analytics implemented with pandas are great performance debugging tools
Rating: 10 out of 10
IncentivizedUse Cases and Deployment Scope
We use pandas in our analytics framework to calculate and analyze performance metrics of the operational data. It is mostly about response time for various APIs and resource consumption.
Pros
- It is easy to do statistical analysis
- It is easy to clean the data
- It is easy to produce graphs and charts to visualize
Cons
- 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
Likelihood to Recommend
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.
Vetted Review
8 years of experience