Python: Functions & Iterables

Functions

Functions are reusable blocks of code that perform specific tasks. They can accept inputs, process them, and return results.

Defining a Function

A function is defined using the def keyword, followed by a name and a set of parentheses.

def greet():
    print("Hello, World!")

Function Parameters

Functions can accept parameters, which are values you pass into the function for processing.

def greet(name):
    print(f"Hello, {name}!")

Return Values

Functions can return values using the return statement.

def add(x, y):
    return x + y

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Lambda (Anonymous Function)

Lambda functions are small, unnamed functions defined using the lambda keyword. They are used for creating quick, throwaway functions without the need for a full def block.

Basic Usage

A lambda function to add two numbers:

add = lambda x, y: x + y
print(add(5, 3))  # Outputs 8

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Map

The map function applies a function to all items in the given iterable, such as a list or tuple.

Using Map with Functions

def square(n):
    return n * n

numbers = [1, 2, 3, 4]
squared_numbers = map(square, numbers)
print(list(squared_numbers))  # Outputs [1, 4, 9, 16]

Using Map with Lambda

numbers = [1, 2, 3, 4]
squared_numbers = map(lambda x: x*x, numbers)
print(list(squared_numbers))  # Outputs [1, 4, 9, 16]

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Filter

The filter function filters the elements from an iterable based on a function that returns either True or False.

Filtering with Functions

def is_even(n):
    return n % 2 == 0

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = filter(is_even, numbers)
print(list(even_numbers))  # Outputs [2, 4, 6]

Filtering with Lambda

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = filter(lambda x: x % 2 == 0, numbers)
print(list(even_numbers))  # Outputs [2, 4, 6]

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Reduce

The reduce function successively applies a function to elements of an iterable, reducing the iterable to a single accumulated result.

Using Reduce

To use reduce, you need to import it from the functools module.

from functools import reduce

def multiply(x, y):
    return x * y

numbers = [1, 2, 3, 4]
product = reduce(multiply, numbers)
print(product)  # Outputs 24

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Iterators

Iterators are objects that can be iterated upon. They implement two methods, __iter__() and __next__(), allowing you to loop through items using a for loop or the next function.

Creating an Iterator

Here's an example of a simple iterator that returns numbers:

class MyNumbers:
    def __iter__(self):
        self.a = 1
        return self

    def __next__(self):
        x = self.a
        self.a += 1
        return x

numbers = MyNumbers()
iterable = iter(numbers)

print(next(iterable))  # Outputs 1
print(next(iterable))  # Outputs 2

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Ranges

The range function generates a sequence of numbers. It's particularly useful in loops when you want to iterate a specific number of times.

Using Range

for i in range(5):
    print(i)  # Outputs 0, 1, 2, 3, 4

Range with Start, Stop, and Step

You can also define a start, stop, and step for the range:

for i in range(2, 10, 2):
    print(i)  # Outputs 2, 4, 6, 8

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Conclusion

Python's support for functions and iterables is a testament to its power and flexibility. As you venture deeper into machine learning, you'll find these tools indispensable in crafting efficient algorithms, preprocessing data, and much more. With functions, lambda expressions, and iterables in your arsenal, you're well on your way to mastering the intricacies of machine learning with Python. Dive in, experiment, and watch your machine learning projects come to life.

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