Complete list of approved manufacturers of cosmetic products with an active license from NPRA. The table provides a preview of the dataset using 10 records as a sample.
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This dataset represents an exact copy of the administrative records managed by the National Pharmaceutical Regulatory Agency (NPRA) using the QUEST3+ system, with any columns containing personal data excluded. QUEST3+ is the administrative system used by NPRA to enable the online management of product registrations, variations, licensing, market sampling, renewal and others.
Although this data represents administrative records, it nevertheless remains a static dataset which is updated once per day, rather than in real time. As such, this dataset should not be used as the basis for any legal action. Any queries on enforcement of the relevant laws of Malaysia should always be referred to the National Pharmaceutical Regulatory Agency (NPRA).
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Complete list of approved manufacturers of cosmetic products with an active license from NPRA. The table provides a preview of the dataset using 10 records as a sample.
Name in Dataset | Variable | Definition |
---|---|---|
company (String) | Company | Name of the company holding the license |
state (Categorical) | State | The state in which the company is located; one of 16 states |
postcode (String) | Postcode | 5-digit postal code for the company's address |
phone (String) | Phone Number | Contact number for the company |
26 Jul 2024, 01:00
02 Aug 2024, 01:00
This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A copy of the license is available Here.
Full Dataset (CSV)
Recommended for individuals seeking an Excel-friendly format.
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Full Dataset (Parquet)
Recommended for data scientists seeking to work with data via code.
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Connect directly to the data with Python.
# If not already installed, do: pip install pandas fastparquet
import pandas as pd
URL_DATA = 'https://storage.data.gov.my/healthcare/cosmetic_manufacturers.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)
This data catalog is not available through OpenAPI as the nature of the data makes it unsuitable for API access. For the full dataset, please use the provided download link as shown in the above section.
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