learn about Python Sets and Set Theory

In this Python tutorial, we will learn about Python Sets and Set Theory. Learn about Python sets: what they are, how to create them, when to use them, built-in functions and their relationship to set theory operations.

Python Sets vs Lists and Tuples

Lists and tuples are standard Python data types that store values in a sequence. Sets are another standard Python data type that also store values. The major difference is that sets, unlike lists or tuples, cannot have multiple occurrences of the same element and store unordered values.

Advantages of a Python Set

Because sets cannot have multiple occurrences of the same element, it makes sets highly useful to efficiently remove duplicate values from a list or tuple and to perform common math operations like unions and intersections.

If you'd like to sharpen your Python skills, or you're just a beginner, be sure to take a look at our Python Programmer career track on DataCamp.

With that, let's get started.

How to Create a Set in Python

Sets are a mutable collection of distinct (unique) immutable values that are unordered.

You can initialize an empty set by using set().

emptySet = set()

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To initialize a set with values, you can pass in a list to set().

dataScientist = set(['Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS'])
dataEngineer = set(['Python', 'Java', 'Scala', 'Git', 'SQL', 'Hadoop'])

If you look at the output of dataScientist and dataEngineer variables above, notice that the values in the set are not in the order added in. This is because sets are unordered.

Sets containing values can also be initialized by using curly braces.

dataScientist = {'Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS'}
dataEngineer = {'Python', 'Java', 'Scala', 'Git', 'SQL', 'Hadoop'}

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Keep in mind that curly braces can only be used to initialize a set containing values. The image below shows that using curly braces without values is one of the ways to initialize a dictionary and not a set.

Add and Remove Values from Python Sets

To add or remove values from a set, you first have to initialize a set.

# Initialize set with values
graphicDesigner = {'InDesign', 'Photoshop', 'Acrobat', 'Premiere', 'Bridge'}

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Add Values to a Python Set

You can use the method add to add a value to a set.

graphicDesigner.add('Illustrator')

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It is important to note that you can only add a value that is immutable (like a string or a tuple) to a set. For example, you would get a TypeError if you try to add a list to a set.

graphicDesigner.add(['Powerpoint', 'Blender'])

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Remove Values from Sets in Python

There are a couple of ways to remove a value from a set.

Option 1: You can use the remove method to remove a value from a set.

graphicDesigner.remove('Illustrator')

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The drawback of this method is that if you try to remove a value that is not in your set, you will get a KeyError.

Option 2: You can use the discard method to remove a value from a set.

graphicDesigner.discard('Premiere')

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The benefit of this approach over the remove method is if you try to remove a value that is not part of the set, you will not get a KeyError. If you are familiar with dictionaries, you might find that this works similarly to the dictionary method get.

Option 3: You can also use the pop method to remove and return an arbitrary value from a set.

graphicDesigner.pop()

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It is important to note that the method raises a KeyError if the set is empty.

Remove All Values from a Python Set

You can use the clear method to remove all values from a set.

graphicDesigner.clear()

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Update Python Set Values

The update method adds the elements from a set to a set. It requires a single argument that can be a set, list, tuples, or dictionary. The .update() method automatically converts other data types into sets and adds them to the set. 

In the example, we have initialized three sets and used an update function to add elements from set2 to set1 and then from set3 to set1. 

# Initialize 3 sets
set1 = set([7, 10, 11, 13])
set2 = set([11, 8, 9, 12, 14, 15])
set3 = {'d', 'f', 'h'}

# Update set1 with set2
set1.update(set2)
print(set1)

# Update set1 with set3
set1.update(set3)
print(set1)

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Iterate through a Python Set

Like many standard python data types, it is possible to iterate through a set.

# Initialize a set
dataScientist = {'Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS'}

for skill in dataScientist:
    print(skill)

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If you look at the output of printing each of the values in dataScientist, notice that the values printed in the set are not in the order they were added in. This is because sets are unordered.

