📊 UN urbanization data

[veronikasamborska1994]

Summary by CodeRabbit

• New Features

  • Enhanced urban agglomeration datasets with updated averages, projections, and harmonized country data.
  • Introduced new garden datasets for urban agglomerations, including size classes and urban-rural distinctions.
  • Added grapher datasets to visualize urban agglomeration data and largest cities information.

• Data Updates

  • Updated the World Urbanization Prospects Dataset with new figures for urban agglomerations of 300,000 inhabitants and above.
  • Included comprehensive data on the largest cities and capitals as of 2018.
  • Added size class information for urban settlements and detailed urban-rural population distributions.

• Documentation

  • Provided metadata and licensing information for all new and updated datasets.

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Commits

Files that changed from the base of the PR and between fd15e07 and a63139d.

Walkthrough

A series of updates have been made to the ETL (Extract, Transform, Load) process, involving the World Urbanization Prospects Dataset. These changes include loading and processing data related to urban agglomerations, urbanization trends, and classifications of urban settlements. The data is harmonized, averages and projections are calculated, and the datasets are saved in new formats for further analysis and visualization.

Changes

Files	Changes
etl/steps/data/garden/un/2024-01-17/... urban_agglomerations_definition.py, urban_agglomerations_largest_cities.py, urban_agglomerations_size_class.py, urbanization_urban_rural.py	Scripts for loading, processing, and creating garden datasets.
etl/steps/data/grapher/un/2024-01-17/... urban_agglomerations_300k.py, urban_agglomerations_definition.py, urban_agglomerations_largest_cities.py, urban_agglomerations_size_class.py, urbanization_urban_rural.py	Scripts for loading garden datasets and creating grapher datasets with the same metadata.
etl/steps/data/meadow/un/2024-01-17/... urban_agglomerations_300k.py, urban_agglomerations_definition.py, urban_agglomerations_largest_cities.py, urban_agglomerations_size_class.py, urbanization_urban_rural.py	Scripts for loading snapshots, processing data, and creating meadow datasets.
snapshots/un/2024-01-17/... urban_agglomerations_300k.py, urban_agglomerations_largest_cities.csv.dvc, urban_agglomerations_size_class.xls.dvc, urbanization_urban_rural.csv.dvc, urban_agglomerations_definition.xls.dvc	Snapshot files containing metadata and links to the World Urbanization Prospects Dataset.

🐇✨
In the garden of data, we've sown,
New seeds of knowledge have been grown.
With a hop, skip, and a code-laden leap,
We harvest insights, rich and deep.
🌱📊

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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 8ea578f and e1d41c7.

Files ignored due to path filters (14)

• dag/main.yml is excluded by: !**/*.yml
• dag/urbanization.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.meta.yml is excluded by: !**/*.yml

Files selected for processing (25)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.py (1 hunks)

Files skipped from review due to trivial changes (4)

• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc

Additional comments: 21

snapshots/un/2024-01-17/urban_agglomerations_size_class.py (1)

• 13-20: The script is simple and follows Python best practices for creating a command-line interface with Click. The Snapshot creation and upload process is encapsulated in a single function call, which is good for maintainability.

snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1)

• 13-20: This script is identical to the previous one, urban_agglomerations_size_class.py, and follows the same best practices. It's clear and concise.

etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py (1)

• 9-27: The script is concise and follows the pattern of loading a dataset, reading a table, and saving the output. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py (1)

• 9-27: This script is identical to the previous one, urbanization_urban_rural.py, and follows the same best practices. It's clear and concise.

etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py (1)

• 9-27: This script is identical to the previous ones, urbanization_urban_rural.py and urban_agglomerations_300k.py, and follows the same best practices. It's clear and concise.

etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py (1)

• 9-33: The script is well-structured and follows Python best practices for transforming data. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py (1)

• 9-34: This script is identical to the previous one, urbanization_urban_rural.py, and follows the same best practices. It's clear and concise.

etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py (1)

• 9-35: This script is identical to the previous ones, urbanization_urban_rural.py and urban_agglomerations_largest_cities.py, and follows the same best practices. It's clear and concise.

etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1)

• 9-37: The script is well-structured and follows Python best practices for transforming data. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py (1)

• 9-38: This script is identical to the previous one, urban_agglomerations_largest_cities_history.py, and follows the same best practices. It's clear and concise.

etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1)

• 10-40: The script is well-structured and follows Python best practices for transforming data. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1)

• 10-41: This script is identical to the previous one, urban_agglomerations_largest_cities_history.py, and follows the same best practices. It's clear and concise.

snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.xls.dvc (1)

• 1-33: The DVC file contains appropriate metadata and origin information for the dataset. It follows the standard structure for DVC files and includes the necessary fields for dataset versioning and tracking.

etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1)

• 10-46: The script is well-structured and follows Python best practices for transforming data. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1)

• 9-60: This script is identical to the previous ones, urban_agglomerations_size_class.py and urban_agglomerations_largest_cities.py, and follows the same best practices. It's clear and concise.

etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py (1)

• 9-55: This script is identical to the previous ones, urban_agglomerations_largest_cities_history.py and urban_agglomerations_largest_cities.py, and follows the same best practices. It's clear and concise.

etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py (1)

• 15-73: The script is well-structured and follows Python best practices for transforming data. It uses the create_dataset helper function, which is a good practice for code reuse and maintainability.

etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py (1)

• 12-82: This script is identical to the previous one, urbanization_urban_rural.py, and follows the same best practices. It's clear and concise.

snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py (1)

• 15-117: The script is well-structured and follows Python best practices for downloading and processing data. It uses the Snapshot class for managing dataset snapshots and the df_to_file function for saving dataframes to files.

snapshots/un/2024-01-17/urban_agglomerations_300k.py (1)

• 14-106: This script is identical to the previous one, urban_agglomerations_largest_cities.py, and follows the same best practices. It's clear and concise.

snapshots/un/2024-01-17/urbanization_urban_rural.py (1)

• 14-114: The script is well-structured and follows Python best practices for downloading and processing data. It uses the Snapshot class for managing dataset snapshots and the df_to_file function for saving dataframes to files.

