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fixed_sliding_window

This is awesome. You’re not just showing code — you’re showing your thinking process, the evolution of the pattern, and the meta-level realizations that make the pattern click.

Let’s build this into a cleaner Markdown article draft that preserves all your insights but tightens the structure so it can be read by someone else (or future-you).

🧠 Sliding Window: Rethinking the Pattern​

You do not have to start at 0, and your loop variable does not need to touch every element.
That’s where the sliding window starts to make sense.

🧱 The Setup​

Given:

arr = [2, 1, 5, 1, 3, 2]
k = 3

Goal: iterate over every contiguous window of size N in a clean, readable, bounded way.

🧪 Version 1: The Common Approach (Textbook/Tutorial)​

def explore_sliding_window_chatgpt(arr, windowSize):
    left = 0
    for right in range(len(arr)):
        if right - left + 1 == windowSize:
            window = arr[left:right + 1]
            print(f"Window [{left}:{right}] =>", window)
            left += 1

✅ Works, but...

  • You’re looping from 0, and guarding with if
  • You check the window length every time
  • You slide left only when the window is valid

I would NEVER be able to write this from scratch... honestly I can barely even read it. There's too much cognitive load.

🔎 Meta Observations​

✅ "We're iterating a non-loop variable (left) only inside a condition"​

  • That variable (left) is part of the condition that guards the loop
  • This is a feedback loop, not a pure iteration

✅ "Our loop bounds are immediately protected"​

  • The outer loop can go longer (e.g. len(arr) + 1)
  • But slicing behavior and guard conditions protect us from going out of bounds

🧪 Version 2: Cleaned Up a Bit​

Start with an invalid state. Let it become valid naturally.

def explore_sliding_window_cleaning_up_thinking(arr, windowSize):
    left = 0 - windowSize # the window is fixed, so lets just "set" it now
    for right in range(len(arr)):
        left += 1
        if left < 0:
            continue
        print("window", arr[left:right + 1])

Highlights:

  • Starts left as out of bounds
  • No window size condition — it's baked into when left becomes valid
  • Cleaner and intuitive

Name: Progressive Guarded Window

✅ Final Form: The Cleanest (Start Valid)​

You don’t need to arrive at a valid state — you can start there.

def explore_sliding_window_cleaning_up_2(arr, windowSize):
    left = 0 
    for right in range(windowSize - 1, len(arr)):
        print("window", arr[left:right + 1])
        left += 1
  • No conditionals
  • Starts right at the first index that gives a full window
  • Slides cleanly by incrementing both left and right

This is the most readable, bounded, and semantically aligned version.

✂️ Python Quirks​

  • Slices are [inclusive:exclusive]
  • That means arr[3:6] gets you elements 3, 4, 5
  • This feels wrong if you’re an inclusive-index thinker

So fix it:

def inclusive_slice(arr, start, end):
    return arr[start:end + 1]

Use when you want to reason like:

"Give me all values from index A through index B (inclusive)"

🔄 Iteration Patterns Takeaway​

  • You can start anywhere — not just at index 0
  • You don’t need to “touch” every index
  • Think in terms of:
    • When is a window valid?
    • What indices are needed?
    • What constraint defines the window?

Once you do that, all the off-by-one noise disappears. (except python inclusive/exclusive slices lol)

🎓 TL;DR​

Sliding window isn't about two pointers — it's about controlling a range.
Once the window length is fixed, the simplest form is to start at the first valid position and iterate forward.

left = 0
for right in range(windowSize - 1, len(arr)):
    window = arr[left:right + 1]
    left += 1

That’s it.

And if you really want to prioritize readability, then you can always pull things out further:

left = 0
firstValidRight = windowSize - 1
for right in range(firstValidRight, len(arr)):
    window = arr[left:right + 1]
    left += 1