Welcome to Part 3 of our comprehensive Python programming series! In Parts 1 and 2, we built a solid foundation with variables, control structures, functions, and data structures. Now we're ready to explore the skills that separate beginner programmers from intermediate ones: handling files, managing errors gracefully, and organizing code with object-oriented principles.
Table of Contents
- Quick Review of Parts 1 & 2
- Working with Files: Your Digital Filing Cabinet
- Error Handling: When Things Go Wrong
- Introduction to Object-Oriented Programming
- Working with External Libraries
- Putting It All Together: Building Real Projects
- What's Next in Part 4
Quick Review of Parts 1 & 2
Before diving into advanced concepts, let's quickly review our programming toolkit. Think of these as the tools you've already mastered in your programming workshop:
From Part 1: Variables (your labeled storage boxes), strings (text manipulation), conditionals (decision making), and loops (efficient repetition).
From Part 2: Functions (your custom tools), scope (where variables live), and data structures - lists (ordered collections), dictionaries (key-value pairs), tuples (unchangeable records), and sets (unique collections).
These fundamentals are like having learned to use basic carpentry tools. Now we're ready to tackle more sophisticated projects that require additional skills and techniques.
Working with Files: Your Digital Filing Cabinet
Understanding File Operations
Imagine you're organizing a traditional office with paper documents. You need to open filing cabinets, read documents, write new ones, and store them safely. Working with files in Python follows the same logical process, but instead of paper documents, we're dealing with digital files on your computer.
Files are how your programs can save information permanently and share data with other programs. Without file operations, everything you create in your program disappears when it ends - like writing notes on a whiteboard that gets erased every day.
Reading Files: Opening Your Digital Documents
Let's start with the most common file operation - reading existing files. Think of this as taking a document out of your filing cabinet to read its contents.
# The basic pattern for reading files
def read_simple_file():
"""Demonstrate basic file reading."""
# Open the file (like opening a filing cabinet drawer)
file = open("sample.txt", "r") # "r" stands for "read mode"
# Read the contents (like reading the document)
content = file.read()
# Close the file (like putting the document back and closing the drawer)
file.close()
return content
# A safer way using 'with' statement (automatic cleanup)
def read_file_safely(filename):
"""Read a file with automatic cleanup."""
try:
with open(filename, "r") as file:
content = file.read()
return content
except FileNotFoundError:
return "File not found!"
# Reading different ways
def demonstrate_reading_methods():
"""Show different ways to read file content."""
filename = "example.txt"
with open(filename, "r") as file:
# Method 1: Read entire file at once
all_content = file.read()
print("Entire file:", all_content[:50] + "...") # Show first 50 characters
with open(filename, "r") as file:
# Method 2: Read line by line
print("Reading line by line:")
for line_number, line in enumerate(file, 1):
print(f"Line {line_number}: {line.strip()}") # strip() removes newline characters
with open(filename, "r") as file:
# Method 3: Read all lines into a list
all_lines = file.readlines()
print(f"Total lines: {len(all_lines)}")
Writing Files: Creating Your Digital Documents
Writing files is like creating new documents or updating existing ones. You can either create a completely new file or add to an existing one.
def write_shopping_list():
"""Create a shopping list file."""
shopping_items = ["Apples", "Bread", "Milk", "Eggs", "Cheese"]
# Write to a new file (creates file if it doesn't exist)
with open("shopping_list.txt", "w") as file: # "w" stands for "write mode"
file.write("My Shopping List\n")
file.write("=" * 20 + "\n")
for item in shopping_items:
file.write(f"- {item}\n")
print("Shopping list saved to shopping_list.txt")
def add_to_diary():
"""Add an entry to a diary file."""
from datetime import datetime
# Get current date and time
current_time = datetime.now().strftime("%Y-%m-%d %H:%M")
# Append to existing file (adds to the end without erasing)
with open("diary.txt", "a") as file: # "a" stands for "append mode"
file.write(f"\n--- Entry on {current_time} ---\n")
file.write("Today I learned about file operations in Python!\n")
file.write("It's like having a digital filing system.\n")
print("Diary entry added!")
