JMP vs Python

JMP vs Python | Popularity, Salary, Pricing, Features, and Applications

JMP and Python are both powerful tools for data analysis, statistics, machine learning, and data visualization but they have some key differences that make them better suited for different types of tasks.

If you want a free, highly customizable tool to use for data analysis, data visualization, machine learning, web scraping, automation tools, etc. you should use Python.

On the other hand, if you want a powerful, highly customizable, easy-to-use software for statistical analysis, machine learning, data analysis, interactive visualization, and more, you should use JMP.

JMP is a statistical software for insight-driven improvements, it is used in applications such as quality control and six sigma.

JMP is also popular for the design of experiments, engineering, pharmaceuticals, and research in science and social sciences.

Python on the other hand is a general-purpose programming language that focuses on simplicity and readability.

Rather than building all of its functionality into its core, Python was designed to be highly extensible through modules.

Python is commonly used for web development, GUI development, system administration, software development, automation, testing tools, machine learning, data analytics, data visualization, and data science.

Python stats

There are many factors to consider when learning a new technology or programming language.

Popularity, opportunities, types of projects, salaries, resources, learning curve, etc. are some of the factors that many people consider when choosing to learn a programming language or software package.


If you want to learn a programming language or a statistical software solely for its popularity among users, you should learn Python over JMP.

As of April 2023, Python is ranked as the number one most popular programming language on the TIOBE index.

Further, according to a  Stack Overflow survey of 2022, Python is the 4th most commonly used programming language, it is used by 43.51% of professional developers.

JMP is not a programming language and is not included in many rankings that include Python.

It is worth noting that JMP has a scripting programming language called JSP but it is used within the JMP package.

Python still remains generally more popular than JMP in many areas where the usage intersects.


Some software packages can be learned more easily than others.

If you want a package or language that you can easily pick up to work on data analysis and statistical projects, you should use JMP over Python.

JMP is generally easier to use than Python, but Python is more versatile and customizable.

JMP is very easy to use, it is straightforward and you can quickly get the results that you want. It has a point-and-click interface that makes it easy even for complete beginners.

Unlike JMP, Python does not have a user-friendly interface, and you must have some programming skills in order to use it effectively.

It is worth noting that Python is one of the easiest programming languages to learn. Python has an English-like, easy-to-understand syntax that makes it very easy to write code.

However, the Python community is large and active, and there are many resources available to help users learn Python, such as tutorials, forums, and documentation.


JMP comes with a scripting language called JSL, an interface for R programming, and an add-in for Excel.

JSL makes automation and customization in JMP easy and straightforward. JSL also helps to extend the functionality of JMP.

You can use JSL scripts to perform analyses and visualizations not available in the point-and-click interface

Power users can develop scripts to extend JMP’s functionality and automate a regularly scheduled analysis in production settings.

And if for some reason you do not want to learn JSL, JMP can easily write the scripts for you.


Python can be integrated into JMP to help Python developers work easily with the JMP software.

JMP allows code written in Python, R, MATLAB, and SAS to be executed in JSL, this integration makes JMP more powerful, robust, and efficient.

JMP is used by many companies such as Nike, Micron, Seagate, Medtronic, Intel, Symrise, NXP, Mulata Finland, Novozymes, DuPont, Dairygold, IQE, NVIDIA, Roche, Siemens Healthineers, Imperial College London, Lufthansa, McDonald’s, and many others.

JMP is a good choice for users who want a user-friendly interface and a wide range of built-in statistical tools, and for those who do not have programming skills.

Python has a large standard library that provides tools and features suited for many applications.

It supports many standard protocols and formats like HTTP, MIME, and many others.

It also includes modules that can be used for relational database connection, unit testing, and manipulation of regular expressions.

Python also has a wide range of packages for data analysis, and users can create custom functions and scripts to perform specific tasks.

Python is also powerful for data visualization, thanks to libraries like Matplotlib, Seaborn, Plotly, and many others.

It has many amazing features, libraries, and packages that make it a popular choice for web development, scientific and numeric applications, system administration, GUI development, and more.

Python powers some of the complex applications developed by companies like Google, NASA, IBM, Microsoft, Meta, Cisco, Duolingo, Pinterest, Reddit, Pixar, Netflix, and many others.

Also read Can I Learn Machine Learning Without Python


Python programming language is free and open source. Many Python libraries are free to use for personal and commercial purposes.

On the other hand, JMP is not a free software, it is a proprietary software package that requires a purchase.

JMP is available in many versions such as JMP, JMP Pro, JMP Live, and JMP Clinical.

Each of these products has its own features and pricing. For example, JMP starts at $1,200 per year.

Because JMP is not free, many users prefer free-to-use, open-source alternatives such as R and Python with many libraries related to data analysis like Pandas, NumPy, ggplot2, and many others.


Both JMP and Python have their own strengths and weaknesses, and the choice between them will depend on the specific needs and goals that you have.

If you are looking for a tool that is easy to use and has a wide range of built-in statistical tools, you should use JMP.

If you have programming skills and want a tool that is highly customizable for data analytics, data visualization, machine learning, web scraping, automation tools, and more, you should use Python.