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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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Ideas: How Bigadata Helps HR Teams

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Big Data is the buzzword of the year. Every leader — whether they’re managing a small team or are at the helm of a multinational corporation with thousands of employees — is wondering how they can use Big Data to better get to know their people, to create a setting that better suits their needs and, in turn, drive recruitment and retention.

As co-authors of The Decoded Company: Know Your Talent Better Than You Know Your Customers, we’ve spent a lot of time thinking about this exact topic. Here are the top five trends you should be thinking about.

  1.  We are living in a data-abundant environment, and it’s changing everything. Gary Hamel, one of the world’s leading thinkers on the topic of management, has written extensively on the topic of the technology of leadership (or what he more accurately calls the technology of human accomplishment).
  2. He believes — and we tend to agree — that this might be the most important technology humanity has ever created. It gives us extraordinary superpowers to organize people into achieving feats that would be otherwise impossible, particularly from an economic perspective. Consider, for example, that Apple has achieved a market cap of $468.99B with 80,300 full-time employees (from its 2013 Annual report), or almost $6m per head.
  3. The challenge is that the management tools we use every day were designed around the assumption that data is expensive to gather and therefore infrequently available. Today’s reality is very different.
  4. Data is abundant and incredibly cheap to gather, store, process, and analyze. This epic shift has led to radically different business models on one hand, but only incremental management philosophy tinkering on the other.
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