# Creating a simple note-taking system
def create_note(title, content):
"""Create a note file with given title and content."""
# Create filename from title (replace spaces with underscores)
filename = title.replace(" ", "_").lower() + ".txt"
with open(filename, "w") as file:
file.write(f"Title: {title}\n")
file.write("=" * len(f"Title: {title}") + "\n\n")
file.write(content)
return filename
# Using the note system
note_file = create_note("Python Learning", "File operations are essential for data persistence.")
print(f"Note saved as: {note_file}")
Working with CSV Files: Organizing Structured Data
CSV (Comma-Separated Values) files are like digital spreadsheets. They're perfect for storing structured data like contact lists, product inventories, or student grades.
def create_student_gradebook():
"""Create a CSV file with student grades."""
import csv # Import the CSV module
# Student data as a list of dictionaries
students = [
{"Name": "Alice", "Math": 95, "Science": 88, "English": 92},
{"Name": "Bob", "Math": 87, "Science": 91, "English": 85},
{"Name": "Charlie", "Math": 92, "Science": 89, "English": 94}
]
# Write to CSV file
with open("gradebook.csv", "w", newline="") as file:
# Create a CSV writer that works with dictionaries
fieldnames = ["Name", "Math", "Science", "English"]
writer = csv.DictWriter(file, fieldnames=fieldnames)
# Write the header row
writer.writeheader()
# Write each student's data
for student in students:
writer.writerow(student)
print("Gradebook saved to gradebook.csv")
def read_student_gradebook():
"""Read and display the student gradebook."""
import csv
try:
with open("gradebook.csv", "r") as file:
reader = csv.DictReader(file) # Reads CSV as dictionaries
print("Student Gradebook:")
print("-" * 40)
for student in reader:
name = student["Name"]
math_grade = int(student["Math"])
science_grade = int(student["Science"])
english_grade = int(student["English"])
# Calculate average
average = (math_grade + science_grade + english_grade) / 3
print(f"{name}: Math={math_grade}, Science={science_grade}, English={english_grade}")
print(f" Average: {average:.1f}")
print()
except FileNotFoundError:
print("Gradebook file not found. Please create it first.")
# Using the gradebook system
create_student_gradebook()
read_student_gradebook()
File Operations Best Practices
Working with files requires careful attention to potential problems. Here are essential practices that will save you from headaches later:
def safe_file_operations():
"""Demonstrate safe file handling practices."""
def check_file_exists(filename):
"""Check if a file exists before trying to work with it."""
import os
return os.path.exists(filename)
def get_file_info(filename):
"""Get information about a file."""
import os
if not check_file_exists(filename):
return "File does not exist"
# Get file size and modification time
size = os.path.getsize(filename)
mod_time = os.path.getmtime(filename)
return {
"size_bytes": size,
"size_kb": size / 1024,
"last_modified": mod_time
}
def backup_file(filename):
"""Create a backup copy of a file."""
if not check_file_exists(filename):
return "Original file not found"
# Create backup filename
backup_name = filename + ".backup"
# Copy the file content
with open(filename, "r") as original:
with open(backup_name, "w") as backup:
backup.write(original.read())
return f"Backup created: {backup_name}"
# Example usage
filename = "important_data.txt"
if check_file_exists(filename):
info = get_file_info(filename)
print(f"File info: {info}")
backup_result = backup_file(filename)
print(backup_result)
else:
print("File not found, creating a sample file...")
with open(filename, "w") as file:
file.write("This is important data that needs backing up!")
safe_file_operations()
Error Handling: When Things Go Wrong
Understanding Errors: The Reality of Programming
Imagine you're following a recipe, but sometimes ingredients are missing, measurements are unclear, or equipment breaks. In programming, similar unexpected situations occur constantly. Error handling is like having a backup plan for every possible thing that could go wrong.
Errors in programming aren't failures - they're normal events that good programmers plan for. The difference between beginner and professional code often lies in how gracefully it handles unexpected situations.
Types of Common Errors
Before learning to handle errors, let's understand what kinds of problems commonly occur:
def demonstrate_common_errors():
"""Show common errors that can occur in Python programs."""
