But how useful is Django for developers, and what are the best Django packages developers should know about? 

To help you with your future web development, our team has created a list of some of the best Django libraries and packages (10 Django packages based on PyPI (The Python Package Index) downloads, Take a look!

Django and Django packages

Django is a high-level Python web framework to perform rapid development processes for secure and maintainable websites. While Python is simple to learn, even for inexperienced developers, it also has an active community and a large number of packages that can be used to extend the functionality of a Django project.

To additionally limit the time it takes to develop high-quality apps, with their incredible scalability, security, and reliable assistance. 

Here are some popular Django packages for Django :

1.Django Rest Framework: 

The Django Rest Framework is a powerful and flexible package that allows developers to easily create RESTful APIs for Django projects. 

The REST framework requires the following:

Python (3.6, 3.7, 3.8, 3.9, 3.10)

Django (2.2, 3.0, 3.1, 3.2, 4.0, 4.1)

Experts recommend using only official support for the latest patch release of each Python and Django series.

Package Installation 

Install using pip, while including any optional packages you want…

pip install djangorestframework

pip install markdown       # Markdown support for the browsable API.

pip install django-filter  # Filtering support

Add ‘rest_framework’ to your INSTALLED_APPS setting.

INSTALLED_APPS = [

    …

    ‘rest_framework’,

]

2.Django Sentry 

Over 3.5 million developers and 85k companies trust Sentry to improve Django apps’ performances and monitor workflow with a full view of releases.

Installation of the Sentry Python SDK using pip

pip install –upgrade sentry-sdk

Run the Python SDK with the Django integration in your settings.py file.

import sentry_sdk

from sentry_sdk.integrations.django import DjangoIntegration

sentry_sdk.init(

    dsn=”https://<key>@sentry.io/<project>”,

    integrations=[DjangoIntegration()],

    # Set traces_sample_rate to 1.0 to capture 100%

    # of transactions for performance monitoring.

    # It is recommended to adjust this value in production,

    traces_sample_rate=1.0,

    # If you want to add users to errors (assuming you are using

    # django.contrib.auth) you may enable sending PII data.

    send_default_pii=True

)

3.Django-Celery

Django-celery integrates Celery into Django; it uses the ORM and cache backends of Django to store results, automatically discovers task modules based on INSTALLED_APPS, and more.

Django uses this package to process asynchronous tasks.

Installing Django-celery in your project

You can install django-celery via the Python Package Index (PyPI) or from source.

To install using pip,:

$ pip install django-celery

To install using easy_install,:

$ easy_install django-celery

Then if you want to create the necessary tables. If you generating schema migrations, you’ll want to run:

$ python manage.py migrate djcelery

4.Django-Filter

Django-filter is a reusable Django application that allows for easy filtering of query sets in the Django Rest Framework. Django-Filter will continue to support all current Django filters for Django versions.

Installation

Install using pip:

pip install django-filter

Then add ‘django_filters’ to your INSTALLED_APPS.

INSTALLED_APPS = [

    …

    ‘django_filters’,

]

5.Django-Guardian: 

This package provides per-object permissions for Django projects.

Requirements

Python 3.5+

A supported version of Django (currently 2.2+)

Installation

To install django-guardian simply run:

pip install django-guardian

Configuration

We need to add django-guardian into our project.

Put guardian into your INSTALLED_APPS in settings module:

INSTALLED_APPS = (

 …

 ‘guardian’,

)

Add extra authorization backend to your settings.py:

AUTHENTICATION_BACKENDS = (

    ‘django.contrib.auth.backends.ModelBackend’, # default

    ‘guardian.backends.ObjectPermissionBackend’,

)

Create guardian database tables by running:

python manage.py migrate

6.Django-Haystack

This package provides a unified/modular search interface for Django projects. That features a unified, familiar API that allows you to plug in different search backends (such as Solr, Elasticsearch, Whoosh, Xapian, etc.) without modifying your code.

Get started with this package 

1. Install package: PyPI’s latest stable (2.6.0): pip instal django-haystack
2. Add haystack to your INSTALLED_APPS.
3. Create search_indexes.py files for your models.
4. Setup the main SearchIndex via autodiscover.
5. Add haystack.urls to your URLconf file.
6. Search!

