Challenge: Room Selection Tool
In architectural practice, you often need to select rooms based on specific requirements such as minimum area and occupancy. Using Python and the pandas library, you can efficiently filter and sort room data to meet these criteria. Imagine you have a DataFrame representing various rooms in a building, with columns for the room name, its area in square meters, and its occupancy capacity.
123456789101112131415161718192021import pandas as pd # Hardcoded DataFrame of rooms data = { "Room": ["Conference", "Office1", "Office2", "Lounge", "Storage", "Lab"], "Area": [35, 20, 28, 40, 15, 30], "Occupancy": [10, 2, 3, 8, 1, 5] } rooms_df = pd.DataFrame(data) def select_rooms(df): # Filter rooms by area > 25 and occupancy >= 3 filtered = df[(df["Area"] > 25) & (df["Occupancy"] >= 3)] # Sort by area in descending order sorted_rooms = filtered.sort_values(by="Area", ascending=False) # Return list of room names return sorted_rooms["Room"].tolist() # Example usage selected = select_rooms(rooms_df) print(selected)
This function first filters the DataFrame to include only those rooms where the area is greater than 25 square meters and the occupancy is at least 3. It then sorts the filtered rooms by area in descending order and returns a list of the room names that meet these criteria. This approach allows you to quickly identify which rooms are suitable for larger groups or specific activities, making architectural planning more efficient.
Swipe to start coding
Write a function called select_rooms that takes a pandas DataFrame with columns 'Room', 'Area', and 'Occupancy'. The function should:
- Filter the DataFrame to include only rooms where area is greater than 25.
- Further filter to include only rooms where occupancy is at least 3.
- Sort the resulting rooms by area in descending order.
- Return a list of the room names in this order.
Test your function with the following DataFrame:
import pandas as pd
data = {
"Room": ["Conference", "Office1", "Office2", "Lounge", "Storage", "Lab"],
"Area": [35, 20, 28, 40, 15, 30],
"Occupancy": [10, 2, 3, 8, 1, 5]
}
rooms_df = pd.DataFrame(data)
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In architectural practice, you often need to select rooms based on specific requirements such as minimum area and occupancy. Using Python and the pandas library, you can efficiently filter and sort room data to meet these criteria. Imagine you have a DataFrame representing various rooms in a building, with columns for the room name, its area in square meters, and its occupancy capacity.
123456789101112131415161718192021import pandas as pd # Hardcoded DataFrame of rooms data = { "Room": ["Conference", "Office1", "Office2", "Lounge", "Storage", "Lab"], "Area": [35, 20, 28, 40, 15, 30], "Occupancy": [10, 2, 3, 8, 1, 5] } rooms_df = pd.DataFrame(data) def select_rooms(df): # Filter rooms by area > 25 and occupancy >= 3 filtered = df[(df["Area"] > 25) & (df["Occupancy"] >= 3)] # Sort by area in descending order sorted_rooms = filtered.sort_values(by="Area", ascending=False) # Return list of room names return sorted_rooms["Room"].tolist() # Example usage selected = select_rooms(rooms_df) print(selected)
This function first filters the DataFrame to include only those rooms where the area is greater than 25 square meters and the occupancy is at least 3. It then sorts the filtered rooms by area in descending order and returns a list of the room names that meet these criteria. This approach allows you to quickly identify which rooms are suitable for larger groups or specific activities, making architectural planning more efficient.
Swipe to start coding
Write a function called select_rooms that takes a pandas DataFrame with columns 'Room', 'Area', and 'Occupancy'. The function should:
- Filter the DataFrame to include only rooms where area is greater than 25.
- Further filter to include only rooms where occupancy is at least 3.
- Sort the resulting rooms by area in descending order.
- Return a list of the room names in this order.
Test your function with the following DataFrame:
import pandas as pd
data = {
"Room": ["Conference", "Office1", "Office2", "Lounge", "Storage", "Lab"],
"Area": [35, 20, 28, 40, 15, 30],
"Occupancy": [10, 2, 3, 8, 1, 5]
}
rooms_df = pd.DataFrame(data)
Oplossing
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