# Error 1: Trying to access a file that doesn't exist
try:
with open("nonexistent_file.txt", "r") as file:
content = file.read()
except FileNotFoundError:
print("Error: The file doesn't exist!")
# Error 2: Converting invalid data to numbers
try:
user_input = "not_a_number"
number = int(user_input)
except ValueError:
print("Error: Cannot convert to number!")
# Error 3: Accessing dictionary keys that don't exist
try:
student_grades = {"Alice": 95, "Bob": 87}
charlie_grade = student_grades["Charlie"] # Charlie doesn't exist
except KeyError:
print("Error: Student not found in gradebook!")
# Error 4: Dividing by zero
try:
result = 10 / 0
except ZeroDivisionError:
print("Error: Cannot divide by zero!")
# Error 5: Accessing list indices that don't exist
try:
fruits = ["apple", "banana"]
third_fruit = fruits[5] # Index 5 doesn't exist
except IndexError:
print("Error: List index out of range!")
demonstrate_common_errors()
The Try-Except Pattern: Your Safety Net
The try-except pattern is like wearing a seatbelt while driving. You hope you won't need it, but it protects you when something unexpected happens.
def safe_calculator():
"""A calculator that handles errors gracefully."""
def get_number(prompt):
"""Get a number from user with error handling."""
while True: # Keep trying until we get valid input
try:
user_input = input(prompt)
number = float(user_input) # Try to convert to number
return number
except ValueError:
print("Please enter a valid number!")
def divide_safely(a, b):
"""Perform division with error handling."""
try:
result = a / b
return result
except ZeroDivisionError:
return "Error: Cannot divide by zero!"
# Using the safe functions
print("Safe Calculator")
print("-" * 15)
num1 = get_number("Enter first number: ")
num2 = get_number("Enter second number: ")
# Perform operations safely
print(f"{num1} + {num2} = {num1 + num2}")
print(f"{num1} - {num2} = {num1 - num2}")
print(f"{num1} * {num2} = {num1 * num2}")
print(f"{num1} / {num2} = {divide_safely(num1, num2)}")
# Example of handling multiple types of errors
def process_student_data(filename):
"""Process student data with comprehensive error handling."""
try:
# Try to open and read the file
with open(filename, "r") as file:
lines = file.readlines()
students = []
for line_num, line in enumerate(lines[1:], 2): # Skip header, start counting from line 2
try:
# Try to parse each line
parts = line.strip().split(",")
name = parts[0]
grade = int(parts[1]) # This might fail if grade isn't a number
students.append({"name": name, "grade": grade})
except ValueError:
print(f"Warning: Invalid grade on line {line_num}, skipping...")
except IndexError:
print(f"Warning: Incomplete data on line {line_num}, skipping...")
return students
except FileNotFoundError:
print(f"Error: File '{filename}' not found!")
return []
except PermissionError:
print(f"Error: No permission to read '{filename}'!")
return []
except Exception as e: # Catch any other unexpected errors
print(f"Unexpected error: {e}")
return []
# Test the student data processor
sample_students = process_student_data("students.csv")
print(f"Successfully processed {len(sample_students)} students")
The Finally Block: Cleanup Operations
Sometimes you need to perform cleanup operations regardless of whether an error occurred. This is like washing your hands after cooking, whether the meal turned out perfectly or not.
def demonstrate_finally_block():
"""Show how finally blocks work for cleanup operations."""
def process_file_with_cleanup(filename):
"""Process a file with guaranteed cleanup."""
file_handle = None
try:
print(f"Attempting to open {filename}...")
file_handle = open(filename, "r")
print("Processing file...")
content = file_handle.read()
# Simulate some processing that might fail
if "error" in content.lower():
raise ValueError("Found error keyword in file!")
print("File processed successfully!")
return content
except FileNotFoundError:
print("File not found!")
return None
except ValueError as e:
print(f"Processing error: {e}")
return None
finally:
# This runs no matter what happened above
if file_handle:
file_handle.close()
print("File closed properly.")
print("Cleanup completed.")
