1635842280
In Python, sets and lists both contain collections of objects, but there are significant differences between these two. If we talk about sets, they are valuable for executing a massive range of intersections and unions. On the other hand, lists provide those operations that help you to find duplicated items. In other words, a list contains identical items, while a set contains exceptional values. Another distinct quality is that the list uses square brackets, while the set uses curly brackets. Keep in mind that a dictionary in Python also uses curly braces. The common thing in sets and lists is that they hold strings, floats, integers, or other data types. So, if you have a set of elements in Python, you can also make a set of strings in python.
1651383480
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
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`
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 pairsgetAttMap
takes a mapping of resource name to attribute/values pairsimportValueMap
takes a mapping of import name to values pairsExample:
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
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'
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:
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:
Implemented by this plugin
#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"]
}
#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=$ctx.args.input.id"]
}
{
"version": "2018-05-29",
"statements": ["UPDATE <name-of-table> set deleted_at=NOW() WHERE id=$ctx.args.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.id"]
}
#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)
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
1677668905
Mocking library for TypeScript inspired by http://mockito.org/
mock
) (also abstract classes) #examplespy
) #examplewhen
) via:verify
)reset
, resetCalls
) #example, #examplecapture
) #example'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).'
)npm install ts-mockito --save-dev
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance from mock
let foo:Foo = instance(mockedFoo);
// Using instance in source code
foo.getBar(3);
foo.getBar(5);
// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();
// Creating mock
let mockedFoo:Foo = mock(Foo);
// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');
// Getting instance
let foo:Foo = instance(mockedFoo);
// prints three
console.log(foo.getBar(3));
// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));
// Creating mock
let mockedFoo:Foo = mock(Foo);
// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');
// Getting instance
let foo:Foo = instance(mockedFoo);
// prints three
console.log(foo.sampleGetter);
Syntax is the same as with getter values.
Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);
// Call count verification
verify(mockedFoo.getBar(1)).once(); // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice(); // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4); // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2); // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4); // was called with any argument max four times
verify(mockedFoo.getBar(4)).never(); // was never called with arg === 4
// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);
// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);
// Some calls
foo.getBar(1);
bar.getFoo(2);
// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2)); // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1)); // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999)); // throws error (mockedBar.getFoo(999999) has never been called)
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));
let foo:Foo = instance(mockedFoo);
try {
foo.getBar(10);
} catch (error:Error) {
console.log(error.message); // 'fatal error'
}
You can also stub method with your own implementation
let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);
when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
return arg1 * arg2;
});
// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));
You can also stub method to resolve / reject promise
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));
You can reset just mock call counter
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice(); // getBar with arg "1" has been called twice
// Reset mock
resetCalls(mockedFoo);
// Call count verification
verify(mockedFoo.getBar(1)).never(); // has never been called after reset
You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)
Or reset mock call counter with all stubs
// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
console.log(foo.getBar(1)); // "one" - as defined in stub
console.log(foo.getBar(1)); // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice(); // getBar with arg "1" has been called twice
// Reset mock
reset(mockedFoo);
// Call count verification
verify(mockedFoo.getBar(1)).never(); // has never been called after reset
console.log(foo.getBar(1)); // null - previously added stub has been removed
You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)
let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);
// Call method
foo.sumTwoNumbers(1, 2);
// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg); // prints 1
console.log(secondArg); // prints 2
You can also get other calls using first()
, second()
, byCallIndex(3)
and more...
You can set multiple returning values for same matching values
const mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');
const foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(1)); // two
console.log(foo.getBar(1)); // three
console.log(foo.getBar(1)); // three - last defined behavior will be repeated infinitely
Another example with specific values
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');
let foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(2)); // two
console.log(foo.getBar(1)); // another one
console.log(foo.getBar(1)); // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2)); // two
console.log(foo.getBar(2)); // two - this is last defined behavior for arg '2' so it will be repeated
Short notation:
const mockedFoo:Foo = mock(Foo);
// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');
const foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(1)); // two
console.log(foo.getBar(1)); // three
console.log(foo.getBar(1)); // three - last defined behavior will be repeated infinity
Possible errors:
const mockedFoo:Foo = mock(Foo);
// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');
const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call
You can mock interfaces too, just instead of passing type to mock
function, set mock
function generic type Mocking interfaces requires Proxy
implementation
let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);
You can mock abstract classes
const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);
You can also mock generic classes, but note that generic type is just needed by mock type definition
const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);
You can partially mock an existing instance:
const foo: Foo = new Foo();
const spiedFoo = spy(foo);
when(spiedFoo.getBar(3)).thenReturn('one');
console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method
You can spy on plain objects too:
const foo = { bar: () => 42 };
const spiedFoo = spy(foo);
foo.bar();
console.log(capture(spiedFoo.bar).last()); // [42]
Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito
License: MIT license
1624429860
List comprehension is used for creating lists based on iterables. It can also be described as representing for and if loops with a simpler and more appealing syntax. List comprehensions are relatively faster than for loops.
