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Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni
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6 Exclusive Differences Between Structured and Unstructured data

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Here's a basic interview question for Big data engineers. Why it's basic means many Bachelor degrees now offering courses on Big data, as a beginner, understanding of data is a little tricky. So interviewers stress this point.

Don't worry, I made it simplified. So you get a clear concept. I share here a total of six differences between these. In today's world, we have a lot of data. That data is the unstructured format.

Structured Vs Unstructured data - 6 Top Differences
 

Structured Data

  1. The major data format is text, which can be string or numeric. The date is also supported.
  2. The data model is fixed before inserting the data.
  3. Data is stored in the form of a table, making it easy to search.
  4. Not easy to scale.
  5. Version is maintained as a column in the table.
  6. Transaction management and concurrency are easy to support.

Unstructured data

  • The data format can be anything from text to images, audio to videos.
  • The data model cannot be fixed since the nature of the data can change. Consider a tweet message that could be text followed by images and audio.
  • Data is not stored in the form of a table.
  • Very easy to scale.
  • Versioning is at an entire level.
  • Transaction management and concurrency are difficult to support.

References

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