Reshaping and Flattening

Reshaping is rearranging the same array elements into a new layout of rows, columns, and dimensions, while flattening is collapsing a multi-dimensional array back down into a single 1D line — neither changes the underlying values.

Learn Reshaping and Flattening in our free NumPy course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick…

Part of the free Numpy course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

In this lesson you'll reshape with explicit sizes, let NumPy infer a dimension with -1 , flatten with ravel() and flatten() , transpose with .T , and add a new axis.

reshape(rows, cols) takes a flat array and folds its elements into a new grid, filling row by row. The data stays the same — only the arrangement changes. The new shape must hold exactly as many elements as the original.

Expected output: a 3×4 grid of 0–11, then (3, 4) .

Pass -1 for one dimension and NumPy figures out its size from the total element count. Useful when you know you want, say, 3 rows but don't want to compute the column count yourself.

Expected output: (3, 4) , (6, 2) , and (12,) .

Both ravel() and flatten() collapse a multi-dimensional array into 1D. The difference is memory: ravel() returns a view (shares memory with the original when possible), while flatten() always returns an independent copy .

Expected output: [1 2 3 4 5 6] , then 99 (ravel changed the source) and 1 (flatten left it alone).

.T transposes an array — it flips rows and columns so the element at [i, j] moves to [j, i] . To add a brand-new axis (e.g. turn a 1D array into a single column), use np.newaxis or an equivalent reshape .

Expected output: the 3×2 transpose, (2, 3) (3, 2) , then the column array and (3, 1) .

Replace each ___ so the program reshapes, infers a dimension, and transposes.

Expected output: (2, 4) , (4, 2) , and the transposed grid. (Answers: 4 , -1 , T .)

❌ ValueError: cannot reshape array of size 12 into shape (5,5)

The new shape needs a different number of elements than the array holds.

✅ Fix: make rows × cols equal the size. 12 fits (3, 4) or (2, 6), not (5, 5).

❌ Editing a flattened array changed the original

You used ravel() , which can return a view that shares memory.

✅ Fix: use flatten() when you want an independent copy.

Reshape a range into a 3×4 grid, flatten it back, and transpose the grid — all without changing the values.

Lesson 8 complete — you can reshape anything!

You now reshape with explicit sizes, let NumPy infer a dimension with -1 , flatten with ravel() and flatten() (knowing the view-vs-copy difference), transpose with .T , and add new axes.

🚀 Up next: Universal Functions — fast, element-wise math built into NumPy.

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