# Send Pandas Construction Analysis to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "pandas-construction-analysis", "name": "Pandas Construction Analysis", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/datadrivenconstruction/pandas-construction-analysis", "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/pandas-construction-analysis", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/pandas-construction-analysis", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=pandas-construction-analysis", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "claw.json", "instructions.md", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/pandas-construction-analysis" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/pandas-construction-analysis", "downloadUrl": "https://openagent3.xyz/downloads/pandas-construction-analysis", "agentUrl": "https://openagent3.xyz/skills/pandas-construction-analysis/agent", "manifestUrl": "https://openagent3.xyz/skills/pandas-construction-analysis/agent.json", "briefUrl": "https://openagent3.xyz/skills/pandas-construction-analysis/agent.md" } } ``` ## 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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: datadrivenconstruction - Version: 2.1.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/pandas-construction-analysis) - [Send to Agent page](https://openagent3.xyz/skills/pandas-construction-analysis/agent) - [JSON manifest](https://openagent3.xyz/skills/pandas-construction-analysis/agent.json) - [Markdown brief](https://openagent3.xyz/skills/pandas-construction-analysis/agent.md) - [Download page](https://openagent3.xyz/downloads/pandas-construction-analysis)