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Complete Guide: String to Float/Int in Python

Learn how to convert strings to float or int in Python with built-in functions. Complete guide with error handling, advanced parsing, and best practices for robust number conversion.

#data-parsing#error-handling#float-int-parsing#numeric-conversion#python-builtins#python-string-conversion#type-conversion
Question

How do I parse a string to a float or int in Python?

How can I convert a string to a float?
Example: “545.2222” → 545.2222

How can I convert a string to an int?
Example: “31” → 31

NeuroAgent

To convert a string to a float or int in Python, you can use the built-in float() and int() functions. For converting “545.2222” to a float, simply use float("545.2222"), and for converting “31” to an int, use int("31"). These functions handle basic string-to-number conversions, though you should be aware of potential ValueError exceptions if the string contains invalid numerical characters.

Contents

Basic String to Float Conversion

The most straightforward way to convert a string to a float in Python is using the built-in float() function. This function takes a string as input and attempts to convert it to a floating-point number.

python
# Basic float conversion
float_string = "545.2222"
number = float(float_string)
print(number)  # Output: 545.2222

# Works with scientific notation
scientific_string = "1.23e-4"
scientific_float = float(scientific_string)
print(scientific_float)  # Output: 0.000123

The float() function can handle various string representations including:

  • Decimal numbers (e.g., “3.14159”)
  • Scientific notation (e.g., “2.5e3”, “1.2E-5”)
  • Positive and negative numbers (e.g., “-42.5”, “+3.14”)

Basic String to Int Conversion

Similarly, converting a string to an integer is done using the int() function. This function converts a string to a whole number.

python
# Basic int conversion
int_string = "31"
number = int(int_string)
print(number)  # Output: 31

# Works with negative numbers
negative_string = "-15"
negative_int = int(negative_string)
print(negative_int)  # Output: -15

# Works with positive numbers with explicit sign
positive_string = "+42"
positive_int = int(positive_string)
print(positive_int)  # Output: 42

The int() function supports:

  • Base 10 numbers (default)
  • Other bases when specified (e.g., binary, hexadecimal, octal)

Error Handling for Invalid Strings

When converting strings that don’t represent valid numbers, both float() and int() will raise a ValueError. It’s important to handle these exceptions in your code.

python
# Handling ValueError for float conversion
try:
    invalid_float = float("abc")
except ValueError:
    print("Cannot convert 'abc' to float")

# Handling ValueError for int conversion
try:
    invalid_int = int("12.34")
except ValueError:
    print("Cannot convert '12.34' to int - contains decimal point")

# Using try-except blocks safely
def safe_float_conversion(s):
    try:
        return float(s)
    except ValueError:
        return None

result = safe_float_conversion("invalid")
print(result)  # Output: None

Common causes of conversion errors include:

  • Non-numeric characters (e.g., “hello”)
  • Strings with decimal points when converting to int
  • Empty strings
  • Strings with leading/trailing whitespace (unless stripped first)

Advanced Parsing Techniques

For more complex scenarios, you might need advanced parsing techniques:

python
# Converting from strings with commas and currency symbols
import re

def parse_currency(currency_string):
    # Remove currency symbols and commas
    cleaned = re.sub(r'[$,€£¥]', '', currency_string)
    return float(cleaned)

price = parse_currency("$1,234.56")
print(price)  # Output: 1234.56

# Parsing mixed numbers and units
def parse_dimension(dimension_string):
    import re
    match = re.match(r'(\d+(?:\.\d+)?)([a-zA-Z]+)', dimension_string)
    if match:
        value = float(match.group(1))
        unit = match.group(2)
        return value, unit
    return None, None

length, unit = parse_dimension("12.5cm")
print(f"{length} {unit}")  # Output: 12.5 cm

Parsing Numeric Strings with Special Formats

Python can handle various numeric string formats:

python
# Different number formats
numbers = [
    "12345",        # Standard decimal
    "-123.45",      # Negative decimal
    "+1.23e4",      # Scientific notation
    "0x1F",         # Hexadecimal (requires int() with base)
    "0o755",        # Octal (requires int() with base)
    "0b1010",       # Binary (requires int() with base)
]

for num_str in numbers:
    try:
        if num_str.startswith(('0x', '0o', '0b')):
            # Convert with base parameter
            if num_str.startswith('0x'):
                value = int(num_str, 16)
            elif num_str.startswith('0o'):
                value = int(num_str, 8)
            else:  # 0b
                value = int(num_str, 2)
        else:
            # Regular decimal or scientific notation
            value = float(num_str)
        print(f"'{num_str}' → {value} ({type(value)})")
    except ValueError as e:
        print(f"Error converting '{num_str}': {e}")

