Скачать или смотреть Efficiently Fetching CSV Files Modified After 2018 Using Python's glob

Discover how to optimize the retrieval of CSV files modified after 2018 using Python's `glob` and `os.scandir`, enhancing overall performance for your file management tasks.
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Unlocking the Power of Python: Filtering CSV Files Modified After 2018

Managing and retrieving files in a digital workspace can sometimes feel overwhelming, particularly when dealing with a large dataset. In a case where you have a remote directory filled with 10,000 files, including CSV formats, you might find yourself in need of a solution to efficiently list only the files modified after a specific date, such as 2018. Here, we will walk through some methods to achieve this in Python and highlight the most effective approach.

The Challenge: Finding Recent CSV Files

If you're tasked with retrieving a specific group of files within a massive directory, the challenge is to first filter for the right file type (in this case, CSV) and then check the modification date. The naive approach would result in excessive computational time simply listing all files, followed by additional filtering. Here's what we aim to accomplish:

List all CSV files

Check their modification date to see if it was edited after January 1, 2018.

A Basic Approach: Using glob and Loops

Initially, one might consider using the Python glob module to list CSV files directly. The glob method is quick, allowing you to filter based on file extensions efficiently:

[[See Video to Reveal this Text or Code Snippet]]

However, this method does not account for file modification dates, necessitating a second phase of verification by checking each file's modification timestamp manually:

[[See Video to Reveal this Text or Code Snippet]]

While functional, this dual-step approach can be inefficient as it processes files twice: once to list them, and once more to check their timestamps.

A More Efficient Solution: Using os.scandir

To enhance performance, especially when handling a large number of files, you can leverage the os.scandir() method. According to its documentation, scandir() is noticeably faster than listdir() due to its ability to fetch file attribute information as files are being iterated over.

Here’s how you can implement this method to filter CSV files modified after 2018 more effectively:

Code Example

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

Import Libraries: We import the necessary libraries, os for directory operations and datetime for managing dates.

Set Directory Path: Define where the files are located. Here, we use . for the current directory.

Define Cutoff Date: We set our cutoff date as January 1, 2018, converting it to timestamp format for easy comparison.

Scan Directory: The os.scandir() method is used to iterate through the directory:

Check if the item is a file using f.is_file().

Compare its modification timestamp with our cutoff date.

Ensure the file ends with .csv to filter out non-CSV files.

Output: The resulting list of CSV files meeting the criteria is printed.

Conclusion

Using Python's os.scandir() is a game-changer for tasks involving file management, particularly when you need efficiency and performance in handling numerous files. By refining the approach that filters both by file type and modification date simultaneously, you can save valuable time and resources.

If you're working with large datasets, consider applying os.scandir() as your go-to method for optimizing file handling tasks.

Happy coding!