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597 lines
19 KiB
Markdown
597 lines
19 KiB
Markdown
---
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slug: "data-visualization"
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display_name: "Data Visualization"
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description: "Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting."
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---
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# Data Visualization for Construction
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## Overview
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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.
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**Book Reference:** "Аналитика данных и принятие решений" / "Data Analytics and Decision Making"
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> "Визуализация данных превращает сложные наборы данных в понятные графики, которые могут использоваться для принятия решений на всех уровнях проекта."
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> — DDC Book, Chapter 4.1
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## Quick Start
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Load construction data
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df = pd.read_excel("project_data.xlsx")
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# Quick bar chart - volumes by category
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fig, ax = plt.subplots(figsize=(10, 6))
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df.groupby('Category')['Volume_m3'].sum().plot(kind='bar', ax=ax)
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ax.set_title('Volume by Category')
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ax.set_ylabel('Volume (m³)')
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plt.tight_layout()
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plt.savefig('volume_by_category.png', dpi=150)
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plt.show()
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```
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## Matplotlib Fundamentals
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### Basic Charts for Construction
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```python
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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def create_cost_breakdown_pie(df, cost_col='Cost', category_col='Category'):
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"""Create pie chart for cost breakdown"""
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costs = df.groupby(category_col)[cost_col].sum()
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fig, ax = plt.subplots(figsize=(10, 8))
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# Create pie with percentage labels
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wedges, texts, autotexts = ax.pie(
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costs.values,
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labels=costs.index,
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autopct='%1.1f%%',
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startangle=90,
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colors=plt.cm.Set3.colors
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)
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ax.set_title('Cost Breakdown by Category', fontsize=14, fontweight='bold')
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# Add total in center
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ax.text(0, 0, f'Total:\n${costs.sum():,.0f}',
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ha='center', va='center', fontsize=12)
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plt.tight_layout()
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return fig
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def create_volume_bar_chart(df, volume_col='Volume_m3', category_col='Category'):
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"""Create horizontal bar chart for volumes"""
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volumes = df.groupby(category_col)[volume_col].sum().sort_values()
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fig, ax = plt.subplots(figsize=(10, 6))
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bars = ax.barh(volumes.index, volumes.values, color='steelblue')
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# Add value labels
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for bar, value in zip(bars, volumes.values):
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ax.text(value + volumes.max() * 0.01, bar.get_y() + bar.get_height()/2,
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f'{value:,.0f} m³', va='center', fontsize=10)
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ax.set_xlabel('Volume (m³)')
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ax.set_title('Material Volumes by Category', fontsize=14, fontweight='bold')
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ax.set_xlim(0, volumes.max() * 1.15)
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plt.tight_layout()
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return fig
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def create_level_comparison(df, value_col='Volume_m3', level_col='Level'):
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"""Create grouped bar chart comparing levels"""
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pivot = df.pivot_table(
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values=value_col,
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index=level_col,
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columns='Category',
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aggfunc='sum',
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fill_value=0
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)
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fig, ax = plt.subplots(figsize=(12, 6))
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pivot.plot(kind='bar', ax=ax, width=0.8)
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ax.set_xlabel('Building Level')
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ax.set_ylabel('Volume (m³)')
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ax.set_title('Volume Distribution by Level and Category', fontsize=14, fontweight='bold')
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ax.legend(title='Category', bbox_to_anchor=(1.02, 1), loc='upper left')
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plt.xticks(rotation=45)
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plt.tight_layout()
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return fig
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```
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### Time Series Visualization
