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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 ]

Talent Analytics on employees to measure real worth

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Human resource managers are currently embracing talent analytics like never before. Companies are evaluating and analysing raw data to derive valuable insights which are helping them to hire the right talent, retain them as well as help them learn and grow internally. Data to do analytics Talent analytics companies take into account all the data, rather than limited samples, so a full-fledged picture emerges.  It looks for patterns in the data and discovers critical connections that might otherwise go unnoticed. Such data can be related to employees' pre-employment assessments to background checks to social media profiles. How data will gather They also gather data on the characteristics of their most successful employees. When big data is tapped this way, HR managers no longer need to depend on intuitions of interviewers and hiring managers or rely on obsolete hiring tools of yesteryears.  Organisations are also leveraging big data to hire and promote t