7.Django-Social-Auth: 

This package allows for easy integration of social authentication (e.g. Facebook, Twitter) into Django projects. Developed using base code from django-twitter-oauth_ and django-openid-auth_, it uses a common interface (login credentials) to define new authentication for third party providers. 

Installation

From pypi_::

 easy_install django-social-auth

8.Django-Extensions

There has been much interest in Django-extensions, thanks to its runserver_plus and shell_plus commands, which make Django development much simpler. This package provides a collection of useful extensions for Django, including management commands and custom fields.

Requirements

Django Extensions requires Django 3.2 or later.

Installation 

Getting Django Extensions by using pip:

$ pip install django-extensions

To access django_extensions in your project, you need to add it to INSTALLED_APPS in your project’s settings.py file:

INSTALLED_APPS = (

    …

    ‘django_extensions’,

    …

)

9.Django-REST-Swagger

This package automatically generates Swagger documentation for Django Rest Framework APIs.

Installation

$ pip install django-rest-swagger

Add ‘rest_framework_swagger’ to INSTALLED_APPS in Django settings.

settings.py

INSTALLED_APPS = [

    …

    ‘rest_framework_swagger’,

    …

]

The Django REST Framework 3.4’s new schema generation features are used in Version 2.0, which fundamentally differs from earlier iterations.

10.Django Debugging Toolbar

This Django Debugging Toolbar package provides improved toolbar debugging for various aspects of a Django project, including SQL queries and performance metrics.

Installation

The best way to install the Debug Toolbar is via pip:

$ python -m pip install django-debug-toolbar

To test an upcoming release, you can install the in-development version instead with the following command (available at github): 

$ python -m pip install -e git+https://github.com/jazzband/django-debug-toolbar.git#egg=django-debug-toolbar

Install the App

Add “debug_toolbar” to your INSTALLED_APPS setting:

INSTALLED_APPS = [

    # …

    “debug_toolbar”,

    # …

]

Modules Vs. Packages

Developers who are working with the Django framework might have heard the terms “modules” and “packages”.

A module is a reusable piece of code that can be included in your Django project and is great for adding advanced functionalities to your apps. Packages are a bit different from modules. A package is simply a collection of files – consisting of Python code and documentation – that is ready to be used by your Django application. 

Unlike modules, which can be used interchangeably within a Django project, packages must be used with an explicit dependency on the package’s name (for example, django-tables). 

Additionally, packages provide an important advantage over modules: they are usually more reliable and easier to maintain. This is because packages usually have been tested more thoroughly by the developers who created them than individual modules.

How To Get The Most Out Of Django Modules And Packages

Django, makes your project more manageable and easier to maintain over time. In which you can break your code down into small, reusable pieces that can be easily added to your project.  

When developing with Django, many of you will want to use both modules and packages at some point in your company. While they can be used alternatively, whichever route you may choose, it should solely depend on what features you want to add or improve within your Django App. 

In the context of developers

In this blog post, we have explored 10 of the best Django packages to make web development easier. Now you have a general idea of what you’re looking for, the next step is to search for packages that fit your needs. You can choose any of the packages listed above that interests you and meet your specific needs and requirements of your project. 

We hope by using this, you will be able to choose the right package for your project and create robust web applications with ease. If you try some of these tools yourself, let us know how they helped you.

The post Top 10 Django Packages Every Developer Should Be Aware Of appeared first on Talent500 blog.

What is WebScrapping?

Use of Selenium and Python in Web Scraping

Implementation with Demo Code

Installation of Selenium & Packages

Open Specific Search Page

Write a function to take the cursor to the end of the page

Write a function to fetch the URL of each Image

Write a function to download Image

Function to save Image in the Destination directory

Conclusion

What is WebScrapping?

In simple words, web-scraping is the automated gathering of content and data from websites or any other resource available on the internet. Most often, the data is fetched in unstructured format and then needs to convert into structured data which can be further utilised.

Now most of the tech giants like Google and Facebook have APIs that allow access to data in structured format. But that is not the case for all the sites –  in fact, large websites still won’t allow you to fetch data in structured format. That’s where scraping comes into existence. Web Scraping helps  to scrape the website for data. There are many ways to scrape the website for the required structured data, in this post we will discuss one of the most popular UI Automation.

Use of Selenium and Python in Web Scraping

There are many Automation tools and languages available in the market. Before we discuss the reason for using Selenium with Python, let’s take a quick overview about Selenium.

