Understanding inputMenu() from PyInputPlus with Examples

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Comprehensive Guide to inputMenu() from PyInputPlus

inputMenu() is a user-friendly function from the PyInputPlus module that allows you to present menu-like options to users. This function is excellent for building interactive command-line interfaces where choices need to be constrained.

🔹 What is inputMenu()?

inputMenu() displays a list of choices and lets the user select one. The function ensures that only valid options are accepted, reducing the need for manual input validation.

📌 Basic Example

This basic example presents a numbered list of programming languages:

import pyinputplus as pyip

lang = pyip.inputMenu(['Python', 'Java', 'C++'], numbered=True)
print(f"Language selected: {lang}")

Explanation: The numbered=True option tells inputMenu() to display the choices with numbers. The user can type either the name or the number.

Note: The inputMenu()has default prompt Please select one of the following:. We will learn how to customise it later in this post

Sample Interaction 1: Choosing Number


Please select one of the following:
1. Python
2. Java
3. C++
java
Language selected: Java

Sample Interaction 2: Choosing Name

Please select one of the following:
1. Python
2. Java
3. C++
Python
Language selected: Python

Note:It is not Case Sensitive So both 'Python' and 'python' have same output

Example:

Please select one of the following:
1. Python
2. Java
3. C++
python
Language selected: python

📌 Custom Prompt

import pyinputplus as pyip

lang = pyip.inputMenu(['Python', 'Java', 'C++'], numbered=True,prompt = "Enter Your Choice: \n")
print(f"Language selected: {lang}")

Sample Interaction :


Enter Your Choice:
1. Python
2. Java
3. C++
java
Language selected: Java

📝 Bulleted Menu

If you prefer a bulleted list instead of numbers, use numbered=False:

It cannot Support numbers(1,2...) as Input 

import pyinputplus as pyip
food = pyip.inputMenu(['Pizza', 'Burger', 'Pasta'], numbered=False)
print(f"Food chosen: {food}")
Please select one of the following:
* Python
* Java
* C++
Python
Language selected: Python

Explanation: This version shows options with * bullets. The user must type the name exactly unless caseSensitive is adjusted.

⛔ Limit Attempts and Provide Default Value

import pyinputplus as pyip

option = pyip.inputMenu(['Yes', 'No'], limit=2, default='No')
print(f"Final decision: {option}")

Explanation: The limit sets how many invalid attempts are allowed. If the user fails to provide a valid response in 2 tries, default='No' is returned automatically.

Sample Interaction:No Correct Input Is Given

Please select one of the following:
* Yes
* No
sorry
'sorry' is not a valid choice.
Please select one of the following:
* Yes
* No
idk
'idk' is not a valid choice.
Final decision: No

Sample Interaction:Correct Input Is Given

Please select one of the following:
* Yes
* No
yes
Final decision: Yes

🔤 Enable Case Sensitivity

Sometimes users may type options in different cases. You can handle this using caseSensitive=False.

import pyinputplus as pyip

choice = pyip.inputMenu(['Red', 'Blue'], caseSensitive=True)
print(f"Color: {choice}")

Explanation: This allows "red", "Red", or "RED" will be treated different.

📦 Real-World Example: Mode Selection

import pyinputplus as pyip

mode = pyip.inputMenu(['Basic', 'Advanced', 'Exit'], numbered=True, prompt="Select mode:\n")
print(f"Running in {mode} mode.")

Use Case: Great for terminal-based apps that switch between different functionalities or exit conditions.

🎯 Summary

  • Safe input: Users can only choose valid options.
  • Flexible display: Numbered or bulleted menus.
  • Custom prompts: Easily adjust user instructions.
  • Error handling: Use limit and default for better control.

Using inputMenu() makes your programs more user-friendly and error-resistant. Ideal for interactive Python scripts and beginner projects.

📚 Related Topics from PyInputPlus:

“In the world of code, Python is the language of simplicity, where logic meets creativity, and every line brings us closer to our goals.”— Only Python

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    Stage 1: Python & Programming Fundamentals 
    
----------------------------------------
1. Python & Programming Fundamentals
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1.1 Environment Setup
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1.2 Core Python Syntax
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1.5 File Handling & Exceptions
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🛠 Tools: VS Code, Git for version control, Jupyter/Colab
    Stage 2: Mathematics for Machine Learning 
    
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2. Mathematics for Machine Learning
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2.1 Linear Algebra
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2.2 Calculus
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2.4 Optimization Basics
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🛠 Tools / References: 
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    Stage 3: Data Handling & Preprocessing 
    
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3. Data Handling & Preprocessing
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3.1 NumPy Essentials
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3.2 Pandas for Tabular Data
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🛠 Tools: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn utilities
    Stage 4: Core Machine Learning 
    
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4. Core Machine Learning
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4.1 ML Concepts & Workflow
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4.4 Unsupervised Learning
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5. Deep Learning Foundations
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5.1 Neural Network Basics
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5.2 Deep Learning Frameworks
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5.4 Basic DL Projects
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6. Advanced Deep Learning & Architectures
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🛠 Tools: TensorFlow / PyTorch, Hugging Face Transformers library
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7. Natural Language Processing (NLP)
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7.1 Text Preprocessing & Representation
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    • BLEU, ROUGE, METEOR (for generation)
    • Accuracy, F1 for classification tasks

