Dictionary Operations

class DictReader:
    """
    CSV reader that returns rows as dictionaries.
    Uses first row as field names unless fieldnames are explicitly provided.
    """
    
    def __init__(
        self,
        f: Iterable[str],
        fieldnames: Optional[Sequence[str]] = None,
        restkey: Optional[str] = None,
        restval: Optional[str] = None,
        dialect: Union[str, SimpleDialect, csv.Dialect] = 'excel',
        *args,
        **kwds
    ):
        """
        Initialize dictionary CSV reader.
        
        Parameters:
        - f: File-like object or iterable of strings
        - fieldnames: Field names to use (first row if None)
        - restkey: Key for fields beyond fieldnames length
        - restval: Value for missing fields
        - dialect: Dialect specification
        - *args, **kwds: Additional arguments passed to underlying reader
        """
    
    def __iter__(self) -> Iterator[Dict[str, str]]:
        """Return iterator over dictionary rows."""
    
    def __next__(self) -> Dict[str, str]:
        """
        Return next row as dictionary.
        
        Returns:
        Dictionary mapping field names to values
        
        Raises:
        StopIteration: When no more rows available
        """
    
    @property
    def fieldnames(self) -> Sequence[str]:
        """Field names (column headers) used for dictionaries."""
    
    @fieldnames.setter
    def fieldnames(self, value: Sequence[str]) -> None:
        """Set field names explicitly."""
    
    @property
    def line_num(self) -> int:
        """Current line number being processed."""

import clevercsv

# Basic dictionary reading
with open('employees.csv', 'r', newline='') as f:
    reader = clevercsv.DictReader(f)
    for row in reader:
        print(f"Name: {row['name']}, Age: {row['age']}, Department: {row['dept']}")

# With automatic dialect detection
with open('data.csv', 'r', newline='') as f:
    sample = f.read()
    dialect = clevercsv.Detector().detect(sample)
    f.seek(0)
    reader = clevercsv.DictReader(f, dialect=dialect)
    records = list(reader)

# Custom field names (ignore first row)
fieldnames = ['id', 'name', 'score', 'grade']
with open('data.csv', 'r', newline='') as f:
    reader = clevercsv.DictReader(f, fieldnames=fieldnames)
    for row in reader:
        print(f"Student {row['name']} scored {row['score']}")

# Handle extra/missing fields
with open('irregular.csv', 'r', newline='') as f:
    reader = clevercsv.DictReader(f, restkey='extra_fields', restval='N/A')
    for row in reader:
        print(f"Regular data: {row}")
        if 'extra_fields' in row:
            print(f"Extra fields: {row['extra_fields']}")

class DictWriter:
    """
    CSV writer that accepts dictionaries and writes them as CSV rows.
    Requires fieldnames to determine column order and content.
    """
    
    def __init__(
        self,
        f: SupportsWrite[str],
        fieldnames: Collection[str],
        restval: Optional[Any] = '',
        extrasaction: Literal['raise', 'ignore'] = 'raise',
        dialect: Union[str, SimpleDialect, csv.Dialect] = 'excel',
        *args,
        **kwds
    ):
        """
        Initialize dictionary CSV writer.
        
        Parameters:
        - f: File-like object that supports writing
        - fieldnames: Field names that determine column order
        - restval: Value for missing dictionary keys
        - extrasaction: Action for extra dictionary keys ('raise' or 'ignore')
        - dialect: Dialect specification
        - *args, **kwds: Additional arguments passed to underlying writer
        """
    
    def writeheader(self) -> Any:
        """
        Write header row containing field names.
        
        Returns:
        Return value from underlying writerow call
        """
    
    def writerow(self, rowdict: Mapping[str, Any]) -> Any:
        """
        Write a single dictionary as a CSV row.
        
        Parameters:
        - rowdict: Dictionary with field values
        
        Returns:
        Return value from underlying writerow call
        
        Raises:
        ValueError: If extrasaction='raise' and dictionary contains extra keys
        """
    
    def writerows(self, rowdicts: Iterable[Mapping[str, Any]]) -> None:
        """
        Write multiple dictionaries as CSV rows.
        
        Parameters:
        - rowdicts: Iterable of dictionaries to write
        
        Raises:
        ValueError: If extrasaction='raise' and any dictionary contains extra keys
        """
    
    @property
    def fieldnames(self) -> Collection[str]:
        """Field names that determine column order."""

import clevercsv

# Basic dictionary writing
data = [
    {'name': 'Alice', 'age': 30, 'city': 'New York'},
    {'name': 'Bob', 'age': 25, 'city': 'San Francisco'},
    {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}
]

fieldnames = ['name', 'age', 'city']
with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(data)

# With specific dialect
dialect = clevercsv.SimpleDialect(';', '"', '')
with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=fieldnames, dialect=dialect)
    writer.writeheader()
    for row in data:
        writer.writerow(row)

# Handle missing values
data_with_missing = [
    {'name': 'Alice', 'age': 30},  # Missing 'city'
    {'name': 'Bob', 'city': 'SF'},  # Missing 'age'
]

with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=['name', 'age', 'city'], restval='Unknown')
    writer.writeheader()
    writer.writerows(data_with_missing)

# Handle extra fields
data_with_extra = [
    {'name': 'Alice', 'age': 30, 'city': 'NYC', 'country': 'USA'},  # Extra 'country'
]

# Ignore extra fields
with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=['name', 'age', 'city'], extrasaction='ignore')
    writer.writeheader()
    writer.writerows(data_with_extra)

import clevercsv

def process_employee_data(input_file, output_file):
    """Process employee data with transformations."""
    
