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JMP VS NumPy

Compare JMP VS NumPy and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

JMP logo JMP

JMP is a data representation tool that empowers the engineers, mathematicians and scientists to explore the any of data visually.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • JMP Landing page
    Landing page //
    2023-04-12
  • NumPy Landing page
    Landing page //
    2023-05-13

JMP features and specs

  • User-friendly Interface
    JMP offers a drag-and-drop interface that is intuitive and easy to navigate, making it accessible for both beginners and advanced users.
  • Comprehensive Data Visualization
    The software provides robust tools for data visualization, enabling users to create a wide variety of charts, graphs, and plots that can help in understanding complex data sets.
  • Advanced Statistical Analysis
    JMP includes a wide range of advanced statistical techniques, such as regression analysis, ANOVA, and multivariate methods, which are suitable for rigorous data analysis.
  • Integration with R and Python
    The software supports integration with R and Python, allowing users to leverage additional functionalities not available in JMP alone.
  • Interactive Data Exploration
    JMP enables interactive data exploration, allowing users to dynamically manipulate data sets and instantly see the results of their changes.
  • Quality Control Features
    The software includes numerous quality control tools, making it ideal for industries where maintaining high standards is critical.

Possible disadvantages of JMP

  • Cost
    JMP is a commercial software with a relatively high price point, which may be a barrier for small businesses or individual users.
  • Learning Curve
    Despite its user-friendly interface, JMP has a steep learning curve for those unfamiliar with statistical analysis and data visualization techniques.
  • Resource Intensive
    The software can be resource-intensive, requiring significant computational power and memory, especially when handling large datasets.
  • Limited Collaboration Features
    JMP lacks extensive features for real-time collaboration compared to some of the more modern data science platforms.
  • Package Ecosystem
    While JMP is powerful, its ecosystem of add-ons and packages is not as extensive as that of R or Python, which might limit its utility for some specialized tasks.
  • OS Compatibility
    JMP is primarily designed for Windows and MacOS. Users on other operating systems might face compatibility issues or may need to use workarounds.

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.

JMP videos

+ Add

Review Of The UAD Marshall JMP 2203 Plug-in From Universal Audio

More videos:

  • Demo - Marshall JMP-1 - In Depth Demo by Leon Todd
  • Review - Marshall JMP 1 Watt Combo - Blues Harmonica Amp Review

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

Category Popularity

0-100% (relative to JMP and NumPy)

JMP

NumPy

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
44 44%
Data Dashboard
56% 56
Data Science Tools
0 0%
Data Science Tools
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 JMP and NumPy

JMP Reviews

25 Best Statistical Analysis Software
JMP is a top-rated tool for data exploration and analysis, delivering dynamic data visualization and an extensive selection of statistical tools to empower users to make well-informed decisions based on their findings.
Source: www.scijournal.org
Top 10 Free Statistical Analysis Software 2023
5. JMP Scripting Language (JSL) scripting is supported for automation, customisation, and expanding functionality.
Source: fellowshipbard.com
9 Best Analysis Software for PC 2023
JMP is a software analysis software that can perform data manipulations and mining. It is a perfect alternative to MS Excel, which is famous for visualization. JMP is available on a free-trial and premium-based plan. The trial plan allows the user to interact with the software before deciding whether to purchase it.
Source: pdf.wps.com

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

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. 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.

JMP mentions (0)

We have not tracked any mentions of JMP yet. Tracking of JMP recommendations started around Mar 2021.

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 / 4 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 / 8 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
View more

What are some alternatives?

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

Stata - Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

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

GraphPad Prism - Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

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

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

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

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