Autorefresh for Crdc_Dual_Enrollment

[Megha18jain]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Megha18jain, 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 establishes a comprehensive and automated pipeline for importing school-level dual enrollment statistics from the U.S. Department of Education's Civil Rights Data Collection (CRDC) into Data Commons. The system is designed to regularly fetch, process, and map CRDC data, ensuring that up-to-date statistical variables related to dual enrollment, disaggregated by various demographic factors, are available. This automation ensures the data remains current and accessible for analysis.

Highlights

• New Data Import Pipeline: Introduces a complete automated pipeline for importing school-level dual enrollment statistics from the U.S. Department of Education's Civil Rights Data Collection (CRDC) into Data Commons.
• Automated Data Processing: Includes Python scripts for downloading raw CRDC data, preprocessing it (e.g., extracting years, handling specific column transformations), and generating statistical variables.
• Comprehensive Configuration: Provides a detailed README, property-value mapping (pvmap), and manifest files to configure the import process, including provenance details and a weekly cron schedule for autorefresh.
• Statistical Variable Generation: The import generates 'Count' statistical variables for dual enrollment students, disaggregated by race, ethnicity, gender, English proficiency status, and disability status.

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[gemini-code-assist]

Code Review

This pull request introduces a new data import pipeline for CRDC Dual Enrollment data, including scripts for downloading, preprocessing, and generating statistical variables. The overall structure is good, and the scripts are well-organized. I've provided a few suggestions to improve maintainability, robustness, and documentation clarity. Key points include making the data download script more dynamic, improving error handling in the preprocessing script to avoid silent data loss, and clarifying the instructions in the README.

[gemini-code-assist]

Using encoding='latin1' and on_bad_lines='skip' can hide issues with the source data. latin1 can read any byte stream without error, which might garble text if the actual encoding is different (like UTF-8). on_bad_lines='skip' can lead to silent data loss if some rows are malformed. It would be more robust to investigate the correct encoding of the source files and handle or log bad lines instead of skipping them. If these settings are intentional to handle known data quality issues, please add a comment explaining why.

[gemini-code-assist]

The instruction for generating the final output is a bit vague. It mentions using the datacommons command-line tool, but the manifest.json specifies a different script (stat_var_processor.py). To improve clarity for users, it would be helpful to provide the exact command to run, similar to the other steps.

Suggested change

	3. **Generate the final output:**
	After preprocessing, the data is ready to be imported into Data Commons using the `datacommons` command-line tool.
	3. **Generate the final output:**
	Run the statvar import script from the `statvar_imports/crdc_dual_enrollment` directory:
	```bash
	python3 ../../tools/statvar_importer/stat_var_processor.py --input_data=preprocessed/*.csv --pv_map=dual_enrollment_pvmap.csv --config_file=metadata.csv --output_path=output/dual_output --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf
	```
	This will generate the final `.mcf`, `.tmcf`, and `.csv` files in the `output/` directory.

[gemini-code-assist]

The re module is imported but not used in this script. It's good practice to remove unused imports to keep the code clean.

[gemini-code-assist]

The end year in range(2012, 2025) is hardcoded. This will require manual updates in the future to download new data as it becomes available. To make the script more maintainable, consider making the end year dynamic based on the current year. You will also need to add import datetime at the top of the file.

Suggested change

	for year in range(2012, 2025):
	for year in range(2012, datetime.date.today().year + 2):

[gemini-code-assist]

Using os.chdir() can be risky as it changes the current working directory for the entire process, which can lead to unexpected behavior. Since you are already using pathlib to construct absolute paths, it would be safer to rely on those and remove this line. If changing the directory is necessary for a downstream tool, consider wrapping its execution in a subprocess where you can control the CWD without affecting the main script.

[gemini-code-assist]

There is a trailing space at the end of this line. It's a minor style issue, but it's good practice to remove trailing whitespace.

Suggested change

	"../../tools/statvar_importer/stat_var_processor.py --input_data=preprocessed/*.csv --pv_map=dual_enrollment_pvmap.csv --config_file=metadata.csv --output_path=output/dual_output --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf "
	"../../tools/statvar_importer/stat_var_processor.py --input_data=preprocessed/*.csv --pv_map=dual_enrollment_pvmap.csv --config_file=metadata.csv --output_path=output/dual_output --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf"