/Curriculum/Core NumPy•Array Transformation

11. NumPy Shape & ReshapingBeginner

Transform dimensions using reshape(), flatten(), ravel(), and transpose (.T).

14 mins

Concept Overview

Reshaping changes the dimensional arrangement of an array without altering its underlying data. The total element count must remain unchanged.

Hardware Mental Model

Reshaping a 1D strip of 6 numbers into a 2x3 grid is like folding a strip of 6 stamps into 2 rows of 3 stamps without ripping any stamps.

Key Concepts (1)Click snippet to load in editor

Pass `-1` as one dimension in `.reshape(r, -1)` and NumPy will automatically compute the required dimension size.

mat = arr.reshape(2, 3); flat = mat.flatten()

Avoid these frequent beginner syntax and logic traps

❌ Incorrect:arr = np.arange(6); arr.reshape(2, 4) # 2x4 = 8 != 6

✓ Correct:arr = np.arange(6); arr.reshape(2, 3)

The product of the new shape dimensions must exactly equal the total number of elements.

Pro Tip: Use `reshape(rows, -1)` to let NumPy calculate the matching column count automatically.

Core takeaway points from this topic

`arr.reshape(r, c)`: Reorganizes dimensions without modifying data.

`arr.T` / `arr.transpose()`: Swaps rows and columns.

`arr.flatten()`: Returns a flattened 1D copy.

`arr.ravel()`: Returns a flattened 1D view.

Editor: 11. NumPy Shape & Reshaping

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Execution Result

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Hands-on Mode

Solve the objective below using NumPy vectorized syntax

Create `arr = np.array([10, 20, 30, 40])`, reshape it into a 2x2 matrix using `.reshape(2, 2)`, and print it.

Expected Output Target:

[[10 20]
 [30 40]]

Practice Workspace

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Initializing Python IDE...

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Execution Result

Click "RUN CODE" to execute and inspect array state