Data Writing

def write_table(
    table: Iterable[Iterable[Any]],
    filename: Union[str, PathLike],
    dialect: Union[str, SimpleDialect, csv.Dialect] = 'excel',
    transpose: bool = False,
    encoding: Optional[str] = None
) -> None:
    """
    Write a table (list of lists) to a CSV file.
    
    Parameters:
    - table: Table data as an iterable of rows (each row is an iterable of values)
    - filename: Path to output CSV file
    - dialect: Dialect to use for writing (default: 'excel' for RFC-4180 compliance)
    - transpose: Transpose table before writing (swap rows and columns)
    - encoding: Text encoding for output file (platform default if None)
    
    Raises:
    ValueError: If table rows have inconsistent lengths
    
    Notes:
    - Empty tables result in no file being created
    - All rows must have the same number of columns (after transposition if applicable)
    - Uses RFC-4180 compliant 'excel' dialect by default for standardized output
    """

import clevercsv

# Basic table writing
data = [
    ['Name', 'Age', 'City'],
    ['Alice', 30, 'New York'],
    ['Bob', 25, 'San Francisco'],
    ['Charlie', 35, 'Chicago']
]

clevercsv.write_table(data, 'employees.csv')

# Write with custom dialect
pipe_dialect = clevercsv.SimpleDialect('|', '"', '')
clevercsv.write_table(data, 'pipe_separated.csv', dialect=pipe_dialect)

# Transpose data (swap rows and columns)
clevercsv.write_table(data, 'transposed.csv', transpose=True)

# Write with specific encoding
clevercsv.write_table(data, 'utf8_output.csv', encoding='utf-8')

# Write numeric data
numeric_data = [
    ['X', 'Y', 'Z'],
    [1.5, 2.7, 3.14159],
    [4.2, 5.8, 6.28318],
    [7.1, 8.9, 9.42477]
]

clevercsv.write_table(numeric_data, 'numeric.csv')

# Write generator data (memory efficient)
def generate_data():
    yield ['ID', 'Value']
    for i in range(1000):
        yield [i, f'Value_{i}']

clevercsv.write_table(generate_data(), 'generated.csv')

import clevercsv
from datetime import datetime

def export_processed_data(input_data, output_file):
    """Process and export data with formatting."""
    
    processed_rows = [['ID', 'Name', 'Email', 'Created Date', 'Active', 'Score']]
    
    for record in input_data:
        processed_row = [
            record['id'],
            record['name'].title(),  # Capitalize names
            record['email'].lower(),  # Lowercase emails
            datetime.now().strftime('%Y-%m-%d'),
            'Yes' if record.get('active', False) else 'No',
            f"{record.get('score', 0):.2f}"  # Format numbers
        ]
        processed_rows.append(processed_row)
    
    # Write with standard CSV format for compatibility
    clevercsv.write_table(processed_rows, output_file)
    print(f"Exported {len(processed_rows)-1} records to {output_file}")

# Usage
raw_data = [
    {'id': 1, 'name': 'alice smith', 'email': 'ALICE@EXAMPLE.COM', 'active': True, 'score': 95.678},
    {'id': 2, 'name': 'bob jones', 'email': 'BOB@EXAMPLE.COM', 'active': False, 'score': 78.234}
]

export_processed_data(raw_data, 'processed_export.csv')

import clevercsv

def export_multiple_formats(data, base_filename):
    """Export data in multiple CSV formats."""
    
    formats = {
        'standard': clevercsv.SimpleDialect(',', '"', ''),
        'excel': 'excel',
        'tab_separated': clevercsv.SimpleDialect('\t', '"', ''),
        'pipe_separated': clevercsv.SimpleDialect('|', '"', ''),
        'semicolon_european': clevercsv.SimpleDialect(';', '"', '')
    }
    
    for format_name, dialect in formats.items():
        output_file = f"{base_filename}_{format_name}.csv"
        clevercsv.write_table(data, output_file, dialect=dialect)
        print(f"Exported {format_name} format to {output_file}")

# Usage
sample_data = [
    ['Product', 'Price', 'Category'],
    ['Laptop', '$999.99', 'Electronics'],
    ['Book', '$19.95', 'Education'],
    ['Coffee Mug', '$12.50', 'Kitchen']
]

export_multiple_formats(sample_data, 'products')

import clevercsv

class StreamingTableExporter:
    """Export large tabular datasets with streaming to manage memory usage."""
    
