datadrivenconstruction/parquet-converter
Convert construction data to/from Parquet format. Optimize storage, enable fast queries, and integrate with data lakehouses.
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill parquet-converterData storage and processing challenges:
Convert construction data to Parquet format for efficient columnar storage, faster queries, and compatibility with data lakehouses.
import pandas as pd
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
import json
class CompressionType:
SNAPPY = "snappy"
GZIP = "gzip"
BROTLI = "brotli"
ZSTD = "zstd"
NONE = None
@dataclass
class ParquetSchema:
columns: Dict[str, str] # column_name: dtype
partitions: List[str] = field(default_factory=list)
row_group_size: int = 100000
@dataclass
class ConversionResult:
source_path: str
output_path: str
source_format: str
rows: int
columns: int
original_size_mb: float
parquet_size_mb: float
compression_ratio: float
duration_seconds: float
class ParquetConverter:
"""Convert construction data to/from Parquet format."""
def __init__(self, project_name: str = "Data Conversion"):
self.project_name = project_name
self.conversions: List[ConversionResult] = []
self.schemas: Dict[str, ParquetSchema] = {}
self._define_standard_schemas()
def _define_standard_schemas(self):
"""Define standard schemas for construction data."""
self.schemas['projects'] = ParquetSchema(
columns={
'project_id': 'string',
'name': 'string',
'project_type': 'category',
'status': 'category',
'start_date': 'datetime64[ns]',
'end_date': 'datetime64[ns]',
'budget': 'float64',
'actual_cost': 'float64',
'size_sf': 'float64',
'location': 'string'
},
partitions=['project_type', 'status']
)
self.schemas['costs'] = ParquetSchema(
columns={
'transaction_id': 'string',
'project_id': 'string',
'cost_code': 'category',
'description': 'string',
'amount': 'float64',
'transaction_date': 'datetime64[ns]',
'vendor': 'string',
'invoice_number': 'string'
},
partitions=['project_id']
)
self.schemas['schedule'] = ParquetSchema(
columns={
'activity_id': 'string',
'project_id': 'string',
'name': 'string',
'wbs_code': 'string',
'start_date': 'datetime64[ns]',
'end_date': 'datetime64[ns]',
'duration': 'int32',
'progress': 'float32',
'status': 'category'
},
partitions=['project_id']
)
self.schemas['qto'] = ParquetSchema(
columns={
'element_id': 'string',
'project_id': 'string',
'element_type': 'category',
'name': 'string',
'quantity': 'float64',
'unit': 'category',
'level': 'string',
'material': 'string'
},
partitions=['project_id', 'element_type']
)
def add_schema(self, name: str, schema: ParquetSchema):
"""Add custom schema."""
self.schemas[name] = schema
def csv_to_parquet(self, csv_path: str, parquet_path: str,
schema_name: str = None,
compression: str = CompressionType.SNAPPY,
partition_cols: List[str] = None) -> ConversionResult:
"""Convert CSV to Parquet."""
start_time = datetime.now()
# Read CSV
df = pd.read_csv(csv_path)
# Apply schema if provided
if schema_name and schema_name in self.schemas:
schema = self.schemas[schema_name]
df = self._apply_schema(df, schema)
partition_cols = partition_cols or schema.partitions
# Get original file size
original_size = Path(csv_path).stat().st_size / (1024 * 1024)
# Write Parquet
if partition_cols:
# Partitioned write
available_partitions = [c for c in partition_cols if c in df.columns]
if available_partitions:
df.to_parquet(
parquet_path,
engine='pyarrow',
compression=compression,
partition_cols=available_partitions,
index=False
)
else:
df.to_parquet(parquet_path, engine='pyarrow',
compression=compression, index=False)
else:
df.to_parquet(parquet_path, engine='pyarrow',
compression=compression, index=False)
# Calculate parquet size
if Path(parquet_path).is_dir():
parquet_size = sum(f.stat().st_size for f in Path(parquet_path).rglob('*.parquet')) / (1024 * 1024)
else:
parquet_size = Path(parquet_path).stat().st_size / (1024 * 1024)
duration = (datetime.now() - start_time).total_seconds()
result = ConversionResult(
source_path=csv_path,
output_path=parquet_path,
source_format='csv',
rows=len(df),
columns=len(df.columns),
original_size_mb=round(original_size, 2),
parquet_size_mb=round(parquet_size, 2),
compression_ratio=round(original_size / parquet_size, 2) if parquet_size > 0 else 0,
duration_seconds=round(duration, 2)
)
self.conversions.append(result)
return result
def excel_to_parquet(self, excel_path: str, parquet_path: str,
sheet_name: Union[str, int] = 0,
schema_name: str = None,
compression: str = CompressionType.SNAPPY) -> ConversionResult:
"""Convert Excel to Parquet."""