Transform a Python Set into Ordered Values

This tutorial has emphasized that sets are unordered. If you find that you need to get the values from your set in an ordered form, you can use the sorted function, which outputs a list that is ordered.

type(sorted(dataScientist))

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The code below outputs the values in the set dataScientist in descending alphabetical order (Z-A in this case).

sorted(dataScientist, reverse = True)

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Remove Duplicates from a List in Python

Part of the content in this section was previously explored in the tutorial 18 Most Common Python List Questions, but it is important to emphasize that sets are the fastest way to remove duplicates from a list. To show this, let's study the performance difference between two approaches.

Approach 1: Use a set to remove duplicates from a list.

print(list(set([1, 2, 3, 1, 7])))

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Approach 2: Use a list comprehension to remove duplicates from a list (If you would like a refresher on list comprehensions, see this tutorial).

def remove_duplicates(original):
    unique = []
    [unique.append(n) for n in original if n not in unique]
    return(unique)

print(remove_duplicates([1, 2, 3, 1, 7]))

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The performance difference can be measured using the the timeit library which allows you to time your Python code. The code below runs the code for each approach 10000 times and outputs the overall time it took in seconds.

import timeit

# Approach 1: Execution time
print(timeit.timeit('list(set([1, 2, 3, 1, 7]))', number=10000))

# Approach 2: Execution time
print(timeit.timeit('remove_duplicates([1, 2, 3, 1, 7])', globals=globals(), number=10000))

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Comparing these two approaches shows that using sets to remove duplicates is more efficient. While it may seem like a small difference in time, it can save you a lot of time if you have very large lists.

Python Set Operations

A common use of sets in Python is computing standard math operations such as union, intersection, difference, and symmetric difference. The image below shows a couple standard math operations on two sets A and B. The red part of each Venn diagram is the resulting set of a given set operation.

Python sets have methods that allow you to perform these mathematical operations as well as operators that give you equivalent results.

Before exploring these methods, let's start by initializing two sets dataScientist and dataEngineer.

dataScientist = set(['Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS'])
dataEngineer = set(['Python', 'Java', 'Scala', 'Git', 'SQL', 'Hadoop'])

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union

A union, denoted dataScientist ∪ dataEngineer, is the set of all values that are values of dataScientist, or dataEngineer, or both. You can use the union method to find out all the unique values in two sets.

# set built-in function union
dataScientist.union(dataEngineer)

# Equivalent Result
dataScientist | dataEngineer

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The set returned from the union can be visualized as the red part of the Venn diagram below.

intersection

An intersection of two sets dataScientist and dataEngineer, denoted dataScientist ∩ dataEngineer, is the set of all values that are values of both dataScientist and dataEngineer.

# Intersection operation
dataScientist.intersection(dataEngineer)

# Equivalent Result
dataScientist & dataEngineer

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The set returned from the intersection can be visualized as the red part of the Venn diagram below.

You may find that you come across a case where you want to make sure that two sets have no value in common. In order words, you want two sets that have an intersection that is empty. These two sets are called disjoint sets. You can test for disjoint sets by using the isdisjoint method.

# Initialize a set
graphicDesigner = {'Illustrator', 'InDesign', 'Photoshop'}

# These sets have elements in common so it would return False
dataScientist.isdisjoint(dataEngineer)

# These sets have no elements in common so it would return True
dataScientist.isdisjoint(graphicDesigner)

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You can notice in the intersection shown in the Venn diagram below that the disjoint sets dataScientist and graphicDesigner have no values in common.

Difference

A difference of two sets dataScientist and dataEngineer, denoted dataScientist \ dataEngineer, is the set of all values of dataScientist that are not values of dataEngineer.

# Difference Operation
dataScientist.difference(dataEngineer)

# Equivalent Result
dataScientist - dataEngineer

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The set returned from the difference can be visualized as the red part of the Venn diagram below.

Symmetric Difference

A symmetric difference of two sets dataScientist and dataEngineer, denoted dataScientist △ dataEngineer, is the set of all values that are values of exactly one of two sets, but not both.