# Test with different scenarios
result = process_file_with_cleanup("test.txt")
demonstrate_finally_block()
Creating Custom Error Messages
Sometimes you want to create your own specific error messages that make sense for your particular program:
def create_custom_exceptions():
"""Demonstrate creating and using custom exceptions."""
# Define custom exception classes
class InsufficientFundsError(Exception):
"""Raised when trying to withdraw more money than available."""
pass
class InvalidAccountError(Exception):
"""Raised when account number is invalid."""
pass
class BankAccount:
"""Simple bank account with error handling."""
def __init__(self, account_number, initial_balance=0):
if not isinstance(account_number, str) or len(account_number) != 10:
raise InvalidAccountError("Account number must be 10 digits")
self.account_number = account_number
self.balance = initial_balance
def withdraw(self, amount):
"""Withdraw money with error checking."""
if amount <= 0:
raise ValueError("Withdrawal amount must be positive")
if amount > self.balance:
raise InsufficientFundsError(
f"Cannot withdraw ${amount}. Available balance: ${self.balance}"
)
self.balance -= amount
return self.balance
def deposit(self, amount):
"""Deposit money with error checking."""
if amount <= 0:
raise ValueError("Deposit amount must be positive")
self.balance += amount
return self.balance
# Using the bank account with error handling
try:
# Create account
account = BankAccount("1234567890", 100)
print(f"Account created with balance: ${account.balance}")
# Make some transactions
account.deposit(50)
print(f"After deposit: ${account.balance}")
account.withdraw(75)
print(f"After withdrawal: ${account.balance}")
# This will cause an error
account.withdraw(200) # More than available balance
except InsufficientFundsError as e:
print(f"Transaction failed: {e}")
except InvalidAccountError as e:
print(f"Account error: {e}")
except ValueError as e:
print(f"Invalid amount: {e}")
create_custom_exceptions()
Introduction to Object-Oriented Programming
Understanding Objects: The Building Blocks of Complex Programs
Think of object-oriented programming like designing a car factory. Instead of building each car from scratch every time, you create blueprints (classes) that define what a car should have - wheels, engine, doors, etc. Then you use these blueprints to manufacture many individual cars (objects), each with its own specific color, model, and features.
In programming, objects help us organize code by grouping related data and functions together. This makes programs easier to understand, maintain, and expand.
Classes: Creating Your Blueprints
A class is like a blueprint or template that defines what properties and abilities an object should have. Let's start with a simple example:
class Dog:
"""A simple class representing a dog."""
def __init__(self, name, breed, age):
"""Initialize a new dog (this runs when creating a dog)."""
self.name = name # Each dog has a name
self.breed = breed # Each dog has a breed
self.age = age # Each dog has an age
self.is_good_boy = True # All dogs are good boys/girls!
def bark(self):
"""Make the dog bark."""
return f"{self.name} says: Woof! Woof!"
def celebrate_birthday(self):
"""Increase the dog's age by 1."""
self.age += 1
return f"Happy birthday {self.name}! Now {self.age} years old."
def describe(self):
"""Return a description of the dog."""
return f"{self.name} is a {self.age}-year-old {self.breed}"
# Creating dog objects from our blueprint
my_dog = Dog("Buddy", "Golden Retriever", 3)
friends_dog = Dog("Luna", "Border Collie", 2)
# Using the dogs' abilities
print(my_dog.describe())
print(friends_dog.bark())
print(my_dog.celebrate_birthday())
Instance Variables vs Class Variables
Understanding the difference between instance variables (unique to each object) and class variables (shared by all objects) is crucial:
class Student:
"""A class representing a student."""
