Pandas Construction Data Analysis

Overview

Based on DDC methodology (Chapter 2.3), this skill provides comprehensive Pandas operations for construction data processing. Pandas is the Swiss Army knife for data analysts - handling everything from simple data filtering to complex aggregations across millions of rows.

Book Reference: "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"

"Используя Pandas, вы можете управлять и анализировать наборы данных, намного превосходящие возможности Excel. В то время как Excel способен обрабатывать до 1 миллиона строк данных, Pandas может без труда работать с наборами данных, содержащими десятки миллионов строк." — DDC Book, Chapter 2.3

Quick Start

import pandas as pd

# Read construction data
df = pd.read_excel("bim_export.xlsx")

# Basic operations
print(df.head())           # First 5 rows
print(df.info())           # Column types and memory
print(df.describe())       # Statistics for numeric columns

# Filter structural elements
structural = df[df['Category'] == 'Structural']

# Calculate total volume
total_volume = df['Volume'].sum()
print(f"Total volume: {total_volume:.2f} m³")

DataFrame Fundamentals

Creating DataFrames

import pandas as pd

# From dictionary (construction elements)
elements = pd.DataFrame({
    'ElementId': ['E001', 'E002', 'E003', 'E004'],
    'Category': ['Wall', 'Floor', 'Wall', 'Column'],
    'Material': ['Concrete', 'Concrete', 'Brick', 'Steel'],
    'Volume_m3': [45.5, 120.0, 32.0, 8.5],
    'Level': ['Level 1', 'Level 1', 'Level 2', 'Level 1']
})

# From CSV
df_csv = pd.read_csv("construction_data.csv")

# From Excel
df_excel = pd.read_excel("project_data.xlsx", sheet_name="Elements")

# From multiple Excel sheets
all_sheets = pd.read_excel("project.xlsx", sheet_name=None)  # Dict of DataFrames

Data Types in Construction

# Common data types for construction
df = pd.DataFrame({
    'element_id': pd.Series(['W001', 'W002'], dtype='string'),
    'quantity': pd.Series([10, 20], dtype='int64'),
    'volume': pd.Series([45.5, 32.0], dtype='float64'),
    'is_structural': pd.Series([True, False], dtype='bool'),
    'created_date': pd.to_datetime(['2024-01-15', '2024-01-16']),
    'category': pd.Categorical(['Wall', 'Slab'])
})

# Check data types
print(df.dtypes)

# Convert types
df['quantity'] = df['quantity'].astype('float64')
df['volume'] = pd.to_numeric(df['volume'], errors='coerce')

Filtering and Selection

Basic Filtering

# Single condition
walls = df[df['Category'] == 'Wall']

# Multiple conditions (AND)
large_concrete = df[(df['Material'] == 'Concrete') & (df['Volume_m3'] > 50)]

# Multiple conditions (OR)
walls_or_floors = df[(df['Category'] == 'Wall') | (df['Category'] == 'Floor')]

# Using isin for multiple values
structural = df[df['Category'].isin(['Wall', 'Column', 'Beam', 'Foundation'])]

# String contains
insulated = df[df['Description'].str.contains('insulated', case=False, na=False)]

# Null value filtering
incomplete = df[df['Cost'].isna()]
complete = df[df['Cost'].notna()]

Advanced Selection

# Select columns
volumes = df[['ElementId', 'Category', 'Volume_m3']]

# Query syntax (SQL-like)
result = df.query("Category == 'Wall' and Volume_m3 > 30")

# Loc and iloc
specific_row = df.loc[0]                    # By label
range_rows = df.iloc[0:10]                  # By position
specific_cell = df.loc[0, 'Volume_m3']      # Row and column
subset = df.loc[0:5, ['Category', 'Volume_m3']]  # Range with columns

Grouping and Aggregation

GroupBy Operations

# Basic groupby
by_category = df.groupby('Category')['Volume_m3'].sum()

# Multiple aggregations
summary = df.groupby('Category').agg({
    'Volume_m3': ['sum', 'mean', 'count'],
    'Cost': ['sum', 'mean']
})

