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NumPy VS Clojure

Compare NumPy VS Clojure and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Clojure logo Clojure

Clojure is a dynamic, general-purpose programming language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Clojure Landing page
    Landing page //
    2023-09-19

We recommend LibHunt Clojure for discovery and comparisons of trending Clojure projects.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Clojure features and specs

  • Functional Programming Paradigm
    Clojure emphasizes immutability and first-class functions, which can lead to more predictable and maintainable code.
  • Interoperability with Java
    Clojure runs on the JVM, allowing seamless integration with the vast ecosystem of Java libraries and tools.
  • REPL Driven Development
    Clojure's Read-Eval-Print Loop (REPL) allows for interactive programming, making it easier to test and debug code in real time.
  • Concise Syntax
    Clojure's syntax is minimalistic and expressive, which can lead to more concise and readable code.
  • Concurrency Support
    Clojure provides strong support for concurrent programming with features like Software Transactional Memory (STM) and immutable data structures.

Possible disadvantages of Clojure

  • Steep Learning Curve
    The functional programming paradigm and Lisp-like syntax can be challenging for newcomers, particularly those from imperative programming backgrounds.
  • Performance Overhead
    Clojure's emphasis on immutability can introduce performance overhead compared to languages that use mutable data structures.
  • Limited Tooling
    While improving, the ecosystem for Clojure is not as mature as for some other mainstream languages, which can pose challenges in finding robust development and debugging tools.
  • Less Mainstream
    Clojure is not as commonly used as languages like Python or Java, which can make it harder to find experienced developers or community support.
  • Verbose Error Messages
    Error messages in Clojure can sometimes be verbose and difficult to understand, which can complicate the debugging process.

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Clojure videos

What is the business value of Clojure?

More videos:

  • Review - Blog in Clojure Code Review
  • Review - Clojure Web App Code Review

Category Popularity

0-100% (relative to NumPy and Clojure)
Data Science And Machine Learning
Programming Language
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100% 100
Data Science Tools
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OOP
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User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Clojure

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Clojure Reviews

We have no reviews of Clojure yet.
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Social recommendations and mentions

Based on our record, NumPy should be more popular than Clojure. It has been mentiond 119 times since March 2021. 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.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
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Clojure mentions (39)

  • Create a Server Driven CLI from your REST API
    Another project of mine Bob can be seen as an example of spec-first design. All its tooling follow that idea and its CLI inspired Climate. A lot of Bob uses Clojure a language that I cherish and who's ideas make me think better in every other place too. - Source: dev.to / 3 months ago
  • Scheming About Clojure
    Clojure is a LISP for the Java Virtual Machine (JVM). As a schemer, I wondered if I should give Clojure a go professionally. After all, I enjoy Rich Hickey's talks and even Uncle Bob is a Clojure fan. So I considered strength and weaknesses from my point of view:. - Source: dev.to / 6 months ago
  • Moving your bugs forward in time
    ‍For the rest of this post I’ll list off some more tactical examples of things that you can do towards this goal. Savvy readers will note that these are not novel ideas of my own, and in fact a lot of the things on this list are popular core features in modern languages such as Kotlin, Rust, and Clojure. Kotlin, in particular, has done an amazing job of emphasizing these best practices while still being an... - Source: dev.to / 12 months ago
  • Let's write a simple microservice in Clojure
    This article will explain how to write a simple service in Clojure. The sweet spot of making applications in Clojure is that you can expressively use an entire rich Java ecosystem. Less code, less boilerplate: it is possible to achieve more with less. In this example, I use most of the libraries from the Java world; everything else is a thin Clojure wrapper around Java libraries. - Source: dev.to / about 1 year ago
  • A new F# compiler feature: graph-based type-checking
    I have a tangential question that is related to this cool new feature. Warning: the question I ask comes from a part of my brain that is currently melted due to heavy thinking. Context: I write a fair amount of Clojure, and in Lisps the code itself is a tree. Just like this F# parallel graph type-checker. In Lisps, one would use Macros to perform compile-time computation to accomplish something like this, I think.... - Source: Hacker News / over 1 year ago
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What are some alternatives?

When comparing NumPy and Clojure, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Elixir - Dynamic, functional language designed for building scalable and maintainable applications

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Python - Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.

OpenCV - OpenCV is the world's biggest computer vision library

Rust - A safe, concurrent, practical language