    transformations = []
    
    with open(input_file, 'r', newline='') as infile:
        reader = clevercsv.DictReader(infile)
        
        for row in reader:
            # Apply transformations
            row['name'] = row['name'].title()  # Capitalize names
            row['age'] = int(row['age']) if row['age'].isdigit() else 0
            row['salary'] = float(row['salary'].replace('$', '').replace(',', ''))
            
            # Add computed fields
            row['seniority'] = 'Senior' if int(row['age']) > 40 else 'Junior'
            
            transformations.append(row)
    
    # Write processed data
    if transformations:
        fieldnames = list(transformations[0].keys())
        with open(output_file, 'w', newline='') as outfile:
            writer = clevercsv.DictWriter(outfile, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(transformations)

# Usage
process_employee_data('employees.csv', 'processed_employees.csv')

import clevercsv

def validate_and_filter_data(filename, validation_rules):
    """Validate and filter CSV data based on rules."""
    
    valid_records = []
    invalid_records = []
    
    with open(filename, 'r', newline='') as f:
        reader = clevercsv.DictReader(f)
        
        for row_num, row in enumerate(reader, 1):
            errors = []
            
            # Apply validation rules
            for field, rule in validation_rules.items():
                if field in row:
                    if not rule(row[field]):
                        errors.append(f"Invalid {field}: {row[field]}")
                else:
                    errors.append(f"Missing required field: {field}")
            
            if errors:
                invalid_records.append({
                    'row_number': row_num,
                    'data': row,
                    'errors': errors
                })
            else:
                valid_records.append(row)
    
    return valid_records, invalid_records

# Usage
validation_rules = {
    'email': lambda x: '@' in x and '.' in x,
    'age': lambda x: x.isdigit() and 0 < int(x) < 120,
    'salary': lambda x: x.replace('$', '').replace(',', '').replace('.', '').isdigit()
}

valid_data, invalid_data = validate_and_filter_data('employees.csv', validation_rules)
print(f"Valid records: {len(valid_data)}")
print(f"Invalid records: {len(invalid_data)}")

import clevercsv

def remap_csv_columns(input_file, output_file, column_mapping):
    """Remap column names and reorganize CSV data."""
    
    with open(input_file, 'r', newline='') as infile:
        reader = clevercsv.DictReader(infile)
        
        # Prepare data with remapped columns
        remapped_data = []
        for row in reader:
            new_row = {}
            for old_name, new_name in column_mapping.items():
                if old_name in row:
                    new_row[new_name] = row[old_name]
                else:
                    new_row[new_name] = ''  # Default for missing columns
            remapped_data.append(new_row)
    
    # Write remapped data
    if remapped_data:
        fieldnames = list(column_mapping.values())
        with open(output_file, 'w', newline='') as outfile:
            writer = clevercsv.DictWriter(outfile, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(remapped_data)

# Usage
column_mapping = {
    'full_name': 'name',
    'years_old': 'age',
    'home_city': 'city',
    'job_title': 'position'
}

remap_csv_columns('input.csv', 'output.csv', column_mapping)

import clevercsv
import warnings

# Capture warnings about duplicate headers
with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter("always")
    
    with open('file_with_duplicate_headers.csv', 'r', newline='') as f:
        reader = clevercsv.DictReader(f)
        data = list(reader)
    
    if w:
        for warning in w:
            print(f"Warning: {warning.message}")

import clevercsv

# Raise error on extra fields
try:
    with open('output.csv', 'w', newline='') as f:
        writer = clevercsv.DictWriter(f, fieldnames=['a', 'b'], extrasaction='raise')
        writer.writerow({'a': '1', 'b': '2', 'c': '3'})  # 'c' is extra
except ValueError as e:
    print(f"Extra field error: {e}")

# Ignore extra fields silently
with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=['a', 'b'], extrasaction='ignore')
    writer.writerow({'a': '1', 'b': '2', 'c': '3'})  # 'c' ignored

import clevercsv

# Use restval for missing fields
data = [{'name': 'Alice'}, {'name': 'Bob', 'age': 25}]  # Missing 'age' in first row

with open('output.csv', 'w', newline='') as f:
    writer = clevercsv.DictWriter(f, fieldnames=['name', 'age'], restval='N/A')
    writer.writeheader()
    writer.writerows(data)

import clevercsv

def process_large_csv_efficiently(filename):
    """Process large CSV files without loading all data into memory."""
    
    with open(filename, 'r', newline='') as f:
        reader = clevercsv.DictReader(f)
        
        # Process one row at a time
        for row in reader:
            # Process row immediately
            process_single_record(row)
            # Don't store in list - keeps memory usage constant

def process_large_csv_inefficiently(filename):
    """Inefficient approach that loads everything into memory."""
    
    with open(filename, 'r', newline='') as f:
        reader = clevercsv.DictReader(f)
        all_records = list(reader)  # Loads entire file into memory
        
        for record in all_records:
            process_single_record(record)

# Efficient: Access fieldnames once
reader = clevercsv.DictReader(file)
fieldnames = reader.fieldnames  # Cache fieldnames
for row in reader:
    # Use cached fieldnames if needed
    process_row(row, fieldnames)

# Less efficient: Access fieldnames repeatedly in loop
reader = clevercsv.DictReader(file)
for row in reader:
    fieldnames = reader.fieldnames  # Repeated access
    process_row(row, fieldnames)