reshape() for changing dimensions in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape the 1D array into a 2D array with 3 rows.

NumPy

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

Drag options to blanks, or click blank then click option'

Areshape

Bflatten

Cravel

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize instead of reshape.

Using flatten which converts to 1D instead of reshaping.

The reshape() method changes the shape of the array without changing its data. Here, it changes a 1D array into a 2D array with 3 rows and 2 columns.

2fill in blank

medium

Complete the code to reshape the array into 3 rows and 2 columns.

NumPy

import numpy as np
arr = np.arange(6)
new_arr = arr.[1]((3, 2))
print(new_arr)

Drag options to blanks, or click blank then click option'

Aflatten

Bresize

Creshape

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize which modifies the original array.

Using flatten which makes the array 1D.

reshape() changes the shape of the array to the given dimensions (3 rows, 2 columns).

3fill in blank

hard

Fix the error in reshaping the array to 2 rows and 4 columns.

NumPy

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

Drag options to blanks, or click blank then click option'

Areshape

Bresize

Cflatten

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape with incompatible shape causes an error.

Using flatten or ravel does not change shape as needed.

The error is because the total size (6) does not match the new shape (2*4=8). Using resize() changes the array size and fills with zeros if needed.

4fill in blank

hard

Fill both blanks to create a 3D array with shape (2, 3, 1).

NumPy

import numpy as np
arr = np.arange(6)
reshaped = arr.[1]((2, [2], 1))
print(reshaped.shape)

Drag options to blanks, or click blank then click option'

Areshape

B3

C4

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize which changes array size.

Choosing wrong middle dimension that does not match total elements.

Use reshape to change the array shape to (2, 3, 1). The middle dimension is 3 to match total elements (2*3*1=6).

5fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 letters.

NumPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

Drag options to blanks, or click blank then click option'

A:

Blen(word)

C>

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary comprehension.

Using wrong comparison operator like '=' instead of '>'.

This dictionary comprehension creates a dictionary where keys are words and values are their lengths, but only for words longer than 3 letters.

Practice

1. What does the reshape() function do in NumPy?

easy

A. Creates a new array with random values

B. Changes the shape of an array without changing its data

C. Sorts the elements of an array

D. Deletes elements from an array

Solution

1. Step 1: Understand the purpose of reshape()

   The reshape() function changes the shape (dimensions) of an existing array but keeps the data intact.

2. Step 2: Compare with other options

   Options A, B, and C describe different functions or actions unrelated to reshaping.

3. Final Answer:

   Changes the shape of an array without changing its data -> Option B

4. Quick Check:

   reshape() = change shape only [OK]

Hint: reshape() changes shape, data stays same [OK]

Common Mistakes:

• Thinking reshape() changes data values
• Confusing reshape() with sorting or deleting
• Assuming reshape() creates new random arrays

2. Which of the following is the correct syntax to reshape a NumPy array arr into 2 rows and 3 columns?

easy

A. arr.reshape(2[3])

B. arr.reshape{2, 3}

C. arr.reshape(2, 3)

D. reshape(arr, 2, 3)

Solution

1. Step 1: Recall correct reshape syntax

   The correct syntax is array.reshape(dim1, dim2, ...) with dimensions as separate arguments or a tuple.

2. Step 2: Evaluate options

   arr.reshape(2, 3) uses correct method call with separate arguments. arr.reshape(2[3]) has invalid syntax (missing comma). arr.reshape{2, 3} uses curly braces which is wrong syntax. reshape(arr, 2, 3) uses a function call without object which is invalid.

3. Final Answer:

   arr.reshape(2, 3) -> Option C

4. Quick Check:

   Use dot and parentheses with dims [OK]

Hint: Use dot and parentheses with dims, no brackets [OK]

Common Mistakes:

• Forgetting comma between dimensions
• Using curly braces instead of parentheses
• Calling reshape as a standalone function

3. What is the output shape of the following code?

import numpy as np
arr = np.arange(12)
new_arr = arr.reshape(3, 4)
print(new_arr.shape)

medium

A. (3, 4)

B. (4, 3)

C. (12,)

D. (2, 6)

Solution

1. Step 1: Understand the original array

   np.arange(12) creates a 1D array with 12 elements from 0 to 11.

2. Step 2: Reshape to (3, 4)

   The reshape changes the array to 3 rows and 4 columns, so the shape becomes (3, 4).

3. Final Answer:

   (3, 4) -> Option A

4. Quick Check:

   reshape(3,4) = (3,4) shape [OK]

Hint: reshape dims become shape tuple [OK]

Common Mistakes:

• Confusing rows and columns order
• Thinking reshape changes total elements
• Expecting original 1D shape after reshape

4. What error will occur when running this code?

import numpy as np
arr = np.arange(10)
new_arr = arr.reshape(3, 4)

medium

A. ValueError: cannot reshape array of size 10 into shape (3,4)

B. TypeError: reshape() takes exactly 1 argument

C. SyntaxError: invalid syntax

D. No error, reshaping works fine

Solution

1. Step 1: Check original array size

   np.arange(10) creates an array with 10 elements.

2. Step 2: Check reshape dimensions

   Trying to reshape into (3, 4) means 12 elements needed, but only 10 exist.

3. Step 3: Identify error type

   This mismatch causes a ValueError about incompatible shape.

4. Final Answer:

   ValueError: cannot reshape array of size 10 into shape (3,4) -> Option A

5. Quick Check:

   Elements must match for reshape [OK]

Hint: Total elements must match for reshape [OK]

Common Mistakes:

• Ignoring total element count mismatch
• Expecting reshape to auto-fill missing elements
• Confusing ValueError with SyntaxError

5. Given a 1D NumPy array arr = np.arange(24), which of the following reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation with -1 in the first position?

hard

A. arr.reshape(2, 3, 4)

B. arr.reshape(2, -1, 4)

C. arr.reshape(2, 3, -1)

D. arr.reshape(-1, 3, 4)

Solution

1. Step 1: Understand total elements and target shape

   The array has 24 elements. The target shape is 3D with dimensions (2, 3, 4), total 24 elements.

2. Step 2: Use -1 for automatic dimension

   Using -1 lets NumPy calculate that dimension automatically. For (2, 3, 4), if we replace one dimension with -1, NumPy calculates it as 24 divided by product of other dims.

3. Step 3: Check each option

   arr.reshape(2, 3, 4) is explicit (2,3,4) - works but no automatic calculation.
   arr.reshape(2, -1, 4): (2, -1, 4) means -1 = 24/(2*4) = 3, shape (2,3,4) correct.
   arr.reshape(-1, 3, 4): (-1, 3, 4) means -1 = 24/(3*4) = 2, shape (2,3,4) correct.
   arr.reshape(2, 3, -1): (2, 3, -1) means -1 = 24/(2*3) = 4, shape (2,3,4) correct.
   All B, C, D produce correct shape, but question asks for reshaping into (2,3,4) using automatic dimension calculation. Only arr.reshape(-1, 3, 4) uses -1 as first dimension, matching question wording.

4. Final Answer:

   arr.reshape(-1, 3, 4) -> Option D

5. Quick Check:

   -1 auto calculates dimension [OK]

Hint: Use -1 for one dimension to auto calculate [OK]

Common Mistakes:

• Using multiple -1 values in reshape
• Not matching total elements with new shape
• Confusing which dimension -1 replaces