Learn Python By Example – Function Basics

Function Basics

Create Function Called print_max

def print_max(x, y):

    /* if a is larger than b */
    if x > y:
        print(x, 'is maximum')

    /* if a is equal to b */
    elif x == y:
        print(x, 'is equal to', y)

    /* otherwise */
        print(y, 'is maximum')

Run Function With Two Arguments

4 is maximum

Note: By default, variables created within functions are local to the function. But you can create a global function that IS defined outside the function.

Create Variable

x = 50

Create Function Called Func

/* Create function */
def func():
    /* Create a global variable called x */
    global x

    /* Print this */
    print('x is', x)
    /* Set x to 2. */
    x = 2
    /* Print this */
    print('Changed global x to', x)

Run func()

x is 50
Changed global x to 2

Create Function Say() Displaying x with default value of 1

/* Create function */
def say(x, times = 1, times2 = 3):
    print(x * times, x * times2)

/* Run the function say() with the default values */

/* Run the function say() with the non-default values of 5 and 10 */
say('!', 5, 10)

! !!!
!!!!! !!!!!!!!!!

VarArgs Parameters (i.e. unlimited number of parameters)

  • * denotes that all positonal arguments from that point to next arg are used
  • ** dnotes that all keyword arguments from that point to the next arg are used

/* Create a function called total() with three parameters */
def total(initial=5, *numbers, **keywords):
    /* Create a variable called count that takes it's value from initial */
    count = initial
    /* for each item in numbers */
    for number in numbers:
        /* add count to that number */
        count += number
    /* for each item in keywords */
    for key in keywords:
        /* add count to keyword's value */
        count += keywords[key]
    /* return counts */
    return count

/* Run function */
total(10, 1, 2, 3, vegetables=50, fruits=100)


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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