Skill Guide
Python scripting for document processing and API integration is the practice of using Python to programmatically extract, transform, and manipulate data from structured/unstructured documents (PDFs, Word, Excel) and to connect disparate software systems via their APIs for automated data exchange and workflow orchestration.
Scenario
You receive a daily email with a PDF attachment containing sales figures. You need to extract the total revenue and log it in a spreadsheet.
Scenario
Customer data resides in a legacy Excel file. New leads are captured via a Typeform API. You need to deduplicate and sync all contacts into a central HubSpot CRM.
Scenario
Build a system where scanned contracts (images in a cloud bucket) are automatically processed: text extracted via OCR, key clauses identified using a custom NLP model, and results indexed in a search engine with alerts sent via API.
`pandas` is the workhorse for tabular data (CSV, Excel). Use `pdfplumber` for precise PDF table extraction. `python-docx` handles Word documents. `Tesseract` is essential for OCR on scanned images. Choose based on the source document format.
`requests` is the standard for synchronous HTTP. Use `httpx` for async support and HTTP/2. `aiohttp` is for fully asynchronous applications. `OAuthLib` simplifies implementing OAuth flows. Always handle retries and timeouts.
`json` and `csv` handle basic data interchange. Use an ORM like `SQLAlchemy` for robust database interaction. `pydantic` is critical for data validation and settings management, ensuring your API payloads and parsed data conform to strict schemas.
`Celery` or `Airflow` orchestrate and schedule complex, multi-step data pipelines. `Docker` containerizes scripts for consistent environment deployment. `Pytest` is non-negotiable for writing maintainable, reliable automation code.
Answer Strategy
Structure your answer around architecture (modular design), performance (parallel processing vs. sequential), resilience (idempotency, error logging), and tooling. Sample: 'I'd build a pipeline with clear stages: file discovery using `os.walk`, parallel extraction using `pdfplumber` with `concurrent.futures`, data validation with `pydantic`, and bulk loading into PostgreSQL via `SQLAlchemy`. Key considerations include idempotent processing to handle retries, comprehensive logging to a file or service, and resumability to avoid reprocessing all files if the script fails midway.'
Answer Strategy
Tests problem-solving, defensive programming, and understanding of distributed systems challenges. Sample: 'I integrated with a payment gateway API that had frequent 5xx errors and rate limits. I implemented exponential backoff retries with jitter using the `tenacity` library. I also added circuit-breaker logic to pause calls after consecutive failures, logged all error states with request IDs, and set up alerting via a monitoring webhook. Crucially, I designed the downstream process to be idempotent, using the API's idempotency keys to ensure retries didn't cause duplicate charges.'
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