    def __init__(self, filename, dialect='excel', encoding=None):
        self.filename = filename
        self.dialect = dialect
        self.encoding = encoding
        self.file = None
        self.writer = None
        self.row_count = 0
    
    def __enter__(self):
        self.file = open(self.filename, 'w', newline='', encoding=self.encoding)
        self.writer = clevercsv.writer(self.file, dialect=self.dialect)
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.file:
            self.file.close()
        print(f"Exported {self.row_count} rows to {self.filename}")
    
    def write_row(self, row):
        """Write a single row."""
        self.writer.writerow(row)
        self.row_count += 1
    
    def write_rows(self, rows):
        """Write multiple rows."""
        for row in rows:
            self.write_row(row)

# Usage
# Export large dataset with streaming
with StreamingTableExporter('large_export.csv') as exporter:
    # Write header
    exporter.write_row(['ID', 'Name', 'Department', 'Salary', 'Hire Date'])
    
    # Process data in batches to manage memory
    for batch_start in range(0, 100000, 1000):  # 100k records in 1k batches
        batch_data = generate_employee_batch(batch_start, 1000)  # Your data generator
        exporter.write_rows(batch_data)

import clevercsv
from typing import List, Any

def validate_and_export_table(data: List[List[Any]], filename: str, validation_rules: dict):
    """Validate tabular data and export with error reporting."""
    
    if not data:
        print("No data to export")
        return 0, 0
    
    header = data[0]
    rows = data[1:]
    
    valid_rows = [header]  # Include header
    invalid_data = []
    
    for i, row in enumerate(rows):
        errors = []
        
        # Check row length
        if len(row) != len(header):
            errors.append(f"Expected {len(header)} columns, got {len(row)}")
        
        # Apply validation rules to each column
        for col_idx, (col_name, validator) in enumerate(validation_rules.items()):
            if col_idx < len(row):
                try:
                    if not validator(row[col_idx]):
                        errors.append(f"Invalid {col_name}: {row[col_idx]}")
                except Exception as e:
                    errors.append(f"Validation error for {col_name}: {e}")
            else:
                errors.append(f"Missing value for {col_name}")
        
        if errors:
            invalid_data.append({
                'row_index': i + 1,  # +1 for header
                'row': row,
                'errors': errors
            })
        else:
            valid_rows.append(row)
    
    # Export valid rows
    if len(valid_rows) > 1:  # More than just header
        clevercsv.write_table(valid_rows, filename)
        print(f"Exported {len(valid_rows)-1} valid rows to {filename}")
    
    # Export invalid rows for review
    if invalid_data:
        error_filename = filename.replace('.csv', '_errors.csv')
        error_rows = [header + ['_errors', '_row_index']]  # Add error columns
        
        for item in invalid_data:
            error_row = list(item['row'])
            # Pad row to match header length
            while len(error_row) < len(header):
                error_row.append('')
            error_row.extend(['; '.join(item['errors']), str(item['row_index'])])
            error_rows.append(error_row)
        
        clevercsv.write_table(error_rows, error_filename)
        print(f"Exported {len(invalid_data)} invalid rows to {error_filename}")
    
    return len(valid_rows) - 1, len(invalid_data)

# Usage
validation_rules = {
    'Name': lambda x: isinstance(x, str) and len(x.strip()) > 0,
    'Age': lambda x: str(x).isdigit() and 0 < int(x) < 150,
    'Email': lambda x: '@' in str(x) and '.' in str(x)
}

test_data = [
    ['Name', 'Age', 'Email'],
    ['Alice', '30', 'alice@example.com'],
    ['', '25', 'bob@example.com'],  # Invalid: empty name
    ['Charlie', '200', 'invalid-email'],  # Invalid: age too high, bad email
    ['Dave', '35']  # Invalid: missing email
]

valid_count, invalid_count = validate_and_export_table(test_data, 'validated_export.csv', validation_rules)
print(f"Validation complete: {valid_count} valid, {invalid_count} invalid")

import clevercsv

# Convert dictionaries to table format for write_table
def write_dict_data_as_table(dict_data, filename, fieldnames=None):
    """Write dictionary data using write_table."""
    
    if not dict_data:
        return
    
    # Get fieldnames from first dictionary if not provided
    if fieldnames is None:
        fieldnames = list(dict_data[0].keys())
    
