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- Guide to NumPy: 2nd Edition
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45313670
Guide to NumPy: 2nd Edition
86% of respondents would recommend this to a friend
Item #:
45313670
ZAR 1354
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This second edition of A Guide to NumPy offers new perspectives, descriptions of distributed processing tools, and insights on using Numba to compile code with NumPy arrays.
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What Stands Out
Comprehensive Coverage
This guide offers extensive insights into NumPy and its functionalities, ensuring that readers gain a thorough understanding of numerical data handling and array manipulation.
Updated Content
The 2nd edition includes updated examples and best practices reflecting the latest developments in NumPy, keeping readers current with the evolving landscape of data science.
User-Friendly Approach
Designed for both beginners and experienced users, the guide simplifies complex concepts using clear explanations and practical examples, making learning NumPy approachable and engaging.
Product Details
| Publisher | CreateSpace Independent Publishing Platform |
| Publication date | September 15, 2015 |
| Edition | 2nd |
| Language | English |
| Print length | 364 pages |
| ISBN-10 | 151730007X |
| ISBN-13 | 978-1517300074 |
| Item Weight | 1.4 pounds (640 grams) |
| Dimensions | 7 x 0.82 x 10 inches (17.8 x 2.1 x 25.4 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists wanting to enhance their data manipulation skills using NumPy for efficient analysis.
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Students and Educators
A great educational resource for learners of Python and numerical computing in academic settings.
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Developers Transitioning
Useful for software developers transitioning into data science who need a solid foundation in NumPy.
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Beginner Programmers
Not suitable for absolute beginners without prior programming experience, as it assumes familiarity with Python.
Product Description
Guide to NumPy: 2nd Edition
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Python Editorial Review
**** The second edition of "Guide to Numpy" serves not only as a significant reference for those already familiar with the library but also as a gateway for advanced users aimed at deepening their understanding of NumPy's underlying mechanics. Developed by Travis Oliphant, one of the primary creators of NumPy, the book provides invaluable insights that are difficult to find in other resources. Readers are encouraged to start with the foundational chapters and then delve into more specialized topics based on their requirements, thus avoiding the potential for information overload. Many reviewers appreciate the comprehensive coverage of both the fundamental concepts and advanced functionalities. Key chapters, such as the exploration of universal functions (ufuncs) and interfacing with C code using tools like Cython and f2py, are praised for their clarity and practical applicability. The approachable explanations and inclusion of relatable examples make complex topics accessible, fostering a better understanding of how to effectively use NumPy in scientific computing or data analysis. While the book excels in countless aspects, some users felt certain sections, notably the final chapter, lacked depth and could benefit from more elaboration. However, despite these minor points for improvement, the Consensus is overwhelmingly positive, making "Guide to Numpy" not just an option, but a necessity for those serious about adopting NumPy in their workflow. **
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Pros
- Written by Travis Oliphant, a foundational figure in NumPy’s development.
- Strong coverage of both basic and advanced concepts.
- Detailed explanations of universal functions and C API interactions.
- Useful practical tips and examples throughout the text.
- Recommended approach for reading to avoid information overload.
Cons
- Some sections are seen as underdeveloped, particularly the final chapter.
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Features & Benefits
- Updated edition providing new perspectives and insights
- Includes descriptions of distributed processing tools in the ecosystem
- Highlights the use of Numba for compiling code with NumPy arrays
- Written by Travis Oliphant, co-founder of Continuum Analytics
- Travis has extensive experience with Python for computational and data science
- Travis is the primary creator of the NumPy package
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