CSV-Wrangler

Patterns for handling real-world messy CSV files.

Encoding Detection

import chardet

def detect_encoding(file_path: str, sample_size: int = 10000) -> str:
    """Detect file encoding from sample."""
    with open(file_path, 'rb') as f:
        raw = f.read(sample_size)
    result = chardet.detect(raw)
    return result['encoding']

def read_with_encoding(path: str) -> pd.DataFrame:
    """Read CSV with auto-detected encoding."""
    encoding = detect_encoding(path)
    # Common fallback chain
    encodings = [encoding, 'utf-8', 'latin-1', 'cp1252']

    for enc in encodings:
        try:
            return pd.read_csv(path, encoding=enc)
        except UnicodeDecodeError:
            continue

    # Last resort: ignore errors
    return pd.read_csv(path, encoding='utf-8', errors='ignore')

Delimiter Detection

import csv

def detect_delimiter(file_path: str) -> str:
    """Detect CSV delimiter from file sample."""
    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
        sample = f.read(4096)

    sniffer = csv.Sniffer()
    try:
        dialect = sniffer.sniff(sample, delimiters=',;\t|')
        return dialect.delimiter
    except csv.Error:
        # Count occurrences and pick most common
        counts = {d: sample.count(d) for d in [',', ';', '\t', '|']}
        return max(counts, key=counts.get)

def read_with_delimiter_detection(path: str) -> pd.DataFrame:
    """Read CSV with auto-detected delimiter."""
    delimiter = detect_delimiter(path)
    return pd.read_csv(path, sep=delimiter)

Handling Malformed Rows

def read_with_error_handling(path: str) -> tuple[pd.DataFrame, list[dict]]:
    """Read CSV, capturing malformed rows separately."""
    good_rows = []
    bad_rows = []

    with open(path, 'r', encoding='utf-8', errors='replace') as f:
        reader = csv.reader(f)
        header = next(reader)
        expected_cols = len(header)

        for line_num, row in enumerate(reader, start=2):
            if len(row) == expected_cols:
                good_rows.append(row)
            else:
                bad_rows.append({
                    'line': line_num,
                    'expected': expected_cols,
                    'actual': len(row),
                    'data': row
                })

    df = pd.DataFrame(good_rows, columns=header)
    return df, bad_rows

# Using pandas error_bad_lines (deprecated) / on_bad_lines
def read_skip_bad_lines(path: str) -> pd.DataFrame:
    """Read CSV, skipping malformed rows."""
    return pd.read_csv(
        path,
        on_bad_lines='skip',  # or 'warn' to see them
        engine='python'
    )

Quoted Fields with Embedded Delimiters

def read_quoted_csv(path: str) -> pd.DataFrame:
    """Handle CSVs with quoted fields containing delimiters."""
    return pd.read_csv(
        path,
        quotechar='"',
        doublequote=True,  # Handle "" as escaped quote
        escapechar='\\',   # Handle \, as escaped comma
        engine='python'
    )

# For really messy files
def read_regex_separated(path: str, pattern: str = r',(?=(?:[^"]*"[^"]*")*[^"]*$)') -> pd.DataFrame:
    """Split on delimiter only outside quotes using regex."""
    import re
    rows = []
    with open(path, 'r') as f:
        for line in f:
            rows.append(re.split(pattern, line.strip()))
    return pd.DataFrame(rows[1:], columns=rows[0])

Header Detection

def detect_header_row(path: str, max_rows: int = 10) -> int:
    """Find the header row in files with metadata at top."""
    with open(path, 'r') as f:
        lines = [f.readline() for _ in range(max_rows)]

    delimiter = detect_delimiter(path)

    for i, line in enumerate(lines):
        parts = line.split(delimiter)
        # Header likely has more columns than metadata
        # and contains text-like values
        if len(parts) > 2 and all(p.strip().replace('_', '').isalnum() for p in parts[:3]):
            return i

    return 0  # Default to first row

def read_with_header_detection(path: str) -> pd.DataFrame:
    """Read CSV, auto-detecting header row."""
    header_row = detect_header_row(path)
    return pd.read_csv(path, skiprows=header_row)

