Python tutorials and worked examples

Read records incrementally and validate input contracts.

• Read large CSV files in Python without loading every row

  Stream a CSV with DictReader, validate malformed records and calculate an exact total while keeping only the current record and running counters.

  tutorial · 4 min read

Trace failures to their causes and verify repairs.

• Read a Python traceback and repair the failing assumption

  Trace a KeyError through three function calls, identify the broken input contract and verify a repair without swallowing unrelated exceptions.

  troubleshooting · 4 min read

Select interpreters and reproduce project dependencies.

• Python virtual environments with uv: select and verify the interpreter

  Create an isolated Python environment with uv, bind package operations to that interpreter and verify isolation before diagnosing import failures.

  tutorial · 4 min read

Reason about references, mutation and the structure of your data.

• Python shallow copy vs deep copy: trace a nested-data bug

  Follow a nested dictionary through assignment, shallow copying and deep copying, then choose the right copying boundary for a data workflow.

  troubleshooting · 4 min read

Understand calls, defaults and the state a function retains.

• Python mutable default arguments: why state survives between calls

  Reproduce a shared-default bug, repair it with a sentinel, and distinguish a fresh collection from intentional accumulation.

  troubleshooting · 4 min read

Combine language fundamentals into complete, testable programs.

• Python projects for beginners: three small programs with complete code

  Build three beginner Python projects with complete code: a study tracker, quiz report and CSV stock checker. Check the output, then extend each one.

  project · 9 min read

Choose the foundations needed for numerical and modeling work.

• Python for machine learning and AI: what to learn first, and what to skip

  The Python you need for machine learning, in order: core language, files and testing, then NumPy, pandas, Matplotlib and scikit-learn, plus what you can safely leave for later.

  learning path · 8 min read