Agent skill · data analytics · datadrivenconstruction
pandas-construction-analysis
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction data.
Why this skill is useful
Provides specific Pandas operations and techniques tailored for construction data analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
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 DataFrame Fundamentals Creating DataFrames Data Types in Construction Filtering and Selection Basic Filtering Advanced Selection Grouping and Aggregation GroupBy Operations Pivot Tables Data Transformation Adding Calculated Columns String Operations Date Operations Merging and Joining Merge DataFrames Concatenating DataFrames Construction-Specific Analyses Quantity Take-Off (QTO) Cost Estimation Material Summary Level-by-Level Analysis Data Export Export to Excel with Multiple Sheets Export to CSV Performance Tips Quick Reference Operation Code ----------- ------ Read Excel pd.readexcel("file.xlsx") Read CSV pd.readcsv("file.csv") Filter rows df[df['Column'] == 'Value'] Select columns df[['Col1', 'Col2']] Group and sum df.groupby('Cat')['Vol'].sum() Pivot table pd.pivottable(df, values='Vol', index='Level') Merge df1.merge(df2, on='key') Add column df['New'] = df['A'] df['B'] Export Excel df.toexcel("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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/pandas-construction-analysis