Selenium is an open source project for a range of tools and libraries aimed at supporting browser automation. To extract the data from these browsers, Selenium provides an interface called WebDriver, which is useful for performing various tasks such as automated testing, cookie retrieval, screenshot retrieval. Some common Selenium use cases for web scraping are form submission, auto-login, data addition and deletion, and alert handling.

Now we have a brief idea on usage of Selenium, but the reason why Python has been used as a supported language with Selenium needs to be discussed. Python has libraries for almost all purposes including libraries for tasks such as web scraping. 

Implementation with Demo Code

Installation of Selenium & Packages

If you have Selenium and Python already installed, then it is quick to start with the code, but for those who doesn’t have, please follow the steps below:

Now let’s install some supporting packages:

To start with our scraper code let’s create a selenium webdriver object and launch a Chrome browser and open talent500, just to make sure all installation and webdriver initialization is working as expected:

Let’s create a directory where we will save our images later.

Open Specific Search Page

In this step, we’re installing a Chrome driver and using a headless browser for web scraping. Now let’s replace the driver get from talent500 to specific search URL, from where we will fetch the images

I’ve used this specific URL to scrape copyright-free images.

Write a function to take the cursor to the end of the page

This snippet of code will scroll down the page

Write a function to fetch the URL of each Image

This function would return a list of URLs for each category (e.g. Cars, horses, etc.)

Write a function to download image

This snippet of code will download the image from each of the URLs.

Function to save Image in the Destination directory

Below function will help to save the images at specific locations for further analysis.

Conclusion

In this present world data is the king, and the information can be used in different areas from product enhancements to market analysis. But the most important benefits of web scraping are Research And Analysis, Monitoring, Machine learning and Marketing.

The data fetched using Selenium can be further used for analysis. Remember that using Selenium always has the upper hand as we can get many resources easily and as it is open source so we don’t have to invest any additional amount.

The post Web Scraping using Selenium and Python appeared first on Talent500 blog.

The previous article discusses the generic questions you might encounter in a technical interview. This part will focus on the two primary backend object-oriented programming languages- Java and Python. 

Java

1. What is a constructor?

A constructor function creates an object and initializes its fields. A constructor can be called only once per class. In Java backend development, constructors can be used to initialize an object’s state and values, which can be useful if you need to perform some kind of initialization on the object when it is first created.

2. What are the different types of constructors?

There are three fundamental types of constructors: 

• No Argument Constructor
• Default Constructor
• Parameterized Constructor

3. Why are multiple inheritances not possible in Java?

Java doesn’t support multiple inheritances because it can create ambiguity. Java also does not support multiple inheritances for simplicity and to reduce complexity in the language itself.

A class, such as B, can extend more than one parent class—such as A and C. This is called multiple inheritances. The diamond problem occurs when a subclass inherits from more than one parent class (A & B)

4. How does a HashMap work?

The HashMap class in the Java programming language uses multiple hashing techniques to store entries. First, its static inner class creates buckets using a number of different methods; then, each bucket points to a Singly Linked List that contains the actual entry objects.
In Java 8, HashMap uses a binary tree to store its data—instead of LinkedList—when the number of elements in each bucket reaches a certain threshold. HashMap allows multiple null values and, at most, one null key; however, only one entry per hash can be non-null

5. What are the different types of exceptions in Java?

Java has two main types of exceptions: checked and unchecked exceptions. When a checked exception occurs, the program can handle it by utilizing a try-catch block or rethrowing the exception. An unchecked exception does not have to be handled; its occurrence is expected during the normal operation of some code (for example, IOException).

6. What is a design pattern?

Design patterns are general reusable solutions to common concerns in software design. These patterns describe the interactions and relationships between objects or classes, and they often show different ways of solving those problems by implementing similar features in varying contexts.

7. What is a lambda expression?

Lambda expressions were introduced into Java as a way to support functional programming. A lambda expression consists of multiple formal parameters, separated by commas and enclosed in parentheses.

8. What is the role of JVM?

The Java Virtual Machine (JVM) offers a runtime environment for executing Java programs. It translates the compiled code from a high-level language called “Java” into instructions that the computer can understand and execute.

9. Explain the compilation process of Java source code.

A Java source code undergoes many stages of analysis: lexical analyzer, syntax analyzer, semantic analyzer and bytecode generator.

