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

Need a 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

Need a 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

Need a 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

Need a hint?

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