# Class variable - shared by all students
school_name = "Python Academy"
total_students = 0
def __init__(self, name, grade):
# Instance variables - unique to each student
self.name = name
self.grade = grade
self.courses = []
# Update the class variable
Student.total_students += 1
def enroll_in_course(self, course):
"""Add a course to the student's schedule."""
self.courses.append(course)
return f"{self.name} enrolled in {course}"
def get_info(self):
"""Return student information."""
course_list = ", ".join(self.courses) if self.courses else "No courses yet"
return f"{self.name} (Grade {self.grade}) - Courses: {course_list}"
@classmethod
def get_school_info(cls):
"""Class method to get school-wide information."""
return f"{cls.school_name} has {cls.total_students} students"
# Creating students
alice = Student("Alice", 10)
bob = Student("Bob", 11)
# Each student has their own courses
alice.enroll_in_course("Math")
alice.enroll_in_course("Science")
bob.enroll_in_course("English")
print(alice.get_info())
print(bob.get_info())
print(Student.get_school_info())
Inheritance: Building on Existing Blueprints
Inheritance is like creating specialized versions of existing blueprints. If you have a blueprint for a vehicle, you can create more specific blueprints for cars, trucks, and motorcycles that inherit the basic vehicle properties but add their own unique features:
class Vehicle:
"""Base class for all vehicles."""
def __init__(self, make, model, year):
self.make = make
self.model = model
self.year = year
self.is_running = False
def start_engine(self):
"""Start the vehicle's engine."""
self.is_running = True
return f"{self.make} {self.model} engine started!"
def stop_engine(self):
"""Stop the vehicle's engine."""
self.is_running = False
return f"{self.make} {self.model} engine stopped."
def get_info(self):
"""Return basic vehicle information."""
status = "running" if self.is_running else "stopped"
return f"{self.year} {self.make} {self.model} - {status}"
class Car(Vehicle):
"""A car class that inherits from Vehicle."""
def __init__(self, make, model, year, num_doors):
# Call the parent class constructor
super().__init__(make, model, year)
self.num_doors = num_doors
def honk_horn(self):
"""Cars can honk their horns."""
return f"{self.make} {self.model}: Beep beep!"
def get_info(self):
"""Override parent method to include door count."""
basic_info = super().get_info() # Get info from parent class
return f"{basic_info} - {self.num_doors} doors"
class Motorcycle(Vehicle):
"""A motorcycle class that inherits from Vehicle."""
def __init__(self, make, model, year, engine_size):
super().__init__(make, model, year)
self.engine_size = engine_size
def wheelie(self):
"""Motorcycles can do wheelies (if running)."""
if self.is_running:
return f"{self.make} {self.model} is doing a wheelie!"
else:
return "Start the engine first!"
def get_info(self):
"""Override parent method to include engine size."""
basic_info = super().get_info()
return f"{basic_info} - {self.engine_size}cc engine"
# Using inheritance
my_car = Car("Toyota", "Camry", 2020, 4)
my_bike = Motorcycle("Honda", "CBR600", 2019, 600)
print(my_car.get_info())
print(my_car.honk_horn())
print(my_bike.get_info())
my_bike.start_engine()
print(my_bike.wheelie())
Practical OOP Example: Library Management System
Let's create a more complex example that shows how OOP helps organize real-world problems:
class Book:
"""Represents a book in the library."""
def __init__(self, title, author, isbn):
self.title = title
self.author = author
self.isbn = isbn
self.is_available = True
self.borrowed_by = None
def checkout(self, borrower_name):
"""Check out the book to a borrower."""
if self.is_available:
self.is_available = False
self.borrowed_by = borrower_name
return f"'{self.title}' checked out to {borrower_name}"
else:
return f"'{self.title}' is already checked out to {self.borrowed_by}"
def return_book(self):
"""Return the book to the library."""
if not self.is_available:
returner = self.borrowed_by
self.is_available = True
self.borrowed_by = None
return f"'{self.title}' returned by {returner}"
else:
return f"'{self.title}' is already available"
def get_status(self):
"""Get the current status of the book."""
if self.is_available:
return f"'{self.title}' by {self.author} - Available"
else:
return f"'{self.title}' by {self.author} - Checked out to {self.borrowed_by}"
class Library:
"""Represents a library that manages books."""
def __init__(self, name):
self.name = name
self.books = [] # List to store all books
def add_book(self, book):
"""Add a book to the library."""
self.books.append(book)
return f"Added '{book.title}' to {self.name}"
def find_book(self, title):
"""Find a book by title."""
