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NumPy VS Julia

Compare NumPy VS Julia and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Julia logo Julia

Julia is a sophisticated programming language designed especially for numerical computing with specializations in analysis and computational science. It is also efficient for web use, general programming, and can be used as a specification language.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Julia Landing page
    Landing page //
    2023-09-15

We recommend LibHunt Julia for discovery and comparisons of trending Julia 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.

Julia features and specs

  • High Performance
    Julia uses Just-In-Time (JIT) compilation which allows it to run at speeds close to those of statically compiled languages like C and Fortran.
  • Ease of Use
    Julia’s syntax is simple and intuitive, similar to that of Python, making it accessible for newcomers and convenient for rapid development.
  • Strong Support for Mathematical Computing
    Designed with numerical and scientific computing in mind, Julia includes powerful mathematical functions and supports arbitrary precision arithmetic.
  • Multiple Dispatch
    Julia's multiple dispatch feature allows functions to be defined across many combinations of argument types which can lead to more flexible and extensible code.
  • Rich Ecosystem
    Julia has a growing ecosystem of libraries and tools, supported by an active community, catering to a wide range of applications including data science, machine learning, and more.
  • Interoperability
    Julia can easily call C and Fortran libraries directly without the need for wrappers, and it can also interact with Python, R, and MATLAB code.
  • First-Class Support for Parallelism
    Julia natively supports parallel and distributed computing, enabling efficient handling of large-scale computations.

Possible disadvantages of Julia

  • Immature Ecosystem
    Despite rapid growth, Julia's ecosystem is still not as mature or extensive as those of older, more established languages like Python or R.
  • Long Compilation Time
    The JIT compilation can lead to longer initial startup times for scripts, which might be a drawback for users accustomed to instantaneous execution.
  • Breaking Changes
    The language is still evolving, and updates sometimes include breaking changes that can disrupt existing codebases.
  • Limited Learning Resources
    Compared to other popular languages, there are fewer tutorials, books, and community resources for learning Julia.
  • Smaller Community
    While growing, the Julia community is smaller compared to well-established languages, which might limit the availability of peer support and community-driven development.
  • Package Management Issues
    Users sometimes experience difficulties with package management and dependency issues, especially when using older packages or packages with many dependencies.
  • Less Enterprise Adoption
    Julia has not been widely adopted in the enterprise sector, which can affect its perceived stability and support for mission-critical applications.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Julia

Overall verdict

  • Julia is considered a good programming language, especially for specific applications.

Why this product is good

  • Ecosystem
    Julia has a growing ecosystem of packages and is used increasingly in research and academia.
  • Easy syntax
    Its syntax is easy to learn, especially for those familiar with other high-level programming languages.
  • Performance
    Julia is designed for high-performance numerical and scientific computing. It combines the ease of use of Python with the speed of C.
  • Interoperability
    It can interoperate with other languages like Python, C, and R, allowing users to leverage existing libraries.
  • Multiple dispatch
    It features multiple dispatch, which enables a more expressive style of programming.

Recommended for

    {"data_science" => "Data scientists who require a fast and flexible language for data manipulation and analysis.", "machine_learning" => "Developers looking to implement machine learning models that benefit from Julia's performance.", "numerical_analysis" => "Engineers and analysts conducting numerical analysis that demands high computational efficiency.", "scientific_computing" => "Researchers and scientists working on mathematical, statistical, and computational problems."}

NumPy videos

+ Add

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

Julia videos

+ Add

Julie & Julia Movie Review: Beyond The Trailer

More videos:

  • Review - 'Julie & Julia' review by Michael Phillips
  • Review - Julie & Julia movie review by Kenneth Turan

Category Popularity

0-100% (relative to NumPy and Julia)

NumPy

Julia

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
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Programming Language
0 0%
Programming Language
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Technical Computing
0 0%
Technical Computing
100% 100

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 Julia

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.
Source: www.analyticsvidhya.com
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

Julia Reviews

7 Best MATLAB alternatives for Linux
Julia is capable of direct calling C and Fortran libraries. You can create scripts in interactive mode (REPL) and by using its embedding API you can use Julia with other programming languages easily.
Source: cyanogenmods.org
15 data science tools to consider using in 2021
Julia 1.0 became available in 2018, nine years after work began on the language; the latest version is 1.6, released in March 2021. The documentation for Julia notes that, because its compiler differs from the interpreters in data science languages like Python and R, new users "may find that Julia's performance is unintuitive at first." But, it claims, "once you understand...
Source: searchbusinessanalytics.techtarget.com
10 Best MATLAB Alternatives [For Beginners and Professionals]
Talking about its capability, Julia can load multidimensional datasets and can perform various actions on them with total ease. Julia has over 13 million downloads as of today. It’s the proof of its flexibility
Source: thetechtop10.com
6 MATLAB Alternatives You Could Use
Strictly speaking, Julia is not a full “alternative” to MATLAB, in the sense that it’s essentially a high-level, dynamic programming language, intended for numerical computing. However, you can easily use it via the free Juno IDE. As for the language itself, it comes with a sophisticated compiler, with support for distributed parallel computing, and a large mathematical...
Source: beebom.com

Social recommendations and mentions

Julia might be a bit more popular than NumPy. We know about 127 links to it since March 2021 and only 121 links to NumPy. 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 (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward. - Source: dev.to / about 2 months ago
  • 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 / 8 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 / about 1 year 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 / about 1 year ago
View more

Julia mentions (127)

  • Ask HN: Let's learn more about each one, shall we?
    Mine is Julia, although I don't use diary. Nowadays I like SuperCollider. https://julialang.org. - Source: Hacker News / 3 months ago
  • Reflections on 2 years of CPython's JIT Compiler: The good, the bad, the ugly
    > I was active in the Python community in the 200x timeframe, and I daresay the common consensus is that language didn't matter and a sufficiently smart compiler/JIT/whatever would eventually make dynamic scripting languages as fast as C, so there was no reason to learn static languages rather than just waiting for this to happen. To be very pedantic, the problem is not that these are dynamic languages _per se_,... - Source: Hacker News / 3 months ago
  • Top Programming Languages for AI Development in 2025
    Julia: Exceptional Numerical Processing. - Source: dev.to / 5 months ago
  • Building a Secret Scanner in Julia: A GitLeaks Alternative
    To use Julia – one of the best programming languages, which is unfairly considered niche. Its applications go far beyond HPC. It’s perfectly suited for solving a wide range of problems. - Source: dev.to / 5 months ago
  • A data scientist's journey building a B2B data product with Julia and Pluto
    In this post, I’m exploring dev tools for data scientists, specifically Julia and Pluto.jl. I interviewed Mandar, a data scientist and software engineer, about his experience adopting Pluto, a reactive notebook environment similar to Jupyter notebooks. What’s different about Pluto is that it’s designed specifically for Julia, a programming language built for scientific computing and machine learning. - Source: dev.to / 7 months ago
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What are some alternatives?

When comparing NumPy and Julia, 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.

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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

GNU Octave - GNU Octave is a programming language for scientific computing.

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