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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 Essential IT Skills for Data Engineers

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Data engineers need the following skills. These skills help you get nice job in any analytics company.
Data engineer skills
Photo Credit: Srini

Five Top Skills Need

Skill-1

Experience working with big data tools such as MapReduce, Pig, Spark, Kafka and NoSQL data stores such as MongoDB, Cassandra, HBase, etc.

Skill-2

Expertise in multi-structured data modeling, reporting on NoSQL & structured database technologies such as HBase and Cassandra, SQL.

Skill-3

Experience with languages such as Python, Perl, Ruby, Java, Scala, R etc.

Skill-4

Strong data & visual presentation skills and ability to explain insights using tools like tableau, D3 charts or other tools.

Skill-5

Basic knowledge and experience of statistical analysis tools such as R.

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