start_time = datetime.now()
# Read Excel
df = pd.read_excel(excel_path, sheet_name=sheet_name)
# Apply schema
if schema_name and schema_name in self.schemas:
df = self._apply_schema(df, self.schemas[schema_name])
original_size = Path(excel_path).stat().st_size / (1024 * 1024)
# Write Parquet
df.to_parquet(parquet_path, engine='pyarrow',
compression=compression, index=False)
parquet_size = Path(parquet_path).stat().st_size / (1024 * 1024)
duration = (datetime.now() - start_time).total_seconds()
result = ConversionResult(
source_path=excel_path,
output_path=parquet_path,
source_format='excel',
rows=len(df),
columns=len(df.columns),
original_size_mb=round(original_size, 2),
parquet_size_mb=round(parquet_size, 2),
compression_ratio=round(original_size / parquet_size, 2) if parquet_size > 0 else 0,
duration_seconds=round(duration, 2)
)
self.conversions.append(result)
return result
def json_to_parquet(self, json_path: str, parquet_path: str,
schema_name: str = None,
compression: str = CompressionType.SNAPPY) -> ConversionResult:
"""Convert JSON to Parquet."""
start_time = datetime.now()
# Read JSON
df = pd.read_json(json_path)
if schema_name and schema_name in self.schemas:
df = self._apply_schema(df, self.schemas[schema_name])
original_size = Path(json_path).stat().st_size / (1024 * 1024)
df.to_parquet(parquet_path, engine='pyarrow',
compression=compression, index=False)
parquet_size = Path(parquet_path).stat().st_size / (1024 * 1024)
duration = (datetime.now() - start_time).total_seconds()
result = ConversionResult(
source_path=json_path,
output_path=parquet_path,
source_format='json',
rows=len(df),
columns=len(df.columns),
original_size_mb=round(original_size, 2),
parquet_size_mb=round(parquet_size, 2),
compression_ratio=round(original_size / parquet_size, 2) if parquet_size > 0 else 0,
duration_seconds=round(duration, 2)
)
self.conversions.append(result)
return result
def parquet_to_csv(self, parquet_path: str, csv_path: str) -> ConversionResult:
"""Convert Parquet to CSV."""
start_time = datetime.now()
df = pd.read_parquet(parquet_path)
if Path(parquet_path).is_dir():
original_size = sum(f.stat().st_size for f in Path(parquet_path).rglob('*.parquet')) / (1024 * 1024)
else:
original_size = Path(parquet_path).stat().st_size / (1024 * 1024)
df.to_csv(csv_path, index=False)
csv_size = Path(csv_path).stat().st_size / (1024 * 1024)
duration = (datetime.now() - start_time).total_seconds()
result = ConversionResult(
source_path=parquet_path,
output_path=csv_path,
source_format='parquet',
rows=len(df),
columns=len(df.columns),
original_size_mb=round(original_size, 2),
parquet_size_mb=round(csv_size, 2), # Actually CSV size
compression_ratio=round(csv_size / original_size, 2) if original_size > 0 else 0,
duration_seconds=round(duration, 2)
)
self.conversions.append(result)
return result
def _apply_schema(self, df: pd.DataFrame, schema: ParquetSchema) -> pd.DataFrame:
"""Apply schema to DataFrame."""
for col, dtype in schema.columns.items():
if col in df.columns:
try:
if dtype == 'category':
df[col] = df[col].astype('category')
elif dtype.startswith('datetime'):
df[col] = pd.to_datetime(df[col])
else:
df[col] = df[col].astype(dtype)
except (ValueError, TypeError):
pass # Keep original type if conversion fails
return df
def get_parquet_info(self, parquet_path: str) -> Dict[str, Any]:
"""Get information about a Parquet file."""