# Symmetric Difference Operation
dataScientist.symmetric_difference(dataEngineer)

# Equivalent Result
dataScientist ^ dataEngineer

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The set returned from the symmetric difference can be visualized as the red part of the Venn diagram below.

Set Comprehension

You may have previously have learned about list comprehensions, dictionary comprehensions, and generator comprehensions. There are also Python set comprehensions. Set comprehensions are very similar. Set comprehensions in Python can be constructed as follows:

{skill for skill in ['SQL', 'SQL', 'PYTHON', 'PYTHON']}

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The output above is a set of 2 values because sets cannot have multiple occurrences of the same element.

The idea behind using set comprehensions is to let you write and reason in code the same way you would do mathematics by hand.

{skill for skill in ['GIT', 'PYTHON', 'SQL'] if skill not in {'GIT', 'PYTHON', 'JAVA'}}

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The code above is similar to a set difference you learned about earlier. It just looks a bit different.

Membership Tests

Membership tests check whether a specific element is contained in a sequence, such as strings, lists, tuples, or sets. One of the main advantages of using sets in Python is that they are highly optimized for membership tests. For example, sets do membership tests a lot more efficiently than lists. In case you are from a computer science background, this is because the average case time complexity of membership tests in sets are O(1) vs O(n) for lists

The code below shows a membership test using a list.

# Initialize a list
possibleList = ['Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS', 'Java', 'Spark', 'Scala']

# Membership test
'Python' in possibleList

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Something similar can be done for sets. Sets just happen to be more efficient.

# Initialize a set possibleSet = {'Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS', 'Java', 'Spark', 'Scala'} # Membership test 'Python' in possibleSet

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Since possibleSet is a set and the value 'Python' is a value of possibleSet, this can be denoted as 'Python' ∈ possibleSet.

If you had a value that wasn't part of the set, like 'Fortran', it would be denoted as 'Fortran' ∉ possibleSet.

Subset

A practical application of understanding membership is subsets.

Let's first initialize two sets.

possibleSkills = {'Python', 'R', 'SQL', 'Git', 'Tableau', 'SAS'}
mySkills = {'Python', 'R'}

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If every value of the set mySkills is also a value of the set possibleSkills, then mySkills is said to be a subset of possibleSkills, mathematically written mySkills ⊆ possibleSkills.

You can check to see if one set is a subset of another using the method issubset.

mySkills.issubset(possibleSkills)

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Since the method returns True in this case, it is a subset. In the Venn diagram below, notice that every value of the set mySkills is also a value of the set possibleSkills.

Frozensets

You have have encountered nested lists and tuples.

# Nested Lists and Tuples
nestedLists = [['the', 12], ['to', 11], ['of', 9], ['and', 7], ['that', 6]]
nestedTuples = (('the', 12), ('to', 11), ('of', 9), ('and', 7), ('that', 6))

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The problem with nested sets is that you cannot normally have nested Python sets, as sets cannot contain mutable values, including sets.

This is one situation where you may wish to use a frozenset. A frozenset is very similar to a set except that a frozenset is immutable.

You make a frozenset by using frozenset().

# Initialize a frozenset
immutableSet = frozenset()

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You can make a nested set if you utilize a frozenset similar to the code below.

nestedSets = set([frozenset()])

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It is important to keep in mind that a major disadvantage of a frozenset is that since they are immutable, it means that you cannot add or remove values.

Conclusion

The Python sets are highly useful to efficiently remove duplicate values from a collection like a list and to perform common math operations like unions and intersections. Some of the challenges people often encounter are when to use the various data types. For example, if you feel like you aren't sure when it is advantageous to use a dictionary versus a set, I encourage you to check out DataCamp's daily practice mode.

Original article sourced at: https://www.datacamp.com

#python 

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A Simple Wrapper Around Amplify AppSync Simulator

This serverless plugin is a wrapper for amplify-appsync-simulator made for testing AppSync APIs built with serverless-appsync-plugin.

