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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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R language five useful real functions

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In Data Science R language plays a crucial role. In the R language, there are five top functions present. These functions I have explained in this post.
#5-key-points-in-r

1. Storing Values

  • Stores a value to variable. The value can be same or mixed data type.
  • It is available /* */ to give comments for your scripts inside
  • Char, Double, Boolean and Decimal are more frequently used data types

2. Reading data from files

  • Large data objects will usually be read as values from external files rather than entered during an R session at the keyboard. 
  • R input facilities are simple and their requirements are fairly strict and even rather inflexible. There is a clear presumption by the designers of R that you will be able to modify your input files using other tools, such as file editors or Perl1 to fit in with the requirements of R. Generally this is very simple.
  • If variables are to be held mainly in data frames, as we strongly suggest they should be, an entire data frame can be read directly with the read.table() function. 
  • There is also a more primitive input function, scan(), that can be called directly. For more details on importing data into R and also exporting data, see the R Data Import/Export manual.

3. Accessing builtin datasets

  • Around 100 datasets are supplied with R (in package datasets), and others are available in packages (including the recommended packages supplied with R). To see the list of datasets currently available use data().
  • All the datasets supplied with R are available directly by name. However, many packages still use the obsolete convention in which data was also used to load datasets into R, for example data(infert) and this can still be used with the standard packages (as in this example). 
  • In most cases this will load an R object of the same name. However, in a few cases it loads several objects, so see the on-line help for the object to see what to expect.

4. Grouped expressions

  • R is an expression language in the sense that its only command type is a function or expression which returns a result. Even an assignment is an expression whose result is the value assigned,and it may be used wherever any expression may be used; in particular multiple assignments are possible. 
  • Commands may be grouped together in braces, {expr_1; ...; expr_m}, in which case the value of the group is the result of the last expression in the group evaluated. Since such a group is also an expression it may, for example, be itself included in parentheses and used a part of an even larger expression, and so on

5. Writing your own functions

  • R language allows the user to create objects of mode function. These are true R functions that are stored in a special internal form and may be used in further expressions and so on. In the process, the language gains enormously in power,convenience and elegance, and learning to write useful functions is one of the main ways to make your use of R comfortable and productive. 
  • It should be emphasized that most of the functions supplied as part of the R system, such as mean(), var(), postscript() and so on, are themselves written in R and thus do not differ materially from user written functions.

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