search
HomeBackend DevelopmentPython Tutorialommon Refactors in Python for Beginners

ommon Refactors in Python for Beginners

Refactoring helps make your code cleaner and more efficient. Here are five common refactors for beginners in Python.

I. Simplifying Boolean Expressions

A common pattern is using an if-else block just to return True or False. For example:

if condition:
    return True
else:
    return False

Refactor it to:

return condition

The condition itself is already a Boolean expression, so the if-else block is unnecessary. By directly returning the condition, the code becomes shorter and more readable. This is a simple but effective way to improve clarity without changing the functionality.

II. List Comprehensions Instead of for / if

Beginners often use for loops with if statements to build lists. For example:

result = []
for item in items:
    if condition(item):
        result.append(item)

Refactor it to a list comprehension:

result = [item for item in items if condition(item)]

List comprehensions provide a more concise way to construct lists. They are also typically faster than equivalent for loops because they are optimized internally by Python. This approach is easier to read as well, especially for simple list creation tasks.

III. Avoid Repeating Calculations

If you call the same function multiple times in a loop, store the result in a variable. For example:

for item in items:
    if len(item) > 5:
        result.append(item)
...

Refactor it to:

for item in items:
    len = len(item)
    if len > 5:
        result.append(item)
...

Imagine if this condition held in multiple elif or nested if statements. Here, the len(item) is called twice for each iteration, which can be inefficient, especially for large lists. Storing the result of len(item) in a variable (len) eliminates the repeated calculation, improving performance and making the code cleaner. This is a basic example.

IV. Replace Loops with map and filter

Instead of writing explicit loops, use Python’s built-in functions like map() and filter(), which can be more efficient and concise. For example, to double each item in a list:

result = []
for item in items:
    result.append(item * 2)

Refactor it to:

result = list(map(lambda x: x * 2, items))

Or to filter items greater than 5:

result = []
for item in items:
    if item > 5:
        result.append(item)

Refactor it to:

result = list(filter(lambda x: x > 5, items))

Both map() and filter() take functions as arguments, so we can use lambda to define small anonymous functions. The lambda function is a concise way to define simple operations. For example, lambda x: x * 2 creates a function that multiplies x by 2. The benefit of map() and filter() is that they are often more efficient than using a for loop and are typically more readable. One could also use list comprehensions (see above).

V Combine Multiple if Statements

When checking multiple conditions, combining them with logical operators (and, or) can simplify your code. For example:

if a > 0:
    if b > 0:
        result = a + b

Refactor it to:

if condition:
    return True
else:
    return False

This reduces unnecessary nesting and makes the code easier to read and maintain. Combining conditions into one if statement makes the flow of logic clearer and eliminates redundancy.

Conclusion

Refactoring is about making your code shorter, clearer, and more efficient without changing what it does. By simplifying Boolean expressions, using list comprehensions, avoiding repeated calculations, leveraging built-in functions like map() and filter(), and merging conditions, you can make your code DRY. Using lambda allows you to define small functions in a single line, keeping the code neat and fast. These practices not only improve performance but also enhance readability, which is crucial for maintaining code in the long run.

Further reading:

https://www.w3schools.com/python/python_lambda.asp

https://www.w3schools.com/python/ref_func_filter.asp

https://www.w3schools.com/python/ref_func_map.asp

The above is the detailed content of ommon Refactors in Python for Beginners. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment