- name
- df-merger
- description
- Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.
- homepage
- https://datadrivenconstruction.io
- metadata
- {"openclaw": {"emoji": "🐼", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
DataFrame Merger for Construction Data
Overview
Construction projects combine data from BIM, schedules, costs, and sensors. This skill merges DataFrames from disparate sources with intelligent key matching and schema reconciliation.
Python Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from difflib import SequenceMatcher
class MergeStrategy(Enum):
"""DataFrame merge strategies."""
INNER = "inner" # Only matching rows
LEFT = "left" # All left, matching right
RIGHT = "right" # Matching left, all right
OUTER = "outer" # All rows from both
CROSS = "cross" # Cartesian product
@dataclass
class MergeResult:
"""Result of merge operation."""
merged_df: pd.DataFrame
matched_rows: int
left_only: int
right_only: int
merge_quality: float # 0-1 score
class ConstructionDFMerger:
"""Merge DataFrames from construction sources."""
# Common construction column name mappings
COLUMN_MAPPINGS = {
'element_id': ['elementid', 'elem_id', 'id', 'guid', 'globalid'],
'type_name': ['typename', 'type', 'element_type', 'category'],
'level': ['level', 'floor', 'storey', 'building_storey'],
'material': ['material', 'mat', 'material_name'],
'volume': ['volume', 'vol', 'volume_m3', 'qty_volume'],
'area': ['area', 'surface_area', 'qty_area', 'area_m2'],
'cost': ['cost', 'price', 'total_cost', 'amount'],
'task_id': ['task_id', 'activity_id', 'wbs', 'activity'],
'start_date': ['start', 'start_date', 'planned_start', 'begin'],
'end_date': ['end', 'end_date', 'planned_finish', 'finish']
}
def __init__(self):
self.column_cache: Dict[str, str] = {}
def find_common_key(self, df1: pd.DataFrame,
df2: pd.DataFrame) -> Optional[str]:
"""Find common key column between DataFrames."""
# Check exact matches first
common = set(df1.columns) & set(df2.columns)
if common:
# Prefer ID-like columns
for col in common:
if 'id' in col.lower() or 'code' in col.lower():
return col
return list(common)[0]
# Try semantic matching
for col1 in df1.columns:
for col2 in df2.columns:
if self._columns_match(col1, col2):
return col1
return None
def _columns_match(self, col1: str, col2: str) -> bool:
"""Check if column names are semantically similar."""
col1_lower = col1.lower().replace('_', '').replace('-', '')
col2_lower = col2.lower().replace('_', '').replace('-', '')
# Exact match after normalization
if col1_lower == col2_lower:
return True
# Check against mappings
for standard, variants in self.COLUMN_MAPPINGS.items():
if col1_lower in variants and col2_lower in variants:
return True
# Similarity check
similarity = SequenceMatcher(None, col1_lower, col2_lower).ratio()
return similarity > 0.8
def harmonize_columns(self, df: pd.DataFrame) -> pd.DataFrame:
"""Standardize column names."""
df = df.copy()
rename_map = {}
for col in df.columns:
col_lower = col.lower().replace('_', '').replace('-', '')
for standard, variants in self.COLUMN_MAPPINGS.items():
if col_lower in variants:
rename_map[col] = standard
break
return df.rename(columns=rename_map)
def merge(self, left: pd.DataFrame,
right: pd.DataFrame,
on: Optional[str] = None,
left_on: Optional[str] = None,
right_on: Optional[str] = None,
how: MergeStrategy = MergeStrategy.LEFT,
harmonize: bool = True) -> MergeResult:
"""Merge two DataFrames."""
if harmonize:
left = self.harmonize_columns(left)
right = self.harmonize_columns(right)
# Determine merge keys
if on is None and left_on is None and right_on is None:
common_key = self.find_common_key(left, right)
if common_key is None:
raise ValueError("No common key found. Specify merge key manually.")
