Why Some Say ‘Don’t Learn Python’: Pros, Cons & Who It’s For

1. Introduction

2. Characteristics of People Who Should Skip Python

People Who Are Not Suited for Python

1. People Who Need High-Performance Development

• Suitable alternatives: C++, Rust, Go

2. People Who Primarily Focus on Mobile App Development

• Suitable alternatives: Swift (iOS), Kotlin (Android), Flutter (Dart)

3. People Who Need Strict Type Checking in Their Systems

• Suitable alternatives: Java, C#, TypeScript

4. People Who Want to Develop Embedded Systems or IoT Devices

• Suitable alternatives: C, Rust

5. People Who Prefer Languages with Greater Code Flexibility

• Suitable alternatives: C++, JavaScript, PHP

People Who Are Suited for Python

1. People Who Want to Do Data Analysis, Machine Learning, or AI Development

2. People Who Want to Start Web Development Easily

3. Beginners Who Want to Learn Programming with a Simple Syntax

4. People Who Want to Leverage It in Their Job

Summary

• High-performance systems that need fast processing
• Want to develop mobile applications
• Prefer statically typed languages
• Want to develop embedded systems
• Value code flexibility

• Data analysis and AI development interest
• Want to develop web applications
• Beginners who want to learn programming
• Want to use Python in their job

3. Basic Overview of Python

What is Python?

• High readability and simple syntax
• Interpreted language (executes code line by line)
• Open source with an active community
• Rich libraries and frameworks are available
• Cross‑platform support, running on various OSes (Windows, macOS, Linux)

Main Uses of Python

1. Data Analysis & AI Development

2. Web Development

3. Automation & Scraping

• Web scraping (data collection)
• Automated processing of Excel and CSV data
• Automated email sending

4. Game Development

5. Networking & Cybersecurity

6. Embedded Systems (with limitations)

Python Adoption Examples

• Google: Uses Python for parts of YouTube and internal tools
• Instagram: Builds its web services using the Django framework
• Dropbox: Much of its server‑side is implemented in Python
• NASA: Uses Python for data analysis and simulations
• Netflix: Uses Python for its recommendation system (recommendation algorithms)

Conclusion

4. Main Reasons Why People Say “Don’t Use Python”

1. Slow Execution Speed

• Systems that require real-time processing
• Large-scale data processing
• Game or VR development

2. Potential Errors from Dynamic Typing

x = "10"   # string
y = 5
print(x + y)  # Error (type mismatch)

• Large-scale projects
• Systems that require robustness

3. Not Suited for Mobile App Development

• Lack of an official development environment
• Performance issues
• Limited support

4. Not Suitable for Embedded Systems or IoT

• High memory usage
• Poor at real-time processing
• Difficult low-level control

5. Enforced Indentation Can Be Stressful

def greet():
    print("Hello")
        print("World")  # ← error if indentation is off

• Inconvenient for people who want to use {}
• Causes trouble in team development

Summary

• Slow execution speed, making it unsuitable for high-performance processing
• Dynamic typing leads to more bugs in large projects
• Not mainstream for mobile app development
• Unsuitable for embedded systems or real-time control
• Enforced indentation doesn’t suit those who dislike it

• Data analysis, machine learning, AI development
• Web development (Django, Flask)
• Automation of tasks (scraping, data processing)

5. Benefits of Python

1. Readable, Simple Syntax for Beginners

def greet():
    print("Hello, World!")

greet()

• Programming beginners
• People who want to write concise code
• People who prioritize maintainability

2. Rich Libraries and Frameworks

• Data analysis (NumPy, Pandas, Matplotlib)
• Machine learning & AI (TensorFlow, PyTorch, scikit-learn)
• Web development (Django, Flask, FastAPI)

import pandas as pd

data = {'Name': ['Tanaka', 'Sato', 'Suzuki'], 'Age': [25, 30, 28]}
df = pd.DataFrame(data)

print(df)

3. Overwhelming Share in Data Science & AI Development

• Comprehensive machine learning libraries (TensorFlow, PyTorch, scikit-learn)
• Strong at numerical computation and statistical processing (NumPy, Pandas)
• Easy graph and data visualization (Matplotlib, Seaborn)

4. Runs on Any OS or Environment

• The same code runs on both Windows and Mac
• Virtual environments (venv, Anaconda) make switching environments easy
• Can also run in cloud environments (Google Colab, AWS Lambda)

5. Large Community and Comprehensive Documentation

• Official documentation is easy to understand
• You can ask questions on technical forums like Stack Overflow
• Abundant resources in Japanese (Qiita, Zenn, etc.)