Date Parsing

def infer_date_columns(df: pd.DataFrame) -> list[str]:
    """Identify columns that look like dates."""
    date_cols = []
    date_patterns = [
        r'\d{4}-\d{2}-\d{2}',           # 2024-01-15
        r'\d{2}/\d{2}/\d{4}',           # 01/15/2024
        r'\d{2}-\d{2}-\d{4}',           # 15-01-2024
        r'\d{4}/\d{2}/\d{2}',           # 2024/01/15
    ]

    for col in df.columns:
        if df[col].dtype == 'object':
            sample = df[col].dropna().head(100)
            for pattern in date_patterns:
                if sample.str.match(pattern).mean() > 0.8:
                    date_cols.append(col)
                    break

    return date_cols

def parse_dates_flexibly(df: pd.DataFrame, columns: list[str]) -> pd.DataFrame:
    """Parse dates with multiple format attempts."""
    for col in columns:
        try:
            df[col] = pd.to_datetime(df[col], infer_datetime_format=True)
        except:
            # Try common formats explicitly
            for fmt in ['%Y-%m-%d', '%m/%d/%Y', '%d/%m/%Y', '%Y/%m/%d']:
                try:
                    df[col] = pd.to_datetime(df[col], format=fmt)
                    break
                except:
                    continue
    return df

Numeric Cleaning

def clean_numeric_column(series: pd.Series) -> pd.Series:
    """Clean numeric column with currency symbols, commas, etc."""
    if series.dtype == 'object':
        cleaned = (series
            .astype(str)
            .str.replace(r'[$€£¥,]', '', regex=True)  # Remove currency/commas
            .str.replace(r'\s+', '', regex=True)       # Remove whitespace
            .str.replace(r'\((.+)\)', r'-\1', regex=True)  # (123) -> -123
        )
        return pd.to_numeric(cleaned, errors='coerce')
    return series

def clean_all_numeric(df: pd.DataFrame) -> pd.DataFrame:
    """Attempt to convert object columns to numeric."""
    for col in df.select_dtypes(include=['object']).columns:
        converted = pd.to_numeric(df[col], errors='coerce')
        # Keep conversion if >80% success
        if converted.notna().mean() > 0.8:
            df[col] = converted
    return df

Complete Wrangling Pipeline

def wrangle_csv(path: str) -> tuple[pd.DataFrame, dict]:
    """Full pipeline for messy CSV handling."""
    report = {'issues': [], 'fixes': []}

    # Step 1: Detect encoding
    encoding = detect_encoding(path)
    report['encoding'] = encoding

    # Step 2: Detect delimiter
    delimiter = detect_delimiter(path)
    report['delimiter'] = delimiter

    # Step 3: Detect header row
    header_row = detect_header_row(path)
    if header_row > 0:
        report['fixes'].append(f"Skipped {header_row} metadata rows")

    # Step 4: Read with error handling
    try:
        df = pd.read_csv(
            path,
            encoding=encoding,
            sep=delimiter,
            skiprows=header_row,
            on_bad_lines='warn',
            engine='python'
        )
    except Exception as e:
        report['issues'].append(f"Read error: {e}")
        df = pd.read_csv(path, encoding='latin-1', on_bad_lines='skip')

    # Step 5: Clean column names
    df.columns = (df.columns
        .str.strip()
        .str.lower()
        .str.replace(r'\s+', '_', regex=True)
        .str.replace(r'[^\w]', '', regex=True)
    )
    report['fixes'].append("Normalized column names")

    # Step 6: Parse dates
    date_cols = infer_date_columns(df)
    if date_cols:
        df = parse_dates_flexibly(df, date_cols)
        report['fixes'].append(f"Parsed dates: {date_cols}")

    # Step 7: Clean numeric columns
    df = clean_all_numeric(df)

    report['shape'] = df.shape
    report['dtypes'] = df.dtypes.to_dict()

    return df, report

Excel File Handling

def read_excel_smart(path: str, sheet: str | int = 0) -> pd.DataFrame:
    """Read Excel with common cleanup."""
    df = pd.read_excel(
        path,
        sheet_name=sheet,
        engine='openpyxl',  # For .xlsx
        # engine='xlrd',    # For .xls
    )

    # Drop fully empty rows/columns
    df = df.dropna(how='all').dropna(axis=1, how='all')

    # Reset index after dropping
    df = df.reset_index(drop=True)

    return df

def list_excel_sheets(path: str) -> list[str]:
    """List all sheets in an Excel file."""
    xl = pd.ExcelFile(path)
    return xl.sheet_names

CSV-to-SQL Conversion

Generate CREATE TABLE and load statements from CSV files with type inference.

