Question

Cubes: A number raised to the third power is called a cube. For example, the cube of 2 is written as 2**3 in Python. Make a list of the first 10 cubes (that is, the cube of each integer from 1 through 10), and use a for loop to print out the value of each cube.

Short answer

Cube each number from 1 to 10 and print the resulting list of cubes.

Step-by-step solution

Understand Cubing a Number

A cube of a number is that number multiplied by itself twice. In Python, this can be represented as raising the number to the power of 3, which is written using the expression **3.

Create a List of Integers 1 to 10

We'll start by creating a list of integers from 1 through 10. This list represents the numbers that we want to cube.

Initialize an Empty List for Cubes

Before calculating the cubes, it's helpful to initialize an empty list named `cubes` where we'll store the cube of each number.

Calculate Cubes Using a For Loop

Use a `for` loop to iterate through each number (from 1 to 10). Within this loop, calculate the cube of the current number and append it to the `cubes` list. The cube is found by raising each number to the power of 3 (number ** 3).

Print Each Cube

Once all the cubes have been calculated and stored in the list, use another `for` loop to iterate over the `cubes` list and print each cube to display the results.

Key concepts

Understanding For Loops in Python

In Python programming, a for loop is a control structure used to repeat a block of code a fixed number of times. The for loop enables you to iterate over a sequence, such as a list, tuple, or string. In the context of the exercise, we use a for loop to iterate through a list of numbers from 1 to 10. This helps us calculate the cubes of each number.
The syntax for a simple for loop is:

• for variable in sequence:
• # Perform some action

Here, "sequence" can be any iterable, like a range of numbers created using range(1, 11). The "variable" acts as a placeholder for each element in the sequence as the loop progresses. This makes for loops a powerful tool for repeating operations on data automatically.

Basics of Data Structures: Lists

Data structures are ways of organizing and storing data so they can be accessed and worked with efficiently. One of the most common data structures in Python is the list. Lists are mutable ordered collections of items, making them perfect for storing sequences like our list of cubes.
Creating a list in Python is simple: use square brackets and separate items with commas. For example, numbers = [1, 2, 3, 4, 5] creates a list of integers.
Lists allow you to store multiple items in a single variable, which is why they're used to keep both the original numbers and the cubes calculated in the exercise. You can easily append new items to a list with the append() method, making lists dynamic and flexible for tasks where the amount of data can change.

Exponentiation in Python

Exponentiation is a mathematical operation that raises one number (the base) to the power of another (the exponent). In Python, exponentiation is performed using the double asterisk operator (**). For example, 2**3 means 2 raised to the power of 3, which equals 8.
This operation is central to the exercise, as it enables the calculation of cubes. By raising each number from 1 to 10 to the power of 3, we obtain the "cubes" of these numbers. Python's ** operator is efficient and supports a broad range of numerical operations, making it a fundamental aspect of calculations in many programming projects.

Iteration Explained

Iteration refers to the process of repeating a specific set of instructions in a program. It involves looping through a set of data, one item at a time, performing operations until a condition is met or data is exhausted.
In programming, iteration is commonly achieved using loops—such as for loops and while loops. In the context of our exercise, iteration is used to cycle through each number from 1 to 10 and compute its cube by applying the exponentiation operation.
Iteration is crucial as it allows us to apply algorithms to large datasets effectively and concisely, automating processes that would be cumbersome to code manually for each data point.