Category: Python Study

Introduction

When dealing with large datasets, displaying all the data on a single screen is inefficient. To provide users with a convenient navigation environment, we utilize the “paging” technique, which allows us to show data appropriately divided. In this course, we will explain how to implement paging functionality in a bulletin board system using Python.

Basic Concepts of Paging

Paging refers to the function of dividing data into certain units and displaying them by pages, allowing users to navigate to the previous, next, or select a specific number. The core idea is to reduce navigation time and improve responsiveness by displaying an appropriate amount of data on the screen.

• Page Size: The number of data items displayed on one page.
• Page Number: The number of the page currently being viewed by the user.
• Total Count: The total size of the data, which is necessary for calculating the number of pages.

Understanding Paging Logic

Designing paging logic can be complex, but a simple pattern can be created with basic mathematical formulas. You can calculate the starting index of a page and dynamically manipulate data when moving to the next or previous page. The basic formula used is as follows:

    #{ For 1-based index pagination }
    Starting Index = (Page Number - 1) * Page Size
    Ending Index = Starting Index + Page Size

Implementing Paging with Python

To implement basic paging functionality in Python, let’s create sample data using a list. We will then divide the data into page size units and return the desired page to the user.

Preparing Sample Data

python
# Preparing a simple dataset - using numbers from 1 to 100 for this example.
data = list(range(1, 101))  # Numbers from 1 to 100

The above data assumes that there are a total of 100 posts represented by numbers from 1 to 100.

Implementing the Paging Function

python
def get_page(data, page_number, page_size):
    if page_number < 1:
        raise ValueError("Page number must be 1 or greater.")
    if page_size < 1:
        raise ValueError("Page size must be 1 or greater.")
    
    start_index = (page_number - 1) * page_size
    end_index = start_index + page_size
    
    # Data range validation
    if start_index >= len(data):
        return []

    return data[start_index:end_index]

Testing the Paging Function

python
# For example, you can request the 3rd page with a page size of 10.
page_number = 3
page_size = 10

# Fetching page data
page_data = get_page(data, page_number, page_size)
print(f"Data on page {page_number}: {page_data}")

The above function provides basic paging functionality. It takes the page number and page size as input and returns the data for that page. If there is insufficient data, it returns an empty list to prevent errors.

Advanced Paging Techniques

In real applications, more complex paging logic combined with database queries is required.

Using with SQL

For example, in databases like PostgreSQL or MySQL, you can utilize paging at the SQL query level using LIMIT and OFFSET statements.

SELECT * FROM board_posts
LIMIT page_size OFFSET (page_number - 1) * page_size;

Adding Page Navigation

Most web applications support paging navigation to allow users to navigate between pages. Additional logic and UI elements are required for this.

Calculating Total Pages

You can calculate how many pages are needed based on the total amount of data and the page size.

python
total_count = len(data)
total_pages = (total_count + page_size - 1) // page_size  # Ceiling calculation

Generating Page Links

Providing the user with page links allows navigation to different pages.

Here, we will generate page navigation links using Python code:

python
def generate_page_links(current_page, total_pages):
    page_links = []
    
    # Generating page links
    for i in range(1, total_pages + 1):
        if i == current_page:
            page_links.append(f"[{i}]")
        else:
            page_links.append(str(i))
    
    return " ".join(page_links)

# If the current page is 3
current_page = 3
page_links = generate_page_links(current_page, total_pages)
print(f"Page links: {page_links}")

The above functions are an example of providing new page links based on page 3.

Conclusion

We discussed how to implement paging functionality in a bulletin board system using Python, providing users with a convenient and efficient data model. We covered everything from basic list management to SQL integration, as well as adding page navigation. These techniques will be useful in developing a better user experience in actual web applications.

When you start learning programming, it’s common to begin with simple problems related to numbers. These problems help to understand the basics of algorithms and programming languages, and they are a great exercise to develop logical thinking. In this course, we will explore how to solve the problem of summing all multiples of 3 and 5 using Python.

Problem Definition

The problem we want to solve is as follows: Sum all natural numbers less than a given positive integer N that are multiples of 3 or 5.

For example, if N is 10, the multiples of 3 are 3, 6, and 9, and the multiple of 5 is 5. Therefore, the sum of all multiples of 3 and 5 is 3 + 5 + 6 + 9 = 23.

Basic Approach

A basic method to solve this problem is to use a loop to check all numbers from 1 to N-1 and check if each number is divisible by 3 or 5. If the number meets the condition, it is added to the total. The simplest form of code is as follows:

def sum_of_multiples(n):
    total = 0
    for i in range(n):
        if i % 3 == 0 or i % 5 == 0:
            total += i
    return total

The code above is very simple. The `sum_of_multiples` function takes an integer `n` as input and calculates the sum of numbers that are multiples of 3 or 5 among the numbers from 0 to `n-1`. This method performs adequately in most cases.