10. What is the structure of JVM?

The JVM is made up of three primary subsystems:

• The Class Loader
• Runtime Data areas
• Execution Engine

11. Briefly talk about communication methods between threads in Java.

• Shared memory: volatile keyword, synchronized keyword
• Wait and notify mechanism: The wait() and notify() methods enable Java programs to interact with other threads. When one thread calls wait(), its execution stops until another thread calls notify().
• Lock/Condition mechanism: The Lock class in Java limits access to an object. The Condition interface defines methods that are invoked by a lock and which place threads in sleep or wake them up—we use these when we want thread 1 (the server) to wait while another thread (a client) performs some work before continuing on.

12. What are volatile and synchronized keywords?

• A volatile keyword applies to a field or variable such that even with multiple threads accessing the same variable, data consistency is ensured by forcing main memory instead of cache memory.
• A synchronized keyword is typically used for a block of code, which enables only one thread to access the resources at any given point in time. When one thread manipulates these resources, others are not allowed to execute until they regain control (this happens when “synchronized” goes out of scope or when another object calls wait() on it).

13. What is the reflection in Java?

Java is a statically-typed language. Reflection in Java enables the program to examine itself at runtime, making it more dynamic. It enables developers to examine or modify the behavior of methods, classes or interfaces during execution time.

Python

1. What is PEP 8, and why is it important?

PEP stands for Python Enhancement Proposal. A PEP is a formal document describing new features or processes of the language to members of its community.

PEP 8 documents the style guidelines for Python code, and violating them can handicap your credibility as a contributor to the open-source community.

2. What is Scope in Python?

In Python, every object is part of some scope. A scope defines a block of code where an object can be used without qualifying it with the module name or other variables defined in that block. However, namespaces also have their own scopes—where you would use them without prefixes.
Examples of scopes created within the process of running Python code are as follows:

• Local scope refers to the local variables and functions that are visible within a given function.
• Global scope applies to all objects that have been created during the execution of a program.
• A module-level scope refers to the global objects of a particular module.
• In Python, the outermost scope refers to all names that can be called in the program. These objects are searched last when a name is referenced.

3. What are lists and tuples? What is the primary difference between the two?

Lists are immutable objects that can be added to and accessed in a very explicit manner. Tuples, on the other hand, are mutable objects that can be modified by their contents and accessed as they are. Lists and tuples have similar semantics, but they’re implemented differently. So let’s take a look at how they differ!

4. What is Pass in Python?

The pass keyword is used in Python to signify a null operation. It fills up empty blocks of code that might execute during runtime but has yet to be written.

5. What are modules and packages in Python?

In Python, a module represents a file with the .py extension that contains functions and classes. Modules can be imported into other files by using the import statement to load their contents into memory. Through the use of packages, you can organize your modules into hierarchies using dot notation.

As modules help avoid collisions among global variable names, packages also provide a way to prevent name clashes among modules. The system can use a folder’s inherent file structure to create packages, so all you have to do is stuff modules into the folder and rename it. To import a module or its contents from this package, prefix the name of that module with your package’s name and join them by dots.

6. Explain global, protected and private attributes in Python.

Global variables are public and can be used anywhere in our code. We use the “global” keyword to let Python know that we want a variable to be accessible from other scopes as well.

Protected attributes are defined with an underscore prefixed to their identifier, for example, _sara. However, they can still be accessed and altered from outside the defined class —but it is irresponsible of a developer to do so.
Private attributes, identified by a leading double underscore (e.g. __ansh), may not be accessed or modified directly from outside of the class that defines them and will raise an AttributeError if such attempts are made.

7. What is the importance of Self in Python?

In Python, a self-variable is used to point to your current function. This is a very important concept in programming. It allows you to access any variables that were defined in the current scope.

How to ace your backend engineer interview?

When preparing for a backend technical interview, remember that the most vital elements interviewers look for are your specific work style, creativity, work philosophy, and whether you have a preference to work within a team or independently.

Here is how you can ace your backend developer interview and set yourself apart from other candidates:

• Have a precise and comprehensive knowledge of the basics of backend development and go through the critical concepts before the interview.
• Pair your interview questions with good examples, presenting the concepts with a use case.
• Consider the implementations which you have done in the project.
• Keep the answer simple. While answering, your delivery and communication skills also carry weight. Remember making the answer exciting and interesting will help engage your interviewer.
• Here is another useful tip. You can subtly and smartly lead the interviewer to the next interview question based on your response.

