Array shapes and dimensions in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 2D NumPy array with shape (3, 2).

Data Analysis Python

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

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

A.size

B.dtype

C.ndim

D.shape

Attempts:

3 left

💡 Hint

Common Mistakes

Using .size instead of .shape returns total elements, not dimensions.

Using .ndim returns number of dimensions, not the shape tuple.

The .shape attribute shows the dimensions of the array. Here, it will print (3, 2).

2fill in blank

medium

Complete the code to find the number of dimensions of the array.

Data Analysis Python

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

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

A.shape

B.ndim

C.size

D.dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using .shape returns the shape tuple, not the number of dimensions.

Using .size returns total elements, not dimensions.

The .ndim attribute returns the number of dimensions of the array. Here, it will print 1 because the array is one-dimensional.

3fill in blank

hard

Fix the error in the code to reshape the array to 2 rows and 3 columns.

Data Analysis Python

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

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

A.reshape

B.resize

C.reshape()

D.shape

Attempts:

3 left

💡 Hint

Common Mistakes

Using .reshape() with extra parentheses causes syntax errors.

Using .resize modifies the array in place and can cause unexpected results.

The .reshape method reshapes the array to the given shape. It must be called without parentheses after the method name in the code before passing the new shape.

4fill in blank

hard

Fill both blanks to create a 3D array with shape (2, 2, 2) and print its number of dimensions.

Data Analysis Python

import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(arr[1])
print(arr[2])

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

A.shape

B.size

C.ndim

D.dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up .size and .ndim attributes.

Using .dtype instead of shape or ndim.

The first print shows the shape (2, 2, 2). The second print shows the number of dimensions, which is 3.

5fill in blank

hard

Complete the code to create a dictionary with word lengths for words longer than 3 characters.

Data Analysis Python

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

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

A:

B>

C)

D]

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma instead of colon in dictionary comprehension.

Using < or == instead of > in the condition.

Missing closing parenthesis in print.

The dictionary comprehension uses : to map words to their lengths. The condition uses > to filter words longer than 3 characters. The print statement closes with ).

Practice

1. What does the shape of a NumPy array represent?

easy

A. The number of elements along each dimension

B. The total number of elements in the array

C. The data type of the elements

D. The memory size of the array

Solution

1. Step 1: Understand the meaning of shape

   The shape of an array is a tuple that shows how many elements are in each dimension.

2. Step 2: Differentiate shape from other properties

   Shape is not the total count, data type, or memory size, but the size per dimension.

3. Final Answer:

   The number of elements along each dimension -> Option A

4. Quick Check:

   Shape = elements per dimension [OK]

Hint: Shape = count of elements per axis [OK]

Common Mistakes:

• Confusing shape with total number of elements
• Mixing shape with data type
• Thinking shape is memory size

2. Which of the following is the correct way to get the shape of a NumPy array arr?

easy

A. arr.shape

B. arr.shape()

C. shape(arr)

D. arr.get_shape()

Solution

1. Step 1: Recall NumPy shape syntax

   In NumPy, shape is an attribute, not a method, so no parentheses are used.

2. Step 2: Check each option

   arr.shape uses arr.shape correctly; others use incorrect syntax or functions.

3. Final Answer:

   arr.shape -> Option A

4. Quick Check:

   Use attribute, no parentheses [OK]

Hint: Use arr.shape without parentheses [OK]

Common Mistakes:

• Adding parentheses after shape
• Using non-existent functions like get_shape()
• Trying to call shape as a function

3. Given the code:

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

What is the output?

medium

A. (3, 2)

B. (6,)

C. (2, 3)

D. (2,)

Solution

1. Step 1: Analyze the array structure

   The array has 2 rows and 3 columns, so shape is (2, 3).

2. Step 2: Confirm shape output

   Printing arr.shape returns the tuple (2, 3).

3. Final Answer:

   (2, 3) -> Option C

4. Quick Check:

   Rows=2, Columns=3 -> (2, 3) [OK]

Hint: Count rows then columns for shape [OK]

Common Mistakes:

• Swapping rows and columns in shape
• Thinking shape is total elements
• Confusing shape with array size

4. What is wrong with this code snippet?

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

medium

A. print statement syntax is incorrect

B. arr is not a NumPy array

C. The array is empty

D. shape is an attribute, not a method; parentheses cause error

Solution

1. Step 1: Identify the error source

   The code calls arr.shape() with parentheses, but shape is an attribute.

2. Step 2: Understand attribute vs method

   Attributes do not use parentheses; calling with () causes a TypeError.

3. Final Answer:

   shape is an attribute, not a method; parentheses cause error -> Option D

4. Quick Check:

   Attributes no parentheses [OK]

Hint: Remember: shape is attribute, no () [OK]

Common Mistakes:

• Using parentheses with shape
• Assuming array is empty causes error
• Misreading print syntax

5. You have a 3D NumPy array with shape (4, 3, 2). Which of the following operations will correctly reshape it into a 2D array with 4 rows and 6 columns?

hard

A. arr.reshape(12, 2)

B. arr.reshape(4, 6)

C. arr.reshape(6, 4)

D. arr.reshape(2, 12)

Solution

1. Step 1: Calculate total elements

   Original shape (4, 3, 2) means 4*3*2 = 24 elements total.

2. Step 2: Determine new shape dimensions

   New shape (4, 6) means 4*6 = 24 elements, matching total elements.

3. Step 3: Verify other options

   arr.reshape(12, 2), arr.reshape(6, 4), and arr.reshape(2, 12) do not produce 4 rows and 6 columns.

4. Final Answer:

   arr.reshape(4, 6) -> Option B

5. Quick Check:

   4*6=24 matches total elements [OK]

Hint: New shape must keep total elements same [OK]

Common Mistakes:

• Mixing up rows and columns in reshape
• Choosing shapes with wrong total elements
• Forgetting reshape must keep total size