Adventures in Machine Learning

Mastering Python’s Built-In Data Types and Functions

Python is an incredibly versatile programming language used by developers for different purposes – from building website backends to developing artificial intelligence applications. This language owes its versatility to its intuitive syntax and extensive libraries.

Additionally, Python is user-friendly, making it a great choice for beginners. Before diving into Python programming, however, it is important to understand the basics of the language, starting with data types and built-in functions.

1. Basic Data Types in Python

Data types refer to the different kinds of values that a programming language can process.

In Python, there are four primary data types – integers, floating-point numbers, strings, and Boolean types. Integers are whole numbers used to represent quantities.

In Python, integers can be base ten, binary, octal, or hexadecimal, depending on how the numbers are written in code. For example,

10 is an integer in base ten, 0b

10

10 is an integer in binary, 0o

12 is an integer in octal, and 0xA is an integer in hexadecimal. Floating-point numbers, on the other hand, are numbers with decimal points or scientific notation.

They are used to represent measurements and quantities that require precise calculations. In Python, floating-point numbers are represented using the “float” keyword and can be written in scientific notation.

For example,

1.0 can be written as

1e0 in scientific notation. Strings are a sequence of characters used to represent text or symbols.

In Python, strings can be represented using single quotes, double quotes, or triple-quoted strings. Single and double quotes can be used interchangeably, but when a string contains a quote within it, the other type of quote can be used to contain the string.

Triple-quoted strings are used for longer strings that span multiple lines. They also preserve all whitespace in the string.

Boolean types are a data type used to represent true/false values. In Python, the keywords “True” and “False” are used to represent these values.

The “truthiness” of values is used to determine if an expression is True or False in a Boolean context. Anything that is not explicitly “False” or “None” is considered “truthy,” while “False” and “None” are considered “falsy.”

2.

Built-in Functions in Python

Python has a vast collection of built-in functions that make programming easier. These functions can be organized into two primary groups – composite data types and variables, references, and scopes.

Composite data types are complex data structures that store multiple variables of different data types. In Python, there are eight composite data types – bytearray, bytes, dict, frozenset, list, object, set, tuple.

Each data type has specific use cases and properties, making them flexible for different scenarios. Variables, references, and scope are programming concepts that involve the creation and naming of objects, assigning of values to variables, and referencing those values in various scopes.

Python has several built-in functions that help with these concepts, including “dir,” which lists the attributes and methods of an object; “globals,” which returns the global symbol table; “id,” which returns an integer representing the ID of the object; “locals,” which returns a dictionary that represents the current local symbol table, and “vars,” which returns the __dict__ attribute of an object.

Conclusion

Understanding the basics of data types and built-in functions in Python is crucial to becoming a proficient programmer. As you learn more about the language, you will discover that there is always something new to learn and explore.

The power of Python lies in its simplicity, versatility, and ability to solve complex problems easily. With these foundational concepts in mind, you can begin your exploration of Python and all it has to offer.

3. Math and

Type Conversion in Python

Python is an incredibly powerful language when it comes to handling numbers, whether they are integers, floating-point numbers, or complex numbers.

Python offers an abundance of built-in functions to perform various mathematical operations and manipulate data types.

Built-in Functions

The built-in functions are a set of callable objects that perform often-used operations without the need for the user to write custom code. In Python, there are several built-in functions for mathematical operations, such as:

– abs(): returns the absolute value of a number.

– pow(): raises a number to a given power. – round(): rounds a number to a given number of decimal places.

For instance, the following code:

“`

x = –

10

print(abs(x))

y = 2

print(pow(y, 3))

z =

1.536536

print(round(z, 2))

“`

will output:

“`

10

8

1.54

“`

Type Conversion

In Python, data types can be converted using built-in functions. This feature allows developers to manipulate data types more effectively.

The primary type conversion functions are:

– int(): converts the given argument to an integer. – float(): converts the given argument to a floating-point number.

– str(): converts the given argument to a string. – bool(): converts the given argument to Boolean.

For example, the following code:

“`

a = ‘

10′

b = ‘

10.0′

c =

1

d = 0

print(int(a))

print(float(b))

print(str(c))

print(bool(d))

“`

will output:

“`

10

10.0

1

False

“`

4. Iterables and Iterators in Python

An iterable is an object that can be traversed, meaning that you can iterate over it using a loop.

