Norway Demographic Census

[abhishekjaisw]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @abhishekjaisw, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new data import pipeline for Norwegian demographic census data. It includes the necessary scripts and configurations to download data from Statistics Norway, transform it into the Data Commons format, and validate the import process through testing. This addition enhances the Data Commons knowledge graph with detailed demographic information for Norway.

Highlights

• Data Import: Adds support for importing demographic census data for Norway.
• New Files: Introduces new files for metadata, property-value mapping, data download, and test data.
• Data Source: Data is sourced from Statistics Norway (SSB) API.
• Data Transformation: Includes a Python script to download, filter, and transform the data into a CSV format suitable for Data Commons.
• Testing: Adds test data and expected output to verify the import process.

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[gemini-code-assist]

Code Review

This pull request adds scripts and data for importing Norway's demographic census data. The changes are generally good, but I have a few suggestions. In data_download.py, the logic for filtering out regions can be simplified for better readability and maintainability. More importantly, there are inconsistencies and omissions in the generated test files. The statistical variable DCIDs in Norway_output.csv and Norway_output_stat_vars.mcf are not consistently named for all age groups. Additionally, Norway_output_stat_vars.mcf is missing definitions for some age groups that are present in the data. Correcting these test files is crucial for ensuring the data import is working as expected.

[gemini-code-assist]

The variableMeasured dcids for some age ranges are inconsistent with others. For age ranges 30-39, 40-49, and 50-59, the Years prefix is missing in the age part of the dcid (e.g., 30To39Years instead of Years30To39). This should be corrected for consistency across all age-related statistical variables in this file.

2023,nuts/NO033,19227,dcid:Count_Person_Years30To39_Female
2024,nuts/NO033,19531,dcid:Count_Person_Years30To39_Female
2025,nuts/NO033,19698,dcid:Count_Person_Years30To39_Female
2023,nuts/NO033,19771,dcid:Count_Person_Years40To49_Female
2024,nuts/NO033,19816,dcid:Count_Person_Years40To49_Female
2025,nuts/NO033,19786,dcid:Count_Person_Years40To49_Female
2023,nuts/NO033,22125,dcid:Count_Person_Years50To59_Female
2024,nuts/NO033,22378,dcid:Count_Person_Years50To59_Female
2025,nuts/NO033,22527,dcid:Count_Person_Years50To59_Female

[gemini-code-assist]

The statvar dcid is inconsistent with its age property and other statvars. The Years prefix is missing from the age part of the dcid. This inconsistency is present for other age ranges (30-39, 40-49) in this file as well and should be corrected for all of them.

Node: dcid:Count_Person_Years50To59_Male

[gemini-code-assist]

This auto-generated MCF file is incomplete. It is missing the statistical variable definitions for the '30-39 years' and '40-49 years' age groups, for both males and females. These are present in the Norway_output.csv and should be included here for a complete test set.

[gemini-code-assist]

The two filtering steps for excluding regions are partially redundant and can be simplified into a single, more robust step. Using str.contains with a regex pattern built from EXCLUDE_REGIONS will handle all cases in a case-insensitive manner, making the code cleaner and less prone to errors if the source data casing changes.

Suggested change

	df = df[~df['Region'].isin(EXCLUDE_REGIONS)]
	df = df[~df['Region'].str.contains('shelf|Unknown|Svalbard', case=False, na=False)]
	df = df[~df['Region'].str.contains('|'.join(EXCLUDE_REGIONS), case=False, na=False)]