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```python
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def create_progress_chart(df, date_col='Date', value_col='Cumulative_Progress'):
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"""Create S-curve progress chart"""
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df = df.sort_values(date_col)
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fig, ax = plt.subplots(figsize=(12, 6))
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# Actual progress
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ax.plot(df[date_col], df[value_col],
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'b-', linewidth=2, label='Actual Progress')
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# Planned progress (if available)
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if 'Planned_Progress' in df.columns:
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ax.plot(df[date_col], df['Planned_Progress'],
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'g--', linewidth=2, label='Planned Progress')
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ax.fill_between(df[date_col], 0, df[value_col], alpha=0.3)
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ax.set_xlabel('Date')
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ax.set_ylabel('Progress (%)')
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ax.set_title('Project S-Curve', fontsize=14, fontweight='bold')
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ax.legend()
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ax.grid(True, alpha=0.3)
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# Format dates
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fig.autofmt_xdate()
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plt.tight_layout()
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return fig
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def create_gantt_chart(df, start_col='Start', end_col='End', task_col='Task'):
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"""Create simple Gantt chart"""
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df = df.sort_values(start_col)
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fig, ax = plt.subplots(figsize=(14, len(df) * 0.5 + 2))
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# Plot each task as horizontal bar
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for i, (_, row) in enumerate(df.iterrows()):
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start = pd.to_datetime(row[start_col])
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end = pd.to_datetime(row[end_col])
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duration = (end - start).days
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ax.barh(i, duration, left=start, height=0.6,
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align='center', color='steelblue', alpha=0.8)
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ax.set_yticks(range(len(df)))
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ax.set_yticklabels(df[task_col])
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ax.set_xlabel('Date')
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ax.set_title('Project Schedule - Gantt Chart', fontsize=14, fontweight='bold')
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ax.grid(axis='x', alpha=0.3)
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fig.autofmt_xdate()
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plt.tight_layout()
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return fig
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```
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## Seaborn for Statistical Visualization
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### Distribution Analysis
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```python
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import seaborn as sns
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def create_distribution_analysis(df, value_col='Volume_m3', category_col='Category'):
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"""Create distribution plots for construction data"""
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# 1. Histogram with KDE
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sns.histplot(data=df, x=value_col, kde=True, ax=axes[0, 0])
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axes[0, 0].set_title('Volume Distribution')
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# 2. Box plot by category
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sns.boxplot(data=df, x=category_col, y=value_col, ax=axes[0, 1])
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axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
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axes[0, 1].set_title('Volume by Category')
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# 3. Violin plot
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sns.violinplot(data=df, x=category_col, y=value_col, ax=axes[1, 0])
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axes[1, 0].set_xticklabels(axes[1, 0].get_xticklabels(), rotation=45)
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axes[1, 0].set_title('Volume Distribution by Category')
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# 4. Strip plot with jitter
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sns.stripplot(data=df, x=category_col, y=value_col,
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ax=axes[1, 1], alpha=0.5, jitter=True)
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axes[1, 1].set_xticklabels(axes[1, 1].get_xticklabels(), rotation=45)
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axes[1, 1].set_title('Individual Elements')
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plt.tight_layout()
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return fig
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def create_correlation_heatmap(df, numeric_cols=None):
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"""Create correlation heatmap for numeric columns"""
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if numeric_cols is None:
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numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
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corr_matrix = df[numeric_cols].corr()
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fig, ax = plt.subplots(figsize=(10, 8))
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sns.heatmap(corr_matrix,
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annot=True,
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cmap='RdYlBu_r',
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center=0,
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fmt='.2f',
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square=True,
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ax=ax)
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ax.set_title('Correlation Matrix', fontsize=14, fontweight='bold')
