestring	pharmacy_postal_codestring	pharmacy_countrystring	drug_idstring	drug_namestring	drug_strengthstring	drug_formstring	purchase_datedate	quantity_purchasedinteger	total_pricefloat	prescriber_idstring	patient_ageinteger	patient_genderstring
PX001	insurance	0.34	true	Cardiologist	PH1001	Metro City Pharmacy	101 Main St.	Boston	MA	02109	USA	D001	Atorvastatin	20mg	tablet	2024-03-13	90	30.6	PR1001	67	male
PX002	insurance	0.21	true	General Practitioner	PH2022	Healthwise Drugs	501 Elm St.	Phoenix	AZ	85001	USA	D002	Amoxicillin	500mg	capsule	2024-01-29	14	2.94	PR1002	41	female
PX003	insurance	0.17	true	Endocrinologist	PH5555	Sun Valley Meds	222 Sunflower Blvd.	San Jose	CA	95112	USA	D003	Metformin	500mg	tablet	2023-12-15	60	10.2	PR1003	59	female
PX004	cash	0.08	false	blank	PH3099	Evergreen Pharmacy	11 Oak Ave.	Chicago	IL	60602	USA	D004	Ibuprofen	200mg	tablet	2024-04-10	24	1.92	blank	35	male
PX005	insurance	0.19	true	Cardiologist	PH4044	Rx Solutions	4500 Market St.	Miami	FL	33101	USA	D005	Lisinopril	10mg	tablet	2024-03-07	30	5.7	PR1004	73	female
PX006	insurance	0.15	true	Endocrinologist	PH1001	Metro City Pharmacy	101 Main St.	Boston	MA	02109	USA	D006	Levothyroxine	75mcg	tablet	2023-10-18	90	13.5	PR1005	48	female
PX007	insurance	0.32	true	Hematologist	PH8888	Good Health Meds	7000 King Rd.	Toronto	ON	M5V 2T6	Canada	D007	Warfarin	5mg	tablet	2023-09-15	30	9.6	PR1006	78	male
PX008	credit_card	0.13	false	blank	PH2022	Healthwise Drugs	501 Elm St.	Phoenix	AZ	85001	USA	D008	Cetirizine	10mg	tablet	2024-02-18	14	1.82	blank	23	female
PX009	insurance	0.26	true	Pulmonologist	PH1212	Central Drugs	12 Pearl St.	Vancouver	BC	V6B 2Y5	Canada	D009	Prednisone	20mg	tablet	2023-07-09	7	1.82	PR1007	64	male
PX010	credit_card	0.07	false	blank	PH9900	OnlineRx Direct	N/A	Online	NA	00000	USA	D010	Ibuprofen	200mg	tablet	2024-04-11	50	3.5	blank	29	male
PX011	insurance	0.44	true	Allergist	PH4044	Rx Solutions	4500 Market St.	Miami	FL	33101	USA	D011	Montelukast	10mg	tablet	2023-11-03	30	13.2	PR1008	35	female
PX012	insurance	0.08	true	Cardiologist	PH1010	Lakeview Pharmacy	1010 Lake St.	Los Angeles	CA	90001	USA	D012	Hydrochlorothiazide	25mg	tablet	2023-06-21	30	2.4	PR1009	75	female
PX013	cash	0.12	false	blank	PH8888	Good Health Meds	7000 King Rd.	Toronto	ON	M5V 2T6	Canada	D013	Paracetamol	500mg	tablet	2024-03-29	16	1.92	blank	28	female
PX014	insurance	28.99	true	Pulmonologist	PH3033	Harbor Med Pharmacy	33 Harbor Dr.	San Diego	CA	92101	USA	D014	Albuterol	90mcg	inhaler	2024-03-14	1	28.99	PR1010	12	male
PX015	credit_card	12.5	true	Urologist	PH5555	Sun Valley Meds	222 Sunflower Blvd.	San Jose	CA	95112	USA	D015	Sildenafil	100mg	tablet	2023-09-28	4	50	PR1011	49	male
PX016	cash	0.09	false	blank	PH6060	West End Pharmacy	606 West Ave.	London	ENG	SW1A 1AA	UK	D016	Aspirin	81mg	tablet	2024-02-01	30	2.7	blank	52	male
PX017	insurance	0.55	true	Cardiologist	PH7777	Peachtree Rx	777 Peachtree St.	Atlanta	GA	30303	USA	D017	Clopidogrel	75mg	tablet	2023-08-10	30	16.5	PR1012	71	female
PX018	insurance	0.18	true	General Practitioner	PH2022	Healthwise Drugs	501 Elm St.	Phoenix	AZ	85001	USA	D018	Lorazepam	1mg	tablet	2024-01-18	14	2.52	PR1013	38	male
PX019	credit_card	0.11	false	blank	PH4044	Rx Solutions	4500 Market St.	Miami	FL	33101	USA	D019	Loratadine	10mg	tablet	2023-12-08	30	3.3	blank	44	female
PX020	insurance	0.27	true	Cardiologist	PH2002	Maple Leaf Pharmacy	2002 Maple Dr.	Vancouver	BC	V6E 1B5	Canada	D020	Metoprolol	50mg	tablet	2022-11-19	60	16.2	PR1014	62	male

