README.md

Algorithms Basics (Python)

This module introduces common algorithmic patterns over lists.

Learning Metadata

• Difficulty: Intermediate.
• Estimated Time: 30-45 minutes.
• Prerequisites: 01-foundations/arrays-and-vectors, 01-foundations/functions.
• Cross-Language Lens: Compare hand-written loops with library helpers and see when explicit iteration stays clearer for beginners.

Quick Run

python example/main.py

Topics Covered

• Linear search for the first matching index.
• Counting occurrences of a target value.
• One-pass minimum and maximum scanning.
• Combining multiple statistics in a single loop.

Common Pitfalls

• Forgetting to handle empty lists before min/max logic.
• Initializing minimum or maximum with invalid defaults.
• Doing multiple passes when one pass is enough.

Cross-Language Notes

• Compared with the C++ baseline, the same traversal and aggregation ideas are expressed with different defaults for loops, collections, and helper functions.
• Relative to Python, the statically typed tracks make intermediate state and accumulator types more explicit.
• The useful comparison is algorithm shape staying constant while the surrounding syntax and safety rails change.

Exercise Focus

• exercises/01.py: linear search and first index.
• exercises/02.py: minimum, maximum, and even-count in one pass.

Exercise Specs

1. exercises/01.py

• Input: integer n, then n values, then a target.
• Output: first index of target or -1.
• Edge cases: n <= 0; target not present.

2. exercises/02.py

• Input: integer n, then n values.
• Output: minimum, maximum, and even count.
• Edge cases: all odd numbers (even count 0); all equal values.

Checkpoint

• I can implement linear search confidently.
• I can compute multiple statistics in one pass.
• I can reason about edge cases before coding.
• I completed exercises/01.py.
• I completed exercises/02.py.