Lære Summarizing Building Data | Architectural Data Analysis with Python

As an architect, you often need to quickly assess key statistics about a building to inform your design choices and communicate effectively with clients and engineers. Some of the most common summary statistics include the total floor area of a building, which gives you an idea of the project's scale and cost; the average room size, which helps evaluate comfort and space efficiency; and material quantities, which are crucial for budgeting and procurement. These summaries provide a foundation for making informed design decisions and ensuring your plans meet both regulatory standards and client expectations.


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import pandas as pd

# Sample data for rooms in a building
data = {
    "Name": ["Living Room", "Bedroom 1", "Bedroom 2", "Kitchen", "Bathroom"],
    "Area": [35.0, 20.0, 18.0, 15.0, 8.0]
}
df = pd.DataFrame(data)

# Calculate total area
total_area = df["Area"].sum()
print("Total floor area:", total_area, "sqm")

# Calculate average room area
average_area = df["Area"].mean()
print("Average room size:", round(average_area, 2), "sqm")

In the code above, you use two essential pandas methods: sum() and mean(). The sum() method adds up all the values in the Area column, giving you the total floor area of the building. This number is vital for estimating construction costs, complying with zoning requirements, and planning building services. The mean() method calculates the average room size, which helps you assess whether spaces are appropriately sized for their functions and compare your design to typical standards or client preferences. By leveraging these summary statistics, you can make better-informed design decisions and clearly communicate the impact of your choices.


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# Define thresholds for room size classification
def classify_room(area):
    if area < 12:
        return "Small"
    elif area <= 25:
        return "Medium"
    else:
        return "Large"

# Add a new column classifying room sizes
df["Size Category"] = df["Area"].apply(classify_room)
print(df)

1. Which pandas method would you use to find the largest room in a building?

2. Why is it useful to classify rooms by size in architectural planning?

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Suggested prompts:

Can you explain how the room size categories were determined?

What other statistics can I calculate from this data?

How can I customize the thresholds for room size classification?

Sveip for å vise menyen


              12345678910111213141516
            
import pandas as pd

# Sample data for rooms in a building
data = {
    "Name": ["Living Room", "Bedroom 1", "Bedroom 2", "Kitchen", "Bathroom"],
    "Area": [35.0, 20.0, 18.0, 15.0, 8.0]
}
df = pd.DataFrame(data)

# Calculate total area
total_area = df["Area"].sum()
print("Total floor area:", total_area, "sqm")

# Calculate average room area
average_area = df["Area"].mean()
print("Average room size:", round(average_area, 2), "sqm")


              123456789101112
            
# Define thresholds for room size classification
def classify_room(area):
    if area < 12:
        return "Small"
    elif area <= 25:
        return "Medium"
    else:
        return "Large"

# Add a new column classifying room sizes
df["Size Category"] = df["Area"].apply(classify_room)
print(df)

1. Which pandas method would you use to find the largest room in a building?

2. Why is it useful to classify rooms by size in architectural planning?

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 2