PriceCatcher: 2023/01
Data as of 31 Jan 2023, 23:59The table below provides a preview of the full dataset, which contains over a million prices. We recommend that you download and work with the data in a coding environment. This data should be used in conjuction with the Item Lookup and Premise Lookup tables.
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How is this data produced?
Prices are collected and verified by groundstaff on a daily basis, with over 2 million prices collected every month.
What caveats I should bear in mind when using this data?
This data is collected for the purpose of price surveillance, and is excellent for high-frequency analysis of specific items in specific locations. Inflation surveillance requires a different approach, in particular to ensure proper representativeness. Inflation analysis should be conducted using DOSM's CPI data.
Publication(s) using this data
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Metadata
Dataset description
The table below provides a preview of the full dataset, which contains over a million prices. We recommend that you download and work with the data in a coding environment. This data should be used in conjuction with the Item Lookup and Premise Lookup tables.
Variable definitions
- Date
- Premise Code
- Item Code
- Price
Name in Dataset | Variable | Definition |
|---|---|---|
date (Date) | Date | The date in YYYY-MM-DD format |
premise_code (Categorical) | Premise Code | Integer representing the premise, to be mapped using the Premise Lookup Table. The lookup table will give you the premise name, address, district, and state. |
item_code (Categorical) | Item Code | Integer representing the item, to be mapped using the Item Lookup Table. The lookup table will give you the item name, unit of measurement, and categorisation. |
price (Float) | Price | Price in RM |
Last updated:
01 Feb 2023, 09:00
Next update:
N/A
Data source(s)
- Ministry of Domestic Trade
- Department of Statistics Malaysia
URLs to dataset
License
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.
Download
Data
Full Dataset (CSV)
Recommended for individuals seeking an Excel-friendly format.
0
Full Dataset (Parquet)
Recommended for data scientists seeking to work with data via code.
0
Code
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/pricecatcher/pricecatcher_2023-01.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)Sample OpenAPI query
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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