# Named aggregations (cleaner output)
summary = df.groupby('Category').agg(
    total_volume=('Volume_m3', 'sum'),
    avg_volume=('Volume_m3', 'mean'),
    element_count=('ElementId', 'count'),
    total_cost=('Cost', 'sum')
).reset_index()

# Multiple grouping columns
by_level_cat = df.groupby(['Level', 'Category']).agg({
    'Volume_m3': 'sum',
    'Cost': 'sum'
}).reset_index()

Pivot Tables

# Create pivot table
pivot = pd.pivot_table(
    df,
    values='Volume_m3',
    index='Level',
    columns='Category',
    aggfunc='sum',
    fill_value=0,
    margins=True,           # Add totals
    margins_name='Total'
)

# Multiple values
pivot_detailed = pd.pivot_table(
    df,
    values=['Volume_m3', 'Cost'],
    index='Level',
    columns='Category',
    aggfunc={'Volume_m3': 'sum', 'Cost': 'mean'}
)

Data Transformation

Adding Calculated Columns

# Simple calculation
df['Cost_Total'] = df['Volume_m3'] * df['Unit_Price']

# Conditional column
df['Size_Category'] = df['Volume_m3'].apply(
    lambda x: 'Large' if x > 50 else ('Medium' if x > 20 else 'Small')
)

# Using np.where for binary conditions
import numpy as np
df['Is_Large'] = np.where(df['Volume_m3'] > 50, True, False)

# Using cut for binning
df['Volume_Bin'] = pd.cut(
    df['Volume_m3'],
    bins=[0, 10, 50, 100, float('inf')],
    labels=['XS', 'S', 'M', 'L']
)

String Operations

# Extract from strings
df['Level_Number'] = df['Level'].str.extract(r'(\d+)').astype(int)

# Split and expand
df[['Building', 'Floor']] = df['Location'].str.split('-', expand=True)

# Clean strings
df['Category'] = df['Category'].str.strip().str.lower().str.title()

# Replace values
df['Material'] = df['Material'].str.replace('Reinforced Concrete', 'RC')

Date Operations

# Parse dates
df['Start_Date'] = pd.to_datetime(df['Start_Date'])

# Extract components
df['Year'] = df['Start_Date'].dt.year
df['Month'] = df['Start_Date'].dt.month
df['Week'] = df['Start_Date'].dt.isocalendar().week
df['DayOfWeek'] = df['Start_Date'].dt.day_name()

# Calculate duration
df['Duration_Days'] = (df['End_Date'] - df['Start_Date']).dt.days

# Filter by date range
recent = df[df['Start_Date'] >= '2024-01-01']

Merging and Joining

Merge DataFrames

# Elements data
elements = pd.DataFrame({
    'ElementId': ['E001', 'E002', 'E003'],
    'Category': ['Wall', 'Floor', 'Column'],
    'Volume_m3': [45.5, 120.0, 8.5]
})

# Unit prices
prices = pd.DataFrame({
    'Category': ['Wall', 'Floor', 'Column', 'Beam'],
    'Unit_Price': [150, 80, 450, 200]
})

# Inner join (only matching)
merged = elements.merge(prices, on='Category', how='inner')

# Left join (keep all elements)
merged = elements.merge(prices, on='Category', how='left')

# Join on different column names
result = df1.merge(df2, left_on='elem_id', right_on='ElementId')

Concatenating DataFrames

# Vertical concatenation (stacking)
all_floors = pd.concat([floor1_df, floor2_df, floor3_df], ignore_index=True)

# Horizontal concatenation
combined = pd.concat([quantities, costs, schedule], axis=1)

# Append new rows
new_elements = pd.DataFrame({'ElementId': ['E004'], 'Category': ['Beam']})
df = pd.concat([df, new_elements], ignore_index=True)