Explanation of Python Syntax

Now let’s take a closer look at the components of the Python code we used.

1. Function Definition

In Python, functions are defined using the `def` keyword. `sum_of_multiples(n):` is a function named `sum_of_multiples` that takes a parameter `n`. The function’s name should be intuitive so that it describes what the function does.

2. Variable Initialization

`total = 0` initializes a variable to store the sum we want to calculate. This variable will be used later to add the multiples of 3 and 5.

3. Loop

The `for i in range(n):` statement sets up a loop that iterates over the numbers from 0 to `n-1`. `range(n)` generates an object similar to a list, which returns a sequence equivalent to `[0, 1, …, n-1]`.

4. Conditional Statement

The `if i % 3 == 0 or i % 5 == 0:` statement checks if each number is a multiple of 3 or 5. The `%` operator returns the remainder, and if the number is divisible by 3 (remainder is 0), it is a multiple of 3. The same applies for multiples of 5. If this condition is true, the number `i` is added to the `total` variable.

Another Method Using List Comprehensions

Python provides various features to improve code readability and conciseness. One of these is list comprehensions. Using list comprehensions allows us to solve the above problem in a single line of code:

def sum_of_multiples_using_comprehension(n):
    return sum(i for i in range(n) if i % 3 == 0 or i % 5 == 0)

This method combines the loop and conditional statements into one line, featuring the use of the `sum()` function to calculate the sum of the list. This form of code is intuitive and is useful when you want to maintain short code.

Considering Efficiency

The methods introduced above are intuitive and simple, but they may not be efficient for large values. As the number of iterations increases, the computational complexity can rise. Fortunately, we can use mathematical formulas to solve this problem more efficiently.

Mathematical Approach

Mathematically, we can use the formula for the sum of an arithmetic series to calculate the sum of multiples of 3 and 5. This method is especially useful when N is very large.

Multiples of 3: 3, 6, 9, …, the largest multiple of 3

Multiples of 5: 5, 10, 15, …, the largest multiple of 5

Common multiples should be excluded since they are counted multiple times.

def arithmetic_sum(n, r):
    count = (n - 1) // r
    return r * count * (count + 1) // 2

def efficient_sum_of_multiples(n):
    sum_3 = arithmetic_sum(n, 3)
    sum_5 = arithmetic_sum(n, 5)
    sum_15 = arithmetic_sum(n, 15)
    return sum_3 + sum_5 - sum_15

`efficient_sum_of_multiples` uses the `arithmetic_sum` function to calculate the sum of an arithmetic series. This function computes the sum of each multiple based on the formula `r x ((n-1)//r) x (((n-1)//r) + 1)/2`. The final result is obtained by adding the sum of the multiples of 3 and 5 and then subtracting the sum of the multiples of 15, which were added multiple times.

Conclusion

In this course, we explored various ways to calculate the sum of multiples of 3 and 5 using Python. We covered the basic iterative approach, a concise implementation using list comprehension, and a mathematically efficient method. By presenting diverse methods to solve this problem, we provided opportunities to enhance understanding of fundamental programming principles and mathematical thinking.

Experiencing solving problems in various ways helps improve programming skills. Additionally, it provides a chance to deepen understanding of algorithms and data structures.

Programming is the process of creating your own tools in the world of computing. This journey offers learning opportunities to hone problem-solving skills, express creativity, and learn how to structure complex problems. Python is an ideal language for this introduction to programming, being friendly to beginners with its concise and intuitive syntax. In this course, we will discuss what programs you can create using Python.

Getting Started with Python: A Tool for Problem Solving

Python is a general-purpose programming language that allows you to write various types of programs and scripts. From web applications and data analysis tools to artificial intelligence models and simple automation scripts, Python plays an essential role. Essentially, Python is a ‘language’ that allows you to command the computer. As a beginner programmer, you will need to learn how to express problems in human language and convert them into a format that a computer can understand using Python.

Understanding Basic Syntax

The concise syntax of Python minimizes the aspects that beginners need to worry about. Here are the basic elements of Python syntax:

• Variables: Variables allow you to store data in memory. x = 10 name = “Alice”
• Data Types: Python supports various data types. These include integers, floats, strings, lists, and dictionaries. age = 25 # integer height = 5.9 # float message = “Hello” # string fruits = [“apple”, “banana”] # list grades = {“math”: 90, “english”: 85} # dictionary
• Conditional Statements: Conditional statements allow you to execute different code based on specific conditions in the program. if age > 18: print(“Adult”) else: print(“Teenager”)
• Loops: Used when you need to repeat the same task multiple times. for fruit in fruits: print(fruit)
• Functions: Functions allow you to make your code reusable. def greet(name): print(“Hello, ” + name) greet(“Alice”)

My First Program: A Simple Calculator

With Python, you can easily create a simple calculator. Let’s create a useful program while keeping it simple.