One of the most suitable ways to prepare for an interview is by reviewing previous questions and practicing answering them. This will help you identify patterns in what kinds of questions are asked – even ace those tricky ones!

The post Backend Developer Interview Questions And Answers Series – Part 2 appeared first on Talent500 blog.

What Is Python?

Python is an interpreted, high-level generic programming language that assists programmers in writing manageable and logical code for both small and large-scale projects.

The benefits of Python:

•             The Edit-Test-Debug cycle runs quickly since there is no compilation
•             Debugging is easy
•             Extensive support library
•             Easy learning data-structure
•             High productivity
•             Team collaboration

Working In Python

• The interpreter reads and evaluates the Python code in the source file for syntax errors.
• If the code is error-free, the interpreter translates it to its ‘Byte code’ counterpart.
• The byte code is then sent to the Python Virtual Machine (PVM), where it is compiled again for errors if any exist.

What is Python Testing

• Automated testing is a well-known context in the testing field. It is when the test plans are conducted utilizing a script rather than a human.
• Python includes tools and modules for automated testing of your system.
• Python test cases are quite simple to develop. Python-based test automation frameworks are becoming more popular as the language gains popularity.

Overview of Python testing framework

The Python testing framework is a dynamic test automation framework created with the Python programming language, which is popular for web development and test automation. Because Python is an open-source programming language, several frameworks are built on it, making it difficult for testers to decide which test automation framework to choose among the many possibilities available.

List Of Python Testing Frameworks

Below are some Python Testing frameworks listed that you should know.

1. Robot
2. PyTest
3. Unittest
4. DocTest
5. Nose2
6.   Testify

1) Robot

• The most popular Robot Framework is an open-source Python-based Automation Testing framework.,
• Built fully in Python and used for Acceptance Testing and Test-driven Development. In the Robot framework, test cases are written in the keyword format.
• The Robot can execute Java and.Net and also offers cross-platform automation testing for desktop apps, mobile applications, online applications, and so on.
• Robot is utilized for Robotic Process Automation in addition to Acceptance Testing (RPA).
• Pip (Python Package Installer) is strongly recommended for Robot installation.
• Some of Robot’s strong characteristics that make it popular among testers are the usage of tabular data syntax, keyword-driven testing, a comprehensive library and tool set, and parallel testing.

Example:

Settings

Library: SeleniumLibrary

 Variables

${SERVER}         localhost:7272

${BROWSER}        Firefox

${DELAY}        0

${VALID USER} demo

${VALID PASSWORD} mode

${LOGIN URL}  http://${SERVER}/

${WELCOME URL} http://${SERVER}/welcome.html

${ERROR URL}  http://${SERVER}/error.html

Open Browser To Login Page

    Open Browser    ${LOGIN URL} ${BROWSER}

    Maximize Browser Window

    Set Selenium Speed ${DELAY}

Login Page Should Be Open

    Title Should Be Login Page

Go To Login Page

    Go To ${LOGIN URL}

    Login Page Should Be Open

Input Username

    [Arguments]    ${username}

    Input Text    username_field ${username}

Input Password

    [Arguments]    ${password}

    Input Text    password_field ${password}

Submit Credentials

    Click Button login_button

Welcome Page Should Be Open

    Location Should Be ${WELCOME URL}

    Title Should Be Welcome Page

Sample of Failed Test Execution

A sample of Successful Test Execution

2) PyTest

• PyTest is a free and open-source Python-based testing framework that is ideal for functional and API testing.
• Pip (Python Package Installer) is required for PyTest installation.
• It allows you to test APIs, databases, and user interfaces using simple or complicated text code.
• Simple syntax facilitates test execution.
• Has a large number of plugins and can run tests in parallel. • Can execute any specified group of tests.

Example:
import pytest                                //Import unittest module//

def test_file1_method():               //Function inside class//

      x=5

      y=6

      assert x+1 == y,”test failed”

Use the py.test command to run the test.

Use the below command If you want to access a test written in a specific file.

py.test <filename>

Define PyTest fixture as shown below.

@pytest.fixture

Example

def test_string_equal():

assert double(55) == 62

assert 25 == 62

+  where 25 = double(55)

3) Unittest

•             Unittest is the first Python-based automated unit test framework built to operate with the Python standard library.
•             Encourages the reuse of test suits and the organizing of tests.
•             It was inspired by JUnit and provides test automation such as test collections, test independence, test setup code, and so on.
•             It is also known as PyUnit.
•             Unittest2 is a backport of further new Unittest capabilities.