🛠 Tools: Hugging Face Transformers, spaCy, NLTK
    Stage 8: Computer Vision Advanced 
    
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8. Computer Vision (CV)
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8.1 Image Preprocessing & Augmentation
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8.2 Advanced CNN Architectures
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    • Instance segmentation concepts

8.3 Vision Transformers (ViT)
    • Applying transformer concepts to images
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8.4 Specialized CV Tasks
    • Face recognition pipelines
    • Video analysis basics: action recognition, object tracking
    • 3D vision intro (depth estimation)

🛠 Tools: OpenCV, TensorFlow/PyTorch, libraries like Detectron2 or YOLO implementations
    Stage 9: Reinforcement Learning & Advanced Topics 
    
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9. Reinforcement Learning & Advanced Topics
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9.1 Reinforcement Learning Foundations
    • Markov Decision Process (MDP)
    • Value functions, policy functions
    • Q-Learning, SARSA (tabular methods)
    • Exploration vs Exploitation

9.2 Deep Reinforcement Learning
    • Deep Q-Networks (DQN)
    • Policy Gradient Methods: REINFORCE, Actor-Critic
    • Advanced: A3C, PPO, DDPG overview

9.3 Other Advanced AI Topics
    • Graph Neural Networks (GNNs): node/graph embeddings (overview)
    • Time Series Forecasting with ML/DL: RNN/LSTM, Prophet intro
    • Bayesian Methods overview
    • AutoML and neural architecture search concepts
    • Federated Learning basics (privacy-aware training)
    • MLOps fundamentals:
        - Model versioning
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        - Monitoring models in production
        - Tools: MLflow, Kubeflow (intro)
    • Edge AI / TinyML overview (deploying models on devices)

🛠 Tools: RL libraries (Stable Baselines3), MLflow, Kubernetes intro, Docker
    Stage 10: Deployment, Production & MLOps 
    
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10. Deployment, Production & MLOps
----------------------------------------
10.1 Model Serving & APIs
    • REST API with Flask / FastAPI
    • gRPC basics (overview)
    • Dockerizing ML applications
    • Serving with TensorFlow Serving or TorchServe

10.2 Cloud Deployment
    • Deploy on AWS Sagemaker / GCP AI Platform / Azure ML (basic workflow)
    • Serverless deployments (AWS Lambda, Cloud Functions) for small models
    • CI/CD pipelines for ML: GitHub Actions or Jenkins integration

10.3 Monitoring & Maintenance
    • Logging model inputs/outputs
    • Drift detection (data/model drift)
    • Retraining pipelines (automated or scheduled)
    • Scaling considerations

10.4 MLOps Tools & Practices
    • Experiment tracking (MLflow, Weights & Biases)
    • Data versioning (DVC)
    • Model registry concepts
    • Infrastructure as Code (Terraform intro)

🛠 Tools: Docker, Kubernetes basics, CI/CD tools, cloud consoles
    Stage 11: Real-World Projects & Portfolio 
    
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11. Real-World Projects & Portfolio
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11.1 Project Ideas by Domain
    • Tabular Data: Predictive analytics (e.g., churn prediction)
    • NLP: Chatbot, summarizer, translation prototype
    • CV: Image classifier, object detector, image segmentation app
    • Time Series: Forecasting stock or weather data
    • RL: Simple game-playing agent
    • Generative: GAN art generation or style transfer demo

11.2 End-to-End Pipeline
    • Data collection & preprocessing
    • Model training & validation
    • Deployment as API or web app (Streamlit/Flask)
    • Monitoring & iteration
    • Documentation & README

11.3 Collaboration & Open Source
    • Participate in Kaggle competitions (beginner → intermediate)
    • Contribute to open-source ML projects
    • Write blog posts/tutorials documenting your projects

11.4 Soft Skills & Communication
    • Clear README, code comments
    • Presentation slides or videos of project demos
    • Networking: sharing work on LinkedIn, GitHub

🛠 Tools: GitHub Pages, Streamlit, Heroku/Netlify, Docker
    Stage 12: Ethics, Explainability & Continuous Learning 
    
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12. Ethics, Explainability & Continuous Learning
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12.1 AI Ethics & Responsible AI
    • Bias & Fairness: identifying and mitigating bias
    • Privacy concerns: GDPR, data protection best practices
    • Transparency: documenting data sources and model decisions

12.2 Explainable AI (XAI)
    • Model interpretability: SHAP, LIME (basic usage)
    • Interpreting black-box models vs inherently interpretable models
    • Communicating explanations to stakeholders

12.3 Continuous Learning & Staying Updated
    • Following research: arXiv alerts, ML conferences (NeurIPS, ICML, CVPR summaries)
    • Blogs, podcasts, newsletters (e.g., “The Batch” by deeplearning.ai)
    • Reading codebases of popular libraries, exploring new architectures
    • Community involvement: forums, study groups

12.4 Advanced Research Topics (Optional/For Aspirants)
    • Research paper reading workflow
    • Experimentation frameworks
    • Contributing to academic research or advanced industrial research

🛠 Tools: arXiv, Google Scholar alerts, RSS readers, community forums

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