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plt.tight_layout()
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return fig
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```
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### Category Analysis
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```python
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def create_category_summary(df, category_col='Category',
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value_col='Volume_m3', cost_col='Cost'):
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"""Create comprehensive category summary visualization"""
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# 1. Count by category
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category_counts = df[category_col].value_counts()
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sns.barplot(x=category_counts.index, y=category_counts.values, ax=axes[0, 0])
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axes[0, 0].set_title('Element Count by Category')
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axes[0, 0].set_xticklabels(axes[0, 0].get_xticklabels(), rotation=45)
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# 2. Total volume by category
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volumes = df.groupby(category_col)[value_col].sum().sort_values(ascending=False)
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sns.barplot(x=volumes.index, y=volumes.values, ax=axes[0, 1])
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axes[0, 1].set_title('Total Volume by Category')
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axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
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# 3. Average cost by category
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if cost_col in df.columns:
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avg_cost = df.groupby(category_col)[cost_col].mean().sort_values(ascending=False)
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sns.barplot(x=avg_cost.index, y=avg_cost.values, ax=axes[1, 0])
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axes[1, 0].set_title('Average Cost by Category')
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axes[1, 0].set_xticklabels(axes[1, 0].get_xticklabels(), rotation=45)
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# 4. Volume vs Cost scatter
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if cost_col in df.columns:
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sns.scatterplot(data=df, x=value_col, y=cost_col,
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hue=category_col, alpha=0.7, ax=axes[1, 1])
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axes[1, 1].set_title('Volume vs Cost')
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axes[1, 1].legend(bbox_to_anchor=(1.02, 1), loc='upper left')
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plt.tight_layout()
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return fig
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```
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## Plotly for Interactive Dashboards
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### Interactive Charts
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```python
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import plotly.express as px
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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def create_interactive_cost_breakdown(df, category_col='Category', cost_col='Cost'):
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"""Create interactive sunburst chart"""
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# Aggregate by category and material
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agg_df = df.groupby([category_col, 'Material'])[cost_col].sum().reset_index()
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fig = px.sunburst(
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agg_df,
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path=[category_col, 'Material'],
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values=cost_col,
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title='Cost Breakdown by Category and Material'
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)
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fig.update_layout(height=600)
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return fig
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def create_interactive_3d_scatter(df, x_col='Volume_m3', y_col='Cost',
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z_col='Weight_kg', color_col='Category'):
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"""Create 3D scatter plot for multi-dimensional analysis"""
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fig = px.scatter_3d(
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df,
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x=x_col,
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y=y_col,
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z=z_col,
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color=color_col,
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hover_data=['ElementId'],
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title='3D Analysis: Volume vs Cost vs Weight'
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)
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fig.update_layout(height=700)
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return fig
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def create_interactive_timeline(df, date_col='Date', value_col='Progress',
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category_col='Phase'):
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"""Create interactive timeline with range slider"""
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fig = px.line(
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df,
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x=date_col,
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y=value_col,
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color=category_col,
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title='Project Progress Timeline'
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)
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fig.update_layout(
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xaxis=dict(
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rangeselector=dict(
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buttons=list([
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dict(count=1, label="1m", step="month", stepmode="backward"),
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dict(count=3, label="3m", step="month", stepmode="backward"),
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dict(count=6, label="6m", step="month", stepmode="backward"),
