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Learn how to use Python Lambda to extract file names from dash-delimited strings efficiently. Get step-by-step guidance and sample code.
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Extracting File Names from Dash-Delimited Strings in Python Lambda

When working with file names, especially those stored in cloud services like Amazon S3, you may encounter various formats that require parsing and manipulation. One common scenario is having dash-delimited file names, from which you want to extract specific parts during runtime. In this guide, we will address this problem specifically for AWS Lambda using Python.

Understanding the Challenge

Imagine you have filenames in the following format stored in S3:

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From these filenames, your goal is to extract relevant parts, such as Photo-verified or Add-to-cart, while ignoring timestamps and other extraneous data. Let’s explore an efficient way to achieve this in a Python Lambda function.

Step-by-Step Solution

To extract the needed parts from the filenames, you can follow these simple steps:

1. Split the String

Using Python's built-in string method, you can split the filename on the dash character (-). This will break the filename into a list of components.

Example Code:

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This would output:

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2. Extract the Required Components

Next, you need to identify which components you're interested in. According to our requirement, we want to ignore the first two parts and the last three parts. You can do this by using Python's list slicing technique.

Example Code:

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This results in:

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3. Join the Extracted Parts

Finally, you'll want to join the extracted parts back into a single string that represents the file name you need. You can use the join() method for this.

Example Code:

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4. Extend This to Different Filenames

To generalize this method, you can wrap it in a function and use it to process multiple filenames.

Complete Function:

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Conclusion

In this guide, we tackled the challenge of extracting specific portions of dash-delimited filenames using Python Lambda functions. By utilizing string splitting, slicing, and joining techniques, we effectively isolated the desired file names Photo-verified and Add-to-cart. This technique can be easily adapted to different filename patterns as needed, making it a versatile solution for file management tasks in AWS S3.

Happy coding! Feel free to reach out with any questions or share your results!