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Numba VS Think Python

Compare Numba VS Think Python and see what are their differences

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Numba logo Numba

Numba gives you the power to speed up your applications with high performance functions written...

Think Python logo Think Python

Learning Resources
  • Numba Landing page
    Landing page //
    2019-09-05
  • Think Python Landing page
    Landing page //
    2023-09-24

Numba features and specs

  • Performance
    Numba can significantly increase the speed of execution for numerically intensive Python code by compiling Python functions to optimized machine code using LLVM.
  • Ease of Use
    Numba is user-friendly and requires minimal code changes. Often, just applying a decorator to functions is enough to gain performance benefits.
  • Integration with NumPy
    Numba works well with NumPy, allowing users to compile functions that utilize NumPy arrays efficiently.
  • JIT Compilation
    It supports Just-In-Time (JIT) compilation, enabling functions to be compiled at runtime, which allows for optimizations based on actual usage.
  • GPGPU Acceleration
    Numba offers support for GPU acceleration, which can further enhance performance by offloading tasks to NVIDIA GPUs using CUDA.

Possible disadvantages of Numba

  • Limited Python Feature Support
    Numba does not support all Python features and standard library modules, which can limit its applicability for certain functions or applications.
  • Compilation Overhead
    The initial compilation of functions can add overhead, which might negate performance gains for small or simple tasks.
  • Debugging Difficulty
    Debugging Numba-compiled code can be challenging due to the compiled nature of the code, which may obscure typical Python error messages.
  • Complex Code Compatibility
    More complex Python constructs, such as classes and closures, are not fully supported, requiring workarounds or alternative solutions.
  • Dependency on LLVM
    Numba heavily relies on the LLVM library for compilation, which can complicate installation and increase dependency size.

Think Python features and specs

  • Accessible for Beginners
    Think Python is written in a clear and approachable style, making it suitable for beginners with no prior programming experience. The author takes care to explain concepts thoroughly, making it easy to follow.
  • Practical Examples
    The book is filled with practical examples that demonstrate how to use Python for various applications. This approach helps readers understand real-world usage of the language.
  • Free Availability
    Think Python is openly accessible in digital format for free, making it easy for anyone to read without financial barriers, supporting open education.
  • Emphasis on Problem Solving
    The book places strong emphasis on teaching readers how to think like programmers, encouraging problem-solving and logical thinking skills.

Possible disadvantages of Think Python

  • Limited Depth
    While suitable for beginners, the book doesn’t delve deeply into advanced features of Python, which might leave learners needing additional resources for more complex topics.
  • Pacing
    Some readers might find the pacing of the book too slow, particularly if they have some prior programming experience, as it aims to accommodate complete beginners.
  • Lack of Exercises
    There are fewer exercises compared to some other programming books, potentially providing less practice for readers to reinforce their learning.
  • Outdated Information
    Depending on the edition, some information may be outdated due to the fast-evolving nature of programming languages. Readers may need to verify with more recent sources.

Analysis of Numba

Overall verdict

  • Numba is considered good, especially if your work involves numerical computations that can take advantage of its just-in-time compilation. Its ability to speed up Python code while allowing you to remain within the Python ecosystem makes it a valuable tool for performance optimization in computationally demanding applications.

Why this product is good

  • Numba is a just-in-time compiler for Python that is particularly effective for numerical and scientific computing. It translates Python functions to optimized machine code at runtime using the LLVM compiler infrastructure. This can significantly accelerate execution speed, especially for operations that involve loops and computationally intensive tasks. It's an attractive option for developers looking for performance optimization without having to write C or C++ code. Numba is also easy to integrate with other popular scientific computing libraries such as NumPy.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Developers involved in scientific computing and numerical analysis.
  • Researchers needing to optimize algorithms for speed without leaving Python.
  • Educational purposes for those learning about compiling and performance acceleration.

Numba videos

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The Criminal History of RondoNumbaNine

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Think Python videos

+ Add

Thoughts on Think Python From a Beginner Programmer

More videos:

Category Popularity

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Numba

Think Python

Website Builder
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Website Builder
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Online Learning
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Online Learning
100% 100
Website Design
100 100%
Website Design
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Development
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Development
100% 100

User comments

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Social recommendations and mentions

Based on our record, Numba seems to be a lot more popular than Think Python. While we know about 93 links to Numba, we've tracked only 9 mentions of Think Python. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Numba mentions (93)

  • I Use Nim Instead of Python for Data Processing
    >Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that... - Source: Hacker News / 9 months ago
  • Gravitational Collapse of Spongebob
    I believe it is using Numba which converts to machine code. https://numba.pydata.org/. - Source: Hacker News / about 1 year ago
  • Mojo🔥: Head -to-Head with Python and Numba
    Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code. - Source: dev.to / over 1 year ago
  • Mojo: The usability of Python with the performance of C
    Or you use numba [1]. Then you can use a subset of plain Python. [1] https://numba.pydata.org/. - Source: Hacker News / over 1 year ago
  • Is anyone using PyPy for real work?
    Simulations are, at least in my experience, numba’s [0] wheelhouse. [0]: https://numba.pydata.org/. - Source: Hacker News / almost 2 years ago
View more

Think Python mentions (9)

  • C949 help and Jay Wengrow's Guide to Data Structures
    This course actually starts with an introduction to Python. Since you don't have access yet, you can give Think Python a whirl - https://greenteapress.com/wp/think-python/ and for a more interactive experience, I really enjoyed this one - https://scrimba.com/learn/python. Source: about 2 years ago
  • Best place to learn and practice python?
    Start with Think Python or learn x in y..both are free resources and good for basic understanding and practise. Source: about 2 years ago
  • Good places to start learning python?
    This free book taught me Python many years ago https://greenteapress.com/wp/think-python/. Source: almost 3 years ago
  • Which books should I read to learn computer science with python language?
    In terms of learning the basics of Python programming, you can get the first edition of Think Python in PDF form for free. Source: over 3 years ago
  • Observations and thoughts from a long time crypto nerd
    Computer Science — For understanding software development. As for a programming language to learn, I recommend Python or Javascript. Try Crash Course's Computer Science videos, the free Think Python book, and/or Part 1 of The Modern JavaScript Tutorial. Source: over 3 years ago
View more

What are some alternatives?

When comparing Numba and Think Python, you can also consider the following products

Cython - Cython is a language that makes writing C extensions for the Python language as easy as Python...

Google's Python Class - Assorted educational materials provided by Google.

PyInstaller - PyInstaller is a program that freezes (packages) Python programs into stand-alone executables...

The New Boston video series - Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

cx_Freeze - cx_Freeze is a set of scripts and modules for freezing Python scripts into executables in much the...

A Byte of Python - A Byte of Python is a Python programming tutorial and learning book that teaches you how to program with the Python programming language.

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