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Скачать или смотреть How to Properly Format CSV Output with Python's List of Lists for DNS Resolvers

  • vlogize
  • 2025-10-04
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How to Properly Format CSV Output with Python's List of Lists for DNS Resolvers
Adding items to list of lists - Pythonpython
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Описание к видео How to Properly Format CSV Output with Python's List of Lists for DNS Resolvers

Learn how to correctly structure your CSV output in Python, ensuring each domain and its DNS resolvers appear on their own row for better readability and organization.
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This video is based on the question https://stackoverflow.com/q/63745748/ asked by the user 'Jordan' ( https://stackoverflow.com/u/14120107/ ) and on the answer https://stackoverflow.com/a/63745893/ provided by the user 'Abhilash' ( https://stackoverflow.com/u/2865057/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Adding items to list of lists - Python

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

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How to Properly Format CSV Output with Python's List of Lists for DNS Resolvers

If you're working with domain names and their corresponding DNS resolvers in Python, you might run into a formatting issue when trying to write this data to a CSV file. Specifically, you may find that all the domains and their DNS resolvers are ending up in the same row, rather than on individual rows as intended. This guide will take you through the steps needed to properly structure your CSV output using a list of lists mechanism in Python, making it clearer and more organized.

The Problem at Hand

You're trying to create a CSV file that records the domains along with their DNS resolver names. The desired output format looks like this:

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

However, without the correct structure in your Python code, you may end up with a faulty format like this:

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

This is misleading and hard to read. So, how do we fix it?

Proposed Solution

The solution involves utilizing a list of lists in Python to keep each domain and its corresponding DNS resolvers neatly separated. Here's a breakdown of the approach:

1. Modify the dns_resolver Function

To start, you should ensure that your dns_resolver function returns a list with the given domain and DNS resolvers. Here’s how it can be structured:

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

In this setup, the *server syntax allows you to unpack the items of the server list into the return list.

2. Read Domains into a List

Next, you will need to read your domains into a list. Here’s how you can do this:

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

For this example, we have hardcoded a few domains, but normally you would read them from a CSV file which looks like this:

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

3. Collect All Results into a List of Lists

Using list comprehension, you can easily iterate through your domain list and collect the results of the dns_resolver function into a list of lists:

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

This will give you an output structure like:

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

4. Write the Output to a CSV File

Finally, you can write the structured output_data into a CSV file where each domain and its DNS resolvers will be printed on a new row:

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

Conclusion

By following the steps above, you can efficiently format your domain and DNS resolver data into a CSV file that is both well-organized and easy to read. The approach of using a list of lists allows for expansion and maintenance of your code as it grows in complexity. Remember, structured data not only makes for better processing but also enhances readability for anyone who may need to use this information in the future. Happy coding!

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