AI Time Estimates for Coding Tasks

Build a Python REST API endpoint with email validation, graceful error handling, and unit tests — a bounded, well-defined coding task suitable for a single developer session.

Write a Python script to parse a messy CSV file, clean null values, and output a normalized JSON summary

Convert a complex multi-join SQL query (multiple tables, join conditions, filters, possibly aggregations) into equivalent pandas DataFrame operations, adding inline comments that explain each transformation step.

Write docstrings for all functions, classes, and methods in an existing undocumented internal Python module, plus a README covering purpose, installation, usage, and examples.

Generate a comprehensive suite of unit tests for a set of existing Python utility functions that currently have no test coverage, targeting high branch and line coverage using a standard framework such as pytest.

Diagnose and fix a REST API endpoint returning intermittent HTTP 500 errors under load. Requires log collection, reproducing the failure under realistic concurrency, identifying root cause (e.g., connection pool exhaustion, thread contention, unhandled exception paths, resource limits), and deploying and validating a fix.

Write Python code to ingest one or more CSV files, compute customer churn metrics (churn rate, retention, cohort analysis, etc.), and render an interactive or static dashboard using a library such as Streamlit, Plotly Dash, or matplotlib. Complexity scales with the sophistication of the metrics and the interactivity of the dashboard.

Write Python code that scrapes product prices from an e-commerce website, handles network and parsing errors gracefully, and writes results to a CSV file.

Build a Python-based web scraping system targeting 20 e-commerce competitor sites, persist the extracted pricing data to a database, and automate daily reporting — a moderately complex engineering project involving scraper development, data modeling, and scheduled automation.

Write Python code for a REST API endpoint that validates incoming request parameters, queries a PostgreSQL database, and returns results as paginated JSON — a common but non-trivial backend feature requiring knowledge of a web framework, an ORM or raw SQL driver, input validation, and pagination logic.

Write Python code to scrape product listings from an e-commerce site, extract and clean the data, and output it as structured JSON.

Write Python code using libraries like requests and BeautifulSoup (or Scrapy) to crawl an e-commerce site's product listing pages, extract structured fields (name, price, URL, etc.), and export them to a CSV file.

Build an interactive analytics dashboard from a Stripe CSV export, computing and visualizing monthly recurring revenue (MRR), customer churn rate, and cohort retention, typically delivered as a web app or BI dashboard.

Build a Python-based web scraper to pull competitor product pricing, clean and normalize the data, and produce an automated weekly comparison report. Complexity scales heavily with the number of target sites, anti-scraping measures, and report polish required.

Building a Stripe Checkout integration with subscription billing for a SaaS product, including webhook event handling and the Stripe customer portal. This is a moderately complex, well-scoped backend/fullstack coding task — Stripe's documentation is excellent, but the surface area (checkout sessions, subscription lifecycle events, webhook signature verification, portal configuration) accumulates quickly for non-experts, and edge cases around subscription state transitions are where most implementations fall short.

Write Python code to connect to a weather API, retrieve forecast data, clean and process it (handle missing values, type conversion, normalization), and produce a weekly forecast visualization using a charting library such as matplotlib or plotly.

Configure a GitHub Actions workflow that automatically runs tests, builds the Node.js project, and deploys it on every push to the main branch, including secrets management and deployment target integration.

Write Python code to scrape product prices from one or more competitor websites and export the results to a structured CSV file. Complexity scales with the number of target sites, whether pages are JavaScript-rendered, and whether anti-bot measures are in place.

Containerise an existing Python web application using a multi-stage Dockerfile to separate build and runtime stages, and a docker-compose.yml for local development with volume mounts, environment variables, and service orchestration.

Build a Python web scraper targeting 10 e-commerce sites to extract competitor pricing data and persist results to a database. Requires per-site selector engineering, anti-bot mitigation, data modeling, and ongoing maintenance as sites change.