analytics implemented with pandas are great performance debugging tools
September 09, 2025

analytics implemented with pandas are great performance debugging tools

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with pandas

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
  • 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
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.
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.

Do you think pandas delivers good value for the price?

Yes

Are you happy with pandas's feature set?

Yes

Did pandas live up to sales and marketing promises?

Yes

Did implementation of pandas go as expected?

Yes

Would you buy pandas again?

Yes

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.

pandas Feature Ratings

8.5
Connect to Multiple Data Sources
8
Extend Existing Data Sources
8
Automatic Data Format Detection
10
MDM Integration
8
Not Rated
Visualization
Not Rated
Interactive Data Analysis
Not Rated
Not Rated
Interactive Data Cleaning and Enrichment
Not Rated
Data Transformations
Not Rated
Data Encryption
Not Rated
Built-in Processors
Not Rated
Not Rated
Multiple Model Development Languages and Tools
Not Rated
Automated Machine Learning
Not Rated
Single platform for multiple model development
Not Rated
Self-Service Model Delivery
Not Rated
Not Rated
Flexible Model Publishing Options
Not Rated
Security, Governance, and Cost Controls
Not Rated

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