Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Pandas Construction Analysis
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate report...
skill openclawclawhub Free
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate report...
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- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.0
Provenance
- Publisher
- datadrivenconstruction
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Documentation
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")
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')
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
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'} )
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']
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)
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
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
OperationCodeRead Excelpd.read_excel("file.xlsx")Read CSVpd.read_csv("file.csv")Filter rowsdf[df['Column'] == 'Value']Select columnsdf[['Col1', 'Col2']]Group and sumdf.groupby('Cat')['Vol'].sum()Pivot tablepd.pivot_table(df, values='Vol', index='Level')Mergedf1.merge(df2, on='key')Add columndf['New'] = df['A'] * df['B']Export Exceldf.to_excel("out.xlsx", index=False)
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
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json