Workflow of Unittest:

•             In the program code, include the Unittest module.
•           The option of creating your own class.
•             Create functions within the Class you’ve established.
•             To execute the test case, place unittest.main(), which is the main method, near the bottom of the code.

Example:

import unittest                                 //Import unittest module//

def add(x,y):

   return x + y

 class Test(unittest.TestCase):          //Define your class with testcase//

   def addition(self):

      self.assertEquals(add(4,5),9)<strong>//Function inside class//

if __name__ == ‘__main__’:

   unittest.main()<strong>//Insert main() method//

4) DocTest

•             Doctest is a module provided in Python’s standard distribution that is used for White-box Unit Testing.
•             It looks for interactive Python sessions to see whether they are functioning properly.
•             It makes use of certain Python features including docstrings, the Python interactive shell, and Python introspection (determining properties of objects at runtime).
•             The functions testfile() and testmod() are used to provide basic interface.

Core Functions:

•             Updating docstring
•             Performing Regression Testing

Example:

Use the testfile () function to check interactive examples in the text file.

Run the test directly from the command line with;

5) Nose2

•             Nose2 is the successor to Nose and is a Python-based Unit Testing framework capable of running Doctests and UnitTests.
•             Nose2 is built using unittest. It is known as extending unittest or unittest with the plugin, and it was created to make testing simpler and easier.
•             Nose employs collective tests from unittest. Testcase and provides a variety of tools for writing tests and exceptions.
•             Nose allows package fixtures, classes, modules, and complicated initialization to be declared once rather than repeatedly.

Example:

from mynum import *

import nose

def test_add_integers():

    assert add(5, 3) == 8

def test_add_floats():

    assert add(1.5, 2.5) == 4

def test_add_strings():

    nose.tools.assert_raises(AssertionError, add, ‘paul’, ‘carol’)

// To throw one of the expected exception to pass

 if __name__ == ‘__main__’:  

    nose.run()

6) Testify

•             Testify was created to take the role of unittest and nose. Testify provides more features than unittest.
•             Testify is well-known as a Java semantic testing implementation (Easy to learn and implement software testing specification).
•             It is simpler to testify while performing automated unit, integration, and system testing.

 Features

•             Simple syntax to fixture method.
•             Improvised test discovery.
•             Class-level setup and teardown fixture method.
•             Extensible plugin system.
•             Easy to handle testing utilities.

Example:
 

from testify import *

 class AdditionTestCase(TestCase):

     @class_setup

    def init_the_variable(self):

        self.variable = 0

     @setup

    def increment_the_variable(self):

        self.variable += 1

    def test_the_variable(self):

        assert_equal(self.variable, 1)

     @suite(‘disabled’, reason=’ticket #123, not equal to 2 places’)

    def test_broken(self):

        # raises ‘AssertionError: 1 !~= 1.01’

        assert_almost_equal(1, 1.01, threshold=2)

     @teardown

    def decrement_the_variable(self):

        self.variable -= 1

     @class_teardown

    def get_rid_of_the_variable(self):

        self.variable = None

 if __name__ == “__main__”:

run()

There are additional lists of frameworks that may become popular in the future.

7) Behave

•             Behave is a test framework for BDD (Behavior Driven Development) that is also used for Black box testing. Behave writes tests in plain language and works with Unicode Strings.
•             The Behave category contains feature files in plain text format that resemble natural language and Python step implementations.

8) Lettuce

• Beneficial for testing Behavior Driven Development. It simplifies and scales the testing process.
•     Lettuce steps:

o   Describing behavior

o   Steps definition in Python.

o   Running the code

o   Modifying code to pass the test.

o   Running the modified code.

These processes are repeated three or four times to ensure that the program is error-free and hence of high quality.

The post Python Testing Framework appeared first on Talent500 blog.

In fact, the data analytics and scientific programming realm readily embraced Python which is, to date, the most notable achievement. 

Apart from this, it also demonstrated spectacular evolution with the web. From the primitive era of Web 1.0 to the groundbreaking advancements in Web2.0 featuring the REST architecture for defining API, several Python-based web development frameworks were conceptualized. 

