MATLAB vs. Python for Data Visualization
Data visualization is a critical aspect of data analysis, and choosing the right tool is just as essential. Two popular tools used for data visualization are MATLAB and Python. Both are great tools, but which one is better? Let's compare MATLAB vs. Python for data visualization and see which one comes out on top (or bottom).
Data Visualization Capabilities
When it comes to data visualization, MATLAB is an industry-standard tool. It has a wide variety of graph types, from basic bar graphs to sophisticated 3D visualizations. Additionally, MATLAB provides a lot of customization options, allowing you to tweak every aspect of your graphs.
On the other hand, Python is relatively newer to the data visualization scene. However, with libraries such as Matplotlib and Seaborn, it has gained some popularity. Matplotlib is a highly customizable library, and Seaborn provides more aesthetically pleasing visualizations.
The winner:
While MATLAB has a wider variety of graph types, Python's visualization libraries provide more options for customization, making Python the winner in this category.
Ease of Use
MATLAB has a very user-friendly interface, making it easy for beginners to get started with data visualization. Additionally, MATLAB provides an easy-to-update environment, making it simple to create, edit and share your visualizations.
Python, on the other hand, provides a more flexible and open-source environment. However, the learning curve can be a bit steep for beginners. Moreover, setting up the necessary libraries can be a bit cumbersome, which makes it a non-trivial task.
The winner:
For beginners, MATLAB provides the best user interface and makes data visualization simple. In contrast, Python provides more flexibility, but some initial effort is needed in setting up the libraries. So, MATLAB is the winner of this category.
Price
Cost is a significant factor for many people when choosing a tool. MATLAB is not free, with licenses starting at $50 for personal use and going up to $5,000 for commercial use. However, MATLAB provides a 30-day free trial, which is a great option for those who want to try out the tool.
Python, on the other hand, is open source and free to use, making it an affordable option for many.
The winner:
Python is the clear winner in this category. With no cost for usage, Python lets you use it with ease and at no cost at all.
Performance
MATLAB is known to be a fast tool, optimized explicitly for mathematical and numerical calculations. With its built-in algorithms, MATLAB can perform complex and computationally intensive tasks with ease.
Python, on the other hand, is a general-purpose language and does not have the same level of optimization as MATLAB. This means that it may be a bit slower than MATLAB, especially when performing complex tasks.
The winner:
MATLAB comes out on top for this category. With its optimized programming and built-in functions, MATLAB is faster and more efficient than Python.
Conclusion
Both MATLAB and Python are great tools for data visualization, and the winner will ultimately depend on the use case. MATLAB is the industry standard with a wider variety of graph types and optimized for mathematical calculations. Additionally, MATLAB is user-friendly and easy for beginners. However, it is costly.
Python, on the other hand, is free to use, has more customization options, and creates aesthetic visualizations. It is a more flexible tool but can be a bit more challenging for beginners.
All in all, both tools are great, and the winner depends on the use case. We hope our comparison helps you make an informed decision about which tool to use.
References
- MATLAB. (2022). MATLAB. [online] Available at: https://www.mathworks.com/products/matlab.html [Accessed 10 Jun. 2022].
- Python.org. (2022). Welcome to Python.org. [online] Available at: https://www.python.org/ [Accessed 10 Jun. 2022].
- Journal of Open Research Software, 9(1), p.10. doi: https://doi.org/10.5334/jors.314. Nunez-Iglesias, J., Kennedy, R., Parnefjord Gustafsson, F. and Tritt, A. (2021) 'A brief survey and comparison of data visualisation tools and libraries for scientific data',, 9(1), p.10. doi: https://doi.org/10.5334/jors.314.