Construction-Specific Analyses

Quantity Take-Off (QTO)

def generate_qto_report(df):
    """Generate Quantity Take-Off summary by category"""
    qto = df.groupby(['Category', 'Material']).agg(
        count=('ElementId', 'count'),
        total_volume=('Volume_m3', 'sum'),
        total_area=('Area_m2', 'sum'),
        avg_volume=('Volume_m3', 'mean')
    ).round(2)

    # Add percentage column
    qto['volume_pct'] = (qto['total_volume'] /
                          qto['total_volume'].sum() * 100).round(1)

    return qto.sort_values('total_volume', ascending=False)

# Usage
qto_report = generate_qto_report(df)
qto_report.to_excel("qto_report.xlsx")

Cost Estimation

def calculate_project_cost(elements_df, prices_df, markup=0.15):
    """Calculate total project cost with markup"""
    # Merge with prices
    df = elements_df.merge(prices_df, on='Category', how='left')

    # Calculate base cost
    df['Base_Cost'] = df['Volume_m3'] * df['Unit_Price']

    # Apply markup
    df['Total_Cost'] = df['Base_Cost'] * (1 + markup)

    # Summary by category
    summary = df.groupby('Category').agg(
        volume=('Volume_m3', 'sum'),
        base_cost=('Base_Cost', 'sum'),
        total_cost=('Total_Cost', 'sum')
    ).round(2)

    return df, summary, summary['total_cost'].sum()

# Usage
detailed, summary, total = calculate_project_cost(elements, prices)
print(f"Project Total: ${total:,.2f}")

Material Summary

def material_summary(df):
    """Summarize materials across project"""
    summary = df.groupby('Material').agg({
        'Volume_m3': 'sum',
        'Weight_kg': 'sum',
        'ElementId': 'nunique'
    }).rename(columns={'ElementId': 'Element_Count'})

    summary['Volume_Pct'] = (summary['Volume_m3'] /
                              summary['Volume_m3'].sum() * 100).round(1)

    return summary.sort_values('Volume_m3', ascending=False)

Level-by-Level Analysis

def analyze_by_level(df):
    """Analyze construction quantities by building level"""
    level_summary = df.pivot_table(
        values=['Volume_m3', 'Cost'],
        index='Level',
        columns='Category',
        aggfunc='sum',
        fill_value=0
    )

    level_summary['Total_Volume'] = level_summary['Volume_m3'].sum(axis=1)
    level_summary['Total_Cost'] = level_summary['Cost'].sum(axis=1)

    return level_summary

Data Export

Export to Excel with Multiple Sheets

def export_to_excel_formatted(df, summary, filepath):
    """Export with multiple sheets"""
    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
        df.to_excel(writer, sheet_name='Details', index=False)
        summary.to_excel(writer, sheet_name='Summary')

        pivot = pd.pivot_table(df, values='Volume_m3',
                               index='Level', columns='Category')
        pivot.to_excel(writer, sheet_name='By_Level')

# Usage
export_to_excel_formatted(elements, qto_summary, "project_report.xlsx")

Export to CSV

# Basic export
df.to_csv("output.csv", index=False)

# With encoding for special characters
df.to_csv("output.csv", index=False, encoding='utf-8-sig')

# Specific columns
df[['ElementId', 'Category', 'Volume_m3']].to_csv("volumes.csv", index=False)

Performance Tips

# Use categories for string columns with few unique values
df['Category'] = df['Category'].astype('category')

# Read only needed columns
df = pd.read_csv("large_file.csv", usecols=['ElementId', 'Category', 'Volume'])

# Use chunking for very large files
chunks = pd.read_csv("huge_file.csv", chunksize=100000)
result = pd.concat([chunk[chunk['Category'] == 'Wall'] for chunk in chunks])

# Check memory usage
print(df.memory_usage(deep=True).sum() / 1024**2, "MB")

Quick Reference

Resources

• Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.3
• Website: https://datadrivenconstruction.io
• Pandas Docs: https://pandas.pydata.org/docs/

Next Steps

• See llm-data-automation for generating Pandas code with AI
• See qto-report for specialized QTO calculations
• See cost-estimation-resource for detailed cost calculations