Lära Challenge: Room Selection Tool | Architectural Data Analysis with Python

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.


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import 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.

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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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              123456789101112131415161718192021
            
import 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)