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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 ]

IBM these are analytics solutions offering to different industries

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Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand.  Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions. IBM has Analytics Solutions in various lines: Banks: Apply analytics to improve customer experiences and operational efficiency, and integrate risk into daily decision making. Communication:Uncover insights about customers, network performance and market trends to make better business decisions. Retail: Build lifetime customer relationships by meeting demands for innovative products while containing costs. Education: Make more informed decisions to improve student performance an