Core CPI by Division (2-digit)

Data as of Dec 2023

National-level core CPI for 12 main groups of goods and services.

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How is this data produced?

The CPI measures the cost of purchasing a constant, representative 'basket' of goods and services. The quantity and quality of goods in the basket are kept constant, so that changes in the cost of the basket are purely due to price changes. The changes in the cost of this basket represent the rate of inflation. The difference between the CPI and core CPI (this dataset) is that core CPI excludes items with volatile prices or government-administered prices. This is usually taken to be a better gauge of the 'underlying' or long-run trend in the price level. For a deeper understanding of the methodology, please refer to the Technical Notes. Finally, it should be noted that Malaysia's CPI classification is deliberately consistent with the United Nations Classification of Individual Consumption According to Purpose (COICOP) to ensure international comparability.

What caveats I should bear in mind when using this data?

The core CPI is not computed for the 'Alcoholic Beverages and Tobacco' category because all items in this category have their prices administered by the government, and are thus excluded from core CPI calculations.

Publication(s) using this data

Consumer Price Index, Dec 2023, the latest edition of the monthly consumer price statistics published by DOSM. OpenDOSM also features a dashboard on consumer prices.


Dataset description

National-level core CPI for 12 main groups of goods and services.

Variable definitions
Last updated:

22 Jan 2024, 12:00

Next update:

23 Feb 2024, 12:00

Data source(s)
  • DOSM

This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A human-readable copy of the license is available Here.


Full Dataset (CSV)

Full Dataset (CSV)

Recommended for individuals seeking an Excel-friendly format.


Full Dataset (Parquet)

Full Dataset (Parquet)

Recommended for data scientists seeking to work with data via code.



Connect directly to the data with Python.

# If not already installed, do: pip install pandas fastparquet import pandas as pd URL_DATA = '' df = pd.read_parquet(URL_DATA) if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) print(df)

Sample OpenAPI query

The following code is an example of how to make an API query to retrieve the data catalogue mentioned above. You can use different programming languages by switching the code accordingly. For a complete guide on possible query parameters and syntax, please refer to the official Open API documentation.
import requests import pprint url = "" response_json = requests.get(url=url).json() pprint.pprint(response_json)
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Department of Statistics Malaysia

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