Table of Contents
Learn to code today.Why you should use coding best practices6 coding best practices for beginner programmers1. Code indentation2. Meaningful naming3. Comments that add context4. Don’t repeat yourself5. Low coupling and high cohesion6. Consider your contextLearn to code today.Coding best practices in action: Refactoring a simple applicationScenario: Student Grade CalculatorOriginal implementationProblems with the original codeDifficult to readDifficult to debugDifficult to extendIncreased risk of bugsRefactored implementationWhich best practices improved the code?IndentationMeaningful namingCommentsDRY principleHigh cohesionLow couplingHow the project scalesWhat professional developers learn from this exampleWrapping up and next stepsContinue learning about coding
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6 coding best practices for beginner programmers

6 coding best practices for beginner programmers

10 mins read
Jun 10, 2026
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Code is written once. Then it’s read, debugged, refactored, and scaled constantly. How you learn code and write it will help you maintain and scale the application later down the road. We’re frequently under the pressure of deadlines in software development. Regardless, it’s important to use coding best practices to help ensure better quality code for yourself and for anyone who may have to extend that code in the future.

Today, we’ll discuss the importance of using coding best practices, and six best practices you should know as a beginner programmer.

Learn to code today.#

Try one of our courses on programming fundamentals:

Why you should use coding best practices#

As a coder, developing a coding style that’s mindful of these best practices will make it easier to extend and debug the code in the future. Even if you’re no longer working with that codebase, the legacy of your work will affect the community of developers who work with that code in the future.

Coding best practices help ensure code quality and codebase health. Using good practices when writing code will support the characteristics of good code, that is: Maintainability, scalability, readability, and portability.

Code is written once and read thousands of times.

Some good coding practices, such as commenting, don’t affect the functionality of a program. However, they do impact the experience for the humans who’ll be reading and maintaining that code. You may be able to get your program to execute, but if you don’t implement best practices, a quickly done task can backfire by creating confusion and problems in the future. Writing good code now will help ease the processes of code reviews, unit tests, and refactoring.

6 coding best practices for beginner programmers#

1. Code indentation#

Proper indentation is the most important thing you can do to ensure that your code is readable and easy to understand. There are many different indentation styles, such as K&R, OTBS, and Stroustrup. These styles all advise that we add spaces and new lines in our code, so that each block of code is readily identifiable.

Consider the following code example:

C++
#include <stdio.h>
int find(int A[], int size) { int ret = -1; for(int i = 0; i < size; i++) {
if(A[i] > ret){ ret = A[i];
}
  }
   return ret;}
int main() { int A[]={1, 4, 7, 13, 99, 0, 8, 5}; printf("\n\n\t%d\n\n", find(A, 8));
return 0; }

You can see that the code works without any errors and even produces correct results. However, even for an expert programmer, it’s difficult to identify the boundaries of functions, loops, and conditional blocks without proper indentation.

In the following example, we rewrite this code with proper indentation. It becomes much easier to understand.

C++
#include <stdio.h>
int find(int A[], int size) {
   int ret = -1;
   for (int i = 0; i < size; i++) {
       if (A[i] > ret) {
           ret = A[i];
       }
   }
   return ret;
}
 
int main() {
   int A[] = {1, 4, 7, 13, 99, 0, 8, 5};
   printf("\n\n\t%d\n\n", find(A, 8));
   return 0;
}

2. Meaningful naming#

Your code’s variable names and function names should be intuitive. Ideally, we should be able to guess what a function does based on the function’s name.

You should give intuitive names to objects, variables, classes, functions, and constants. However, we also strive to keep our code concise and readable. If the most intuitive name is too long to keep code concise, you’re welcome to use its shorthand. Just be mindful that the shorthand should remain intuitive as well.

The following example of code does not follow meaningful naming conventions. This makes it difficult to understand and reuse.

C++
#include <stdio.h>
 
int find(int A[], int size) {
   int ret = -1;
   for (int i = 0; i < size; i++) {
       if (A[i] > ret) {
           ret = A[i];
       }
   }
   return ret;
}
int main() {
   int A[]={1, 4, 7, 13, 99, 0, 8, 5};
   printf("\n\n\t%d\n\n", find(A, 8));
   return 0;
}

In contrast, the following code example has the same functionality, but meaningful naming makes it easier to understand.

C++
#include <stdio.h>
 
int findLargest(int inputAry[], int inputArySize) {
   int largest = -1;
   for (int loop = 0; loop < inputArySize; loop++) {
       if (inputAry[loop] > largest) {
           largest = inputAry[loop];
       }
   }
   return largest;
}
 
int main() {
   int A[]={1, 4, 7, 13, 99, 0, 8, 5};
   printf("\n\n\t%d\n\n", findLargest(A, 8));
   return 0;
}

3. Comments that add context#

Code is for the compiler, while comments are for coders.

