Shape Manipulation & Reshape

Understanding shape

shape tells the size of each dimension.

shape

import numpy as np
 
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # (2, 3)

Reshape with .reshape()

Reshape changes the dimension layout without changing the underlying data or copying it — it just reinterprets the same flat block of memory with a new shape.

reshape

import numpy as np
 
arr = np.arange(1, 13)  # 1..12
mat = arr.reshape(3, 4) # 3 rows, 4 cols
print(mat)

sketch Reshaping (3, 4) into (4, 3) p5.js Pause Restart

The same 12 values, read out in the same order, just wrapped into a different grid shape.

Using -1 (auto infer)

reshape-auto

import numpy as np
 
arr = np.arange(12)
mat = arr.reshape(3, -1)
print(mat.shape)  # (3, 4)

Caution

The total size must match. If the array has 12 elements, only shapes that multiply to 12 are valid.

Flattening arrays

.ravel() (often a view)

ravel

import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.ravel()
print(flat)

.flatten() (always copy)

flatten

import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.flatten()
print(flat)

From the book

.reshape() and .ravel() return views whenever possible — no data is copied. .flatten() always makes a copy. If you only need to read the flattened data (not mutate it), .ravel() is the cheaper choice.

Add or remove dimensions

np.newaxis / None

Convert a 1D array into a column vector:

newaxis

import numpy as np
 
arr = np.array([1, 2, 3])
col = arr[:, None]
print(col)
print(col.shape)  # (3, 1)

np.expand_dims

expand

import numpy as np
 
arr = np.array([1, 2, 3])
arr2 = np.expand_dims(arr, axis=0)
print(arr2.shape)  # (1, 3)

np.squeeze (remove size-1 dims)

squeeze

import numpy as np
 
arr = np.array([[[1], [2], [3]]])
print(arr.shape)           # (1, 3, 1)
print(np.squeeze(arr).shape)  # (3,)

Transpose (swap axes)

Transposing is a special case of reshaping — it also returns a view, never a copy.

For 2D:

transpose

import numpy as np
 
mat = np.array([[1, 2, 3], [4, 5, 6]])
print(mat.T)

diagram Shape-changing toolbox mermaid

  flowchart LR
  A["ndarray"] --> B["reshape(r, c)
same data, new grid"]
  A --> C["ravel() / flatten()
collapse to 1D"]
  A --> D["expand_dims / newaxis
add a dimension"]
  A --> E["squeeze()
remove size-1 dims"]
  A --> F["T / swapaxes()
reorder axes"]

Different tools for growing, shrinking, or rearranging dimensions without touching the underlying data.

Next

Continue to: Broadcasting in NumPy to learn how NumPy applies operations between different shapes.

🧪 Try It Yourself

Exercise 1 – Reshape with -1

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Exercise 2 – Flatten a Matrix

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Exercise 3 – Column Vector with newaxis

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