on = common_key
# Perform merge
merged = pd.merge(
left, right,
on=on,
left_on=left_on,
right_on=right_on,
how=how.value,
indicator=True,
suffixes=('_left', '_right')
)
# Calculate statistics
matched = len(merged[merged['_merge'] == 'both'])
left_only = len(merged[merged['_merge'] == 'left_only'])
right_only = len(merged[merged['_merge'] == 'right_only'])
# Quality score
total = len(left) + len(right)
quality = (matched * 2) / total if total > 0 else 0
# Clean up
merged = merged.drop('_merge', axis=1)
return MergeResult(
merged_df=merged,
matched_rows=matched,
left_only=left_only,
right_only=right_only,
merge_quality=round(quality, 2)
)
def merge_multiple(self, dfs: List[pd.DataFrame],
on: Optional[str] = None,
how: MergeStrategy = MergeStrategy.OUTER) -> pd.DataFrame:
"""Merge multiple DataFrames sequentially."""
if not dfs:
return pd.DataFrame()
result = dfs[0].copy()
for i, df in enumerate(dfs[1:], 1):
result_obj = self.merge(result, df, on=on, how=how)
result = result_obj.merged_df
return result
def fuzzy_merge(self, left: pd.DataFrame,
right: pd.DataFrame,
left_on: str,
right_on: str,
threshold: float = 0.8) -> pd.DataFrame:
"""Merge using fuzzy string matching."""
matches = []
left_values = left[left_on].dropna().unique()
right_values = right[right_on].dropna().unique()
for lval in left_values:
best_match = None
best_score = 0
for rval in right_values:
score = SequenceMatcher(None, str(lval).lower(),
str(rval).lower()).ratio()
if score > best_score and score >= threshold:
best_score = score
best_match = rval
if best_match:
matches.append({
'left_key': lval,
'right_key': best_match,
'match_score': best_score
})
match_df = pd.DataFrame(matches)
# Join using match mapping
left_with_key = left.merge(match_df, left_on=left_on, right_on='left_key', how='left')
result = left_with_key.merge(right, left_on='right_key', right_on=right_on, how='left')
return result
class BIMScheduleMerger(ConstructionDFMerger):
"""Specialized merger for BIM and schedule data."""
def merge_bim_schedule(self, bim_df: pd.DataFrame,
schedule_df: pd.DataFrame,
bim_type_col: str = 'Type Name',
schedule_wbs_col: str = 'WBS') -> pd.DataFrame:
"""Merge BIM elements with schedule activities."""
# This typically requires a mapping table
# For now, use fuzzy matching on descriptions
bim_df = self.harmonize_columns(bim_df)
schedule_df = self.harmonize_columns(schedule_df)
# Try to match type names to WBS descriptions
result = self.fuzzy_merge(
bim_df, schedule_df,
left_on=bim_type_col,
right_on=schedule_wbs_col,
threshold=0.6
)
return result
class CostQTOMerger(ConstructionDFMerger):
"""Merge cost data with quantity takeoffs."""
def merge_cost_qto(self, cost_df: pd.DataFrame,
qto_df: pd.DataFrame) -> pd.DataFrame:
"""Merge cost rates with QTO quantities."""
cost_df = self.harmonize_columns(cost_df)
qto_df = self.harmonize_columns(qto_df)
# Try common merge keys
for key in ['work_item_code', 'type_name', 'material', 'element_id']:
if key in cost_df.columns and key in qto_df.columns:
result = self.merge(cost_df, qto_df, on=key)
# Calculate extended costs
result.merged_df['extended_cost'] = (
result.merged_df.get('quantity', 0) *
result.merged_df.get('unit_price', 0)
)
return result.merged_df
# Fallback to fuzzy merge
return self.fuzzy_merge(
qto_df, cost_df,
left_on='type_name' if 'type_name' in qto_df.columns else qto_df.columns[0],
right_on='description' if 'description' in cost_df.columns else cost_df.columns[0]
)Quick Start
merger = ConstructionDFMerger()
# Merge two DataFrames
result = merger.merge(bim_df, schedule_df)
print(f"Matched: {result.matched_rows}, Quality: {result.merge_quality}")
# Access merged data
merged = result.merged_dfCommon Use Cases
1. BIM + Schedule Integration
bim_schedule = BIMScheduleMerger()
integrated = bim_schedule.merge_bim_schedule(bim_elements, schedule_activities)2. Cost + QTO
cost_merger = CostQTOMerger()
priced_qto = cost_merger.merge_cost_qto(cost_database, quantities)
print(f"Total: ${priced_qto['extended_cost'].sum():,.2f}")3. Multiple Sources
all_data = merger.merge_multiple(
[bim_df, schedule_df, cost_df, resource_df],
on='element_id'
)Resources
- DDC Book: Chapter 2.3 - Pandas DataFrame