Even if other best practices are accounted for, source code can’t always be self-explanatory. When code can’t explain itself, comments should step in. Here, good practices for writing stories apply to writing code: The more you anticipate your reader’s thoughts, the more impactful your comments will be.

Here are some general guidelines for code comments:

  • Prioritize quality over quantity: Don’t go commenting on every line of code. Comments should still support code readability. If your function name or variable name already infers what is happening, you don’t need to let readers know.

  • Don’t assume your reader has context: Let your readers know the context behind the code so they can understand why each part is necessary. If you’ve modified code to fix a bug, comments help keep that bug fixed.

  • Explain the “Why”: Don’t tell us what we can already see in the code. Explain the why behind it. We can see which method or function you’re using, but knowing why helps readers better understand the code.

Code commenting helps make codebases and projects more maintainable. When done well, good commenting can streamline code reviews. Comments also support developer onboarding by helping newcomers familiarize themselves more quickly with a codebase.

The following is an example of code commenting done well.

C++
#include <stdio.h>
 
/**
* Finds the largest integer from the given array (inputAry)
* of size (inputArySize).
*/
int findLargest(int inputAry[], int inputArySize) {
   //Assumption: array will have +ve elements.
   //Thus, the largest is initialized with -1 (smallest possible value).
  int largest = -1;
 
   // Iterate through all elements of the array.
  for (int loop = 0; loop < inputArySize; loop++) {
 
      //Replace largest with element greater than it.
      if (inputAry[loop] > largest) {
          largest = inputAry[loop];
      }
 
  }
  //returns the largest element of the array
  return largest;
}
 
int main() {
   int A[]={1, 4, 7, 13, 99, 0, 8, 5};
   printf("\n\n\t%d\n\n", findLargest(A, 8));
   return 0;
}

4. Don’t repeat yourself#

Also known as the DRY principle, “Don’t repeat yourself” strives to reduce code duplication. The idea here is that if you have code that’s doing the same thing twice, it should be made into a function. By abstracting code into functions, you can reuse that code and make development more efficient. In addition, avoiding code duplication makes debugging easier, as you won’t have to fix a bug in every instance of repeated code throughout your program.

5. Low coupling and high cohesion#

Low coupling and high cohesion are different yet complementary principles. Low coupling encourages separation between unrelated parts of a codebase, while high cohesion encourages integration between related parts of a codebase.

GOOD practice: Low coupling between unrelated units and high cohesion between related units.

They may sound like opposing principles, but low coupling and high cohesion work together to ensure the maintainability, scalability, and testability of our applications. High cohesion strives to keep a close relation between units that need to know about each other. When it’s time to extend code, we benefit from finding related code in the same places. On the other hand, low coupling strives to reduce dependencies between unrelated units.

If we don’t follow this best practice, we risk trending toward high coupling and low cohesion. This results in excessive dependencies, which has several negative impacts. One undesirable result is an increased vulnerability to bugs, as a bug in one unit will affect its dependent units as well.

This is NOT a good practice: Avoid high coupling between unrelated units and and low cohesion between related units.

Low coupling and high cohesion apply to how we treat any of our language constructs, from methods and classes to libraries and APIs. These good practices can be achieved through what are known as SOLID principles in object-oriented programming.

6. Consider your context#

Coding guidelines vary across different contexts. Depending on your programming language, company, or industry, there may be different coding guidelines for naming conventions, coding style, indentation, and file structures. Be mindful of your project’s individual needs and honor those coding standards when you can.

Use your best judgment, and adjust to whatever your situation calls for.

It’s important to know good coding practices, but rules are simply generalizations without context. To be used well, they need your good judgment. Keeping all these principles in mind, you should follow your instincts as a programmer. There will be times where your context challenges a general principle, and you know your case best. This is what others mean when they say not to take these rules “pragmatically,” and we agree.

Learn to code today.#

Try one of our courses on programming fundamentals:

Coding best practices in action: Refactoring a simple application#

Learning coding best practices is important, but their real value becomes clear when you see how they improve actual code. Concepts such as meaningful naming, proper indentation, DRY (Don't Repeat Yourself), low coupling, and high cohesion may sound straightforward in theory, but they have a significant impact on how easy software is to maintain and extend.

Let's walk through a simple example to see how these principles work together. We'll start with a small application that calculates student grades. The first version works correctly, but as new requirements arrive, the code quickly becomes difficult to understand and modify.

Scenario: Student Grade Calculator#

Imagine you're building a simple application that:

  • Accepts student scores

  • Calculates averages

  • Determines pass/fail status

  • Generates a summary report

The initial version was written quickly to meet a deadline. It works, but it doesn't follow many coding best practices.

Original implementation#

s1 = [85, 90, 78]
s2 = [60, 55, 70]
a = (s1[0] + s1[1] + s1[2]) / 3
if a >= 60:
 print("Student 1 Passed")
else:
 print("Student 1 Failed")
b = (s2[0] + s2[1] + s2[2]) / 3
if b >= 60:
 print("Student 2 Passed")
else:
 print("Student 2 Failed")
print("Student 1 Average:", a)
print("Student 2 Average:", b)

At first glance, the program seems simple enough. However, problems start appearing as soon as the application grows.

