Скачать или смотреть Fix NumPy ValueError operands could not be broadcast together with shapes (2,3), (3,2)

Are you getting the NumPy error “ValueError: operands could not be broadcast together with shapes (2,3) (3,2)” when trying to add two arrays?
You’re not alone — this is one of the most common and confusing NumPy errors for beginners and intermediate Python users.

In this tutorial, we break down exactly why this error happens, how NumPy broadcasting rules work, and why using transpose (.T) fixes the issue. By visualizing array shapes step by step, you’ll clearly understand how NumPy aligns dimensions and why mismatched shapes cause failures.

You’ll also learn how linear algebra concepts like transpose apply directly to NumPy operations, helping you debug shape errors faster and write cleaner numerical code.

🔍 What You’ll Learn in This Video

✅ Why NumPy raises broadcasting errors
✅ How NumPy compares array shapes dimension by dimension
✅ Why shapes (2,3) and (3,2) cannot be added directly
✅ What transpose means in linear algebra and NumPy
✅ How .T changes array orientation
✅ How to fix the error using B.T
✅ Best practices to avoid shape mismatch errors in NumPy

⏱️ Timestamps

00:00 – Problem overview: NumPy broadcasting error
00:07 – Understanding the shape mismatch error
00:17 – Visualizing Array A (2×3)
00:34 – Visualizing Array B (3×2)
00:47 – Why NumPy fails element-wise alignment
01:06 – Why shapes must match exactly
01:13 – What transpose means in linear algebra
01:17 – Using .T in NumPy
01:28 – Shape becomes (2,3) after transpose
01:39 – Fixing the code with A + B.T
01:59 – Final result and key takeaway

💡 Key Takeaway

Always check array shapes first.
If rows and columns are swapped, transpose the array using .T before performing arithmetic operations.

🚀 Who This Video Is For

Python beginners learning NumPy
Data science & machine learning students
Engineers working with matrices
Anyone debugging NumPy broadcasting errors

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