pandas-construction-analysis

Pandas 建筑数据分析

概述

基于 DDC 方法论（第 2.3 章），此技能提供全面的 Pandas 操作，用于处理建筑数据。Pandas 是数据分析师的瑞士军刀，可处理从简单的数据筛选到跨数百万行数据的复杂汇总等各种任务。

书籍参考：《Pandas DataFrame и LLM ChatGPT》/《Pandas DataFrame and LLM ChatGPT》

“使用 Pandas，您可以管理和分析远超 Excel 处理能力的数据集。Excel 最多只能处理 100 万行数据，而 Pandas 可以轻松处理包含数千万行的数据集。” — DDC Book，第 2.3 章

快速入门

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 基础

创建 DataFrame

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

建筑数据中的数据类型

# 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')

筛选与选择

基本筛选

# 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()]

高级选择

# 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

分组与汇总

GroupBy 操作

# 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()

数据透视表

# 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'}
)

数据转换

添加计算列

# 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']
)

字符串操作

# 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')

日期操作

# 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']

合并与连接

合并 DataFrame

# 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')

拼接 DataFrame

# 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)

建筑专项分析

工程量计算（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")

成本估算

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}")

材料汇总

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)

按楼层分析

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

数据导出

导出到包含多个工作表的 Excel 文件

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")

导出到 CSV 文件

# 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)

性能提示

# 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")

快速参考

操作	代码
读取 Excel	pd.read_excel("file.xlsx")
读取 CSV	pd.read_csv("file.csv")
筛选行	df[df['Column'] == 'Value']
选择列	df[['Col1', 'Col2']]
分组并求和	df.groupby('Cat')['Vol'].sum()
数据透视表	pd.pivot_table(df, values='Vol', index='Level')
合并	df1.merge(df2, on='key')
添加列	df['New'] = df['A'] * df['B']
导出 Excel	df.to_excel("out.xlsx", index=False)

资源

• 书籍：《Data-Driven Construction》，Artem Boiko 著，第 2.3 章
• 网站：https://datadrivenconstruction.io
• Pandas 文档：https://pandas.pydata.org/docs/

后续步骤

• 查看 llm-data-automation，了解如何使用 AI 生成 Pandas 代码
• 查看 qto-report，了解专门的 QTO 计算
• 查看 cost-estimation-resource，了解详细的成本计算