import pyarrow.parquet as pq
if Path(parquet_path).is_dir():
# Partitioned dataset
files = list(Path(parquet_path).rglob('*.parquet'))
total_size = sum(f.stat().st_size for f in files) / (1024 * 1024)
if files:
sample = pq.read_table(str(files[0]))
schema = sample.schema
else:
return {'error': 'No parquet files found'}
return {
'path': parquet_path,
'type': 'partitioned',
'num_files': len(files),
'total_size_mb': round(total_size, 2),
'columns': [f.name for f in schema],
'dtypes': {f.name: str(f.type) for f in schema}
}
else:
# Single file
pf = pq.ParquetFile(parquet_path)
metadata = pf.metadata
return {
'path': parquet_path,
'type': 'single_file',
'size_mb': round(Path(parquet_path).stat().st_size / (1024 * 1024), 2),
'num_rows': metadata.num_rows,
'num_columns': metadata.num_columns,
'num_row_groups': metadata.num_row_groups,
'columns': [pf.schema_arrow.field(i).name for i in range(metadata.num_columns)],
'created_by': metadata.created_by
}
def query_parquet(self, parquet_path: str, columns: List[str] = None,
filters: List[tuple] = None) -> pd.DataFrame:
"""Query Parquet file with column selection and filtering."""
return pd.read_parquet(
parquet_path,
columns=columns,
filters=filters
)
def merge_parquet_files(self, input_paths: List[str],
output_path: str,
compression: str = CompressionType.SNAPPY) -> ConversionResult:
"""Merge multiple Parquet files into one."""
start_time = datetime.now()
dfs = [pd.read_parquet(p) for p in input_paths]
merged = pd.concat(dfs, ignore_index=True)
original_size = sum(Path(p).stat().st_size for p in input_paths) / (1024 * 1024)
merged.to_parquet(output_path, engine='pyarrow',
compression=compression, index=False)
parquet_size = Path(output_path).stat().st_size / (1024 * 1024)
duration = (datetime.now() - start_time).total_seconds()
return ConversionResult(
source_path=str(input_paths),
output_path=output_path,
source_format='parquet_merge',
rows=len(merged),
columns=len(merged.columns),
original_size_mb=round(original_size, 2),
parquet_size_mb=round(parquet_size, 2),
compression_ratio=round(original_size / parquet_size, 2) if parquet_size > 0 else 0,
duration_seconds=round(duration, 2)
)
def get_conversion_summary(self) -> Dict[str, Any]:
"""Get summary of all conversions."""
if not self.conversions:
return {'total_conversions': 0}
return {
'total_conversions': len(self.conversions),
'total_rows_processed': sum(c.rows for c in self.conversions),
'original_size_mb': sum(c.original_size_mb for c in self.conversions),
'parquet_size_mb': sum(c.parquet_size_mb for c in self.conversions),
'avg_compression_ratio': round(
sum(c.compression_ratio for c in self.conversions) / len(self.conversions), 2
),
'total_duration_seconds': sum(c.duration_seconds for c in self.conversions)
}
def export_conversion_log(self, output_path: str) -> str:
"""Export conversion log to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary = self.get_conversion_summary()
summary_df = pd.DataFrame([summary])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Detailed log
log_df = pd.DataFrame([{
'Source': c.source_path,
'Output': c.output_path,
'Format': c.source_format,
'Rows': c.rows,
'Columns': c.columns,
'Original Size (MB)': c.original_size_mb,
'Parquet Size (MB)': c.parquet_size_mb,
'Compression Ratio': c.compression_ratio,
'Duration (s)': c.duration_seconds
} for c in self.conversions])
log_df.to_excel(writer, sheet_name='Conversions', index=False)
return output_path
# Create converter
converter = ParquetConverter("Project Data Migration")
# Convert CSV to Parquet
result = converter.csv_to_parquet(
"costs.csv",
"costs.parquet",
schema_name="costs",
compression="snappy"
)
print(f"Converted {result.rows} rows")
print(f"Compression ratio: {result.compression_ratio}x")
print(f"Size: {result.original_size_mb}MB -> {result.parquet_size_mb}MB")
result = converter.excel_to_parquet(
"project_data.xlsx",
"project_data.parquet",
schema_name="projects"
)
# Select specific columns with filter
df = converter.query_parquet(
"costs.parquet",
columns=['project_id', 'amount', 'transaction_date'],
filters=[('amount', '>', 10000)]
)
info = converter.get_parquet_info("costs.parquet")
print(f"Rows: {info['num_rows']}")
print(f"Columns: {info['columns']}")
result = converter.merge_parquet_files(
["costs_2023.parquet", "costs_2024.parquet"],
"costs_all.parquet"
)
Take datadrivenconstruction/parquet-converter from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.