Lernen Introduction to Architectural Data Structures | Architectural Data Analysis with Python

Understanding how to represent and organize architectural data in Python is a crucial skill for anyone working with building information, schedules, or material specifications. In architectural workflows, you will often need to store lists of spaces, keep track of material properties, and manage complex schedules. Python provides several built-in and external data structures that make this organization practical and efficient. Common choices include lists for storing collections such as room names, dictionaries for mapping material properties, and pandas DataFrames for handling tabular data like room schedules.


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import pandas as pd

# List of room names in a building
room_names = ["Lobby", "Conference Room", "Office 1", "Office 2", "Restroom"]

# Dictionary of material properties
material_properties = {
    "Concrete": {"density": 2400, "cost": 100},
    "Glass": {"density": 2500, "cost": 300},
    "Steel": {"density": 7850, "cost": 500}
}

# Creating a DataFrame for a room schedule
room_schedule = pd.DataFrame({
    "Room": room_names,
    "Area (sqm)": [50, 30, 20, 20, 10],
    "Occupancy": [10, 20, 3, 3, 2]
})

print(room_schedule)

Lists are ideal for storing simple collections, such as the names of rooms within a building. Dictionaries allow you to associate each material with its specific properties, enabling quick lookups and calculations based on density or cost. The pandas DataFrame structure is especially useful for architectural schedules, where each row represents a room and each column holds an attribute like area or occupancy. These structures help you organize and access information efficiently, making it easier to update schedules, calculate material quantities, or analyze building layouts.


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# Access and modify data in the DataFrame

# Update the area of "Conference Room" to 35 sqm
room_schedule.loc[room_schedule["Room"] == "Conference Room", "Area (sqm)"] = 35

# Print the updated DataFrame
print(room_schedule)

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Abschnitt 1. Kapitel 1

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              1234567891011121314151617181920
            
import pandas as pd

# List of room names in a building
room_names = ["Lobby", "Conference Room", "Office 1", "Office 2", "Restroom"]

# Dictionary of material properties
material_properties = {
    "Concrete": {"density": 2400, "cost": 100},
    "Glass": {"density": 2500, "cost": 300},
    "Steel": {"density": 7850, "cost": 500}
}

# Creating a DataFrame for a room schedule
room_schedule = pd.DataFrame({
    "Room": room_names,
    "Area (sqm)": [50, 30, 20, 20, 10],
    "Occupancy": [10, 20, 3, 3, 2]
})

print(room_schedule)


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# Access and modify data in the DataFrame

# Update the area of "Conference Room" to 35 sqm
room_schedule.loc[room_schedule["Room"] == "Conference Room", "Area (sqm)"] = 35

# Print the updated DataFrame
print(room_schedule)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 1

Introduction to Architectural Data Structures

1. Which Python data structure is best suited for storing a schedule of rooms with multiple attributes (e.g., area, occupancy)?

2. What is the advantage of using a dictionary for material properties in architectural calculations?

3. How does a pandas DataFrame differ from a list when handling tabular architectural data?

Introduction to Architectural Data Structures

1. Which Python data structure is best suited for storing a schedule of rooms with multiple attributes (e.g., area, occupancy)?

2. What is the advantage of using a dictionary for material properties in architectural calculations?

3. How does a pandas DataFrame differ from a list when handling tabular architectural data?