Python IDLE - quick insights
January 29, 2021
Python IDLE - quick insights
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Python IDLE
We use Python IDLE for machine learning and AI projects at our organization. To perform analysis on health data and for decision making like whether a health center has to get an award or not based on their annual data reported, we perform some predictive analysis and make decisions.
- Firstly, I would say Python IDLE interface is user friendly.
- Easy to learn for the beginners.
- Syntax highlighting is nice features.
- Smart indent helps a lot.
- Debugging could be more advanced.
- Can have more data science packages.
- Output features can be better.
- In a short time, we were able to develop several ML models for various teams to make accurate decisions.
- Beginners can easily understand and adapt to GUI.
- We could automate several manual validation tasks and so could reduce human intervention.
I have used Visual Studio Code as well but, I would prefer to use Python IDLE as it helps to debug easily and is user-friendly which ultimately helps to perform the tasks quickly.
Do you think Python IDLE delivers good value for the price?
Yes
Are you happy with Python IDLE's feature set?
Yes
Did Python IDLE live up to sales and marketing promises?
Yes
Did implementation of Python IDLE go as expected?
I wasn't involved with the implementation phase
Would you buy Python IDLE again?
Yes