There’s a famous saying that goes, “Less is more,” a principle that also extends to the world of programming. As a developer, I’ve found the mantra, “Doing the Simplest Thing That Could Possibly Work,” to be a beacon of guidance. This principle advocates for simplicity, advocating the creation of code that does the job in the most straightforward manner possible. Let me explain why this approach works with an example. ## Finding the Average: A Case Study

### Phase 1: Starting Simple

Imagine being tasked with calculating the average of a list of numbers. The standard procedure is straightforward: sum up all the numbers and then divide by the total count. This is a simple, efficient, and effective solution that takes advantage of Python’s built-in functionality and operators.

```def find_average(numbers):
return sum(numbers) / len(numbers)
```

### Phase2: When the Simplest Thing Needs an Upgrade

There will inevitably be times when the simplest solution might not be sufficient. Let’s consider our earlier example. If the list of numbers is huge, summing all of them upfront might consume a lot of memory, possibly leading to an out-of-memory error.

A more complex solution would be to use an algorithm that handles the numbers in chunks, thereby reducing the memory footprint. In this situation, the initial simple solution served as a stepping stone, providing a clear direction to the problem that needed to be solved. The point is, more complexity can be introduced when the need arises, but it’s not where we start.

```def find_average_large_list(numbers):
average = 0
for i, number in enumerate(numbers):
average = average + (number - average) / (i + 1)
return average
```

## Time Efficiency Through Simplicity

First, by using the simplest solution, we save a lot of time that would be otherwise spent creating a more complicated solution. While there may be numerous complex ways to solve the same problem, the simplest method tends to consume the least amount of time, both in terms of writing and executing the code. A sophisticated algorithm might look impressive, but if it doesn’t improve the speed or efficiency of the program significantly, it’s generally not worth the extra effort.

## Readability and Comprehensibility Through Simplicity

The next advantage of this simple solution lies in its readability. Clear, concise code is easier for other developers (or future you) to understand, reducing the time needed to decipher what the code is doing. This results in easier debugging and maintenance, as well as smoother collaboration when working in a team.

## Flexibility Through Simplicity

The simplest solution also provides a good starting point that can be built upon if necessary. If you find that the initial simple solution isn’t sufficient, you can always add layers of complexity later. By not over-complicating things at the outset, you retain the flexibility to adapt and extend your code as requirements evolve.

What is simplicity in programming and why does it matter?

SimpleMadeEasy.md at master · matthiasn/talk-transcripts (github.com)

SimpleMadeEasy-mostly-text.md at master · matthiasn/talk-transcripts (github.com)

SimplicityMatters.md at master · matthiasn/talk-transcripts (github.com)

## Conclusion

In conclusion, “Doing the Simplest Thing That Could Possibly Work” is a principle that serves not only as a time and effort saver, but also as a guideline for writing readable, maintainable, and adaptable code. It doesn’t mean that you should never introduce complexity into your programs. Rather, it suggests that you start with the simplest solution, assess its efficiency, and then progressively enhance it if the need arises. In this way, simplicity becomes the seed that sprouts into robust and effective coding.

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