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

def subtract(x, y):
    return x - y

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

def divide(x, y):
    if y == 0:
        return "Cannot divide by 0."
    return x / y

print("Select the operation you want:")
print("1. Addition")
print("2. Subtraction")
print("3. Multiplication")
print("4. Division")

while True:
    choice = input("Choose an operation: ")

    if choice in ['1', '2', '3', '4']:
        num1 = float(input("Enter the first number: "))
        num2 = float(input("Enter the second number: "))

        if choice == '1':
            print(num1, "+", num2, "=", add(num1, num2))

        elif choice == '2':
            print(num1, "-", num2, "=", subtract(num1, num2))

        elif choice == '3':
            print(num1, "*", num2, "=", multiply(num1, num2))

        elif choice == '4':
            print(num1, "/", num2, "=", divide(num1, num2))
    else:
        print("Invalid input.")

By creating such a simple calculator, you can understand various programming concepts. Basic elements such as function definitions, user input, and conditional statements are all included.

Improving Skills Through Practice

The best way to improve your programming skills is to write and modify code directly while trying multiple times. Start with simple programs and gradually expand to more complex projects. Project ideas are endless. For example:

• A simple reminder application that gives alerts based on specific dates
• A program that can search for specific words in text files
• Collecting the latest news articles through web scraping

Such small projects will rapidly enhance your coding skills.

Conclusion

The possibilities of creating programs with Python are endless. Discover problems, write code, and solve them yourself. The essence of programming lies in trying, learning from mistakes, and continuously improving. Python is just the starting point, and you will challenge yourself with deeper understanding and more complex problems in the future. Good luck on your programming journey!

Introduction

This chapter covers advanced topics that go beyond the basics of Python usage, aiming to maximize productivity and write more efficient code. The code developed through this chapter will be more robust, scalable, and easier to maintain.

1. Context Managers

Context managers are a Python feature that automates resource allocation and release in scenarios such as file opening, database connections, and using locks. They enhance code readability and reduce the likelihood of bugs.

1.1 Basic Usage of Context Managers

The most common example of a context manager in Python is opening a file using the with statement.

with open('example.txt', 'r') as file:
    data = file.read()
    # The file is automatically closed when the block ends.

1.2 Custom Context Managers

To implement a context manager, you just need to define a class with __enter__ and __exit__ methods.

class CustomContext:
    def __enter__(self):
        # Resource allocation or setup
        print("Resource allocated")
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        # Resource release
        print("Resource released")

with CustomContext() as context:
    print("Inside the block")

This example manages resources by recognizing the start and end of a block.

2. Generators

Generators are a simplified version of iterators and can save memory when processing large datasets. Generators yield one value at a time and wait until the next value is needed. This property allows generators to efficiently handle large datasets.

2.1 Generator Functions

Generator functions are defined like regular functions but use yield instead of return to provide values.

def simple_generator():
    yield 1
    yield 2
    yield 3

The above function creates a generator object that returns 1, 2, and 3 sequentially each time it is called.

2.2 Infinite Generators

Generators can easily create infinite loops, making them useful for processes that repeat periodically.

def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

This function returns an increasing number starting from 0 indefinitely until stopped.

3. Decorators

Decorators are powerful tools that dynamically alter or extend the behavior of functions or methods. They greatly enhance code reusability and are primarily used for logging, access control, and metrics.

3.1 Definition and Use of Decorators

Decorators can wrap another function to add specific logic or modify the input and output of an existing function.

def my_decorator(func):
    def wrapper():
        print("Before function call")
        func()
        print("After function call")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

In this example, the functionality added by the decorator runs when the say_hello function is called.

3.2 Combining Multiple Decorators

Multiple decorators can be applied to a single function, and their behavior can vary based on the order of the decorators.

def decorator_one(func):
    def wrapper():
        print("Decorator 1 applied")
        func()
    return wrapper

def decorator_two(func):
    def wrapper():
        print("Decorator 2 applied")
        func()
    return wrapper

@decorator_two
@decorator_one
def display():
    print("Display function")

display()

4. Parallelism and Multithreading

To improve program performance, you can use parallelism or multithreading. This allows the code to utilize multiple CPU cores to perform tasks simultaneously.

4.1 Multithreading

Multithreading is useful in cases where there are many I/O bound tasks. You can create threads using Python’s threading module.

import threading
import time

def thread_function(name):
    print(f"Thread {name} started")
    time.sleep(2)
    print(f"Thread {name} ended")

threads = []
for i in range(3):
    thread = threading.Thread(target=thread_function, args=(i,))
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

This code creates three threads, each performing a task for 2 seconds.

