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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 ]

Cloud Computing: Horizontal Vs. Vertical Scaling

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The purpose of cloud computing is resource utilization. You can scale up the resources in two ways - vertical and horizontal. Adding resources, you can do either horizontally and vertically . The advantages and drawbacks you can find in simple words. Scaling 1. Horizontal Scaling Advantages You can increase workloads in small steps. The upgrade-cost is far less. Scale the system as much as needed. Drawbacks Dependency on software applications is more for Data distribution and parallel processing. On top of that, fewer software applications exist in the market. You May Also Like:  9 Top Services AWS Provided 2. Vertical Scaling Advantages Since it is a single machine, it is easy to manage. On the fly, you can increase workloads. Drawbacks It is expensive. You need a huge investment. The machine should be powerful to take more workloads - future use. Below is the List of Resources that You can do both Horizontal and Vertical Scaling Platform Scaling Network Scaling Container Scaling Dat