CSE 101: Introduction To Computers Lab 2 Spring 2017 Assignm

CSE 101: Introduction to Computers Lab #2 Spring 2017 Assignment Due

Complete the ddg() function, which takes a list of strings as its only argument. Each string value will be either “duck”, “goose”, or “egg” (all lowercase). The function returns a non-negative integer according to the rules: each “duck” is worth 1 point, each “goose” is worth 2 points, and encountering an “egg” resets the current count to 0. For example, the sequence [“duck”, “duck”, “goose”] would produce the count value 4, while [“duck”, “egg”, “goose”, “egg”] would return 0, and [“duck”, “goose”, “egg”, “duck”] would return 1.

Complete the schedule() function, which takes a list of positive integers (average temperatures) as input. It should return a list of strings representing the daily activities based on the temperature: 90 or higher – “swimming”, 80–89 – “hiking”, 70–79 – “tennis”, 60–69 – “softball”, 50–59 – “football”, 49 or below – “movie”. For example, [72, 84, 55] should return [“tennis”, “hiking”, “football”].

Complete the intersection() function, which takes two lists of integers (each representing a set with no duplicates) and returns a list of integers that are common to both lists. The order does not matter. If there are no common elements, return an empty list. For example, [1, 2, 3, 4, 5] and [2, 4, 6, 8, 10] should return [2, 4].

Paper For Above instruction

This paper provides comprehensive solutions to three fundamental programming problems designed to enhance understanding and mastery of Python programming. The problems involve handling strings with control flow, list processing with conditional logic, and set operations. These exercises are essential for developing skills in algorithm design, data manipulation, and function implementation in Python.

Introduction

In contemporary programming, the ability to manipulate data effectively is crucial. Python offers rich data structures like lists and strings, along with control flow constructs that enable developers to solve complex problems with concise code. This document addresses three core challenges: scoring poultry animals based on specific rules, categorizing daily activities based on temperature forecasts, and computing intersections of two sets represented as lists.

Part I: Counting Poultry Animals with Conditions

Tasked with implementing the ddg() function, the goal is to process a sequence of animals represented by strings: “duck”, “goose”, and “egg”. Each duck yields one point, each goose yields two points, and an egg resets the total score to zero. The process involves iterating through the list of strings, updating the score based on the current item. If the item is “egg”, the score resets; otherwise, points are added accordingly. This problem exemplifies fundamental control flow and list traversal techniques in Python, demonstrating how to accumulate values conditionally.

Part II: Determining Activities Based on Weather Forecasts

The schedule() function requires mapping integer temperature values to activities according to specified ranges. This involves simple conditional tests and list processing. By iterating through the temperature list, the function assigns the appropriate activity based on threshold comparisons, which can be efficiently implemented using if-elif-else statements. Solving this problem enhances decision-making skills in programming and introduces the concept of categorization based on numerical ranges.

Part III: Set Intersection via Lists

The intersection() function explores fundamental set operations applied through lists. Given two lists containing unique elements, the goal is to extract common elements. While Python sets could directly compute intersections, the problem emphasizes working with lists and includes understanding membership testing. Implementing this involves nested loops or list comprehensions, reinforcing knowledge about iteration, membership checking, and list construction. It’s an important exercise in simulating set behavior without relying solely on built-in set data structures.

Implementation Strategies and Best Practices

For each problem, careful attention must be paid to details such as resetting counters, handling boundary conditions in ranges, and ensuring no duplicates are included in the final output for set intersection. Utilizing Python’s built-in data structures appropriately can improve efficiency; for the intersection problem, converting lists to sets could be considered, but understanding manual iteration is equally valuable. Testing each function with provided and additional test cases ensures correctness before submission, which is critical for successful programming education.

Conclusion

Mastering these problems illustrates vital programming concepts: conditional logic, list traversal, and set operations. These skills form the foundation for more advanced algorithm development. Continuing practice with such exercises will strengthen problem-solving abilities, and proficiency in Python syntax and idioms will foster confidence in tackling diverse computational challenges in academic and real-world contexts.

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