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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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Aws QuickSight quick tutorial

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aws quicksight

Amazon QuickSight is a very fast, cloud-powered business intelligence (BI) service that makes it easy for all employees to build visualizations, perform ad-hoc analysis, and quickly get business insights from their data.

Amazon QuickSight Architecture uses a new, Super-fast, Parallel, In-memory Calculation Engine (“SPICE”) to perform advanced calculations and render visualizations rapidly.

Amazon QuickSight integrates automatically with AWS data services, enables organizations to scale to hundreds of thousands of users, and delivers fast and responsive query performance to them via SPICE’s query engine.

At one-tenth the cost of traditional solutions, Amazon QuickSight enables you to deliver rich BI functionality to everyone in your organization.

  1. Easily connect Amazon QuickSight to AWS data services, including Amazon Redshift, Amazon RDS, Amazon Aurora, Amazon EMR, Amazon DynamoDB, Amazon S3, and Amazon Kinesis; upload CSV, TSV and spreadsheet files; or connect to third-party data sources such as Salesforce.
  2. Amazon QuickSight automatically infers data types and relationships and provides suggestions for the best possible visualizations, optimized for your data, to help you get quick, actionable business insights.
  3. Amazon QuickSight uses SPICE – a Super-fast, Parallel, In-memory optimized Calculation Engine built from the ground up to generate answers on large datasets.
  4. Securely share your analysis with others in your organization by building interactive stories for collaboration using the storyboard and annotations. 
  5. Recipients can further explore the data and respond back with their insights and knowledge, making the whole organization efficient and effective.

Related: AWS - Cloud computing online Training

Amazon QuickSight provides partners a simple SQL-like interface to query the data stored in SPICE so that customers can continue using their existing BI tools from AWS BI Partners while benefiting from the faster performance delivered by SPICE.

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