Type Mapping

Detected Pattern	PostgreSQL	MySQL	SQLite
Integer	INTEGER / BIGINT	INT / BIGINT	INTEGER
Decimal	NUMERIC(p,s)	DECIMAL(p,s)	REAL
Boolean	BOOLEAN	TINYINT(1)	INTEGER
Date	DATE	DATE	TEXT
Timestamp	TIMESTAMP	DATETIME	TEXT
UUID	UUID	CHAR(36)	TEXT
Short text	VARCHAR(n)	VARCHAR(n)	TEXT
Long text	TEXT	TEXT	TEXT
JSON	JSONB	JSON	TEXT

PostgreSQL Output

-- Generated from: orders.csv
-- Rows: 50,000 | Columns: 8

DROP TABLE IF EXISTS orders CASCADE;

CREATE TABLE orders (
    order_id BIGINT PRIMARY KEY,
    customer_id BIGINT NOT NULL,
    order_date DATE NOT NULL,
    status VARCHAR(20) NOT NULL CHECK (status IN ('pending', 'shipped', 'delivered')),
    total_amount NUMERIC(10, 2) NOT NULL CHECK (total_amount >= 0),
    shipping_address TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_orders_customer ON orders(customer_id);
CREATE INDEX idx_orders_date ON orders(order_date);

\COPY orders FROM 'orders.csv' WITH (FORMAT csv, HEADER true, NULL '');
SELECT COUNT(*) as loaded_rows FROM orders;

MySQL Output

DROP TABLE IF EXISTS orders;

CREATE TABLE orders (
    order_id BIGINT PRIMARY KEY,
    customer_id BIGINT NOT NULL,
    order_date DATE NOT NULL,
    status ENUM('pending', 'shipped', 'delivered') NOT NULL,
    total_amount DECIMAL(10, 2) NOT NULL,
    shipping_address TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    INDEX idx_customer (customer_id),
    INDEX idx_date (order_date)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

LOAD DATA INFILE '/path/to/orders.csv'
INTO TABLE orders
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

SQLite Output

DROP TABLE IF EXISTS orders;

CREATE TABLE orders (
    order_id INTEGER PRIMARY KEY,
    customer_id INTEGER NOT NULL,
    order_date TEXT NOT NULL,
    status TEXT NOT NULL CHECK (status IN ('pending', 'shipped', 'delivered')),
    total_amount REAL NOT NULL CHECK (total_amount >= 0),
    shipping_address TEXT,
    created_at TEXT DEFAULT (datetime('now')),
    updated_at TEXT DEFAULT (datetime('now'))
);

CREATE INDEX idx_orders_customer ON orders(customer_id);
CREATE INDEX idx_orders_date ON orders(order_date);

.mode csv
.import --skip 1 orders.csv orders

Python Load Script

import pandas as pd
from sqlalchemy import create_engine

def load_csv_to_db(
    csv_path: str,
    table_name: str,
    connection_string: str,
    if_exists: str = 'replace',
    chunksize: int = 10000
) -> int:
    """Load CSV to database with progress."""
    engine = create_engine(connection_string)
    total_rows = 0
    for chunk in pd.read_csv(csv_path, chunksize=chunksize):
        chunk.to_sql(
            table_name, engine,
            if_exists=if_exists if total_rows == 0 else 'append',
            index=False
        )
        total_rows += len(chunk)
    return total_rows

Constraints Detection

Automatically detect and suggest:

1. Primary Key - Column named 'id', '*_id', 'pk'; 100% unique, no nulls
2. Foreign Keys - Columns ending in '_id'; reference table inferred from prefix
3. NOT NULL - Columns with 0% null rate
4. UNIQUE - Columns with 100% unique values; email, username patterns
5. CHECK Constraints - Categorical columns -> IN list; numeric ranges -> >= / <=