Install

npm install serverless-appsync-simulator
# or
yarn add serverless-appsync-simulator

Usage

This plugin relies on your serverless yml file and on the serverless-offline plugin.

plugins:
  - serverless-dynamodb-local # only if you need dynamodb resolvers and you don't have an external dynamodb
  - serverless-appsync-simulator
  - serverless-offline

Note: Order is important serverless-appsync-simulator must go before serverless-offline

To start the simulator, run the following command:

sls offline start

You should see in the logs something like:

...
Serverless: AppSync endpoint: http://localhost:20002/graphql
Serverless: GraphiQl: http://localhost:20002
...

Configuration

Put options under custom.appsync-simulator in your serverless.yml file

| option | default | description | | ------------------------ | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | | apiKey | 0123456789 | When using API_KEY as authentication type, the key to authenticate to the endpoint. | | port | 20002 | AppSync operations port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20002, 20012, 20022, etc.) | | wsPort | 20003 | AppSync subscriptions port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20003, 20013, 20023, etc.) | | location | . (base directory) | Location of the lambda functions handlers. | | refMap | {} | A mapping of resource resolutions for the Ref function | | getAttMap | {} | A mapping of resource resolutions for the GetAtt function | | importValueMap | {} | A mapping of resource resolutions for the ImportValue function | | functions | {} | A mapping of external functions for providing invoke url for external fucntions | | dynamoDb.endpoint | http://localhost:8000 | Dynamodb endpoint. Specify it if you're not using serverless-dynamodb-local. Otherwise, port is taken from dynamodb-local conf | | dynamoDb.region | localhost | Dynamodb region. Specify it if you're connecting to a remote Dynamodb intance. | | dynamoDb.accessKeyId | DEFAULT_ACCESS_KEY | AWS Access Key ID to access DynamoDB | | dynamoDb.secretAccessKey | DEFAULT_SECRET | AWS Secret Key to access DynamoDB | | dynamoDb.sessionToken | DEFAULT_ACCESS_TOKEEN | AWS Session Token to access DynamoDB, only if you have temporary security credentials configured on AWS | | dynamoDb.* | | You can add every configuration accepted by DynamoDB SDK | | rds.dbName | | Name of the database | | rds.dbHost | | Database host | | rds.dbDialect | | Database dialect. Possible values (mysql | postgres) | | rds.dbUsername | | Database username | | rds.dbPassword | | Database password | | rds.dbPort | | Database port | | watch | - *.graphql
- *.vtl | Array of glob patterns to watch for hot-reloading. |

Example:

custom:
  appsync-simulator:
    location: '.webpack/service' # use webpack build directory
    dynamoDb:
      endpoint: 'http://my-custom-dynamo:8000'

Hot-reloading

By default, the simulator will hot-relad when changes to *.graphql or *.vtl files are detected. Changes to *.yml files are not supported (yet? - this is a Serverless Framework limitation). You will need to restart the simulator each time you change yml files.

Hot-reloading relies on watchman. Make sure it is installed on your system.

You can change the files being watched with the watch option, which is then passed to watchman as the match expression.

e.g.

custom:
  appsync-simulator:
    watch:
      - ["match", "handlers/**/*.vtl", "wholename"] # => array is interpreted as the literal match expression
      - "*.graphql"                                 # => string like this is equivalent to `["match", "*.graphql"]`

Or you can opt-out by leaving an empty array or set the option to false

Note: Functions should not require hot-reloading, unless you are using a transpiler or a bundler (such as webpack, babel or typescript), un which case you should delegate hot-reloading to that instead.

Resource CloudFormation functions resolution

This plugin supports some resources resolution from the Ref, Fn::GetAtt and Fn::ImportValue functions in your yaml file. It also supports some other Cfn functions such as Fn::Join, Fb::Sub, etc.

