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Coles Python Algorithms in easy steps: Boost your coding skills at Village Green Shopping Centre in Vernon, BC
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Python Algorithms in easy steps presents a clear, practical tour of the algorithms every programmer benefits from knowing. Each chapter focuses on a small, self-contained family of techniques, fast number methods, smarter ways to search and sort, essential string handling, core data structure patterns, and the recursive tools that simplify complex problems. The emphasis is on clarity and mechanical understanding: minimal examples, predictable behavior, and ideas that transfer directly into everyday code. By working throughthese chapters, readers build a compact toolkit for writing programs that are faster, cleaner, and easier to reason about across a wide range of tasks.
Python Algorithms in easy steps assumes the reader is familiar with Python basics - for example, the use of Python built-in functions. But algorithms go one step further. . .
- Built ins hide the mechanism - they give results but not the step by step process, cost, or behavior.
- Custom behavior is often required - real code needs early exit, partial scans, custom comparisons, and hybrid strategies.
- Understanding algorithms improves debugging - you can spot slow paths, reason about edge cases, and choose better data structures.
- Algorithmic thinking transfers across languages - the habits you build here apply everywhere; not just in Python.
- Algorithms are the map; built ins are the destination - knowing the mechanics lets you adapt, extend, and design solutions; not just call them.
Python Algorithms in easy steps presents a clear, practical tour of the algorithms every programmer benefits from knowing. Each chapter focuses on a small, self-contained family of techniques, fast number methods, smarter ways to search and sort, essential string handling, core data structure patterns, and the recursive tools that simplify complex problems. The emphasis is on clarity and mechanical understanding: minimal examples, predictable behavior, and ideas that transfer directly into everyday code. By working throughthese chapters, readers build a compact toolkit for writing programs that are faster, cleaner, and easier to reason about across a wide range of tasks.
Python Algorithms in easy steps assumes the reader is familiar with Python basics - for example, the use of Python built-in functions. But algorithms go one step further. . .
- Built ins hide the mechanism - they give results but not the step by step process, cost, or behavior.
- Custom behavior is often required - real code needs early exit, partial scans, custom comparisons, and hybrid strategies.
- Understanding algorithms improves debugging - you can spot slow paths, reason about edge cases, and choose better data structures.
- Algorithmic thinking transfers across languages - the habits you build here apply everywhere; not just in Python.
- Algorithms are the map; built ins are the destination - knowing the mechanics lets you adapt, extend, and design solutions; not just call them.
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