for book in self.books:
if book.title.lower() == title.lower():
return book
return None
def checkout_book(self, title, borrower_name):
"""Check out a book to a borrower."""
book = self.find_book(title)
if book:
return book.checkout(borrower_name)
else:
return f"Book '{title}' not found in {self.name}"
def return_book(self, title):
"""Return a book to the library."""
book = self.find_book(title)
if book:
return book.return_book()
else:
return f"Book '{title}' not found in {self.name}"
def list_available_books(self):
"""List all available books."""
available_books = [book for book in self.books if book.is_available]
if available_books:
print(f"Available books at {self.name}:")
for book in available_books:
print(f"- '{book.title}' by {book.author}")
else:
print(f"No books currently available at {self.name}")
# Using the library system
# Create a library
city_library = Library("City Public Library")
# Create some books
book1 = Book("Python Programming", "John Smith", "123-456-789")
book2 = Book("Data Science Basics", "Jane Doe", "987-654-321")
book3 = Book("Web Development", "Bob Johnson", "456-789-123")
# Add books to library
print(city_library.add_book(book1))
print(city_library.add_book(book2))
print(city_library.add_book(book3))
# List available books
city_library.list_available_books()
# Check out some books
print(city_library.checkout_book("Python Programming", "Alice"))
print(city_library.checkout_book("Data Science Basics", "Bob"))
# Try to check out an already borrowed book
print(city_library.checkout_book("Python Programming", "Charlie"))
# Return a book
print(city_library.return_book("Python Programming"))
# List available books again
city_library.list_available_books()
Working with External Libraries
Understanding Libraries: Standing on the Shoulders of Giants
Imagine you're building a house. You could make your own nails, cut your own lumber, and manufacture your own windows, but that would be incredibly inefficient. Instead, you use materials and tools that others have already perfected. Python libraries work the same way - they're collections of pre-written code that solve common problems.
The Python community has created thousands of libraries for everything from working with dates to building web applications to analyzing data. Learning to use these libraries effectively is like learning to use power tools instead of building everything by hand.
Built-in Libraries: Tools That Come with Python
Python comes with many useful libraries already installed. These are like the basic tools that come with your programming toolkit:
def explore_builtin_libraries():
"""Demonstrate some useful built-in Python libraries."""
# datetime - working with dates and times
from datetime import datetime, timedelta
current_time = datetime.now()
print(f"Current time: {current_time}")
# Calculate a future date
future_date = current_time + timedelta(days=30)
print(f"30 days from now: {future_date.strftime('%Y-%m-%d')}")
# random - generating random numbers and choices
import random
# Random numbers
print(f"Random number 1-10: {random.randint(1, 10)}")
# Random choice from a list
colors = ["red", "blue", "green", "yellow"]
print(f"Random color: {random.choice(colors)}")
# math - mathematical functions
import math
print(f"Square root of 16: {math.sqrt(16)}")
print(f"π (pi): {math.pi:.4f}")
# os - operating system interface
import os
print(f"Current directory: {os.getcwd()}")
print(f"Files in current directory: {os.listdir('.')[:5]}") # Show first 5 files
explore_builtin_libraries()
Creating Useful Utilities with Built-in Libraries
Let's build some practical tools using Python's built-in libraries. Think of these as your first power tools - they show you how combining simple functions can create genuinely useful programs:
def create_utility_functions():
"""Create useful utility functions using built-in libraries."""
import os
import shutil
from datetime import datetime
import json
def organize_downloads_folder():
"""Organize files in downloads by type - like having a filing assistant."""
