開発スタッフがアプリ関連の最新技術やニュースをご紹介します。

In the digital age, we often find ourselves overwhelmed with files scattered across our computers. Whether it’s photos from last summer’s vacation, important documents for work, or music and videos for entertainment, keeping track of everything can be a challenge. But fear not! With the power of Python, you can automate the process of organizing your files into neat categories based on their types.

In this tutorial, we’ll walk you through a simple Python script that automatically organizes your files into categories like images, documents, videos, and more.

Before we dive in, make sure you have Python installed on your computer.

Step 1: Setting Up the Script

First, let’s create a Python script that will do the heavy lifting for us. Open your favorite text editor and copy the following code:

import os
import shutil

def classify_files(directory):
    # Dictionary of file categories and their extensions
    categories = {
        'Images': ['jpeg', 'jpg', 'png', 'gif', 'bmp'],
        'Documents': ['pdf', 'doc', 'docx', 'txt', 'rtf'],
        'Spreadsheets': ['xls', 'xlsx', 'csv'],
        'Videos': ['mp4', 'avi', 'mov', 'mkv'],
        'Music': ['mp3', 'wav', 'flac', 'aac'],
        'Archives': ['zip', 'rar', '7z', 'tar', 'gz']
    }

    # Create category directories if not exist
    for category in categories.keys():
        os.makedirs(os.path.join(directory, category), exist_ok=True)

    # List files in the directory
    files = os.listdir(directory)

    # Classify files
    for filename in files:
        # Get file extension
        _, ext = os.path.splitext(filename)
        ext = ext[1:].lower()  # Remove leading dot and convert to lowercase

        # Classify the file into appropriate category
        for category, extensions in categories.items():
            if ext in extensions:
                # Move the file to the corresponding category directory
                source_path = os.path.join(directory, filename)
                dest_path = os.path.join(directory, category, filename)
                shutil.move(source_path, dest_path)
                print(f"Moved '{filename}' to '{category}' category.")
                break  # Once classified, move to next file


def main():
    # Input directory path
    directory = input("Enter the directory path to classify files: ")

    # Check if directory exists
    if not os.path.exists(directory):
        print("Directory not found.")
        return

    # Classify files
    classify_files(directory)
    print("File classification completed.")


if __name__ == "__main__":
    main()

Save the file with a meaningful name like `organize_files.py`.

Step 2: Understanding the Code

Now, let’s break down what the code does:

– The script defines a dictionary of file categories and their corresponding extensions. For example, images have extensions like JPEG, PNG, etc.

– It prompts you to enter the directory path where your files are located.

– It creates category directories if they don’t exist already.

– It loops through all the files in the directory, classifies them into appropriate categories based on their extensions, and moves them to the respective category folders.

Step 3: Running the Script

Navigate to the directory where you saved the Python script using the command line or terminal. Then, run the script by typing `python organize_files.py` and press Enter.

Step 4: Sit Back and Relax

Watch as the script works its magic, organizing your files into tidy categories. Once it’s done, you will see your newly organized file system!

Conclusion

You’ve just automated the tedious task of file organization using Python. From now on, keeping your digital files organized will be a breeze. Feel free to customize the script to suit your specific needs and explore other ways Python can simplify your life.

Hope you enjoy that.

By Asahi

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LinuxをインストールしたPCにtargetcliを使用してiSCSIターゲットを作成してみます。

CUIで操作するのでターミナルを起動します。
ターミナル上に apt-get install targetcli を入力して targetcli をインストールします。

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今回はGoogle AI StudioのTemperatureパラメータ設定について説明します。

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Microsoft is gearing up for its Build event with a preview online event, shifting the spotlight to AI advancements, particularly through its Copilot feature. The unveiling of the Surface Pro 10 for Business and Surface Laptop 6 for Business at this event puts Copilot front and center, with a dedicated key embedded in the keyboard. This move underscores Microsoft’s commitment to integrating AI into its hardware, making tasks like planning, document retrieval, and website analysis more accessible to users with a simple button press.

These new Surface PCs mark a significant step in Microsoft’s journey towards what they’re terming as “the first Surface PCs optimized for AI.” By incorporating Copilot directly into the keyboard, Microsoft demonstrates a deep investment in leveraging AI technology. While it’s still relatively early days for Copilot, with its launch just a year ago, this move signals a strategic alignment with the burgeoning trend of AI integration in computing devices. Despite the past presence of Cortana keys, Microsoft’s focus on Copilot underscores its dedication to enhancing user experiences through AI.

The industry at large has seen a surge in branding around AI, with terms like “AI PC” and “AI smartphones” becoming commonplace. While these labels may seem abstract to consumers, they reflect a broader push towards AI-powered functionalities. Microsoft’s approach of branding these Surface devices as “optimized for AI” strikes a balance between acknowledging the trend and providing tangible benefits to users. Ultimately, while a Copilot key may seem like a small addition, its presence underscores Microsoft’s commitment to integrating AI seamlessly into everyday computing tasks, all while respecting the constraints of physical device design.

Yuuma

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In the realm of programming languages, each one comes with its own set of strengths and characteristics, tailored to suit different needs and preferences of developers. Two prominent languages that often find themselves in comparison are C and Go (or Golang). Let’s delve into the key differences between these two languages across various aspects.

1. Memory Management

– In C, developers need to manually manage memory, allocating and deallocating it for variables. This hands-on approach gives precise control but also opens doors to memory-related bugs.

– Go takes a different route with automatic garbage collection, simplifying memory management tasks for developers and reducing the likelihood of memory leaks.

2. Concurrency

– Go shines in this department with its built-in support for concurrency. Goroutines and channels make it easier to write concurrent programs without worrying about low-level threading intricacies.

– C, lacking built-in concurrency primitives, often resorts to external threading libraries like POSIX threads, which can be more complex and error-prone.

3. Type System

– C boasts a weak static type system, allowing implicit type conversions and offering limited type checking.

– Go’s strong static type system enhances type safety and reduces common programming errors by enforcing stricter type rules.

4. Error Handling

– Go introduces a unique error handling mechanism using return values, eschewing exceptions. This approach encourages explicit error handling, leading to more robust and predictable code.

– C relies on error codes, return values, and functions like perror() for error handling, which can be less structured and more error-prone compared to Go’s approach.

5. Standard Library

– C’s standard library is minimalistic, often necessitating reliance on third-party libraries for additional functionalities.

– Go boasts a rich standard library covering a wide range of tasks, from networking to encryption, reducing the need for external dependencies and streamlining development.

6. Memory Safety

– C’s flexibility comes at a cost, as direct memory manipulation can lead to common pitfalls like buffer overflows and dangling pointers.

– Go prioritizes memory safety, employing automatic bounds checking and garbage collection to mitigate memory-related bugs, enhancing overall code reliability.

In conclusion, while C and Go share some similarities, they diverge significantly in their approach to memory management, concurrency, type systems, error handling, standard libraries, and memory safety. Understanding these differences can help developers make informed decisions when selecting the right language for their projects, based on factors such as performance requirements, ease of development, and code reliability.

Hope you enjoy that.
Asahi

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