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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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5 Top R Vs SAS Differences

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Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects.


SAS is an Analysis-suite software and R is a programming language.

1. R Language

  1. R supports both statistical analysis and Graphics
  2. R is an open source project.
  3. R is 18th most popular Language
  4. R packages are written in C, C++, Java, Python and.Net
  5. R is popular in Machine learning, data mining and Statistical analysis projects.

a). R Advantages

  • R is flexible since a lot of packages are available.
  • R is best suited for data related projects and Machine learning.
  • Less cost since it is open source language.
  • R Studio is the best tool to develop R programming modules.
Ref: imartcus.org (read more advantages)

R vs SAS Read Today


b). R Disadvantages

  • R language architecture model is out of date. So may not use it for critical applications.
  • R is not suitable for Server programming, due to lack of security.
  • R code you cannot use in web browsers.

SAS

SAS is a statistical analysis suite. Developed to process data sets in mainframe computers. Later developed to support multi-platforms. Like Mainframe, Windows, and Linux, SAS has multiple products. SAS/ Base is very basic level. SAS is popular in data related projects.

a). SAS Advantages

  1. The data integration from any data source is faster in SAS.
  2. The licensed software suite, so you will get support from SAS organization for any issues.
  3. SAS has multiple products. Most popular in creating reports and statistical analysis.
  4. Best suited for data-oriented projects.

b). SAS Disadvantages

  1. Mining of text is hard in SAS.
  2. Graphical visualization is not present in SAS.
  3. SAS is not suitable for Machine learning projects.
  4. The SAS software is expensive.
  5. SAS studio is a useful tool to work on it.


References

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