Skill Guide

Python scripting for automation: basic to intermediate Python for chaining prompts, parsing outputs, and data wrangling

The use of Python to create automated workflows that sequence AI prompt interactions, extract and transform structured/unstructured data from responses, and perform systematic data cleansing and transformation.

This skill directly reduces manual, repetitive data processing labor, enabling rapid prototyping of AI-driven solutions and accelerating data-to-insight cycles. It increases operational efficiency by allowing teams to scale personalized outputs and complex data manipulation tasks reliably.

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How to Learn Python scripting for automation: basic to intermediate Python for chaining prompts, parsing outputs, and data wrangling

Focus 1: Core Python syntax - variables, data types, loops, functions, and file I/O (open/read/write). Focus 2: String manipulation and basic parsing with str methods and the re module. Focus 3: Understanding JSON and CSV formats and using the json and csv modules.

Master Python's requests library for API interaction and error handling with try/except. Practice chaining multiple API calls using stateful data (passing output from one call as input to the next). Common mistake: Failing to parse nested JSON structures correctly or handling API rate limits and timeouts.

Design robust, fault-tolerant automation pipelines using logging, configuration files (configparser, pydantic), and orchestration tools (Airflow, Prefect). Architect systems for idempotency and data validation. Focus on building reusable modules for prompt templating, output parsing, and data wrangling that can be version-controlled and tested.

Practice Projects

Beginner

Project

API-to-CSV Weather Report Generator

Scenario

Automate fetching daily weather data from a public API (e.g., OpenWeatherMap) for multiple cities and saving a clean CSV report.

How to Execute

1. Sign up for a free API key. 2. Write a script to make GET requests for each city using the requests library. 3. Parse the JSON response to extract temperature, humidity, and description. 4. Use the csv module to write the results to a file with appropriate headers.

Intermediate

Project

Prompt Chain for Research Summarization

Scenario

Create a script that takes a research topic, generates 3 sub-questions using an LLM, fetches answers for each, and then synthesizes a final summary.

How to Execute

1. Use an API client (e.g., for OpenAI) to send a prompt: 'Generate 3 key research questions about [topic]'. 2. Parse the response to get a list of questions. 3. Loop through questions, sending each to the LLM to get an answer. 4. Concatenate all answers into a new prompt asking for a synthesis, and parse the final output.

Advanced

Project

Automated Data Cleaning & Enrichment Pipeline

Scenario

Build a pipeline that ingests messy CSV/JSON files from a directory, applies a series of cleaning rules (deduplication, null handling, normalization), enriches records via an external API, and loads the result into a SQLite database.

How to Execute

1. Use pathlib to monitor an input directory. 2. Create a processing module with functions for each cleaning step using pandas. 3. Design an enrichment function that makes API calls for each record, implementing exponential backoff for retries. 4. Use SQLAlchemy or sqlite3 to create and load data into a database table with proper schema. 5. Add logging and a configuration file to control the pipeline.

Tools & Frameworks

Core Python Libraries

requestsjsoncsvrepandassqlite3

requests for HTTP calls; json/csv for data serialization; re for regex parsing; pandas for high-performance data wrangling and cleaning; sqlite3 for lightweight database storage.

API & Data Handling

OpenAI Python clientPydanticBeautifulSoup4httpx

OpenAI client for LLM interactions; Pydantic for data validation and settings management; BeautifulSoup4 for HTML/XML parsing; httpx for async-capable HTTP requests.

Orchestration & Infrastructure

Apache AirflowPrefectDockerAWS Lambda

Airflow/Prefect for scheduling and managing complex workflows; Docker for containerizing scripts to ensure consistent environments; serverless platforms like AWS Lambda for event-driven automation.

Interview Questions

Answer Strategy

Focus on a concrete example. Explain the flow: initial prompt, parsing the response (e.g., using json.loads or regex), passing data to subsequent calls, and aggregating results. Emphasize error handling (try/except, status codes) and data validation. Sample: 'I built a script for generating product descriptions. It first called the LLM for features, parsed the JSON list, then iterated to get a benefit for each feature. I used Pydantic to validate the parsed data and implemented retry logic with exponential backoff for API failures.'

Answer Strategy

This tests debugging and defensive programming. Strategy: Identify failure points (parsing, data access). Propose adding structured logging (logging module), input validation (Pydantic models or try/except with specific exceptions), and writing unit tests with edge cases. Sample: 'I'd first add detailed logging around data ingestion and parsing steps. Then, I'd introduce a Pydantic model to validate each incoming record, catching validation errors gracefully and logging them. I'd write tests using pytest with malformed data to ensure the fixes work.'