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Fundamentals of Function Parameters and Arguments in Python
Python has become a popular language in data science because of its versatility, simplicity, and powerful libraries. Functions, with their ability to encapsulate reusable code, play an important role in streamlining and enhancing data science workflows in Python. Understanding the nuances of function arguments and parameters is essential to harnessing the true potential of Python functions in a data science context.
Parameters vs 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, whereas an argument is what you pass in as its parameter when calling a function. For example:
def my_func(param1, param2):
print(f"param1 param2")my_func("Arg1", "Arg2")
# Out:
# Arg1 Arg2
param1 And param2 while functional parameters are "Arg1" And "Arg2" There are arguments.
positional vs keyword arguments
In this example, “Arg1” and “Arg2” are passed as positional arguments. This is because the parameters corresponding to each argument are not specified in the function call. This means that because of their order “Arg1” takes the place of param1and replaces “Arg2” param2 ,
We can change the order by taking advantage of keyword arguments. This is where the parameters corresponding to each argument are explicitly defined using the correct keywords.
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 though the positions of the arguments have been changed because the parameters corresponding to each argument were defined using the corresponding keyword.
default parameter
Another thing you’ll often see are default parameters. These parameters often have a generic value or “default” value that can often be ignored when calling the function. They are established in…
