Appearance
question:Write a python function to calculate the monthly budget allocation for a deployed service member's family, given their total monthly income, fixed monthly expenses, and a list of additional expenses. The function should return the remaining amount after all expenses have been deducted. Input:- `total_income`: A float representing the total monthly income of the family.- `fixed_expenses`: A float representing the sum of fixed monthly expenses (like rent, utilities, etc.).- `additional_expenses`: A list of floats where each element represents an additional expense for that month.Output:- A float representing the remaining amount after all expenses have been deducted from the total income.Example:```pythoncalculate_remaining_budget(5000.0, 2000.0, [300.0, 150.0, 50.0])```Expected output: `2500.0`
answer:def calculate_remaining_budget(total_income, fixed_expenses, additional_expenses): total_expenses = fixed_expenses + sum(additional_expenses) remaining_budget = total_income - total_expenses return remaining_budget
question:Write a python function to calculate the average solar energy absorption over a given period, based on daily absorption data and a list of climate factors affecting absorption efficiency. The input to your function should include:1. A list of daily solar energy absorption values (in kilowatt-hours), which represents the solar energy absorbed over a series of days.2. A dictionary of climate factors, where keys are strings describing the factor (e.g., "cloud cover", "humidity", "temperature") and values are their respective impact on absorption efficiency as percentages (e.g., 90 means 90% efficiency).The function should return the average solar energy absorption adjusted for the impact of these climate factors. Assume that each climate factor independently affects the energy absorption.Example:```pythondaily_absorption = [4.5, 5.0, 4.8, 5.1, 4.9]climate_factors = {"cloud cover": 80, "humidity": 95, "temperature": 85}# Expected output: A float representing the average solar energy absorption adjusted for climate factors.```Your task is to implement the logic to adjust the daily absorption values based on the given climate factors and calculate the average of the adjusted values.
answer:def calculate_avg_solar_absorption(daily_absorption, climate_factors): adjusted_absorption = [] for absorption in daily_absorption: adjustment_factor = 1 for factor, efficiency in climate_factors.items(): adjustment_factor *= efficiency / 100 adjusted_absorption.append(absorption * adjustment_factor) avg_absorption = sum(adjusted_absorption) / len(adjusted_absorption) return avg_absorption
question:Write a python function to take a list of dictionaries, where each dictionary represents a project with keys 'name', 'status', and 'budget'. The function should return a new list of project names that have a status of 'completed' and a budget greater than 1,000,000 units. Input:- A list of dictionaries. Each dictionary contains: - 'name': a string representing the name of the project. - 'status': a string that can be 'completed', 'in-progress', or 'not-started'. - 'budget': an integer representing the project's budget.Output:- A list of strings representing the names of projects that are 'completed' and have a budget greater than 1,000,000 units.Example:```pythonprojects = [ {'name': 'Project A', 'status': 'completed', 'budget': 1500000}, {'name': 'Project B', 'status': 'in-progress', 'budget': 2000000}, {'name': 'Project C', 'status': 'completed', 'budget': 900000}, {'name': 'Project D', 'status': 'completed', 'budget': 1200000}]# Expected Output: ['Project A', 'Project D']```
answer:def filter_completed_projects(projects): completed_projects = [] for project in projects: if project['status'] == 'completed' and project['budget'] > 1000000: completed_projects.append(project['name']) return completed_projects
question:Write a python function to calculate the total carbon footprint from a list of daily emissions data. Each day's emissions data is provided as a dictionary with keys representing different emission sources (e.g., 'car', 'electricity', 'flights') and values representing the amount of CO2 emitted in kilograms. The function should accept a list of these dictionaries and return the total carbon footprint as a single float representing the sum of all emissions from all days.Input: A list of dictionaries, where each dictionary's keys are strings (emission sources) and values are floats (CO2 emissions in kilograms).Output: A single float representing the total carbon footprint.Example:```pythondaily_emissions = [ {'car': 5.0, 'electricity': 10.0, 'flights': 50.0}, {'car': 3.0, 'electricity': 12.0, 'flights': 30.0}, {'car': 7.0, 'electricity': 9.0, 'flights': 20.0}]# Output should be 146.0```
answer:def calculate_total_carbon_footprint(daily_emissions): total_footprint = 0.0 for day_emissions in daily_emissions: day_total = sum(day_emissions.values()) total_footprint += day_total return total_footprint
question:Write a python function to simulate a simple hypnosis session that helps to address the root cause of a phobia. The function should take a list of strings as input, where each string represents a thought or emotion related to the phobia. It should return a new list of strings where each thought or emotion has been gently transformed into a positive affirmation.For example, for the input `["I'm scared of heights", "I feel anxious when I look down", "What if I fall?"]`, the expected output should be something like `["I am safe and secure", "I feel calm and confident", "I trust in my ability to stay balanced"]`. The transformation does not need to be perfect but should reflect a calming and positive shift in perspective.The function should:1. Take as input: a list of strings (`thoughts`).2. Return as output: a list of transformed strings (`positive_affirmations`).You can assume that the input list will contain at least one string.
answer:def transform_thoughts(thoughts): positive_affirmations = [] for thought in thoughts: if "scared" in thought: positive_affirmations.append("I am safe and secure") elif "anxious" in thought: positive_affirmations.append("I feel calm and confident") elif "fall" in thought: positive_affirmations.append("I trust in my ability to stay balanced") else: positive_affirmations.append("I am at peace and in control") return positive_affirmations
question:Write a python function to help me manage the inventory of custom car parts in my shop. I need a function that takes in a list of car parts where each part is represented as a dictionary with the keys "name", "category", and "quantity". The function should return a new list containing only the parts that have a quantity greater than zero, sorted alphabetically by their "name". Input:- A list of dictionaries, where each dictionary represents a car part with the keys: - "name" (a string): the name of the car part. - "category" (a string): the category of the car part. - "quantity" (an integer): the quantity of the car part in stock.Output:- A list of dictionaries, containing only the parts with a quantity greater than zero, sorted alphabetically by "name".Example:```pythonparts = [ {"name": "Spoiler", "category": "Aerodynamics", "quantity": 5}, {"name": "Turbocharger", "category": "Engine", "quantity": 0}, {"name": "Alloy Wheels", "category": "Wheels", "quantity": 3}, {"name": "Brake Pads", "category": "Brakes", "quantity": 7}]# Expected output:# [# {"name": "Alloy Wheels", "category": "Wheels", "quantity": 3},# {"name": "Brake Pads", "category": "Brakes", "quantity": 7},# {"name": "Spoiler", "category": "Aerodynamics", "quantity": 5}# ]```
answer:def manage_inventory(parts): available_parts = [part for part in parts if part["quantity"] > 0] sorted_parts = sorted(available_parts, key=lambda x: x["name"]) return sorted_parts