Problems with the original code#

The code works, but it has several maintenance issues.

Difficult to read#

Variables such as a and b don't explain what they represent. Someone reading the code must spend extra time figuring out their purpose.

Difficult to debug#

If an average calculation is incorrect, the logic is repeated in multiple places, making bugs harder to track down.

Difficult to extend#

What happens when you need to support 100 students instead of two? The current approach requires copying and modifying large sections of code.

Increased risk of bugs#

Duplicated logic often leads to inconsistencies when one section gets updated and another does not.

As projects grow, these issues compound and make development increasingly expensive.

Refactored implementation#

Now let's apply several coding best practices.

PASSING_SCORE = 60 
def calculate_average(scores): 
"""Calculate the average score for a student.""" 
return sum(scores) / len(scores) 
def determine_status(average_score): 
"""Determine whether the student passed.""" 
return "Passed" if average_score >= PASSING_SCORE else "Failed" 
def generate_report(student_name, scores): 
"""Generate a student performance summary.""" 
average_score = calculate_average(scores) 
status = determine_status(average_score) 
print(f"{student_name}:") 
print(f" Average Score: {average_score:.2f}") 
print(f" Status: {status}") 
student_scores = { 
"Student 1": [85, 90, 78], 
"Student 2": [60, 55, 70] 
} 
for student_name, scores in student_scores.items(): generate_report(student_name, scores)

The functionality is exactly the same, but the structure is dramatically improved.

Which best practices improved the code?#

Best Practice

Improvement

Indentation

Easier readability

Meaningful naming

Faster understanding

Comments

Better context

DRY principle

Less duplication

High cohesion

Related logic grouped together

Low coupling

Easier maintenance

Indentation#

Consistent indentation makes the flow of execution immediately clear.

Meaningful naming#

Names such as calculate_average() and average_score communicate intent without requiring additional explanation.

Comments#

Short comments and docstrings provide context without overwhelming the code.

DRY principle#

Average calculation logic now exists in a single location. If the calculation changes, you only update one function.

High cohesion#

Each function performs one specific responsibility:

  • Calculate averages

  • Determine pass/fail status

  • Generate reports

Low coupling#

Functions depend on minimal external information, making them easier to test and reuse.

How the project scales#

Now imagine a new requirement arrives:

Add letter grades (A–F) to the report.

In the original version, you would likely need to add grading logic in multiple places, increasing duplication and maintenance effort.

In the refactored version, you simply create a new function:

def calculate_letter_grade(average_score): 
if average_score >= 90: 
return "A" 
elif average_score >= 80: 
return "B" 
elif average_score >= 70: 
return "C" 
elif average_score >= 60: 
return "D" 
return "F"

Then update the report generator:

letter_grade = calculate_letter_grade(average_score) 
print(f" Grade: {letter_grade}")

No existing business logic needs to be duplicated or rewritten.

This is one of the biggest benefits of clean code: change becomes easier.

What professional developers learn from this example#

One of the most important lessons in software engineering is that most code spends far more time being maintained than being written.

Features evolve. Requirements change. Teams grow. New developers join projects. Code that seemed "good enough" initially can become a major obstacle later.

Professional developers understand that:

  • Clean code reduces future development costs.

  • Readable code helps teams collaborate more effectively.

  • Well-structured applications are easier to test and debug.

  • Readability usually matters more than cleverness.

A solution that takes five extra minutes to organize today can save hours of maintenance effort later.

The goal of clean code is not to impress the compiler—it's to help future developers, including your future self.

Coding best practices are not about achieving perfection. They're about making software easier to understand, maintain, test, and extend as requirements evolve.

Simple improvements such as better naming, proper indentation, reusable functions, and reduced duplication may seem small in isolation. Over the lifetime of a project, however, these improvements compound into cleaner codebases, fewer bugs, and more productive development teams.

The earlier you build these habits, the easier it becomes to write code that scales with both your projects and your career.

Wrapping up and next steps#

Writing good code is essential to your success as a developer. By keeping these coding practices in mind, you can ensure that your code is easier to maintain, scale, and debug. The more you code, the better you’ll get at applying and adapting these practices across different languages and projects.

To master best practices while learning to code, check out Educative’s courses for absolute beginners. Available in six programming languages, these courses will help you understand the logic of programming and begin writing code. Start your journey today!

Happy learning!

Continue learning about coding#

Frequently Asked Questions

What’s the best way to practice coding?

The best way to practice coding is by consistently working on real-world projects that challenge your skills, participating in coding challenges and hackathons to test your abilities under pressure, and contributing to open-source projects to gain collaborative experience and feedback from the programming community.

Written By:
Erica Vartanian
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