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Learn how to list all file names from an S3 bucket and its subfolders using Boto3 in Python, and save the results in a CSV format.
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How to Effectively Retrieve File Names from an S3 Bucket with Boto3

When working with Amazon S3, retrieving file names from buckets is often straightforward. However, when you need to extract file names that are nested inside subfolders, the task can become a bit more complex. This guide will guide you through the process of retrieving file names from a specific S3 bucket and its subfolders, using Boto3, Amazon's SDK for Python.

The Problem: Retrieving File Names from an S3 Bucket

Imagine you have an S3 bucket that contains numerous files stored in various subfolders. For example, your bucket structure might look like this:

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Your task is to retrieve the file names in a structured way, particularly those files stored in specific folders. This often requires filtering and navigating through potentially deep nesting.

The Solution

To solve the problem of retrieving file names from both the main bucket and its subdirectories, follow these steps:

Step 1: Set Up Your Boto3 Environment

Before diving into the code, make sure you have the Boto3 library installed and correctly set up. If you haven't installed it yet, you can do so with pip:

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Step 2: Create a Connection to the S3 Bucket

Use the following code snippet as a starting point to establish a connection to your S3 bucket. Replace XXXXXXXXXXXXX and YYYYYYYYYYYYYYY with your AWS access credentials.

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Step 3: List All Objects in the Bucket

Now that you've connected to the S3 bucket, you can start listing all objects. The following code demonstrates how to retrieve file names from a particular prefix (subfolder).

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Step 4: Handle Subfolders

Your specific requirement is to also include files from subsequent subfolders (like /001, /002, etc.). By using the filter method as shown above, this process will automatically include files from those subfolders.

Step 5: Create a Data Structure for Easy Output

To manage the results efficiently, you might find it convenient to create a dictionary or a dataframe. Here’s how you can create a DataFrame using Pandas:

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Conclusion

By following the steps outlined in this guide, you can effectively retrieve file names from an S3 bucket and its subfolders using Boto3. This process includes establishing a connection to your S3 instance, filtering through objects in specific directories, and ultimately, exporting the results into a tidy CSV format.

If you run into issues or want to optimize this code further, feel free to reach out for help in the comments. Happy coding!