Python IDLE--for basic stats analysis and model development
January 05, 2021

Python IDLE--for basic stats analysis and model development

Yaxian Xie | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with Python IDLE

I think it depends on users. I prefer Python IDLE for machine learning
model development. It's widely used across science teams for ML
solutions in production as it's well integrated with most production
systems and AWS tools. Also, it is used as the default tool for machine
learning university internally.



  • User friendly for basic stats analysis
  • Well-developed packages for ML development
  • Well integrated with production system
  • More user-friendly tutorials
  • Easier output format
  • Quick intro guide to new features
  • Positive on ML model in production
It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop.

Do you think Python IDLE delivers good value for the price?

Not sure

Are you happy with Python IDLE's feature set?

Yes

Did Python IDLE live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of Python IDLE go as expected?

I wasn't involved with the implementation phase

Would you buy Python IDLE again?

No

I prefer to use Python IDLE for basic stats analysis and model development. The codes can be directly integrated with production systems and AWS tools. I think Python IDLE could provide more user-friendly tutorials or quick intros for new features as well as ML-related functions or packages for ML model development.