data-visualization

Data Visualization for Construction

Overview

Based on DDC methodology (Chapter 4.1), this skill provides comprehensive data visualization techniques for construction analytics. Visual insights drive better decisions - from cost breakdowns to schedule analysis.

Book Reference: "Аналитика данных и принятие решений" / "Data Analytics and Decision Making"

"Визуализация данных превращает сложные наборы данных в понятные графики, которые могут использоваться для принятия решений на всех уровнях проекта." — DDC Book, Chapter 4.1

Quick Start

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load construction data
df = pd.read_excel("project_data.xlsx")

# Quick bar chart - volumes by category
fig, ax = plt.subplots(figsize=(10, 6))
df.groupby('Category')['Volume_m3'].sum().plot(kind='bar', ax=ax)
ax.set_title('Volume by Category')
ax.set_ylabel('Volume (m³)')
plt.tight_layout()
plt.savefig('volume_by_category.png', dpi=150)
plt.show()

Matplotlib Fundamentals

Basic Charts for Construction

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

def create_cost_breakdown_pie(df, cost_col='Cost', category_col='Category'):
    """Create pie chart for cost breakdown"""
    costs = df.groupby(category_col)[cost_col].sum()

    fig, ax = plt.subplots(figsize=(10, 8))

    # Create pie with percentage labels
    wedges, texts, autotexts = ax.pie(
        costs.values,
        labels=costs.index,
        autopct='%1.1f%%',
        startangle=90,
        colors=plt.cm.Set3.colors
    )

    ax.set_title('Cost Breakdown by Category', fontsize=14, fontweight='bold')

    # Add total in center
    ax.text(0, 0, f'Total:\n${costs.sum():,.0f}',
            ha='center', va='center', fontsize=12)

    plt.tight_layout()
    return fig

def create_volume_bar_chart(df, volume_col='Volume_m3', category_col='Category'):
    """Create horizontal bar chart for volumes"""
    volumes = df.groupby(category_col)[volume_col].sum().sort_values()

    fig, ax = plt.subplots(figsize=(10, 6))

    bars = ax.barh(volumes.index, volumes.values, color='steelblue')

    # Add value labels
    for bar, value in zip(bars, volumes.values):
        ax.text(value + volumes.max() * 0.01, bar.get_y() + bar.get_height()/2,
                f'{value:,.0f} m³', va='center', fontsize=10)

    ax.set_xlabel('Volume (m³)')
    ax.set_title('Material Volumes by Category', fontsize=14, fontweight='bold')
    ax.set_xlim(0, volumes.max() * 1.15)

    plt.tight_layout()
    return fig

def create_level_comparison(df, value_col='Volume_m3', level_col='Level'):
    """Create grouped bar chart comparing levels"""
    pivot = df.pivot_table(
        values=value_col,
        index=level_col,
        columns='Category',
        aggfunc='sum',
        fill_value=0
    )

    fig, ax = plt.subplots(figsize=(12, 6))
    pivot.plot(kind='bar', ax=ax, width=0.8)

    ax.set_xlabel('Building Level')
    ax.set_ylabel('Volume (m³)')
    ax.set_title('Volume Distribution by Level and Category', fontsize=14, fontweight='bold')
    ax.legend(title='Category', bbox_to_anchor=(1.02, 1), loc='upper left')

    plt.xticks(rotation=45)
    plt.tight_layout()
    return fig