爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
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爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
爆速Python Programmer’s SELECTION
Understand the characteristics of Python, maximize its performance, and use high-performance libraries correctly to process data faster.
爆速Python Programmer’s SELECTION
Item #: 88212401

爆速Python Programmer’s SELECTION

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Item #: 88212401

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What Stands Out

Engaging Content
Explosive Python Tankobon combines action-packed storytelling with relatable characters, ensuring readers of all ages stay captivated from start to finish.
Durable Build
The softcover design is crafted for longevity, allowing fans to enjoy the series multiple times without wear, suitable for collectors and casual readers alike.
Vibrant Artwork
Featuring stunning illustrations, the book enhances the reading experience, drawing readers into the vibrant world of Explosive Python with eye-catching visuals.

Product Details

Shop 爆速Python Programmer’s SELECTION online at a best price in Isle of Man. 4798183733
  • 「そう、すべては速さのために」Pythonでデータセットを扱う際、最後に重要になってくるのはやはり「処理速度」です。データが巨大であればあるほど、ちょっとした工夫が処理速度を向上させ、「データの氾濫」ともいえる現状を打開する鍵になってくれます。Pythonの特性を理解しつつそのパフォーマンスを最大限に引き出し、さらにハイパフォーマンスなライブラリを正しく利用することができれば、遅い遅いといわれがちなPythonにおいても、爆速な処理を手にすることができるのです。本書は、組み込み機能やスレッディング特性、CPythonのグローバルインタプリタロック（GIL）などに始まり、Cythonへの移行やGPUの利用に至るまでの多面的なアプローチを紹介し、単にマシン性能を上げたり、マシンの数を増やすだけでは得られない、効率的なPythonアプリケーションの記述をサポートします。【本書は『Fast Python: High performance techniques for large datasets』の邦訳書です。】◆◆◆◆◆もくじ◆◆◆◆◆●Part 1　基礎的なアプローチ　・Chapter 1　データ処理の効率化が急がれている　・Chapter 2　組み込み機能のパフォーマンスを最大限に引き出す　・Chapter 3　並行性、並列性、非同期処理　・Chapter 4　ハイパフォーマンスなNumPy●Part 2　ハードウェア　・Chapter 5　Cythonを使って重要なコードを再実装する　・Chapter 6　メモリ階層、ストレージ、ネットワーク●Part 3　現代のデータ処理のためのアプリケーションとライブラリ　・Chapter 7　ハイパフォーマンスなpandasとApache Arrow　・Chapter 8　ビッグデータの格納●Part 4　高度なトピック　・Chapter 9　GPUコンピューティングを使ったデータ分析　・Chapter 10　Daskを使ったビッグデータの分析　・付録A　環境のセットアップ　・付録B　Numbaを使って効率的な低レベルコードを生成する
Publisher 翔泳社
Publication date June 24, 2024
Language Japanese
Print length 352 pages
ISBN-10 4798183733
ISBN-13 978-4798183732
Item Weight 1 g
Dimensions 7.17 x 0.91 x 9.06 inches (18.2 x 2.3 x 23 cm)

Who Should Buy?

Suitable For
  • Beginner Programmers

    This product provides a solid introduction to Python, making it great for those just starting out in programming.

  • Data Science Enthusiasts

    The book covers essential Python libraries, allowing readers to quickly apply Python in data analysis and scientific computing.

  • Coding Educators

    Teachers and instructors will find this resource valuable for creating engaging lesson plans and teaching materials for Python courses.

Not Suitable For
  • Advanced Developers

    Experienced programmers may find the content overly simplistic and lacking in depth, offering little new knowledge.

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Tiago Rodrigues Antão (Original Author) Techniques Editorial Review

爆速Python Programmer’s SELECTION is a comprehensive guide for Python enthusiasts published by 笠間社 on June 24, 2024. This 352-page book, written in Japanese, offers a deep dive into programming concepts and best practices in Python. Readers appreciate its clarity and detailed explanations, making it suitable for both beginners and experienced developers. The dimensions of the book allow for easy handling, and its well-structured chapters provide a smooth learning curve. However, some readers might find that it primarily targets Japanese speakers, which limits accessibility for a broader audience.

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Pros

  • In-depth content for Python programming
  • Clear explanations for beginners
  • Well-structured chapters
  • Compact and easy to handle
  • Suitable for a range of skill levels

Cons

  • Primarily available in Japanese language

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Features & Benefits

  • Enhance your Python data processing skills with this comprehensive guide.
  • Explore built-in features and threading characteristics to boost efficiency.
  • Learn to utilize Cython and GPUs for significant performance improvements.
  • Discover high-performance libraries like NumPy, pandas, and Apache Arrow.
  • Develop concurrent and asynchronous processing techniques for better data management.
  • Gain insights into reimplementing code and optimizing hardware setup for big data tasks.

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