downloads_path = os.path.expanduser("~/Downloads") # Gets user's downloads folder
# Create folders for different file types
folders = {
'images': ['.jpg', '.png', '.gif', '.bmp'],
'documents': ['.pdf', '.docx', '.txt', '.xlsx'],
'videos': ['.mp4', '.avi', '.mov']
}
for folder_name in folders.keys():
folder_path = os.path.join(downloads_path, folder_name)
os.makedirs(folder_path, exist_ok=True) # Create folder if it doesn't exist
# Move files to appropriate folders
for filename in os.listdir(downloads_path):
if os.path.isfile(os.path.join(downloads_path, filename)):
file_extension = os.path.splitext(filename)[1].lower()
for folder, extensions in folders.items():
if file_extension in extensions:
source = os.path.join(downloads_path, filename)
destination = os.path.join(downloads_path, folder, filename)
shutil.move(source, destination)
print(f"Moved {filename} to {folder}")
break
def create_daily_journal():
"""Create a simple daily journal entry system."""
journal_dir = "my_journal"
os.makedirs(journal_dir, exist_ok=True)
today = datetime.now().strftime("%Y-%m-%d")
journal_file = os.path.join(journal_dir, f"journal_{today}.txt")
with open(journal_file, "a") as file:
timestamp = datetime.now().strftime("%H:%M")
file.write(f"\n--- Entry at {timestamp} ---\n")
file.write("What did I learn today? ")
file.write("What am I grateful for? ")
file.write("What's my goal for tomorrow? ")
return f"Journal created: {journal_file}"
create_utility_functions()
Installing External Libraries: Expanding Your Toolkit
Installing external libraries is like shopping for specialized tools. Python makes this incredibly easy with pip (Python's package installer). Think of pip as an app store for Python code:
# In your terminal or command prompt, you would type:
# pip install requests
# pip install beautifulsoup4
# pip install matplotlib
def demonstrate_external_libraries():
"""Show how external libraries extend Python's capabilities."""
# Note: You need to install these first with pip
try:
import requests # For making web requests
# Get information about a GitHub user
response = requests.get("https://api.github.com/users/octocat")
if response.status_code == 200:
user_data = response.json()
print(f"GitHub user: {user_data['name']}")
print(f"Public repos: {user_data['public_repos']}")
except ImportError:
print("Install requests with: pip install requests")
try:
import matplotlib.pyplot as plt
# Create a simple chart
months = ['Jan', 'Feb', 'Mar', 'Apr']
savings = [100, 250, 180, 400]
plt.bar(months, savings)
plt.title('Monthly Savings')
plt.ylabel('Amount ($)')
plt.savefig('savings_chart.png')
print("Chart saved as savings_chart.png")
except ImportError:
print("Install matplotlib with: pip install matplotlib")
demonstrate_external_libraries()
Putting It All Together: Building Real Projects
Project 1: Personal Expense Tracker
Let's combine everything we've learned to build something genuinely useful. This project demonstrates file handling, error management, and object-oriented design working together:
class ExpenseTracker:
"""A simple expense tracking system that saves data to files."""
def __init__(self, filename="expenses.json"):
self.filename = filename
self.expenses = self.load_expenses()
def load_expenses(self):
"""Load expenses from file, creating empty list if file doesn't exist."""
try:
with open(self.filename, 'r') as file:
return json.load(file)
except FileNotFoundError:
return [] # Start with empty list if no file exists
def save_expenses(self):
"""Save expenses to file."""
with open(self.filename, 'w') as file:
json.dump(self.expenses, file, indent=2)
def add_expense(self, amount, category, description=""):
"""Add a new expense with error checking."""
try:
amount = float(amount)
if amount <= 0:
raise ValueError("Amount must be positive")
expense = {
'date': datetime.now().strftime("%Y-%m-%d"),
'amount': amount,
'category': category,
'description': description
}
self.expenses.append(expense)
self.save_expenses()
return f"Added ${amount:.2f} expense for {category}"
except ValueError as e:
return f"Error: {e}"
def get_monthly_summary(self):
"""Get summary of expenses by category."""
from collections import defaultdict
current_month = datetime.now().strftime("%Y-%m")
monthly_expenses = defaultdict(float)
for expense in self.expenses:
if expense['date'].startswith(current_month):
monthly_expenses[expense['category']] += expense['amount']
return dict(monthly_expenses)
# Using the expense tracker
tracker = ExpenseTracker()
print(tracker.add_expense(50.00, "Groceries", "Weekly shopping"))
print(tracker.add_expense(25.00, "Gas", "Fill up"))
print("Monthly summary:", tracker.get_monthly_summary())
Project 2: Simple Web Scraper
This project shows how external libraries can solve complex problems with just a few lines of code:
def create_news_summary():
"""Create a simple news headline collector."""
