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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]

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 Data The major data format is text, which can be string or numeric. The date is also supported. The data model is fixed before inserting the data. Data is stored in the form of a table, making it easy to search. Not easy to scale. Version is maintained as a column in the table. 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 foll