Csv Handler

Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.

CSV Handler for Construction Data

Overview

CSV is the universal exchange format in construction - from scheduling exports to cost databases. This skill handles encoding issues, delimiter detection, and data cleaning.

Python Implementation

python

import pandas as pd
import csv
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path
from dataclasses import dataclass
import chardet


@dataclass
class CSVProfile:
    """Profile of CSV file."""
    encoding: str
    delimiter: str
    has_header: bool
    row_count: int
    column_count: int
    columns: List[str]


class ConstructionCSVHandler:
    """Handle CSV files from construction software."""

    COMMON_DELIMITERS = [',', ';', '\t', '|']
    COMMON_ENCODINGS = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252', 'iso-8859-1']

    def __init__(self):
        self.last_profile: Optional[CSVProfile] = None

    def detect_encoding(self, file_path: str) -> str:
        """Detect file encoding."""
        with open(file_path, 'rb') as f:
            raw = f.read(10000)
        result = chardet.detect(raw)
        return result.get('encoding', 'utf-8') or 'utf-8'

    def detect_delimiter(self, file_path: str, encoding: str) -> str:
        """Detect CSV delimiter."""
        with open(file_path, 'r', encoding=encoding, errors='replace') as f:
            sample = f.read(5000)

        # Count occurrences
        counts = {d: sample.count(d) for d in self.COMMON_DELIMITERS}

        # Return most common that appears consistently
        if counts:
            return max(counts, key=counts.get)
        return ','

    def profile_csv(self, file_path: str) -> CSVProfile:
        """Profile CSV file."""
        encoding = self.detect_encoding(file_path)
        delimiter = self.detect_delimiter(file_path, encoding)

        # Read sample
        df = pd.read_csv(file_path, encoding=encoding, delimiter=delimiter,
                         nrows=10, on_bad_lines='skip')

        has_header = not df.columns[0].replace('.', '').replace('-', '').isdigit()

        # Full row count
        with open(file_path, 'r', encoding=encoding, errors='replace') as f:
            row_count = sum(1 for _ in f) - (1 if has_header else 0)

        profile = CSVProfile(
            encoding=encoding,
            delimiter=delimiter,
            has_header=has_header,
            row_count=row_count,
            column_count=len(df.columns),
            columns=list(df.columns)
        )
        self.last_profile = profile
        return profile

    def read_csv(self, file_path: str,
                 encoding: Optional[str] = None,
                 delimiter: Optional[str] = None,
                 clean: bool = True) -> pd.DataFrame:
        """Read CSV with auto-detection."""

        # Auto-detect if not provided
        if encoding is None:
            encoding = self.detect_encoding(file_path)
        if delimiter is None:
            delimiter = self.detect_delimiter(file_path, encoding)

        # Read with error handling
        df = pd.read_csv(
            file_path,
            encoding=encoding,
            delimiter=delimiter,
            on_bad_lines='skip',
            low_memory=False
        )

        if clean:
            df = self.clean_dataframe(df)

        return df

    def clean_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
        """Clean construction CSV data."""
        # Clean column names
        df.columns = [self._clean_column_name(c) for c in df.columns]

        # Remove empty rows and columns
        df = df.dropna(how='all')
        df = df.dropna(axis=1, how='all')

        # Strip whitespace from strings
        for col in df.select_dtypes(include=['object']):
            df[col] = df[col].str.strip() if df[col].dtype == 'object' else df[col]

        return df

    def _clean_column_name(self, name: str) -> str:
        """Clean column name."""
        if not isinstance(name, str):
            return str(name)