Performance Considerations

When dealing with large-scale string conversions, performance matters:

python
# Performance comparison
import timeit

def performance_test():
    # Method 1: Direct conversion
    def direct_conversion():
        numbers = ["123", "456", "789"] * 1000
        return [float(n) for n in numbers]
    
    # Method 2: With validation
    def safe_conversion():
        numbers = ["123", "456", "789"] * 1000
        result = []
        for n in numbers:
            try:
                result.append(float(n))
            except ValueError:
                result.append(0.0)
        return result
    
    # Method 3: Pre-compiled regex for validation
    import re
    number_pattern = re.compile(r'^-?\d+\.?\d*$')
    
    def regex_conversion():
        numbers = ["123", "456", "789"] * 1000
        result = []
        for n in numbers:
            if number_pattern.match(n):
                result.append(float(n))
            else:
                result.append(0.0)
        return result
    
    print("Direct conversion:", timeit.timeit(direct_conversion, number=100))
    print("Safe conversion:", timeit.timeit(safe_conversion, number=100))
    print("Regex conversion:", timeit.timeit(regex_conversion, number=100))

performance_test()

For better performance:

  • Use direct conversion when you know strings are valid
  • Consider list comprehensions for batch operations
  • Avoid regex validation if not needed for your use case

Best Practices for String Conversion

Here are comprehensive best practices for converting strings to numbers:

python
# Comprehensive conversion function
def convert_string_to_number(input_string, target_type='auto', default=None, strip_whitespace=True):
    """
    Robust string to number conversion with multiple options.
    
    Args:
        input_string: String to convert
        target_type: 'auto', 'int', or 'float'
        default: Value to return on conversion failure
        strip_whitespace: Whether to strip whitespace before conversion
    
    Returns:
        Converted number or default value
    """
    if not input_string:
        return default
    
    # Handle whitespace
    if strip_whitespace:
        input_string = input_string.strip()
    
    try:
        if target_type == 'auto':
            # Try int first, then float
            try:
                return int(input_string)
            except ValueError:
                return float(input_string)
        elif target_type == 'int':
            return int(input_string)
        elif target_type == 'float':
            return float(input_string)
        else:
            raise ValueError(f"Invalid target_type: {target_type}")
    except ValueError:
        return default

# Usage examples
print(convert_string_to_number("42"))           # Output: 42 (int)
print(convert_string_to_number("3.14"))         # Output: 3.14 (float)
print(convert_string_to_number("42", 'float'))  # Output: 42.0 (float)
print(convert_string_to_number("invalid"))      # Output: None (default)
print(convert_string_to_number("  123  "))      # Output: 123 (stripped whitespace)

Additional best practices:

  1. Always handle potential exceptions in production code
  2. Consider the source of your strings - user input, files, APIs, etc.
  3. Be aware of locale-specific number formats if dealing with international data
  4. Use type hints in your function signatures for better code documentation
  5. Consider using data validation libraries for complex parsing scenarios

Conclusion

Python provides simple and effective methods for converting strings to numbers using float() and int() functions. The key takeaways are:

  1. Basic conversion: Use float("545.2222") for decimal numbers and int("31") for integers
  2. Error handling: Always implement try-except blocks or use validation functions to handle invalid strings
  3. Advanced parsing: For complex formats like currency or mixed units, use regular expressions and custom parsing logic
  4. Performance: Choose appropriate conversion methods based on your data quality and performance requirements
  5. Robustness: Implement comprehensive conversion functions with proper validation and error handling

For most use cases, the built-in float() and int() functions will suffice, but understanding how to handle edge cases and invalid inputs is crucial for building reliable applications. When working with user input or external data sources, always validate and sanitize strings before attempting numeric conversion.

How to handle ValueError when converting strings to numbers in Python?What's the difference between float() and int() conversion in Python?How to convert strings with commas or currency symbols to numbers in Python?Can I convert hexadecimal or binary strings to integers in Python?What are the performance considerations for large-scale string to number conversions?How to convert strings to numbers with automatic type detection in Python?
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