Array attributes (shape, dtype, ndim, size) in NumPy - Mini Project: Build & Apply

Explore NumPy Array Attributes

📖 Scenario: You are working with data collected from a small weather station. The data is stored in arrays representing temperature readings over several days.

🎯 Goal: Learn how to create a NumPy array and explore its basic attributes: shape, dtype, ndim, and size.

📋 What You'll Learn

Create a NumPy array with specific temperature data

Create a variable to hold the array

Access and print the array's shape, dtype, ndim, and size attributes

💡 Why This Matters

🌍 Real World

Data scientists often check array attributes to understand the data's structure before processing it.

💼 Career

Knowing array attributes is essential for data cleaning, transformation, and ensuring compatibility with machine learning models.

1

Create a NumPy array with temperature data

Import the NumPy library as np and create a NumPy array called temps with these exact values: [[22, 21, 23], [20, 19, 18], [25, 24, 26]].

NumPy

# Import numpy as np and create the array temps with the given values
# Your code here

Hint

Use np.array() to create the array with the nested list of temperatures.

2

Create a variable to hold the array's shape

Create a variable called shape and set it to the shape attribute of the temps array.

NumPy

import numpy as np

temps = np.array([[22, 21, 23], [20, 19, 18], [25, 24, 26]])

# Create variable shape to hold temps.shape
# Your code here

Hint

Use temps.shape to get the shape of the array.

3

Create variables for dtype, ndim, and size

Create three variables: dtype set to temps.dtype, ndim set to temps.ndim, and size set to temps.size.

NumPy

import numpy as np

temps = np.array([[22, 21, 23], [20, 19, 18], [25, 24, 26]])

shape = temps.shape

# Create dtype, ndim, and size variables from temps
# Your code here

Hint

Access each attribute directly from the temps array.

4

Print the array attributes

Print the variables shape, dtype, ndim, and size each on a separate line in this exact order.

NumPy

import numpy as np

temps = np.array([[22, 21, 23], [20, 19, 18], [25, 24, 26]])

shape = temps.shape

dtype = temps.dtype
ndim = temps.ndim
size = temps.size

# Print shape, dtype, ndim, and size each on its own line
# Your code here

Hint

Use four print() statements, one for each variable.

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