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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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India is gearing for awesome Data Analytics Jobs

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There is no surprise in India, all companies started building Data analytics team and infrastructure. With a lot of Indian companies building their data analytics team, the requirement in the domestic market for this skill will increase over the next couple of years.

The requirement for Data Analytics

  •  There will be an increased demand for data analytics professionals.
  • Industry experts believe that currently big data and analytics is one of the top three skills in demand in India. 
  • Organizations are looking at their internal set of data to understand the business better – as a result, there will be an explosion of various job opportunities in this area.
Top three segments where huge demand for data analytics engineers are Data Science, Statistics, Technical specialists with multiple skills.

Top Demand Roles in Data Analytics

  • Some of the requirements are for tech personnel, statistician, econometrician, data scientist, analytical consultant, functional consultant, etc. 
  • However, there is a short supply of data scientists, according to experts, followed by a functional consultant. 
  • Industries in Utilities and Manufacturing will be the next set to invest in Big Data.

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