Python Built-in Libraries

Imagine you just got a new smartphone. Right after purchase, it already has a calendar, calculator, camera, and other useful applications installed. Python's built-in libraries work in a similar way! 🧰

Built-in libraries are modules that come with Python and are available immediately after installation. They provide ready-made solutions for the most common programming tasks.

What are built-in libraries?

Built-in libraries (standard library) are a set of modules that are included in the Python distribution and can be used without additional installation.

The Python standard library includes:

• Modules for system interaction
• Tools for data processing
• Utilities for network operations
• Tools for creating user interfaces
• And much more

Main built-in libraries

Let's look at several of the most useful Python built-in libraries.

math — mathematical functions

The math module provides access to mathematical functions defined in the C language standard:

Python 3.13
>>> import math

# Constants
>>> print(f"Number π: {math.pi}")
>>> print(f"Number e: {math.e}")
Number π: 3.141592653589793
Number e: 2.718281828459045

# Trigonometric functions
>>> angle = math.pi / 4  # 45 degrees in radians
>>> print(f"Sine of 45°: {math.sin(angle):.4f}")
>>> print(f"Cosine of 45°: {math.cos(angle):.4f}")
Sine of 45°: 0.7071
Cosine of 45°: 0.7071

# Other functions
>>> print(f"Factorial of 5: {math.factorial(5)}")
>>> print(f"Greatest common divisor of 12 and 18: {math.gcd(12, 18)}")
Factorial of 5: 120
Greatest common divisor of 12 and 18: 6

random — generating random numbers

The random module provides functions for generating random numbers and selecting random elements:

Python 3.13
>>> import random

# Generating a random integer in a range
>>> print(f"Random number from 1 to 10: {random.randint(1, 10)}")
Random number from 1 to 10: 7

# Random floating-point number from 0 to 1
>>> print(f"Random number from 0 to 1: {random.random():.4f}")
Random number from 0 to 1: 0.3528

# Selecting a random element from a sequence
>>> fruits = ["apple", "banana", "orange", "pear"]
>>> print(f"Random fruit: {random.choice(fruits)}")
Random fruit: orange

# Shuffling a sequence
>>> numbers = [1, 2, 3, 4, 5]
>>> random.shuffle(numbers)
>>> print(f"Shuffled numbers: {numbers}")
Shuffled numbers: [3, 1, 5, 2, 4]

datetime — working with dates and time

The datetime module provides classes for working with dates and time:

Python 3.13
>>> import datetime

# Current date and time
>>> now = datetime.datetime.now()
>>> print(f"Current date and time: {now}")
Current date and time: 2023-07-15 15:42:23.123456

# Creating a specific date
>>> specific_date = datetime.date(2023, 12, 31)
>>> print(f"Specified date: {specific_date}")
Specified date: 2023-12-31

# Difference between dates
>>> today = datetime.date.today()
>>> new_year = datetime.date(today.year + 1, 1, 1)
>>> days_until_new_year = (new_year - today).days
>>> print(f"Days until New Year: {days_until_new_year}")
Days until New Year: 170

# Formatting dates
>>> formatted_date = now.strftime("%d.%m.%Y %H:%M")
>>> print(f"Formatted date: {formatted_date}")
Formatted date: 15.07.2023 15:42

os — interacting with the operating system

The os module provides functions for interacting with the operating system:

Python 3.13
>>> import os

# Getting the current working directory
>>> print(f"Current directory: {os.getcwd()}")
Current directory: /Users/username/projects

# List of files and folders in a directory
>>> files = os.listdir('.')
>>> print(f"First 3 files in the current directory: {files[:3]}")
First 3 files in the current directory: ['file1.txt', 'folder1', 'file2.py']

# System information
>>> print(f"Operating system name: {os.name}")
Operating system name: posix

# Check if a file or directory exists
>>> file_exists = os.path.exists('example.txt')
>>> print(f"File example.txt exists: {file_exists}")
File example.txt exists: False

json — working with JSON format

The json module provides functions for working with data in JSON format:

Python 3.13
>>> import json

# Python dictionary
>>> person = {
...     "name": "John",
...     "age": 30,
...     "city": "New York",
...     "languages": ["Python", "JavaScript", "SQL"]
... }

# Converting a dictionary to a JSON string
>>> person_json = json.dumps(person, indent=4)
>>> print("JSON string:")
>>> print(person_json)
JSON string:
{
    "name": "John",
    "age": 30,
    "city": "New York",
    "languages": [
        "Python",
        "JavaScript",
        "SQL"
    ]
}

# Converting a JSON string to a Python object
>>> json_string = '{"name": "Mary", "age": 25, "city": "San Francisco"}'
>>> person_dict = json.loads(json_string)
>>> print(f"Name: {person_dict['name']}, Age: {person_dict['age']}")
Name: Mary, Age: 25

collections — specialized data types

The collections module provides alternative data structures for Python:

Python 3.13
>>> from collections import Counter, defaultdict, namedtuple

# Counter - counting elements
>>> text = "Programming in Python is interesting and enjoyable"
>>> character_count = Counter(text.lower())
>>> print("Three most frequent characters:")
>>> for char, count in character_count.most_common(3):
...     print(f"'{char}': {count}")
Three most frequent characters:
' ': 5
'n': 5
'i': 4

# defaultdict - dictionary with default value
>>> fruit_categories = defaultdict(list)
>>> fruit_categories["yellow"].append("banana")
>>> fruit_categories["red"].append("apple")
>>> fruit_categories["red"].append("strawberry")
>>> print(f"Yellow fruits: {fruit_categories['yellow']}")
>>> print(f"Green fruits: {fruit_categories['green']}")  # Empty list
Yellow fruits: ['banana']
Green fruits: []

# namedtuple - named tuples
>>> Person = namedtuple('Person', ['name', 'age', 'job'])
>>> alice = Person('Alice', 30, 'engineer')
>>> print(f"{alice.name}, {alice.age} years old, works as an {alice.job}")
Alice, 30 years old, works as an engineer

Understanding Check

Let's check how well you've understood the topic of built-in libraries:

Which library is best suited for working with dates in Python?