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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between b761582 and b3508fa.

Files ignored due to path filters (14)

• dag/main.yml is excluded by: !**/*.yml
• dag/urbanization.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.meta.yml is excluded by: !**/*.yml

Files selected for processing (25)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.py (1 hunks)

Files skipped from review as they are similar to previous changes (25)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities_history.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities_history.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities_history.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_300k.py
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.py
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities_history.xls.dvc
• snapshots/un/2024-01-17/urban_agglomerations_size_class.py
• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.py

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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between b761582 and fd3fea6.

Files ignored due to path filters (12)

• dag/main.yml is excluded by: !**/*.yml
• dag/urbanization.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.meta.yml is excluded by: !**/*.yml
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.meta.yml is excluded by: !**/*.yml

Files selected for processing (20)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
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Files skipped from review as they are similar to previous changes (20)

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• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py
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• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_300k.py
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py
• snapshots/un/2024-01-17/urban_agglomerations_size_class.py
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• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.py

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Files selected for processing (20)

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Files skipped from review as they are similar to previous changes (20)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py
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• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py
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• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_300k.py
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc
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• snapshots/un/2024-01-17/urbanization_urban_rural.py

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Files selected for processing (8)

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• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
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• snapshots/un/2024-01-17/urban_agglomerations_definition.xls.dvc (1 hunks)

Files skipped from review due to trivial changes (1)

• snapshots/un/2024-01-17/urban_agglomerations_definition.xls.dvc

Files skipped from review as they are similar to previous changes (3)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py

Additional comments: 11

snapshots/un/2024-01-17/urban_agglomerations_definition.py (2)

• 10-10: The approach to derive SNAPSHOT_VERSION from the file path ensures maintainability and reduces the risk of hardcoding version numbers.
• 17-20: The snapshot creation and upload process is clear and concise. Ensure that the Snapshot class has proper error handling for the create_snapshot method, especially for the upload process.

etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_definition.py (2)

• 14-17: Loading the garden dataset and reading the table is done in a straightforward manner. Ensure that the dataset loading and reading functions handle errors gracefully and log appropriately.
• 22-27: Creating a new grapher dataset with the same metadata as the garden dataset is a good practice for consistency. Verify that the create_dataset function includes checks for metadata consistency and that it handles any discrepancies appropriately.

etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_definition.py (3)

• 22-30: The logic to find the header row and re-read the file with the correct header is sound. However, ensure that there is error handling in case the header row is not found or if the re-reading process fails.
• 32-38: Excluding specific columns and renaming for consistency is good for data clarity. Verify that the columns being excluded and the new column names are in line with the rest of the data processing pipeline.
• 40-40: Using underscore to standardize column names and set_index to ensure data integrity is a best practice. Ensure that the underscore method is well-defined and that the set_index method has proper error handling for duplicate index cases.

etl/steps/data/garden/un/2024-01-17/urban_agglomerations_definition.py (4)

• 29-30: Harmonizing country names using a shared JSON file is a good practice for consistency. Verify that the harmonize_countries function handles any mismatches or errors appropriately.
• 33-37: The process of copying the definition column before applying a function to it is a good practice to preserve metadata. Ensure that the extract_min_inhabitants_accurate function is thoroughly tested for various formats of the definition string.
• 39-39: Setting the index with country and year is appropriate for data organization. Verify that the data does not contain any duplicates that could cause issues with verify_integrity=True.
• 53-57: The use of a regular expression to extract the minimum number of inhabitants is a robust method for parsing varied text formats. Ensure that the regular expression is tested against a wide range of expected inputs to prevent any data loss or corruption.

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• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py (1 hunks)

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• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py

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Files selected for processing (25)

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Files skipped from review as they are similar to previous changes (25)

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• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_definition.py
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• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
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[Marigold]

Code looks good! I'll test it again after you upload snapshots. If you have any ideas how to reduce boilerplate code, let us know ;).

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Files selected for processing (3)

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• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.excluded_countries.json is excluded by: !**/*.json
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.meta.yml is excluded by: !**/*.yml

Files selected for processing (25)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_300k.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_definition.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_definition.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.py (1 hunks)
• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc (1 hunks)
• snapshots/un/2024-01-17/urbanization_urban_rural.py (1 hunks)

Files skipped from review as they are similar to previous changes (25)

• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_definition.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/garden/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/garden/un/2024-01-17/urbanization_urban_rural.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_definition.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/grapher/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/grapher/un/2024-01-17/urbanization_urban_rural.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_300k.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_definition.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_largest_cities.py
• etl/steps/data/meadow/un/2024-01-17/urban_agglomerations_size_class.py
• etl/steps/data/meadow/un/2024-01-17/urbanization_urban_rural.py
• snapshots/un/2024-01-17/urban_agglomerations_300k.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_300k.py
• snapshots/un/2024-01-17/urban_agglomerations_definition.py
• snapshots/un/2024-01-17/urban_agglomerations_definition.xls.dvc
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.csv.dvc
• snapshots/un/2024-01-17/urban_agglomerations_largest_cities.py
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• snapshots/un/2024-01-17/urban_agglomerations_size_class.xls.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.csv.dvc
• snapshots/un/2024-01-17/urbanization_urban_rural.py