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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 ]
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The best answer for 'Efficient Workbook' in Tableau

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There are several factors that define an “efficient” workbook. Some of these factors are technical and some more user-focused. An efficient workbook is:

  • A workbook that takes advantage of the “principles of visual analysis” to effectively communicate the message of the author and the data, possibly by engaging the user in an interactive experience.
  • A workbook that responds in a timely fashion. This can be a somewhat subjective measure, but in general we would want the workbook to provide an initial display of information and to respond to user interactions within a couple of (< 5) seconds. 
  • Tableau latest version is 9.1.2 as on writing this post
  • Tableau version 8 and Version 9 differences
  1. Individual Query time improved by 10x
  2. Dashboard Query times improved by 9x
  3. Query Fusion improving times by 2x
  4. And Query Caching improving times by 50x

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