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A type-checking utility function that determines whether a given object is a floating-point scalar value, supporting both Python's native float type and NumPy floating-point types.

Purpose

This function provides a unified way to check if an object is a floating-point scalar, handling both standard Python floats and NumPy floating-point types (float16, float32, float64, etc.). It's commonly used in data validation, type checking, and preprocessing pipelines where distinguishing floating-point scalars from other numeric types (integers, complex numbers) or collections is necessary.

Source Code

def is_float(obj):
    """
    Checks if the argument is a floating-point scalar.
    """
    return isinstance(obj, (float, np.floating))

Parameters

Parameter Details

obj: The object to be checked. Can be any Python object. The function will return True only if this object is an instance of Python's built-in float type or any NumPy floating-point type (np.float16, np.float32, np.float64, np.floating, etc.).

Return Value

Returns a boolean value: True if the input object is a floating-point scalar (either Python float or NumPy floating type), False otherwise. This includes all NumPy floating-point subtypes like float16, float32, float64, longdouble, etc.

Dependencies

• numpy

Required Imports

import numpy as np

Usage Example

import numpy as np

def is_float(obj):
    return isinstance(obj, (float, np.floating))

# Test with Python float
result1 = is_float(3.14)
print(result1)  # True

# Test with NumPy float
result2 = is_float(np.float64(2.718))
print(result2)  # True

# Test with integer
result3 = is_float(42)
print(result3)  # False

# Test with string
result4 = is_float("3.14")
print(result4)  # False

# Test with NumPy array
result5 = is_float(np.array([1.0, 2.0]))
print(result5)  # False (array, not scalar)

# Test with different NumPy float types
result6 = is_float(np.float32(1.5))
print(result6)  # True

Best Practices

• This function only checks for scalar floating-point values; it will return False for arrays or collections of floats
• The function uses isinstance() which is the Pythonic way to check types and properly handles inheritance (all NumPy floating types inherit from np.floating)
• This function does not perform type coercion; it only checks the actual type of the object
• For checking if a value can be converted to float (like the string '3.14'), use a different approach with try/except around float() conversion
• When working with pandas Series or DataFrames, this function checks the container type, not the dtype; use dtype checking methods for those cases
• NumPy's np.floating base class covers all NumPy float types (float16, float32, float64, float128/longdouble), making this check comprehensive for NumPy floats

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