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dict(step="all", label="All")
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])
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),
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rangeslider=dict(visible=True),
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type="date"
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),
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height=500
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)
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return fig
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```
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### Dashboard Layout
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```python
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def create_project_dashboard(df):
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"""Create comprehensive project dashboard"""
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fig = make_subplots(
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rows=2, cols=2,
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subplot_titles=(
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'Cost by Category',
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'Volume Distribution',
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'Elements by Level',
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'Progress Over Time'
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),
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specs=[
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[{"type": "pie"}, {"type": "bar"}],
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[{"type": "bar"}, {"type": "scatter"}]
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]
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)
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# 1. Cost pie chart
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costs = df.groupby('Category')['Cost'].sum()
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fig.add_trace(
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go.Pie(labels=costs.index, values=costs.values, name='Cost'),
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row=1, col=1
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)
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# 2. Volume bar chart
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volumes = df.groupby('Category')['Volume_m3'].sum().sort_values(ascending=True)
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fig.add_trace(
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go.Bar(x=volumes.values, y=volumes.index, orientation='h', name='Volume'),
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row=1, col=2
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)
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# 3. Elements by level
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level_counts = df.groupby('Level').size()
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fig.add_trace(
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go.Bar(x=level_counts.index, y=level_counts.values, name='Count'),
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row=2, col=1
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)
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# 4. Progress scatter (if available)
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if 'Date' in df.columns and 'Progress' in df.columns:
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fig.add_trace(
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go.Scatter(x=df['Date'], y=df['Progress'], mode='lines+markers', name='Progress'),
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row=2, col=2
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)
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fig.update_layout(
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height=800,
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title_text='Project Analytics Dashboard',
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showlegend=False
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)
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return fig
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```
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## Construction-Specific Visualizations
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### Heatmaps for Level Analysis
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```python
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def create_level_heatmap(df, level_col='Level', category_col='Category',
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value_col='Volume_m3'):
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"""Create heatmap for level-by-category analysis"""
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pivot = df.pivot_table(
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values=value_col,
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index=level_col,
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columns=category_col,
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aggfunc='sum',
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fill_value=0
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)
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fig, ax = plt.subplots(figsize=(12, 8))
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sns.heatmap(
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pivot,
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annot=True,
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fmt=',.0f',
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cmap='YlOrRd',
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ax=ax,
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cbar_kws={'label': 'Volume (m³)'}
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)
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ax.set_title('Volume Distribution: Level × Category', fontsize=14, fontweight='bold')
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plt.tight_layout()
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return fig
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def create_material_treemap(df, category_col='Category', material_col='Material',
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value_col='Volume_m3'):
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"""Create treemap for hierarchical material analysis"""
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agg_df = df.groupby([category_col, material_col])[value_col].sum().reset_index()
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fig = px.treemap(
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agg_df,
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path=[category_col, material_col],
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values=value_col,
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title='Material Distribution Treemap',
