pandas-construction-analysis

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---
name: "pandas-construction-analysis"
description: "Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction data."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw": {"emoji": "🐼", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
---
# 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

```python
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")
```

## DataFrame Fundamentals

### Creating DataFrames

```python
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

```python
# 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')
```

## Filtering and Selection

### Basic Filtering

```python
# 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

```python
# 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', 'V