In Python, any object that provides the iter() method is considered iterable. Some examples of iterables include lists, tuples, sets, and dictionaries.

Built-In Functions

Python offers several built-in functions to work with iterables and iterators:

– iter(): returns an iterator from an iterable. – next(): retrieves the next element from an iterator.

– range(): generates a sequence of numbers. – enumerate(): adds a counter to an iterable and returns it in a form of enumerate object.

For instance, the following code:

“`

numbers = [

1, 2, 3, 4, 5]

iterable = iter(numbers)

print(next(iterable))

print(next(iterable))

print(range(5))

fruits = [‘Apple’, ‘Banana’, ‘Mango’]

for index, fruit in enumerate(fruits):

print(index, fruit)

“`

will output:

“`

1

2

range(0, 5)

0 Apple

1 Banana

2 Mango

“`

Composite Data Type

Python has several built-in composite data types, including lists, tuples, sets, and dictionaries. –

Lists: are ordered, mutable, and dynamic data structures that can contain elements of different data types.

Lists are created using square brackets [] and can be indexed and sliced in various ways. –

Tuples: are ordered, immutable, and dynamic data structures that can contain elements of different data types.

Tuples are created using round brackets () and can also be indexed and sliced. –

Sets: are unordered and distinct collections of unique elements.

Sets are created by enclosing elements in curly braces {} or by using the set() constructor. –

Dictionaries: are unordered collections of key-value pairs, similar to maps in Java.

Dictionaries are created using curly braces {} or using the dict() constructor. For example, the following code:

“`

fruits = [‘Apple’, ‘Banana’, ‘Mango’]

print(fruits[

1:])

t = (

1, 2, 3)

print(t[0])

s = {

1, 2, 3}

print(s)

d = {‘name’: ‘

John’, ‘age’: 25}

print(d[‘name’])

“`

will output:

“`

[‘Banana’, ‘Mango’]

1

{

1, 2, 3}

John

“`

Conclusion

In conclusion, Python is a powerful language that offers an abundance of built-in functions and data types. It also provides developers with the ability to convert data types and work with iterables and iterators using built-in functions.

By learning and understanding these concepts, developers can effectively manipulate data, perform mathematical operations, and build robust applications. 5.

Composite Data Types:

Lists and

Tuples

Python offers two built-in data structures for storing collections of data: lists and tuples. These data types are similar in that they can store multiple items in one variable, but they differ in the way that they can be modified.

Lists

Lists are one of the most commonly used Python data types. A list is a mutable data structure that can hold objects of different data types, including other lists.

Lists can be accessed and modified using a variety of built-in functions and operators. Some of the commonly used ones are:

– List: is a data structure used to store a collection of items.

It is created using the square brackets [] operator. – Indexing: used to access the elements of a list by their position.

– Slicing: used to extract a part of a list by specifying two indices and returning a new list. – Append: used to add an item to the end of the list.

– Pop: used to remove an item from the list. – Sort: used to sort the list in ascending or descending order.

For example, the following code demonstrates how to create, access, and modify a list:

“`

fruits = [‘apple’, ‘banana’, ‘cherry’]

print(fruits[

1])

print(fruits[:2])

fruits.append(‘orange’)

print(fruits)

fruits.pop(

1)

print(fruits)

fruits.sort(reverse=True)

print(fruits)

“`

Tuples

Tuples are similar to lists, but they are immutable, which means that they cannot be changed once they are created.

Tuples are created using the round brackets () operator, and like lists, they can contain objects of different data types.

Some of the commonly used functions for tuples are:

– Tuple: is a data structure used to store a collection of items. It is created using the round brackets () operator.

– Immutability: once created, the values in a tuple cannot be changed. – Packing: used to combine multiple values into a single tuple.

– Unpacking: used to extract the values of a tuple and assign them to individual variables. For example, the following code demonstrates how to create, access, and unpack a tuple:

“`

fruits = (‘apple’, ‘banana’, ‘cherry’)

print(fruits[

1])

a, b, c = fruits

print(a, b, c)

“`

6.

Composite Data Types:

Sets and

Dictionaries

Python also offers two other built-in data structures for storing collections of data: sets and dictionaries.

Sets

Sets are unordered collections of unique objects. They do not allow duplicate values, and they have a variety of built-in functions that can be used to manipulate their contents.