What the 200 rows show

from the 200-row sample

Insurance (payment method) stands out: 138 of its 138 rows have prescription_required = true, against 10 of 62 for the rest.

74%prescription_required = true
0.26median unit_price
3pharmacy countries
4patient genders
5drug forms
20pharmacy states

Prescription required rate by payment_methodprescription_required = true

unit_price200 rows, in bands of 2,000

Median 0.26, from 0.06 to 15,000.

prescriber_specialty148 rows with a value · 52 left blank

Cardiologist30
General Practitioner30
Endocrinologist22
Pain Specialist14
Allergist12
Pulmonologist10
Pediatrician8
Gastroenterologist8
Hematologist5
Urologist5

22 columns by typefrom the column list below

string 16
integer 2
float 2
date 1
boolean 1

Columns

22 columns in four groups

blueprint · 22 columns
column	type	description	example
Text 16 columns
`purchase_id`	string	Unique identifier for each prescription drug purchase transaction.unique	PX001
`pharmacy_id`	string	Unique identifier for the pharmacy where the purchase was made.	PH1001
`pharmacy_name`	string	Name of the pharmacy where the purchase occurred.	Metro City Pharmacy
`pharmacy_street_address`	string	Street address of the pharmacy.	101 Main St.
`pharmacy_city`	string	City where the pharmacy is located.	Boston
`pharmacy_state`	string	State or province of the pharmacy location.	MA
`pharmacy_postal_code`	string	Postal or ZIP code of the pharmacy.	02109
`pharmacy_country`	string	Country where the pharmacy is located.3 countries	USA
`drug_id`	string	Unique identifier for the prescription drug.	D001
`drug_name`	string	Brand or generic name of the prescription drug.	Atorvastatin
`drug_strength`	string	Strength or dosage of the drug (e.g., 500mg, 10ml).optional	20mg
`drug_form`	string	Form of the drug (e.g., tablet, capsule, syrup, injection).5 forms · optional	tablet
`prescriber_id`	string	Unique identifier for the prescribing healthcare provider (if applicable).optional	PR1001
`prescriber_specialty`	string	Medical specialty of the prescriber (e.g., General Practitioner, Cardiologist).12 specialties · optional	Cardiologist
`patient_gender`	string	Gender of the patient (if available).male · female · other · unknown · optional	male
`payment_method`	string	Method of payment used for the purchase (e.g., cash, credit_card, insurance, other).cash · credit_card · insurance · other · optional	insurance
Numbers 4 columns
`quantity_purchased`	integer	Number of units of the drug purchased in the transaction.1 or more	90
`unit_price`	float	Price per unit of the drug at the time of purchase.0 or more	0.34
`total_price`	float	Total price paid for the purchased quantity (quantity_purchased * unit_price).0 or more	30.6
`patient_age`	integer	Age of the patient at the time of purchase (if available).0 or more · optional	67
Dates and times 1 column
`purchase_date`	date	Date when the prescription drug was purchased.	2024-03-13
True or false 1 column
`prescription_required`	boolean	Indicates if a prescription was required for this drug purchase.	true

Use it for

A healthcare dashboard
The prescription_required rate, unit_price by payment_method and a breakdown of prescriber_specialty. Excel, Power BI or Tableau.
Why do 148 of 200 rows have prescription_required = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Purchases200
PX0010.34insurance
PX0020.21insurance
PX0040.08cash
A software demo
Believable purchases with pharmacy_id, pharmacy_name and pharmacy_street_address to fill a screen in front of a buyer.

This dataset200 rows22 columns

Yours1,000 rows22 columnspharmacy_street_address: UK only

blueprint · prescription-drug-purchase-trends

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each row captures a unique transaction
Includes drug name, category, quantity, and price
Contains anonymized customer demographic
Tracks refill vs new prescription
Excludes over-the-counter medicines

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: prescription-drug-purchase-trends

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?