I’m working on a data project focused on SRD (Social Relief of Distress) datasets and trying to build a clean, efficient environment in Anaconda to manage the entire workflow. Since SRD data often comes in mixed formats—PDF reports, CSV exports, scraped tables, and public API snapshots—it becomes challenging to maintain a consistent setup that handles all these sources without running into dependency issues.
So far, I’ve created a basic environment using pandas, NumPy, and Jupyter Notebook, but I’m running into conflicts when adding libraries for PDF extraction, automated validation, and visualisation. Some packages install fine on their own, but cause version mismatches once combined in the same environment.
Before I rebuild everything from scratch, I’d really appreciate advice from anyone who has worked with welfare-related or SRD datasets in Anaconda. Specifically:
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What core libraries or versions worked best for you?
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Did you rely on conda-forge, pip, or a hybrid environment?
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Any suggested environment.yml examples for handling multi-format SRD data?
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Tools or workflows that helped maintain reproducibility over time?
For context, here’s a general reference link I’m checking: click here.
Thanks in advance for any insights on creating a stable and scalable setup for SRD data analysis!