Some of the common functions for sets include:

– Set: is a data structure used to store a collection of unique items. It is created using the curly braces {} operator.

– Union: used to combine two or more sets into a new set. – Intersection: used to find the common elements between two or more sets.

– Difference: used to find the elements that are in one set but not in another. – Symmetric difference: used to find the elements that are in either of the sets, but not in both.

For example, the following code demonstrates how to create, access, and manipulate a set:

“`

fruits = {‘apple’, ‘banana’, ‘cherry’}

print(‘banana’ in fruits)

berries = {‘blueberry’, ‘strawberry’, ‘raspberry’}

print(fruits.union(berries))

print(fruits.intersection(berries))

print(fruits.difference(berries))

print(fruits.symmetric_difference(berries))

“`

Dictionaries

A dictionary is a collection of key-value pairs, where each key has a corresponding value.

Dictionaries are useful for storing data in a structured format that can be easily searched and retrieved.

Python provides several built-in functions that can be used to manipulate dictionaries, including:

– Dictionary: is a data structure used to store a collection of key-value pairs. It is created using the curly braces {} operator.

– Key-value pairs: each dictionary item consists of a key and a corresponding value. – Keys: the unique identifiers used to access the values in a dictionary.

– Values: the data associated with each key in a dictionary. – Get(): used to retrieve the value associated with a key in a dictionary.

– Items(): used to retrieve a list of tuples containing the key-value pairs. For example, the following code demonstrates how to create, access, and manipulate a dictionary:

“`

person = {‘name’: ‘

John’, ‘age’: 25, ‘gender’: ‘Male’}

print(person[‘age’])

print(person.get(‘gender’))

print(person.items())

person[‘location’] = ‘New York’

print(person)

del person[‘age’]

print(person)

“`

Conclusion

Using the right data structure is vital in programming, and Python provides an array of built-in data types to choose from. Understanding the functions and properties of each data structure is crucial in optimizing the performance of programs, resulting in cleaner coding and better readability.

By learning the features and differences of lists, tuples, sets, and dictionaries, developers can choose the appropriate data structure for their specific needs and build more powerful and efficient programs. 7.

Classes, Attributes, and

Inheritance in Python

Python is an Object-Oriented Programming (OOP) language, which means that it is based on the concept of classes and objects. Object-oriented programming is a programming paradigm that emphasizes the use of objects as the core of a program.

In Python, classes are defined using the class keyword, and objects are instances of classes.

Classes and Objects

A class is a blueprint for an object, which defines its attributes and methods. The attributes of a class are variables that hold data, while methods are functions that perform operations on that data.

To create a new object from a class, we use the class name followed by parentheses. For example, the following code demonstrates how to define a class and create an object from that class:

“`

class Car:

def __init__(self, make, model, year):

self.make = make

self.model = model

self.year = year

my_car = Car(‘Toyota’, ‘Camry’, 2020)

print(my_car.make)

“`

Inheritance

Inheritance is the process by which one class takes on the methods and attributes of another class. The class that is being inherited is called the superclass, while the class that inherits is called the subclass or child class.

The child class can add additional functionality or override existing methods of the superclass. For example, the following code demonstrates how to create a subclass that inherits from a superclass:

“`

class Vehicle:

def __init__(self, make, model, year):

self.make = make

self.model = model

self.year = year

class Car(Vehicle):

def __init__(self, make, model, year):

super().__init__(make, model, year)

my_car = Car(‘Toyota’, ‘Camry’, 2020)

print(my_car.make)

“`

In this example, the Car class is a subclass of the Vehicle class, and it inherits the attributes and methods of Vehicle.

The `super()` function is used to call the superclass constructor and initialize the attributes of the Car class. 8.

Input/Output in Python

Input and output functionality is essential in programming. In Python, there are built-in functions that allow you to prompt the user for input and display output on the screen.

Input

Input refers to the data that a program receives from the user. Python offers a built-in function called `input()` that allows a program to prompt the user for input.

The `input()` function displays a message asking the user for input and waits for the user to enter their response. The response is returned as a string.

For example, the following code demonstrates how to use the `input()` function:

“`

name = input(‘Please enter your name: ‘)

print(‘Hello, ‘ + name + ‘!’)

“`

The user is prompted to enter their name, and the program displays a greeting message using the entered name.

Output

Output refers to the data

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