The syntax of a list comprehension is actually easy to understand. However, when it comes to complex and nested operations, it might get a little tricky to figure out how to structure a list comprehension.
In such cases, writing the loop version first makes it easier to write the code for the list comprehension. We will go over several examples that demonstrate how to convert a loop-wise syntax to a list comprehension.
Basic structure of list comprehension (image by author)
Let’s start with a simple example. We have a list of 5 integers and want to create a list that contains the squares of each item. Following is the for loop that performs this operation.
lst_a = [1, 2, 3, 4, 5]
lst_b = []
for i in lst_a:
lst_b.append(i**2)
print(lst_b)
[1, 4, 9, 16, 25]
#python #programming #how to convert loops to list comprehension in python #convert loops #list comprehension #how to convert loops to list comprehension
1640973720
The beyonic APIs Docs Reference: https://apidocs.beyonic.com/
Discuss Beyonic API on slack
The Beyonic API is a representational state transfer, REST based application programming interface that lets you extend the Beyonic dashboard features into your application and systems, allowing you to build amazing payment experiences.
With the Beyonic API you can:
For usage, general questions, and discussions the best place to go to is Beyhive Slack Community, also feel free to clone and edit this repository to meet your project, application or system requirements.
To start using the Beyonic Python API, you need to start by downloading the Beyonic API official Python client library and setting your secret key.
Install the Beyonic API Python Official client library
>>> pip install beyonic
Setting your secrete key.
To set the secrete key install the python-dotenv modeule, Python-dotenv is a Python module that allows you to specify environment variables in traditional UNIX-like “.env” (dot-env) file within your Python project directory, it helps us work with SECRETS and KEYS without exposing them to the outside world, and keep them safe during development too.
Installing python-dotenv modeule
>>> pip install python-dotenv
Creating a .env file to keep our secrete keys.
>>> touch .env
Inside your .env file specify the Beyonic API Token .
.env file
BEYONIC_ACCESS_KEY = "enter your API "
You will get your API Token by clicking your user name on the bottom left of the left sidebar menu in the Beyonic web portal and selecting ‘Manage my account’ from the dropdown menu. The API Token is shown at the very bottom of the page.
import os
import beyonic
from dotenv import load_dotenv
load_dotenv()
myapi = os.environ['BEYONIC_ACCESS_KEY']
beyonic.api_key = myapi
# Listing account: Working.
accounts = beyonic.Account.list()
print(accounts)
#Listing currencies: Not working yet.
'''
supported_currencies = beyonic.Currency.list()
print(supported_currencies)
Supported currencies are: USD, UGX, KES, BXC, GHS, TZS, RWF, ZMW, MWK, BIF, EUR, XAF, GNF, XOF, XOF
'''
#Listing networks: Not working yet.
"""
networks = beyonic.Network.list()
print(networks)
"""
#Listing transactions: Working.
transactions = beyonic.Transaction.list()
print(transactions)
#Listing contact: Working.
mycontacts = beyonic.Contact.list()
print(mycontacts)
#Listing events: Not working yet.
'''
events = beyonic.Event.list()
print(events)
Error: AttributeError: module 'beyonic' has no attribute 'Event'
'''
Docker file
FROM python:3.8-slim-buster
COPY . .
COPY ./requirements.txt ./requirements.txt
WORKDIR .
RUN pip install -r requirements.txt
CMD [ "python3", "getExamples.py" ]
Build docker image called demo
>>> docker build -t bey .
Run docker image called demo
>>>docker run -t -i bey
Now, I’ll create a Docker compose file to run a Docker container using the Docker image we just created.
version: "3.6"
services:
app:
build: .
command: python getExamples.py
volumes:
- .:/pythonBeyonicExamples
Now we are going to run the following command from the same directory where the docker-compose.yml file is located. The docker compose up command will start and run the entire app.
docker compose up
NB: The screenshot below might differ according to your account deatils and your transcations in deatils.
To stop the container running on daemon mode use the below command.
docker compose stop
Output
Contributing to this repository. All contributions, bug reports, bug fixes, enhancements, and ideas are welcome, You can get in touch with me on twitter @HarunMbaabu.
Download Details:
Author: HarunMbaabu
Source Code: https://github.com/HarunMbaabu/BeyonicAPI-Python-Examples
License:
1626775355
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.
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.
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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