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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 ]

Data Science in Retail Marketing and Opportunities

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Data Science and Analytics started by all big companies including Shoppers top and Wal-mart. Data Science in Retail Shoppers Stop started big data analytics to study customers behavior. Started with one of the easiest programs. Where they studied customer loyalty program. Some Insights Studied patterns Studied customer buying styles Some people buy shirt-only and some people buy both Shirt and Trouser Studied buying patterns of Women buyers Read more at Live Mint

Retail Analytics Solution from Leader in Data Science SAS

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Retail market is changed now. So many companies now open shops across the world. Every business owner tries to increase sales. This is possible with analytics. Retail Analytics The study also includes an in-depth look at how best-in-class retailers use analytics, as well as the business analytics software vendor landscape. Walmart started retail business in India. So the real use of data analytics started now. References Download here