Question

What will the following code display? numbers1 = [1, 2, 3] numbers2 = [10, 20, 30] numbers2 += numbers1 print(numbers1) print(numbers2)

Short answer

```python numbers1 = [1, 2, 3] numbers2 = [10, 20, 30] numbers2 += numbers1 print(numbers1) print(numbers2) ``` Answer: The output of the given Python code is: ``` [1, 2, 3] [10, 20, 30, 1, 2, 3] ```

Step-by-step solution

Declare the Lists

Declare two lists, numbers1 and numbers2. numbers1 contains the integer values 1, 2, and 3, while numbers2 contains the integer values 10, 20, and 30: numbers1 = [1, 2, 3] numbers2 = [10, 20, 30]

List Concatenation with the += Operator

The += operator is used to concatenate lists. In this case, the elements of numbers1 will be appended to the end of numbers2, resulting in a new version of numbers2: numbers2 += numbers1

Result of List Concatenation

After the list concatenation, numbers1 remains unchanged since its elements are just added to numbers2. The list numbers2 now contains the elements [10, 20, 30, 1, 2, 3].

Print the Lists

Finally, the code prints the contents of both lists: print(numbers1) # Output: [1, 2, 3] print(numbers2) # Output: [10, 20, 30, 1, 2, 3]

Summary

The given code will display the following output: [1, 2, 3] [10, 20, 30, 1, 2, 3]

Key concepts

List Operations in Python

Lists are a fundamental part of Python, serving as one of the language's built-in data types that are both versatile and user-friendly. In Python, a list can store a sequence of objects in a specific order, and these objects could be anything: numbers, strings, or even other lists.

The beauty of Python lists lies in the wide range of operations that can be performed on them. Common list operations include adding elements (append, extend, insert), removing elements (remove, pop), and accessing elements by index. Perhaps one of the most intuitive features is the ability to concatenate lists, that is, to join two lists end-to-end to form a single list. This operation is often done using the '+=' operator or the 'extend()' method.

Another operation is slicing, where you can extract a subset of the list by specifying start, end, and step indices. This lets you work with just the portion of the list you're interested in. With these operations, lists become incredibly versatile for managing ordered data in Python programs.

List Concatenation with += Operator

The '+=' operator in Python is an example of an 'in-place' operator, which modifies the variable it is used with, rather than creating a new one. When it comes to lists, this operator has the effect of extending a list without explicitly calling the 'extend()' method.

To demonstrate list concatenation with the '+=' operator, consider two lists, A and B. The operation 'A += B' will append all the elements of list B to the end of list A, and critically, it mutates list A rather than creating a new list. In terms of memory and performance, this can be more efficient than 'A = A + B', which creates a new list and reassigns it back to A.

For example, if you have list1 = [1, 2] and list2 = [3, 4], after executing list1 += list2, list1 will be [1, 2, 3, 4]. It's essential to realize that list2 remains unchanged, and list1 has been modified in place.

Understanding Python Code Execution

Grasping the flow of execution is critical for debugging and writing efficient Python code. When a Python script is run, the interpreter reads and executes the code sequentially, line by line, from the start of the script to the end.

The code execution for list concatenation using the '+=' operator is a perfect example of understanding how Python modifies data structures during runtime. As Python executes the code, it reaches the concatenation statement and performs the operation in place. This means it doesn't need to create a new list; instead, it appends the items from the second list to the first list directly.

In the context of our exercise, the key takeaway is recognizing how the '+=' operator changed 'numbers2' while 'numbers1' remained unaffected. This kind of understanding ensures that as a programmer, you're not caught off guard by unintended side-effects in your data structures. It also aids in writing code that is not only correct but also optimized for performance.