        # Remove special characters, replace spaces
        clean = name.strip().lower()
        clean = clean.replace(' ', '_').replace('-', '_')
        clean = ''.join(c for c in clean if c.isalnum() or c == '_')
        return clean

    def merge_csvs(self, file_paths: List[str],
                   on_column: Optional[str] = None) -> pd.DataFrame:
        """Merge multiple CSV files."""
        dfs = []
        for path in file_paths:
            df = self.read_csv(path)
            df['_source_file'] = Path(path).name
            dfs.append(df)

        if not dfs:
            return pd.DataFrame()

        if on_column and on_column in dfs[0].columns:
            result = dfs[0]
            for df in dfs[1:]:
                result = pd.merge(result, df, on=on_column, how='outer')
            return result

        return pd.concat(dfs, ignore_index=True)

    def split_csv(self, df: pd.DataFrame,
                  group_column: str,
                  output_dir: str) -> List[str]:
        """Split CSV by column values."""
        output_path = Path(output_dir)
        output_path.mkdir(parents=True, exist_ok=True)

        files = []
        for value in df[group_column].unique():
            subset = df[df[group_column] == value]
            filename = f"{group_column}_{value}.csv"
            filepath = output_path / filename
            subset.to_csv(filepath, index=False)
            files.append(str(filepath))

        return files

    def convert_types(self, df: pd.DataFrame,
                      type_map: Dict[str, str] = None) -> pd.DataFrame:
        """Convert column types intelligently."""
        df = df.copy()

        if type_map:
            for col, dtype in type_map.items():
                if col in df.columns:
                    try:
                        df[col] = df[col].astype(dtype)
                    except:
                        pass
        else:
            # Auto-convert
            for col in df.columns:
                # Try numeric
                try:
                    df[col] = pd.to_numeric(df[col])
                    continue
                except:
                    pass

                # Try datetime
                try:
                    df[col] = pd.to_datetime(df[col])
                except:
                    pass

        return df

    def export_csv(self, df: pd.DataFrame,
                   file_path: str,
                   encoding: str = 'utf-8-sig',
                   delimiter: str = ',') -> str:
        """Export DataFrame to CSV."""
        df.to_csv(file_path, encoding=encoding, sep=delimiter, index=False)
        return file_path


# Specialized handlers
class ScheduleCSVHandler(ConstructionCSVHandler):
    """Handler for project schedule CSVs."""

    SCHEDULE_COLUMNS = ['task_id', 'task_name', 'start_date', 'end_date',
                        'duration', 'predecessors', 'resources']

    def parse_schedule(self, file_path: str) -> pd.DataFrame:
        """Parse schedule CSV."""
        df = self.read_csv(file_path)

        # Convert date columns
        for col in df.columns:
            if 'date' in col.lower() or 'start' in col.lower() or 'end' in col.lower():
                try:
                    df[col] = pd.to_datetime(df[col])
                except:
                    pass

        return df


class CostCSVHandler(ConstructionCSVHandler):
    """Handler for cost/estimate CSVs."""

    def parse_costs(self, file_path: str) -> pd.DataFrame:
        """Parse cost CSV."""
        df = self.read_csv(file_path)

        # Find and convert numeric columns
        for col in df.columns:
            if any(word in col.lower() for word in ['cost', 'price', 'amount', 'total', 'qty', 'quantity']):
                df[col] = pd.to_numeric(df[col].replace(r'[\$,]', '', regex=True), errors='coerce')

        return df

Quick Start

python

handler = ConstructionCSVHandler()

# Profile CSV first
profile = handler.profile_csv("export.csv")
print(f"Encoding: {profile.encoding}, Delimiter: '{profile.delimiter}'")

# Read with auto-detection
df = handler.read_csv("export.csv")
print(f"Loaded {len(df)} rows, {len(df.columns)} columns")

Common Use Cases

1. Merge Multiple Exports

python

files = ["jan_export.csv", "feb_export.csv", "mar_export.csv"]
merged = handler.merge_csvs(files)

2. Split by Category

python

handler.split_csv(df, group_column='category', output_dir='./split_files')

3. Schedule Import

python

schedule_handler = ScheduleCSVHandler()
schedule = schedule_handler.parse_schedule("p6_export.csv")

Resources

• DDC Book: Chapter 2.1 - Structured Data