pandas vs. Wolfram Mathematica

Pandas are great for quick and relatively simple analytics and visualizations

Pandas work well for exploratory ad-hoc analytic work

But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.

Incentivized

We are the judgement that Wolfram Mathematica is despite many critics based on the paradigms selected a mark in the fields of the markets for computations of all kind. Wolfram Mathematica is even a choice in fields where other bolide systems reign most of the market. Wolfram Mathematica offers rich flexibility and internally standardizes the right methodologies for his user community. Wolfram Mathematica is not cheap and in need of a hard an long learner journey. That makes it weak in comparison with of-the-shelf-solution packages or even other programming languages. But for systematization of methods Wolfram Mathematica is far in front of almost all the other. Scientist and interested people are able to develop themself further and Wolfram Matheamatica users are a human variant for themself. The reach out for modern mathematics based science is deep and a unique unified framework makes the whole field of mathematics accessable comparable to the brain of Albert Einstein. The paradigms incorporated are the most efficients and consist in assembly on the market. The mathematics is covering and fullfills not just education requirements but the demands and needs of experts.

Mathematica is incompatible with other systems for mCAx and therefore the borders between the systems are hard to overcome. Wolfram Mathematica should be consider one of the more open systems because other code can be imported and run but on the export side it is rathe incompatible by design purposes. A better standard for all that might solve the crisis but there is none in sight. Selection of knowledge of what works will be in the future even more focussed and general system might be one the lossy side. Knowledge of esthetics of what will be in the highest demand in necessary and Wolfram is not a leader in this field of science. Mathematics leves from gathering problems from application fields and less from the glory of itself and the formalization of this.

• It is easy to do statistical analysis
• It is easy to clean the data
• It is easy to produce graphs and charts to visualize

Incentivized

• Doing the analysis is very good, plotting the data, getting the insight from data using this is easy and output looks pretty good
• Solving mathematical problems, with detailed solutions step by step (mainly calculus problems)
• You can also query this knowledge engine in simple English. It has ability to interpret that and will give you answer accordingly
• Nowadays, it provides API to use, so you can do image processing, video/audio using this

Incentivized

• There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
• Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
• In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case

Incentivized

• Should include more libraries and functions.
• Should include more functions that can be used in Machine Learning.
• Should include more functions that can be used in Data Science.

Incentivized

Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.

It is lightweight, flexible and easy to implement.

Incentivized

Wolfram Mathematica is a nice software package. It has very nice features and easy to install and use in your machine. Besides this, there is a nice support from Wolfram. They come to the university frequently to give seminars in Mathematica. I think this is the best thing they are doing. That is very helpful for graduate and undergraduate students who are using Mathematica in their research.

Incentivized

All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.

Incentivized

The ability to manipulate algebraic expressions, nested lists, and data structures in Mathematica was unequalled when I first did the comparison. Since then, I've stuck with Mathematica mostly because it's "the tool I know."

Incentivized

• Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
• Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers

Incentivized

• Mathematica is our "go to" environment for developing solutions for our clients, so I suppose you could say that it is solely responsible for our revenues. On occasion we do use other platforms but Mathematica is a core component of our offer to clients.

Incentivized