Note: Under the hood, this features relies on the cfn-resolver-lib package. For more info on supported cfn functions, refer to the documentation

Basic usage

You can reference resources in your functions' environment variables (that will be accessible from your lambda functions) or datasource definitions. The plugin will automatically resolve them for you.

provider:
  environment:
    BUCKET_NAME:
      Ref: MyBucket # resolves to `my-bucket-name`

resources:
  Resources:
    MyDbTable:
      Type: AWS::DynamoDB::Table
      Properties:
        TableName: myTable
      ...
    MyBucket:
      Type: AWS::S3::Bucket
      Properties:
        BucketName: my-bucket-name
    ...

# in your appsync config
dataSources:
  - type: AMAZON_DYNAMODB
    name: dynamosource
    config:
      tableName:
        Ref: MyDbTable # resolves to `myTable`

Override (or mock) values

Sometimes, some references cannot be resolved, as they come from an Output from Cloudformation; or you might want to use mocked values in your local environment.

In those cases, you can define (or override) those values using the refMap, getAttMap and importValueMap options.

• refMap takes a mapping of resource name to value pairs
• getAttMap takes a mapping of resource name to attribute/values pairs
• importValueMap takes a mapping of import name to values pairs

Example:

custom:
  appsync-simulator:
    refMap:
      # Override `MyDbTable` resolution from the previous example.
      MyDbTable: 'mock-myTable'
    getAttMap:
      # define ElasticSearchInstance DomainName
      ElasticSearchInstance:
        DomainEndpoint: 'localhost:9200'
    importValueMap:
      other-service-api-url: 'https://other.api.url.com/graphql'

# in your appsync config
dataSources:
  - type: AMAZON_ELASTICSEARCH
    name: elasticsource
    config:
      # endpoint resolves as 'http://localhost:9200'
      endpoint:
        Fn::Join:
          - ''
          - - https://
            - Fn::GetAtt:
                - ElasticSearchInstance
                - DomainEndpoint

Key-value mock notation

In some special cases you will need to use key-value mock nottation. Good example can be case when you need to include serverless stage value (${self:provider.stage}) in the import name.

This notation can be used with all mocks - refMap, getAttMap and importValueMap

provider:
  environment:
    FINISH_ACTIVITY_FUNCTION_ARN:
      Fn::ImportValue: other-service-api-${self:provider.stage}-url

custom:
  serverless-appsync-simulator:
    importValueMap:
      - key: other-service-api-${self:provider.stage}-url
        value: 'https://other.api.url.com/graphql'

Limitations

This plugin only tries to resolve the following parts of the yml tree:

• provider.environment
• functions[*].environment
• custom.appSync

If you have the need of resolving others, feel free to open an issue and explain your use case.

For now, the supported resources to be automatically resovled by Ref: are:

• DynamoDb tables
• S3 Buckets

Feel free to open a PR or an issue to extend them as well.

External functions

When a function is not defined withing the current serverless file you can still call it by providing an invoke url which should point to a REST method. Make sure you specify "get" or "post" for the method. Default is "get", but you probably want "post".

custom:
  appsync-simulator:
    functions:
      addUser:
        url: http://localhost:3016/2015-03-31/functions/addUser/invocations
        method: post
      addPost:
        url: https://jsonplaceholder.typicode.com/posts
        method: post

Supported Resolver types

This plugin supports resolvers implemented by amplify-appsync-simulator, as well as custom resolvers.

From Aws Amplify:

• NONE
• AWS_LAMBDA
• AMAZON_DYNAMODB
• PIPELINE

Implemented by this plugin

• AMAZON_ELASTIC_SEARCH
• HTTP
• RELATIONAL_DATABASE

Relational Database

Sample VTL for a create mutation

#set( $cols = [] )
#set( $vals = [] )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #set( $discard = $cols.add("$toSnake") )
  #if( $util.isBoolean($ctx.args.input[$entry]) )
      #if( $ctx.args.input[$entry] )
        #set( $discard = $vals.add("1") )
      #else
        #set( $discard = $vals.add("0") )
      #end
  #else
      #set( $discard = $vals.add("'$ctx.args.input[$entry]'") )
  #end
#end
#set( $valStr = $vals.toString().replace("[","(").replace("]",")") )
#set( $colStr = $cols.toString().replace("[","(").replace("]",")") )
#if ( $valStr.substring(0, 1) != '(' )
  #set( $valStr = "($valStr)" )
#end
#if ( $colStr.substring(0, 1) != '(' )
  #set( $colStr = "($colStr)" )
#end
{
  "version": "2018-05-29",
  "statements":   ["INSERT INTO <name-of-table> $colStr VALUES $valStr", "SELECT * FROM    <name-of-table> ORDER BY id DESC LIMIT 1"]
}

