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Python Algorithms in easy steps (Boost your coding skills)

List Price: $24.99
SKU:
9781787910638
Quantity:
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25 unit(s)
Expected release date is Sep 29th 2026
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  • Product Details

    Author:
    Mike McGrath
    Format:
    Paperback
    Pages:
    192
    Publisher:
    In Easy Steps Limited (September 29, 2026)
    Imprint:
    In Easy Steps Limited
    Release Date:
    September 29, 2026
    Language:
    English
    ISBN-13:
    9781787910638
    ISBN-10:
    1787910636
    Weight:
    12oz
    Dimensions:
    7.38" x 8.88" x 0.39"
    File:
    PGW-LEGATO-Metadata_Only_Publishers_Group_West_Customer_Group_Metadata_20260423123951-20260423.xml
    Folder:
    PGW
    List Price:
    $24.99
    Country of Origin:
    United Kingdom
    Series:
    In Easy Steps
    As low as:
    $19.24
    Publisher Identifier:
    P-PER
    Discount Code:
    A
    Pub Discount:
    65
  • Overview

    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 through these 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.