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NumPy

1.8K
696
+ 1
10
R Language

2.9K
1.7K
+ 1
399
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NumPy vs R: What are the differences?

NumPy: Fundamental package for scientific computing with Python. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases; R: A language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

NumPy belongs to "Data Science Tools" category of the tech stack, while R can be primarily classified under "Languages".

NumPy is an open source tool with 11.1K GitHub stars and 3.67K GitHub forks. Here's a link to NumPy's open source repository on GitHub.

Instacart, Zalando, and Thumbtack are some of the popular companies that use R, whereas NumPy is used by Instacart, Suggestic, and Twilio SendGrid. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to NumPy, which is listed in 63 company stacks and 34 developer stacks.

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Pros of NumPy
Pros of R Language
  • 8
    Great for data analysis
  • 2
    Faster than list
  • 82
    Data analysis
  • 61
    Graphics and data visualization
  • 53
    Free
  • 44
    Great community
  • 37
    Flexible statistical analysis toolkit
  • 26
    Easy packages setup
  • 26
    Access to powerful, cutting-edge analytics
  • 18
    Interactive
  • 12
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Shiny interactive plots
  • 5
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 2
    Machine Learning
  • 2
    Graphical visualization

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Cons of NumPy
Cons of R Language
    Be the first to leave a con
    • 6
      Very messy syntax
    • 4
      Tables must fit in RAM
    • 3
      Arrays indices start with 1
    • 2
      Messy syntax for string concatenation
    • 2
      No push command for vectors/lists
    • 1
      Messy character encoding
    • 0
      Poor syntax for classes
    • 0
      Messy syntax for array/vector combination

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    - No public GitHub repository available -

    What is NumPy?

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

    What is R Language?

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

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    Jobs that mention NumPy and R Language as a desired skillset
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United Kingdom of Great Britain and Northern Ireland England London
    CBRE
    Philippines National Capital Region Makati City
    What companies use NumPy?
    What companies use R Language?
    See which teams inside your own company are using NumPy or R Language.
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    What tools integrate with NumPy?
    What tools integrate with R Language?

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    7
    2324
    GitHubPythonReact+42
    48
    39987
    GitHubGitDocker+34
    29
    41685
    What are some alternatives to NumPy and R Language?
    Pandas
    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
    MATLAB
    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
    SciPy
    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
    Panda
    Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    See all alternatives