Sample VTL for an update mutation

#set( $update = "" )
#set( $equals = "=" )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $cur = $ctx.args.input[$entry] )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #if( $util.isBoolean($cur) )
      #if( $cur )
        #set ( $cur = "1" )
      #else
        #set ( $cur = "0" )
      #end
  #end
  #if ( $util.isNullOrEmpty($update) )
      #set($update = "$toSnake$equals'$cur'" )
  #else
      #set($update = "$update,$toSnake$equals'$cur'" )
  #end
#end
{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> SET $update WHERE id=$ctx.args.input.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.input.id"]
}

Sample resolver for delete mutation

{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> set deleted_at=NOW() WHERE id=$ctx.args.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.id"]
}

Sample mutation response VTL with support for handling AWSDateTime

#set ( $index = -1)
#set ( $result = $util.parseJson($ctx.result) )
#set ( $meta = $result.sqlStatementResults[1].columnMetadata)
#foreach ($column in $meta)
    #set ($index = $index + 1)
    #if ( $column["typeName"] == "timestamptz" )
        #set ($time = $result["sqlStatementResults"][1]["records"][0][$index]["stringValue"] )
        #set ( $nowEpochMillis = $util.time.parseFormattedToEpochMilliSeconds("$time.substring(0,19)+0000", "yyyy-MM-dd HH:mm:ssZ") )
        #set ( $isoDateTime = $util.time.epochMilliSecondsToISO8601($nowEpochMillis) )
        $util.qr( $result["sqlStatementResults"][1]["records"][0][$index].put("stringValue", "$isoDateTime") )
    #end
#end
#set ( $res = $util.parseJson($util.rds.toJsonString($util.toJson($result)))[1][0] )
#set ( $response = {} )
#foreach($mapKey in $res.keySet())
    #set ( $s = $mapKey.split("_") )
    #set ( $camelCase="" )
    #set ( $isFirst=true )
    #foreach($entry in $s)
        #if ( $isFirst )
          #set ( $first = $entry.substring(0,1) )
        #else
          #set ( $first = $entry.substring(0,1).toUpperCase() )
        #end
        #set ( $isFirst=false )
        #set ( $stringLength = $entry.length() )
        #set ( $remaining = $entry.substring(1, $stringLength) )
        #set ( $camelCase = "$camelCase$first$remaining" )
    #end
    $util.qr( $response.put("$camelCase", $res[$mapKey]) )
#end
$utils.toJson($response)

Using Variable Map

Variable map support is limited and does not differentiate numbers and strings data types, please inject them directly if needed.

Will be escaped properly: null, true, and false values.

{
  "version": "2018-05-29",
  "statements":   [
    "UPDATE <name-of-table> set deleted_at=NOW() WHERE id=:ID",
    "SELECT * FROM <name-of-table> WHERE id=:ID and unix_timestamp > $ctx.args.newerThan"
  ],
  variableMap: {
    ":ID": $ctx.args.id,
##    ":TIMESTAMP": $ctx.args.newerThan -- This will be handled as a string!!!
  }
}

Requires

• serverless framework
• serverless-appsync-plugin
• serverless-offline
• serverless-dynamodb-local (when using dynamodb resolvers only)
• watchman (if Hot-reloading is desactivated it is not required)

Author: Serverless-appsync
Source Code: https://github.com/serverless-appsync/serverless-appsync-simulator 
License: MIT License

#serverless #sync #graphql 

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

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Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

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