Python has become the go-to language in Data Science for its versatility, simplicity, and powerful libraries. Functions, with their ability to encapsulate reusable code, play a key role in streamlining and enhancing the data science workflow in Python. Understanding the nuances of function arguments and parameters is essential for harnessing the true potential of Python functions in the context of Data Science.
Parameters v Arguments
The first thing to understand when working with functions in Python is the difference between parameters and arguments. A parameter is a variable within a function definition, while an argument is what you pass into the function’s parameters when you call it. For example:
def my_func(param1, param2):
print(f"{param1} {param2}")my_func("Arg1", "Arg2")
# Out:
# Arg1 Arg2
param1 and param2 are functional parameters, while "Arg1" and "Arg2" are the arguments.
Positional v Keyword arguments
In this example, “Arg1” and “Arg2” are passed in as positional arguments. This is because the parameter that each argument relates to are not specified in the functional call. This means that due to their order “Arg1” takes the position of param1, and “Arg2” takes the position of param2 .
We can change the order by taking advantage of keyword arguments. This is where the parameter that each argument relates to, is clearly defined using the correct keyword.
def my_func(param1, param2):
print(f"{param1} {param2}")my_func(param2 = "Arg2", param1 = "Arg1")
# Out:
# Arg1 Arg2
This example produces the same output as the first function call, even when the position of the arguments has been switched because the parameter that each argument relates to was defined using the corresponding keyword.
Default Parameters
The second thing you will often see is default parameters. These parameters often have a common value or “default” value that can often be ignored when calling the function. They are set in the…