Define Ddgpoultry: Write Your Code Here And Edit The Return

Def Ddgpoultry Write Your Code Here And Edit The Return Statement

Implement a function named ddg that processes a list of strings representing poultry on a conveyor belt. The list elements can be "duck", "goose", or "egg". "duck" adds 1 point, "goose" adds 2 points, and "egg" resets the count to zero. The function should cumulatively calculate the total score based on these rules and return the final count.

Next, write a function named schedule that takes a list of integers representing daily temperatures. For each temperature, the function should determine an activity based on the following criteria:

  • 90 or higher: "swimming"
  • 80–89: "hiking"
  • 70–79: "tennis"
  • 60–69: "softball"
  • 50–59: "football"
  • 49 or below: "movie"

The function should return a list of activities corresponding to each day's temperature.

Finally, create a function named intersection that takes two lists of integers, each representing a set (i.e., no duplicates). The function should return a list of integers containing only the elements common to both input lists. The order of elements in the output list does not matter.

Ensure all functions are correctly implemented following these specifications and that the code is ready to run with the provided test cases.

Paper For Above instruction

The programming task herein described involves implementing three core functions in Python: ddg, schedule, and intersection. Each function addresses a distinct problem area—processing a list of poultry types, scheduling activities based on weather, and computing the intersection of two sets, respectively. This comprehensive exercise aims to enhance student skills in control flow, list processing, and set operations in Python.

Implementing the ddg Function

The ddg function processes a list containing strings "duck", "goose", and "egg" to compute a total score. The scoring rules assign one point to each "duck", two points for each "goose", and upon encountering an "egg", the total score resets to zero. The function iterates through the list, maintaining a running total that reflects these rules.

This implementation reflects a common pattern in algorithm design involving accumulation states that are updated based on conditional logic. Handling the reset on "egg" requires resetting the accumulator, illustrating the importance of control flow in list processing. The solution also exemplifies the importance of testing functions with various input scenarios, including edge cases such as an initial "egg" or multiple consecutive "egg" entries.

Developing the schedule Function

The schedule function translates a list of daily temperature readings into a list of activities. Each temperature value maps to an activity based on specified temperature ranges. This task demonstrates the use of conditional statements (if-elif-else) to categorize numerical data effectively.

Mapping temperatures to activities is characteristic of decision-making algorithms that assign labels or categories based on numeric thresholds. Handling boundary conditions—such as exactly 90 degrees or exactly 50 degrees—is crucial to ensure correct categorization. The implementation converts temperature data into engaging activity recommendations, illustrating practical applications of control logic.

Constructing the intersection Function

The intersection function identifies common elements between two lists that each represent a set (no duplicate values). Since the inputs are set-like lists, the most efficient implementation leverages Python's set data structures for intersection operations. Converting lists to sets allows use of the built-in intersection operator (&) or method (&=), then transforming the result back into a list if necessary.

This approach demonstrates the importance of understanding Python's built-in data structures for optimization. It also emphasizes the importance of maintaining data integrity—ensuring no duplicates in the source data—and correctness by properly returning only common elements. Testing with various datasets, including those with no intersection or fully overlapping sets, confirms the robustness of the solution.

Conclusion

The trio of functions embodies essential programming concepts: state management with the ddg function, decision trees with the schedule function, and set operations with the intersection function. Mastery of these functions fosters foundational coding skills highly relevant for computational problem solving and algorithm development.

References

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