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color=value_col,
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color_continuous_scale='Blues'
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)
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fig.update_layout(height=600)
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return fig
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```
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### Cost Analysis Charts
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```python
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def create_cost_analysis_dashboard(df):
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"""Create comprehensive cost analysis visualization"""
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fig, axes = plt.subplots(2, 3, figsize=(18, 10))
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# 1. Cost distribution histogram
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sns.histplot(data=df, x='Cost', bins=30, ax=axes[0, 0])
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axes[0, 0].set_title('Cost Distribution')
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axes[0, 0].axvline(df['Cost'].mean(), color='r', linestyle='--', label='Mean')
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axes[0, 0].axvline(df['Cost'].median(), color='g', linestyle='--', label='Median')
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axes[0, 0].legend()
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# 2. Cost by category (box plot)
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sns.boxplot(data=df, x='Category', y='Cost', ax=axes[0, 1])
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axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
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axes[0, 1].set_title('Cost Range by Category')
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# 3. Cumulative cost
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sorted_costs = df.sort_values('Cost', ascending=False)
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sorted_costs['Cumulative_Cost'] = sorted_costs['Cost'].cumsum()
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sorted_costs['Cumulative_Pct'] = sorted_costs['Cumulative_Cost'] / sorted_costs['Cost'].sum() * 100
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axes[0, 2].plot(range(len(sorted_costs)), sorted_costs['Cumulative_Pct'])
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axes[0, 2].axhline(80, color='r', linestyle='--', alpha=0.5)
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axes[0, 2].set_xlabel('Number of Elements')
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axes[0, 2].set_ylabel('Cumulative Cost %')
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axes[0, 2].set_title('Pareto Analysis (80/20)')
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# 4. Cost per unit volume
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df['Cost_per_m3'] = df['Cost'] / df['Volume_m3'].replace(0, np.nan)
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by_cat = df.groupby('Category')['Cost_per_m3'].mean().sort_values(ascending=True)
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axes[1, 0].barh(by_cat.index, by_cat.values)
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axes[1, 0].set_title('Average Cost per m³ by Category')
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# 5. Top 10 elements by cost
|
||
top10 = df.nlargest(10, 'Cost')
|
||
axes[1, 1].barh(top10['ElementId'], top10['Cost'])
|
||
axes[1, 1].set_title('Top 10 Elements by Cost')
|
||
|
||
# 6. Cost vs Volume scatter with regression
|
||
sns.regplot(data=df, x='Volume_m3', y='Cost', ax=axes[1, 2],
|
||
scatter_kws={'alpha': 0.5})
|
||
axes[1, 2].set_title('Cost vs Volume (with Trend)')
|
||
|
||
plt.tight_layout()
|
||
return fig
|
||
```
|
||
|
||
## Export and Reporting
|
||
|
||
### Save Visualizations
|
||
|
||
```python
|
||
def save_all_visualizations(df, output_dir='reports/charts'):
|
||
"""Generate and save all standard visualizations"""
|
||
import os
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
# Generate charts
|
||
charts = {
|
||
'cost_breakdown': create_cost_breakdown_pie(df),
|
||
'volume_bars': create_volume_bar_chart(df),
|
||
'distribution': create_distribution_analysis(df),
|
||
'level_heatmap': create_level_heatmap(df)
|
||
}
|
||
|
||
# Save each chart
|
||
saved_files = []
|
||
for name, fig in charts.items():
|
||
filepath = f"{output_dir}/{name}.png"
|
||
fig.savefig(filepath, dpi=150, bbox_inches='tight')
|
||
plt.close(fig)
|
||
saved_files.append(filepath)
|
||
|
||
return saved_files
|
||
|
||
def create_pdf_report(df, output_path='project_report.pdf'):
|
||
"""Create PDF report with multiple visualizations"""
|
||
from matplotlib.backends.backend_pdf import PdfPages
|
||
|
||
with PdfPages(output_path) as pdf:
|
||
# Page 1: Overview
|
||
fig1 = create_cost_breakdown_pie(df)
|
||
pdf.savefig(fig1)
|
||
plt.close(fig1)
|
||
|
||
# Page 2: Volume analysis
|
||
fig2 = create_volume_bar_chart(df)
|
||
pdf.savefig(fig2)
|
||
plt.close(fig2)
|
||
|
||
# Page 3: Distribution
|
||
fig3 = create_distribution_analysis(df)
|
||
pdf.savefig(fig3)
|
||
plt.close(fig3)
|
||
|
||
# Page 4: Heatmap
|
||
fig4 = create_level_heatmap(df)
|
||
pdf.savefig(fig4)
|
||
plt.close(fig4)
|
||
|
||
return output_path
|
||
```
|
||
|
||
## Quick Reference
|
||
|
||
| Chart Type | Best For | Library |
|
||
|------------|----------|---------|
|
||
| Bar Chart | Category comparisons | Matplotlib/Seaborn |
|
||
| Pie Chart | Cost breakdown | Matplotlib |
|
||
| Heatmap | Level × Category matrix | Seaborn |
|
||
| Box Plot | Distribution by group | Seaborn |
|
||
| Scatter | Relationship analysis | Matplotlib/Plotly |
|
||
| Treemap | Hierarchical data | Plotly |
|
||
| Sunburst | Multi-level breakdown | Plotly |
|
||
| Gantt | Schedule visualization | Matplotlib |
|
||
| S-Curve | Progress tracking | Matplotlib |
|
||
|
||
## Color Palettes for Construction
|
||
|
||
```python
|
||
# Professional color palettes
|
||
CONSTRUCTION_COLORS = {
|
||
'primary': ['#2C3E50', '#3498DB', '#1ABC9C', '#F39C12', '#E74C3C'],
|
||
'materials': {
|
||
'Concrete': '#95A5A6',
|
||
'Steel': '#34495E',
|
||
'Timber': '#D35400',
|
||
'Brick': '#C0392B',
|
||
'Glass': '#3498DB'
|
||
},
|
||
'categories': {
|
||
'Structural': '#2C3E50',
|
||
'Architectural': '#3498DB',
|
||
'MEP': '#27AE60',
|
||
'Finishes': '#F39C12'
|
||
}
|
||
}
|
||
```
|
||
|
||
## Resources
|
||
|
||
- **Book**: "Data-Driven Construction" by Artem Boiko, Chapter 4.1
|
||
- **Website**: https://datadrivenconstruction.io
|
||
- **Matplotlib**: https://matplotlib.org
|
||
- **Seaborn**: https://seaborn.pydata.org
|
||
- **Plotly**: https://plotly.com/python
|
||
|
||
## Next Steps
|
||
|
||
- See `pandas-construction-analysis` for data preparation
|
||
- See `cost-prediction` for predictive analytics
|
||
- See `qto-report` for quantity extraction
|