🧠 Learning Journal – Python & Computer Science | Day 1

Arrays, Lists… and Jagged What!?

Today I dove into one of the most basic yet deep concepts in programming: arrays.

At first glance, arrays might seem like a beginner's topic, just a list of items, right?

But the more I explored, the more I realized… there's a whole ecosystem of array types, especially in Python, and each has a specific use case.

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🔍 What I learned

✅ Python Lists

In Python, lists are super flexible:

• They can grow and shrink dynamically.

• They can store different data types.

• They're great for everyday programming.

✅ NumPy Arrays

For serious number crunching, you’ll want to use NumPy arrays:

• Fixed type (all elements must be the same type)

• Faster and more memory-efficient

• Optimized for math, data analysis, machine learning

✅ Multidimensional & Jagged Arrays

This is where things got interesting.

• 2D arrays: Think of spreadsheets or matrices.

• 3D arrays: Layers of 2D arrays… great for image processing or tensor operations.

• Jagged arrays: Arrays where each "row" can have a different length.
  Example? Storing student grades where each student took a different number of exams.

  notes = [[90, 85], [100, 92, 88], [70]]

🤯 Key Takeaways

• Arrays are not all equal — use the right type for the right task.

• Multidimensional arrays open the door to real-world data (images, tables, nested structures).

• Jagged arrays are powerful when data is irregular.

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🔄 Coming up next

➡️ Linked Lists : those weird pointer chains that haunt CS students 😅
But I’m excited to break them down and understand their purpose.

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✍️ Why I'm doing this

I’m learning in public to reinforce my understanding and connect with others on the same path, toward data science, machine learning, or just becoming a better programmer.

If you’ve got any cool tricks or real-life use cases for arrays, I’m all ears 👂

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Thanks for reading!
📅 See you in Day 2.