Sofia Kelly

Sofia Kelly

1609765860

Is Go set to take on Python?

When Rossum developed Python in the 1980s, did he know that it would become the world’s most widely used programming language? More than 8 million developers today, use Python as their primary development language. Thanks to its abundance of libraries, and plenty of applications. Be it website development, machine learning, or analytics, Python is being used everywhere that people can think.

One of the most important implementations of Python is observed while analyzing data. Ever since the wave of digitization swept industries off their feet. Be it healthcare, business, or any other industry. Developers are utilizing Python for data analytics, and it is proving to be great.

There are a few advantages of data analytics. On the one hand, it is helping people see the future with help from the past and the present. On the other hand, it is helping us to make better decision making in all the processes. The point is, whatever people or industries want to accomplish with the data, Python is assisting in it. Python becomes a tool that makes complex and tangled data appear sorted and clear.

As the world evolves, new programming languages keep on emerging. They are born out of the shortcomings of existing languages and help improve the performance of the system in one way or another. One such programming language is Google’s Go.

Golang or Go is a programming language developed by Google, whose idea was realized in the year 2007. However, it was only in 2009 that the world saw its first release. Go is relatively much fresh than Python. Being known for ten years, why are people realizing Golang’s importance now more than ever? Is it because the light around Python is dimming? Whatever be the reason, recent events have suggested that Google’s Go has more than a few advantages over Python, especially when we talk of data analytics.

Google’s Go was born out of the need for a language that was based on the syntax of C. As a result of this, the lead developers at Google Robert Greisemer, Rob Pike, and Ken Thompson created Go with many features of modern languages. Having said this, developers can easily find object-oriented features such as operator overloading, pointer arithmetic, along with others. Apart from this, Go also has a robust library with unmatched performance and speed.

Even though Python can do a lot of what Go does, it lacks in some aspects. When it comes to speed, dynamic typing, GIL, concurrency support, etc. Python shows clear signs of limitations for analytics. Let’s take a more in-depth look at what this comparison between Go and Python mean for analytics.

#python #data #go #data-science

What is GEEK

Buddha Community

Is Go set to take on Python?
Hermann Frami

Hermann Frami

1651383480

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

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

#serverless #sync #graphql 

Ray Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Ray Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

藤本 結衣

藤本 結衣

1636296420

線形検索のためのPythonプログラム

このチュートリアルでは、Pythonで線形検索プログラムを作成する方法を学習します。

まず、線形検索（シーケンシャル検索とも呼ばれます）は、リストまたは配列内の要素を見つけるために使用されます。一致するものが見つかるか、リスト全体が検索されるまで、リストの各要素を1つずつ/順番にチェックします。

線形探索アルゴリズム

以下の手順に従って線形検索を実装します。

  • ループを使用してリスト/配列をトラバースします。
  • すべての反復で、target 値をリスト/配列の指定された値に関連付け ます。
    • 値が一致する場合は、リスト/配列の現在のインデックスを返します。
    • それ以外の場合は、次の配列/リスト要素に移動します。
  • 一致するものが見つからない場合は、を返し -1ます。

線形検索のためのPythonプログラム

  • whileループを使用した線形検索用のPythonプログラム
  • Forループを使用した線形検索用のPythonプログラム
  • 再帰を使用したPythonプログラムでの線形検索

whileループを使用した線形検索用のPythonプログラム

# python program for linear search using while loop
 
#define list
lst = []
 
#take input list size
num = int(input("Enter size of list :- "))
 
for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)
 
#take input number to be find in list   
x = int(input("Enter number to search in list :- "))
 
i = 0
flag = False
 
while i < len(lst):
    if lst[i] == x:
        flag = True
        break
  
    i = i + 1
  
if flag == 1:
    print('{} was found at index {}.'.format(x, i))
else:
    print('{} was not found.'.format(x))

プログラムの実行後、出力は次のようになります。

Enter size of list :-  5
Enter the array of 0 element :-  10
Enter the array of 1 element :-  23
Enter the array of 2 element :-  56
Enter the array of 3 element :-  89
Enter the array of 4 element :-  200
Enter number to search in list :-  89
89 was found at index 3.

Forループを使用した線形検索用のPythonプログラム

# python program for linear search using for loop
 
#define list
lst = []
 
#take input list size
num = int(input("Enter size of list :- "))
 
for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)
 
#take input number to be find in list   
x = int(input("Enter number to search in list :- "))
 
i = 0
flag = False
 
for i in range(len(lst)):
    if lst[i] == x:
        flag = True
        break
  
if flag == 1:
    print('{} was found at index {}.'.format(x, i))
else:
    print('{} was not found.'.format(x))

プログラムの実行後、出力は次のようになります。

Enter size of list :-  6
Enter the array of 0 element :-  25
Enter the array of 1 element :-  50
Enter the array of 2 element :-  100
Enter the array of 3 element :-  200
Enter the array of 4 element :-  250
Enter the array of 5 element :-  650
Enter number to search in list :-  200
200 was found at index 3.