try:
import requests
from bs4 import BeautifulSoup
# Get a simple news page (this is just an example)
url = "https://news.ycombinator.com"
response = requests.get(url)
if response.status_code == 200:
# Parse the HTML
soup = BeautifulSoup(response.content, 'html.parser')
# Find headlines (this depends on the website structure)
headlines = soup.find_all('a', class_='storylink')[:5] # Get first 5
# Save to file
with open('tech_news.txt', 'w') as file:
file.write(f"Tech News - {datetime.now().strftime('%Y-%m-%d')}\n")
file.write("=" * 50 + "\n\n")
for i, headline in enumerate(headlines, 1):
file.write(f"{i}. {headline.text}\n")
print("News headlines saved to tech_news.txt")
except ImportError:
print("Install required libraries: pip install requests beautifulsoup4")
except Exception as e:
print(f"Error fetching news: {e}")
create_news_summary()
What's Next in Part 4
Congratulations! You've now learned the essential skills that transform you from a beginner into an intermediate Python programmer. You understand how to work with files, handle errors gracefully, organize code with classes, and leverage external libraries.
In Part 4 of our series, we'll explore advanced topics that will make you a truly proficient Python developer. We'll cover building complete applications with graphical interfaces (streamlit).
Resources for Continued Learning
Essential Documentation and References
The official Python documentation is your most reliable resource as you continue learning. Think of it as the owner's manual for Python - it contains everything you need to know, explained by the people who built the language. Visit python.org/doc for comprehensive guides and tutorials.
For hands-on practice, realpython.com offers excellent tutorials that bridge the gap between beginner and professional code. Their articles explain not just how to do something, but why certain approaches work better than others.
GitHub Repositories for Learning
GitHub serves as both a learning resource and a place to share your own projects. Search for repositories tagged with "python-learning" or "python-beginner" to find code examples and projects you can study and modify.
Some particularly valuable repositories include:
TheAlgorithms/Python - A collection of algorithms implemented in Python, perfect for understanding how classic computer science problems are solved.
donnemartin/system-design-primer - While advanced, this repository shows how Python fits into larger system architectures.
vinta/awesome-python - A curated list of Python frameworks, libraries, and resources organized by category.
Video Learning Resources
YouTube offers countless Python programming channels, but focus on those that emphasize understanding over memorization. Corey Schafer's Python tutorials are particularly well-regarded for their clear explanations and practical examples.
Programming with Mosh provides excellent beginner-friendly content that builds projects while teaching concepts.
For more structured learning, consider edX and Coursera courses from universities. These often provide assignments and projects that challenge you to apply what you've learned.
Practice Platforms
LeetCode and HackerRank offer coding challenges that help you apply your skills to algorithmic problems. Start with their "Easy" problems and work your way up.
Project Euler provides mathematical programming challenges that are perfect for practicing loops, functions, and problem-solving skills.
GitHub itself can be your practice platform - create repositories for your projects, contribute to open-source projects, and showcase your growing skills.
Building Your Next Steps
Remember that becoming proficient at programming is like learning a musical instrument - consistent practice matters more than intense cramming sessions. Set aside time each week to work on small projects that interest you personally.
Consider joining local Python meetups or online communities like r/learnpython on Reddit, where you can ask questions and help others with their coding challenges.
Most importantly, start building things that solve real problems in your life. Whether it's organizing your photo collection, tracking your fitness goals, or automating repetitive tasks, the best learning happens when you're solving problems you actually care about.
The journey from beginner to expert is long, but every line of code you write makes you a better programmer. Keep building, keep learning, and most importantly, keep enjoying the process of bringing your ideas to life through code.
This concludes Part 3 of our Python Programming Fundamentals series. You now have the tools to handle files, manage errors, organize code with objects, and use external libraries. In Part 4, we'll explore web development, data analysis, and building complete applications. Happy coding!
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