Array attributes (shape, dtype, ndim, size) in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the shape attribute of a NumPy array tell you?

The shape attribute shows the size of the array in each dimension. For example, (3, 4) means 3 rows and 4 columns.

beginner

What information does the dtype attribute provide in a NumPy array?

The dtype attribute tells you the type of data stored in the array, like integers (int32) or floating-point numbers (float64).

beginner

Explain the ndim attribute of a NumPy array.

The ndim attribute tells you how many dimensions the array has. For example, 1 means a vector, 2 means a matrix, and so on.

beginner

What does the size attribute represent in a NumPy array?

The size attribute shows the total number of elements in the array, no matter how many dimensions it has.

intermediate

How can you use these attributes to understand a NumPy array quickly?

By checking shape, dtype, ndim, and size, you learn the array's layout, data type, number of dimensions, and total elements. This helps you know what kind of data you have and how it is organized.

What does arr.shape return for a NumPy array?

A The total number of elements in the array

B The number of dimensions of the array

C The data type of the array elements

D The size of each dimension of the array

shape gives the size of each dimension, like (rows, columns).

If arr.ndim is 3, what does it mean?

A The array has 3 dimensions

B The array has 3 elements

C The array has 3 rows

D The array has 3 columns

ndim tells how many dimensions the array has.

Which attribute tells you the total number of elements in a NumPy array?

A shape

B size

C dtype

D ndim

size is the total count of all elements in the array.

What does arr.dtype show?

A The shape of the array

B The number of dimensions

C The data type of elements

D The total size of the array

dtype tells the type of data stored, like int or float.

If an array has shape = (4, 5), what is its size?

A 20

B 5

C 9

D 4

Size is the product of shape dimensions: 4 * 5 = 20.

Describe what each of these NumPy array attributes means: shape, dtype, ndim, and size.

Think about how these attributes help you understand the array's structure and data.

How would you use the shape and size attributes to find out how many elements are in each dimension of a NumPy array?

Remember that size is the total number of elements, and shape tells the size of each dimension.

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