    # Convert to table format
    table_data = [fieldnames]  # Header row
    for record in dict_data:
        row = [record.get(field, '') for field in fieldnames]
        table_data.append(row)
    
    clevercsv.write_table(table_data, filename)

# Alternative: Use DictWriter directly
def write_dict_data_with_dictwriter(dict_data, filename, fieldnames=None):
    """Write dictionary data using DictWriter."""
    
    if not dict_data:
        return
    
    if fieldnames is None:
        fieldnames = list(dict_data[0].keys())
    
    with open(filename, 'w', newline='') as f:
        writer = clevercsv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(dict_data)

# Usage
records = [
    {'name': 'Alice', 'age': 30, 'city': 'New York'},
    {'name': 'Bob', 'age': 25, 'city': 'San Francisco'},
    {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}
]

# Method 1: Convert to table
write_dict_data_as_table(records, 'method1_output.csv')

# Method 2: Use DictWriter
write_dict_data_with_dictwriter(records, 'method2_output.csv')

# Memory efficient: Use generators or iterators
def generate_large_table():
    yield ['ID', 'Value', 'Timestamp']
    for i in range(1000000):
        yield [i, f'value_{i}', datetime.now().isoformat()]

clevercsv.write_table(generate_large_table(), 'large_file.csv')  # Constant memory usage

# Memory intensive: Load all data first
large_data = list(generate_large_table())  # Loads all 1M rows into memory
clevercsv.write_table(large_data, 'large_file.csv')  # High memory usage

# Faster: Prepare all data first, then write once
all_rows = prepare_all_data()
clevercsv.write_table(all_rows, 'output.csv')

# Slower: Multiple file operations (avoid this pattern)
for i, row_data in enumerate(data_source):
    mode = 'w' if i == 0 else 'a'
    # Opening file repeatedly is inefficient
    clevercsv.write_table([row_data], 'output.csv')  # Don't do this

# Maximum compatibility: Use 'excel' dialect (RFC-4180)
clevercsv.write_table(data, 'compatible.csv', dialect='excel')

# Custom requirements: Create appropriate dialect
european_dialect = clevercsv.SimpleDialect(';', '"', '')  # Common in Europe
clevercsv.write_table(data, 'european.csv', dialect=european_dialect)

import clevercsv

def safe_csv_write(data, filename):
    """Write CSV with error handling."""
    try:
        clevercsv.write_table(data, filename)
        print(f"Successfully wrote {len(data)} rows to {filename}")
        return True
    except ValueError as e:
        print(f"Data validation error: {e}")
        return False
    except IOError as e:
        print(f"File write error: {e}")
        return False
    except Exception as e:
        print(f"Unexpected error: {e}")
        return False

# Usage
test_data = [
    ['A', 'B', 'C'],
    ['1', '2', '3'],
    ['4', '5']  # Inconsistent row length - will cause ValueError
]

success = safe_csv_write(test_data, 'test_output.csv')
if not success:
    print("Write operation failed, check data consistency")

import clevercsv

def write_with_validation(table, filename):
    """Write table with row length validation and repair."""
    if not table:
        print("Empty table - no file created")
        return
    
    # Check for consistent row lengths
    row_lengths = [len(row) for row in table]
    if len(set(row_lengths)) > 1:
        print(f"Inconsistent row lengths detected: {set(row_lengths)}")
        
        # Option 1: Pad short rows
        max_length = max(row_lengths)
        padded_table = []
        for row in table:
            padded_row = list(row) + [''] * (max_length - len(row))
            padded_table.append(padded_row)
        
        print(f"Padded short rows to {max_length} columns")
        clevercsv.write_table(padded_table, filename)
        
        # Option 2: Truncate long rows (alternative approach)
        # min_length = min(row_lengths)
        # truncated_table = [row[:min_length] for row in table]
        # clevercsv.write_table(truncated_table, filename)
    else:
        clevercsv.write_table(table, filename)
        print(f"Successfully wrote consistent table with {row_lengths[0]} columns")

# Usage
inconsistent_data = [
    ['Name', 'Age', 'City', 'Country'],
    ['Alice', '30', 'New York'],  # Missing country
    ['Bob', '25', 'SF', 'USA', 'Extra']  # Extra field
]

write_with_validation(inconsistent_data, 'repaired_output.csv')