Skill Guide

Basic Python scripting for document processing and API integration

The application of Python scripting to programmatically manipulate files (PDFs, Word docs, spreadsheets) and connect to external services via RESTful APIs to automate data extraction, transformation, and integration workflows.

This skill directly reduces manual data entry errors and processing time by orders of magnitude, enabling faster business intelligence and operational agility. It automates repetitive information tasks, freeing human capital for higher-value analysis and decision-making.

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8.7 Avg Demand

35% Avg AI Risk

How to Learn Basic Python scripting for document processing and API integration

1. Master Python fundamentals: variables, loops, functions, and exception handling. 2. Learn to navigate file systems using `os` and `pathlib`. 3. Understand HTTP methods (GET, POST) and REST principles conceptually.

1. Implement document parsing with libraries like `PyPDF2`, `python-docx`, and `openpyxl`. 2. Use the `requests` library for authenticated API calls, handling pagination, rate limits, and JSON responses. 3. Common mistake: not implementing robust error handling for network failures or malformed data.

1. Architect scalable, resilient ingestion pipelines using async (`asyncio`, `aiohttp`) or task queues (`Celery`). 2. Implement data validation/transformation layers (e.g., with `pydantic`). 3. Mentor on design patterns for maintainable code and production-ready logging/monitoring.

Practice Projects

Beginner

Project

Automated PDF Invoice Data Extractor

Scenario

Extract key fields (Invoice #, Date, Total) from a folder of PDF invoices and compile them into a single CSV file.

How to Execute

1. Use `PyPDF2` or `pdfplumber` to read text from each PDF. 2. Write regex patterns to locate and extract the target data fields. 3. Use the `csv` module to write each extracted row to a output.csv file. 4. Wrap the logic in a function that processes all files in a specified directory.

Intermediate

Project

Salesforce-to-Excel Contact Sync

Scenario

Create a script that pulls contact data from the Salesforce REST API, transforms it (e.g., formats phone numbers), and updates a master Excel report.

How to Execute

1. Use `requests` with OAuth2 to authenticate with Salesforce. 2. Write a query loop to paginate through all contacts via the `/query` endpoint. 3. Use `pandas` to clean/transform the JSON response data. 4. Use `openpyxl` to write the transformed data to a formatted Excel sheet, overwriting or appending as needed.

Advanced

Project

Real-time Document Processing Microservice

Scenario

Build a service that watches a cloud storage bucket (e.g., AWS S3) for new documents, processes them (extracts text, classifies type), and posts the structured data to a central API.

How to Execute

1. Use a cloud SDK (e.g., `boto3`) with event notifications or a poller to detect new files. 2. Implement a document processing pipeline with appropriate parsers and a text classifier (e.g., using a pre-trained model). 3. Design the service as a containerized FastAPI/Flask app. 4. Implement circuit breakers and dead-letter queues for robust API communication and error handling.

Tools & Frameworks

Python Libraries & Packages

requestspandaspython-docxPyPDF2/pdfplumberopenpyxlpydantic

`requests` is the standard for HTTP calls. `pandas` excels at tabular data transformation. `python-docx`, `PyPDF2`, and `openpyxl` are the essential triad for reading/writing Office documents. `pydantic` is critical for robust data validation and serialization in API payloads.

Infrastructure & Deployment

DockerAirflow/PrefectCelery/RQ

Docker containers ensure consistent environments. Workflow orchestrators like Airflow schedule and monitor complex document pipelines. Task queues like Celery handle asynchronous, long-running processing jobs.

Interview Questions

Answer Strategy

The interviewer is testing your understanding of production reliability and defensive programming. Structure your answer around three pillars: resilience, observability, and maintainability. Sample answer: "First, I'd implement robust error handling with specific exception catches and retries with exponential backoff for network issues. For malformed files, I'd use try-except blocks around parsers, log the error with the filename, and move failed files to a quarantine folder. Second, I'd add structured logging and metrics to monitor success/failure rates. Finally, I'd refactor the code into a class with clear separation of concerns for fetching, parsing, and saving, and write unit tests for each component."

Answer Strategy

Tests debugging tenacity and systematic problem-solving. Focus on methodology: API exploration, testing, and documentation. Sample answer: "I started by using tools like Postman or curl to manually hit the endpoints, testing different parameters and observing raw responses to reverse-engineer the contract. I documented every finding, including hidden pagination and authentication quirks, in a shared wiki. I then built the integration incrementally in Python, using detailed logging to trace every request and response, which helped me quickly identify and work around undocumented rate limits and field-specific validation rules."