How to Get a List of Argument Names Inside a Python Function

To extract the number and names of arguments from a function or function[something] to return (“arg1”, “arg2”), we use the inspect module.

The given code is written using the inspect module to find the arguments inside the functions aMethod and foo.

Example

import inspect
def aMethod(arg1, arg2): pass
print(inspect.getargspec(aMethod))
def foo(a,b,c=4, *arglist, **keywords): pass
print(inspect.getargspec(foo))

Output

ArgSpec(args=['arg1', 'arg2'], varargs=None, keywords=None, defaults=None)
ArgSpec(args=['a', 'b', 'c'], varargs='arglist', keywords='keywords', defaults=(4,))

To get a list of the argument names within a Python function, you can use the inspect module. This module has a number of functions that allow you to inspect the attributes of Python objects, including functions.

Here is an example function that uses the inspect module to get a list of parameter names:

Example

import inspect
def get_parameter_names(func):
""Returns a list of parameter names for the given function."""
signature = inspect.signature(func)
return [param.name for param in signature.parameters.values()]

# Example usage
def my_function(a, b, c=1, *args, **kwargs):
pass

parameter_names = get_parameter_names(my_function)
print(parameter_names)

Output

['a', 'b', 'c', 'args', 'kwargs']

In this example, the get_parameter_names function accepts a single argument, func, which is the function to inspect.

Use the inspect.signature function to obtain a signature object for a given function. The signature object contains information about the function’s parameters, including their names and default values.

The values property of the parameters object returns an ordered dictionary of Parameter objects, each of which corresponds to a parameter in the function’s signature. We then loop through these Parameter objects and extract the parameter names using the name property.

To test the function, we define an example function called my_function that takes several arguments, including some positional and keyword arguments with default values. We then call get_parameter_names on my_function and it returns a list of the parameter names:

This list includes all the parameters defined in my_function, including the positional parameters a and b, the keyword argument c, and the variable-length argument lists args and kwargs.

Note that the get_parameter_names function does not include the self parameter for instance methods, as this parameter is implicit and not explicitly defined in the function signature.

Here is another example demonstrating how to use this function to inspect function objects defined in different modules:

Example

import module_name

parameter_names = get_parameter_names(module_name.function_name)
print(parameter_names)

In this example, we assume there is a Python module named module_name that defines a function named function_name. We can import the module using the import statement and then pass the function object module_name.function_name to the get_parameter_names function to obtain a list of its parameter names.

This technique can be useful if you are working with a complex codebase with functions defined across multiple modules, or if you are writing utility functions that need to inspect functions defined by other developers.

Finally, note that the get_parameter_names function is designed for use with Python 3. For Python 2, similar functionality can be achieved using the inspect.getargspec function. However, note that the inspect.signature function has been superseded by this function, as it is no longer supported in Python 3.

Here is another example demonstrating how to use the get_parameter_names function to generate function documentation on the fly:

Example

def my_function(a, b, c=1, *args, **kwargs):
""
This is an example function.

Parameters:
---------
{}

Returns:
-----
None
""
pass

parameter_names = get_parameter_names(my_function)
parameter_docs = "n ".join(f"{name}: " for name in parameter_names)

docstring = my_function.__doc__.format(parameter_docs)
my_function.__doc__ = docstring

print(my_function.__doc__)

In this example, we define an example function called my_function that takes several arguments, including some positional and keyword arguments with default values. The function’s docstring has a docstring that explains what the function does and what its arguments are.

We then use the get_parameter_names function to get a list of the function’s parameter names. This then generates a formatted string containing a blank line for each argument, followed by the argument name and a colon.

Finally, we use the function’s docstring’s format method to replace the argument docstring with the docstring. This generates a dynamically generated docstring that contains a description of each argument.

We can then print the docstring to verify that it contains the dynamically generated parameter documentation.

This technique can be useful if you are writing functions with a large number of arguments or want to ensure that the documentation for a function is always up-to-date with the function’s signature.

Here is another example showing how to use the get_parameter_names function to validate a function’s input parameters:

Example

def my_function(a, b, c=1, *args, **kwargs):
"""
This is an example function.

Parameters
----------
a: int
An integer.
b: float
A float.
c: int, optional
An optional integer.
args: tuple
Variable-length argument list.
kwargs: dict
Arbitrary keyword arguments.

Returns
------
float
A float value.
"""
if not isinstance(a, int):
raise TypeError("a must be an integer")
if not isinstance(b, float):
raise TypeError("b must be a float")
# ...more validation code ...

return a + b + c

def validate_input(func):
    parameter_names = get_parameter_names(func)
    def wrapper(*args, **kwargs):
        signature = inspect.signature(func)
        bound_arguments = signature.bind(*args, **kwargs)
        bound_arguments.apply_defaults()
        for name, value in bound_arguments.arguments.items():
            parameter = signature.parameters[name]
            if not isinstance(value, parameter.annotation):
                raise TypeError(f"{name} must be {parameter.annotation}")
        return func(*args, **kwargs)
    return wrapper

my_function = validate_input(my_function)
result = my_function(1, 2.0, c=3)
print(result)

In this example, we define a sample function called my_function that takes several arguments, including some positional and keyword arguments with default values. The function’s docstring includes a docstring explaining what the function does and what its arguments are.

Then, we define a decorator function called validate_input that takes a function as its argument. The decorator uses the get_parameter_names function to obtain a list of the function’s parameter names and then defines a new wrapper function that performs input validation before calling the original function.

The wrapper function first uses the signature function to obtain the signature object of the original function and then uses the bind method to bind the input arguments to the function’s parameters. We then apply default values to any missing parameters.

Next, we loop through each parameter name and its corresponding value in the bound parameters, checking that the value is of the correct type according to the parameter’s annotation.

If any input arguments fail validation, the wrapper function will raise a TypeError with the argument name and its expected type.

Finally, we return the result of calling the original function with the input arguments.

To apply the decorator to my_function, simply call validate_input with my_function as its argument and then assign the result back to my_function. We can then call my_function with the arguments, and the decorator will validate the arguments before calling the original function.

This technique can be useful if you are writing functions that require strict input validation to ensure that the function’s behavior is well-defined for all inputs.