Array attributes (shape, dtype, ndim, size) in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of array shape and size

What is the output of the following code snippet?

NumPy

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

A (2, 3) 5

B (3, 2) 6

C (2, 3) 6

D (3, 2) 5

💡 Hint

Shape shows rows and columns; size is total elements.

The array has 2 rows and 3 columns, so shape is (2, 3). Total elements are 2*3=6.

❓ Predict Output

intermediate

Determine array data type

What will be printed by this code?

NumPy

import numpy as np
arr = np.array([1, 2, 3], dtype='float32')
print(arr.dtype)

A float32

B int32

C float64

D int64

💡 Hint

dtype shows the type of elements stored.

The array was created with dtype='float32', so arr.dtype is float32.

❓ data_output

advanced

Number of dimensions of an array

What is the value of arr.ndim after running this code?

NumPy

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

A 4

B 2

C 1

D 3

💡 Hint

Count how many levels of nested lists there are.

The array has 3 levels of nested lists, so ndim is 3.

❓ Predict Output

advanced

Shape and size of a reshaped array

What is the output of this code?

NumPy

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

A (3, 4) 12

B (4, 3) 12

C (3, 4) 11

D (4, 3) 11

💡 Hint

Reshape changes shape but size stays same.

The array is reshaped to 3 rows and 4 columns; size remains 12 elements.

🧠 Conceptual

expert

Understanding dtype impact on size

Given two arrays with the same shape but different dtypes, which statement is true?

A Both arrays have the same size attribute value.

B The array with larger dtype uses more memory but size attribute is same.

C The size attribute changes depending on dtype size in bytes.

D The array with smaller dtype has a larger size attribute.

💡 Hint

Size counts elements, not memory used.

Size counts number of elements, not memory. Memory depends on dtype but size attribute is element count.

Practice

1. Which numpy attribute tells you the total number of elements in an array?

easy

A. size

B. shape

C. ndim

D. dtype

Solution

1. Step 1: Understand the meaning of each attribute

   shape shows dimensions, dtype shows data type, ndim shows number of dimensions, and size shows total elements.

2. Step 2: Identify the attribute for total elements

   The attribute size gives the total number of elements in the array.

3. Final Answer:

   size -> Option A

4. Quick Check:

   Total elements = size [OK]

Hint: Total elements? Use size attribute [OK]

Common Mistakes:

• Confusing shape with total elements
• Thinking ndim gives total elements
• Mixing dtype with size

2. Which of the following is the correct way to get the number of dimensions of a numpy array arr?

easy

A. arr.size()

B. arr.ndim

C. arr.shape()

D. arr.dtype

Solution

1. Step 1: Recall attribute vs method syntax in numpy

   Attributes like ndim and dtype are accessed without parentheses, methods like size() do not exist.

2. Step 2: Identify correct attribute for number of dimensions

   arr.ndim gives the number of dimensions directly.

3. Final Answer:

   arr.ndim -> Option B

4. Quick Check:

   Number of dimensions = arr.ndim [OK]

Hint: Use attribute without () for ndim, size, dtype [OK]

Common Mistakes:

• Using parentheses with attributes
• Confusing shape with ndim
• Trying to call size() as method

3. Given the code:

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

What is the output?

medium

A. (2, 3) float64 2 6

B. (3, 2) int64 2 6

C. (2, 3) int64 2 6

D. (2, 3) int32 2 6

Solution

1. Step 1: Analyze the array shape and data type

   The array has 2 rows and 3 columns, so shape is (2, 3). The default integer type on many systems is int64.

2. Step 2: Check number of dimensions and total size

   It is a 2D array, so ndim = 2. Total elements = 2*3 = 6.

3. Final Answer:

   (2, 3) int64 2 6 -> Option C

4. Quick Check:

   shape=(2,3), dtype=int64, ndim=2, size=6 [OK]

Hint: Shape = rows x columns, size = product of shape [OK]

Common Mistakes:

• Mixing rows and columns in shape
• Assuming dtype is int32 by default
• Confusing ndim with 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 cannot display tuples

B. arr is not defined

C. shape returns a number, not a tuple

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

Solution

1. Step 1: Identify usage of shape

   shape is an attribute, so it should be accessed without parentheses.

2. Step 2: Correct the code

   Change arr.shape() to arr.shape to fix the error.

3. Final Answer:

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

4. Quick Check:

   Attributes have no () [OK]

Hint: Attributes like shape have no () [OK]

Common Mistakes:

• Using parentheses with attributes
• Thinking shape returns a single number
• Assuming print can't show tuples

5. You have a numpy array arr with shape (4, 5, 6). Which attribute would you use to find the total number of elements, and what is that number?

hard

A. arr.size, 120

B. arr.ndim, 3

C. arr.shape, (4, 5, 6)

D. arr.dtype, depends on data

Solution

1. Step 1: Understand the attributes

   size gives total elements, ndim gives number of dimensions, shape gives dimensions, dtype gives data type.

2. Step 2: Calculate total elements from shape

   Total elements = 4 * 5 * 6 = 120, so arr.size = 120.

3. Final Answer:

   arr.size, 120 -> Option A

4. Quick Check:

   Total elements = product of shape = 120 [OK]

Hint: Multiply shape dimensions or use size [OK]

Common Mistakes:

• Confusing ndim with total elements
• Using shape instead of size
• Ignoring that dtype is unrelated to size