Summary

1. Simple, beginner‑friendly syntax
2. Powerful libraries and frameworks
3. Overwhelming share in data analysis & AI development
4. Cross‑platform operation
5. Robust learning environment and community

6. Who Is Suited for Python and Who Isn’t

People Who Are Suited for Python

1. Those Who Want to Do Data Analysis, Machine Learning, or AI Development

• Data Science (statistical analysis, data analytics)
• Machine Learning & Deep Learning (image recognition, natural language processing)
• Finance & Marketing Analytics (data‑driven decision making)

2. Those Who Want to Develop Web Applications

• Simple web app development (Flask)
• Large‑scale web service development (Django)
• REST API development (FastAPI)

3. Those Who Want to Automate Tasks or Scrape Data

• Web scraping (Selenium, BeautifulSoup)
• Data processing & report generation (Pandas, openpyxl)
• Automated email sending (smtplib)
• File organization & manipulation (os, shutil)

4. Beginners Who Want to Learn Programming with a Simple Syntax

• Simple syntax → concise and readable code
• Clear error messages → easy to debug
• Abundant learning resources → YouTube, Udemy, official documentation, etc.

5. Professionals Who Want to Leverage IT Skills in Their Careers

• Data Scientist
• Machine Learning Engineer
• Web Engineer
• IT Consultant
• Systems Developer

People Who Are Not Suited for Python

1. Those Who Need to Build High‑Performance Systems

• Game engine development
• High‑frequency trading (HFT) and other finance systems
• Real‑time control of IoT devices

• C++ (games, high‑performance processing)
• Rust (safety and speed)
• Go (high‑performance server‑side)

2. Those Who Want to Develop Mobile Apps

7. FAQ (Frequently Asked Questions)

Q1. Is Python suitable for beginners?

• Simple syntax (uses indentation instead of braces {} to define structure)
• Readable and easy to understand
• Clear error messages
• Abundant tutorials and learning resources

Q2. How much of an issue is Python’s slower execution speed?

• Game development (requires real‑time rendering)
• High‑frequency trading (financial systems requiring millisecond‑level processing)
• Real‑time control of IoT devices

• Cython (combines Python with C for speed)
• Numba (uses JIT compilation to boost performance)
• Implement performance‑critical parts in C++ or Rust and call them from Python

Q3. Can you develop mobile apps with Python?

• Weak official support
• Low performance
• Scarce information and rarely used in real‑world development

• iOS → Swift
• Android → Kotlin
• Cross‑platform → Flutter (Dart)

Q4. Does Python have a promising future?

• Growth alongside AI and machine learning advancements
• Dominant share in the data science field
• Increasing use in web development (Django, Flask)
• Used for enterprise automation (RPA)

Q5. What are the differences between Python and other programming languages?

Summary

8. Summary

Overall Summary of Python’s Pros and Cons

1. Simple, beginner-friendly syntax
2. Powerful libraries and frameworks
3. Dominant share in data analysis and AI development
4. Cross‑platform compatibility
5. Rich learning resources and community support

1. Slower execution speed
2. Potential errors due to dynamic typing
3. Not well suited for mobile app development
4. Unsuitable for embedded systems and real‑time processing
5. Mandatory indentation can be stressful for some

Characteristics of People Who Should Learn Python

• Want to do data analysis or AI development
• Want to build web applications
• Want to automate tasks
• Want to learn a beginner-friendly programming language
• Want to leverage IT skills in your job

• Need high‑performance processing for development
• Focus primarily on mobile app development
• Work on large‑scale projects that require strict type safety
• Prioritize code flexibility

Conclusion: Should You Learn Python?

If You Decide to Learn Python, What Should You Do Next?

1. Learn the fundamentals of Python (e.g., Progate, Udemy, YouTube, etc.)
2. Write simple programs (practice basic syntax)
3. Take on practical projects (web scraping, data analysis, etc.)
4. Dive deeper into Python specialties (data analysis, machine learning, web development, etc.)

Final Summary