4.2 Multiprocessing

For CPU bound tasks, the multiprocessing module is more efficient. It creates processes to fully utilize CPU cores.

from multiprocessing import Process

def process_function(name):
    print(f"Process {name} started")
    time.sleep(2)
    print(f"Process {name} ended")

processes = []
for i in range(3):
    process = Process(target=process_function, args=(i,))
    processes.append(process)
    process.start()

for process in processes:
    process.join()

5. Advanced Exception Handling

Exception handling is essential for enhancing the reliability of programs. In this section, we will explore advanced exception handling techniques.

5.1 Creating Custom Exceptions

You can create custom exceptions to explicitly express exceptions that may occur in specific situations.

class CustomError(Exception):
    pass

try:
    raise CustomError("This is a custom exception")
except CustomError as e:
    print(e)

5.2 Exception Chaining

One exception may be the result of another exception. In Python, you can create exception chains using the raise ... from ... syntax.

try:
    raise ValueError("First exception")
except ValueError as ve:
    raise KeyError("Second exception") from ve

6. Conclusion

This chapter has delved deeper into Python’s advanced features. By leveraging these techniques, you can write more robust and scalable code. In the next chapter, we will explore how to use Python for data analysis.

To enhance functionality and process various data, Python developers actively utilize external libraries. An external library in Python refers to a collection of code made publicly available by developers to fit the development cycle. These libraries help expand Python’s basic functionalities and efficiently perform complex tasks.

What is an external library?

An external library is a collection of code developed independently to perform specific functions. By using these libraries, you can easily solve tasks that are complex or time-consuming to implement directly.

Libraries are typically composed of units called modules, each of which includes one or more related functions. For example, the math module, which supports mathematical operations, provides various arithmetic calculation functions.

Installing and managing external libraries

Python external libraries are mainly downloaded from a central repository called PyPI (Python Package Index). PyPI hosts hundreds of thousands of registered packages, allowing you to easily find libraries that meet most requirements.

Installing libraries using pip

Using Python’s package manager pip, you can easily install libraries. Here is the basic pip command:

$ pip install package_name

For example, to install the widely used numpy library for data analysis:

$ pip install numpy

The installed libraries can be used in Python scripts through the import statement:

import numpy as np

Maintaining project independence using virtual environments

There are often cases where different versions of libraries need to be used across multiple projects. To achieve this, Python allows you to create virtual environments using the venv module. Virtual environments help manage independent dependencies for each project, preventing conflicts.

Introduction to essential external libraries

Here are some widely used Python libraries in various fields. They are essential for developing applications in data analysis, web development, machine learning, and more.

1. NumPy

NumPy is a widely used library for scientific computing that provides high-performance multidimensional array objects and various tools. It efficiently performs array-based operations, demonstrating outstanding performance in data analysis.

2. Pandas

Pandas is a library that provides data structures and data analysis tools. It helps manipulate and analyze multidimensional data easily and allows you to handle various data sources effortlessly using data frames.

3. Matplotlib

Matplotlib is a powerful library for data visualization. It allows you to create various types of charts and is customizable, making it suitable for complex data visualization tasks.

4. Requests

Requests is a library that helps make HTTP requests easily. It is useful for simple API calls and web crawling tasks, allowing you to perform efficient and human-friendly HTTP requests.

5. Flask & Django

Flask and Django are Python-based web frameworks. Flask is lightweight and modular, suitable for small projects. Django provides powerful features for developing large-scale web applications.

6. TensorFlow & PyTorch

These libraries are mainly used for deep learning and machine learning tasks. TensorFlow is a library developed by Google that is efficient for large-scale data processing and deep learning model implementation. PyTorch is popular among researchers for its dynamic computation graph and natural code writing advantages.

7. Scikit-learn

Scikit-learn is a library for machine learning that makes it easy to implement various machine learning algorithms quickly. It supports easy learning, evaluation, and model selection.

8. Beautiful Soup

Beautiful Soup is a library for parsing and navigating HTML and XML documents. It helps scrape web data easily.

Tips for utilizing external libraries

Here are some tips for effectively utilizing external libraries:

• Check documentation: The official documentation of the library provides usage instructions, examples, and explanations of functions and classes. Be sure to check it before use.
• Leverage the community: You can ask questions and resolve issues through platforms like Stack Overflow and GitHub Issues.
• Investigate use cases: You can look at how the library is used in projects with similar objectives.

Conclusion

Python’s external libraries greatly enhance developer productivity. By learning how to install and use them, and by appropriately utilizing essential libraries tailored to each field, you can develop better programs. Efficiently integrating libraries allows you to solve complex problems simply.