再帰を使用したPythonプログラムでの線形検索

# python program for linear search using for loop
 
#define list
lst = []
 
#take input list size
num = int(input("Enter size of list :- "))
 
for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)
 
#take input number to be find in list   
x = int(input("Enter number to search in list :- "))
 
# Recursive function to linear search x in arr[l..r]  
def recLinearSearch( arr, l, r, x): 
    if r < l: 
        return -1
    if arr[l] == x: 
        return l 
    if arr[r] == x: 
        return r 
    return recLinearSearch(arr, l+1, r-1, x) 
     
 
res = recLinearSearch(lst, 0, len(lst)-1, x) 
  
if res != -1:
    print('{} was found at index {}.'.format(x, res))
else:
    print('{} was not found.'.format(x))

プログラムの実行後、出力は次のようになります。

Enter size of list :-  5
Enter the array of 0 element :-  14
Enter the array of 1 element :-  25
Enter the array of 2 element :-  63
Enter the array of 3 element :-  42
Enter the array of 4 element :-  78
Enter number to search in list :-  78
78 was found at index 4.

リンク: https://www.tutsmake.com/linear-search-in-python/

#python 

André Schürrle

1637592240

Lineare Suche in Python

In diesem Python-Beitrag erfahren Sie Folgendes:

  • Was ist eine lineare Suche?
  • Linearer Suchalgorithmus
  • Schreiben Sie ein Python-Programm für die lineare Suche mit While-Schleife
  • Schreiben Sie ein Python-Programm für die lineare Suche mit der For-Schleife
  • Lineare Suche im Python-Programm mit Rekursion

Was ist eine lineare Suche?

Eine lineare Suche, auch bekannt als sequentielle Suche, diese Methode wird verwendet, um ein Element innerhalb einer Liste oder eines Arrays zu finden. Es überprüft jedes Element der Liste nacheinander / sequentiell, bis eine Übereinstimmung gefunden wird oder die gesamte Liste durchsucht wurde.

Linearer Suchalgorithmus

Implementieren Sie die lineare Suche mit den folgenden Schritten:

  • Durchlaufen Sie die Liste/das Array mit einer Schleife.
  • Verknüpfen Sie in jeder Iteration den  target Wert mit dem angegebenen Wert der Liste/des Arrays.
    • Wenn die Werte übereinstimmen, geben Sie den aktuellen Index der Liste/des Arrays zurück.
    • Fahren Sie andernfalls mit dem nächsten Array-/Listenelement fort.
  • Wenn keine Übereinstimmung gefunden wird, geben Sie zurück  -1.

Schreiben Sie ein Python-Programm für die lineare Suche mit While-Schleife

# python program for linear search using while loop

#define list
lst = []

#take input list size
num = int(input("Enter size of list :- "))

for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)

#take input number to be find in list   
x = int(input("Enter number to search in list :- "))

i = 0
flag = False

while i < len(lst):
	if lst[i] == x:
		flag = True
		break

	i = i + 1

if flag == 1:
	print('{} was found at index {}.'.format(x, i))
else:
	print('{} was not found.'.format(x))

Nach der Ausführung des Programms lautet die Ausgabe:

Enter size of list :-  5
Enter the array of 0 element :-  10
Enter the array of 1 element :-  23
Enter the array of 2 element :-  56
Enter the array of 3 element :-  89
Enter the array of 4 element :-  200
Enter number to search in list :-  89
89 was found at index 3.

Schreiben Sie ein Python-Programm für die lineare Suche mit der For-Schleife

# python program for linear search using for loop

#define list
lst = []

#take input list size
num = int(input("Enter size of list :- "))

for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)

#take input number to be find in list   
x = int(input("Enter number to search in list :- "))

i = 0
flag = False

for i in range(len(lst)):
    if lst[i] == x:
        flag = True
        break

if flag == 1:
	print('{} was found at index {}.'.format(x, i))
else:
	print('{} was not found.'.format(x))

Nach der Ausführung des Programms lautet die Ausgabe:

Enter size of list :-  6
Enter the array of 0 element :-  25
Enter the array of 1 element :-  50
Enter the array of 2 element :-  100
Enter the array of 3 element :-  200
Enter the array of 4 element :-  250
Enter the array of 5 element :-  650
Enter number to search in list :-  200
200 was found at index 3.

Lineare Suche im Python-Programm mit Rekursion

# python program for linear search using for loop

#define list
lst = []

#take input list size
num = int(input("Enter size of list :- "))

for n in range(num):
    #append element in list/array
    numbers = int(input("Enter the array of %d element :- " %n))
    lst.append(numbers)

#take input number to be find in list   
x = int(input("Enter number to search in list :- "))

# Recursive function to linear search x in arr[l..r]  
def recLinearSearch( arr, l, r, x): 
    if r < l: 
        return -1
    if arr[l] == x: 
        return l 
    if arr[r] == x: 
        return r 
    return recLinearSearch(arr, l+1, r-1, x) 

res = recLinearSearch(lst, 0, len(lst)-1, x) 

if res != -1:
	print('{} was found at index {}.'.format(x, res))
else:
	print('{} was not found.'.format(x))

Nach der Ausführung des Programms lautet die Ausgabe:

Enter size of list :-  5
Enter the array of 0 element :-  14
Enter the array of 1 element :-  25
Enter the array of 2 element :-  63
Enter the array of 3 element :-  42
Enter the array of 4 element :-  78
Enter number to search in list :-  78
78 was found at index 4.