So what exactly are these APIs? Let’s find out!

To understand python API, first, let’s discuss in brief what an API is.

Explaining API

API in the engineering realm, an acronym for Application Programming Interface, is a set of protocols shared by a particular service. 

APIs allow a product, service or database to communicate with other interfaces without having to know their implementation intricacies. APIs can, therefore, significantly streamline the app development process.

Simplified app development leads to considerable money, resource and time savings. Whether you’re building a new tool from scratch or managing already existing ones—APIs provides you incredible flexibility, design simplification and easy usage. Thus developers enjoy broader opportunities for bringing innovation.

So how did Python and API become a winning combo? 

From a Python perspective, you’ll generally make a request for retrieving information or data. As A response to this request, the API returns a response with your requested details. This process is known as API calling. 

To work with APIs in Python programming, you’ll need a set of tools to make those requests. Therefore, Python developers should create APIs using various frameworks to connect to other services, platforms or products and establish a streamlined workflow. 

As discussed previously, Python is an incredibly versatile programming language. And therefore, it’s well-suited to tackle the intricacies of API development. 

In fact, developers prefer using Python language for API development due to its extensively supportive library, user-friendliness and simple syntax. 

This article will discuss the top 10 API frameworks that developers can use for Python-based software development. 

Top 10 Python API Framework For Web Development

The most popular Python API frameworks include: 

Django

Django is one of the most popular, open-source and free Python API frameworks. With Django API Framework, developers can easily and quickly develop intricate codebases and high-performing applications. 

Django Framework is the foundation of more than 12000 successful projects. Additionally, it is one of the earliest web development Python frameworks. 

The most striking features of Django Framework are its extensive library, less requirement for coding and reusable elements. 

The eminent features of Django API Framework include:

• Helps in defining application URL patterns. 
• Integrated authentication support 
• Supports URL routing with a powerful system
• Prebuilt libraries empower full-stack development.
• Developers can enjoy one of the most secure data storage and recovery with this object-oriented programming language database.
• The automatic t500_prod_admin interface enables the editing and deleting functionality with flexibility for customization.
• Follows MVC-MVT architecture.
• Cache framework for supporting multiple cache mechanisms.

Pyramid

Pyramid is well known among developers for its immense versatility. From complex web applications to simple software, you can develop almost everything using the Pyramid framework. 

Pyramid presents the best blend of micro-framework and full-stack python API development framework. You can start developing your projects with small and simple models and advance to complicated ones as you progress through the pipeline. 

Additionally, Pyramid also offers extensive support documentation. 

The eminent features of Pyramid API Framework include:

• Performs exceptionally well on both simple and large apps
• URL mapping powered by URL dispatch and WebHelpers
• Validation and generation of HTML structure
• WSGI framework is supported by MVC architecture.
• All-embracing templating and asset details
• Flexible yet secure authentication and approval

Bottle

Searching for a Framework for small app development and Python API building? A bottle is a perfect choice for you. It is one of the most extensive Python API web development frameworks. 

The primary reason for its popularity is that it doesn’t require third-party tools and libraries to support web development. Instead, it leverages the Standard Python Library, allowing developers to work just with simple hardware. 

Previously Bottle supported Python 2.5-2.7 and 3.0. However, the recent version release dropped support for older Python versions 2.5 and 2.6. 

The versatile platform availability allows developers to perform cross-platform development with Bottle. 

Features of bottle API Framework include:

• Allows Request-dispatching route
• Speed optimizations supporting testing and high-performance. 
• Quick and pythonic integrated template engine and backing.
• Integrated HTTP server
• Plug-ins available for various databases
• Allows Third-party template engines and WSGI/HTTP servers
• Seamless and dynamic URL-mapping powered by simplified syntax.
• Allows easy access to data, file uploads, cookies, and other HTTP-related metadata.

TurboGears

TurboGears allows streamlined Python website development using versatile JavaScript development tools. 

Developers can create highly functional web applications using various tools such as SQLAlchemy, Repoze, Genshi, etc., making the development faster compared to other similar frameworks. 

Furthermore, Turbogears supports several databases and web servers such as Pylons. The framework is based on an MVC (Model-View-Controller) design. 

Leading Enterprises using Turbo wear for API and web development include ShowMeDo and SourceForge.

Eminent features of TurboGear

• MVC-design architecture and PasteScript templates.
• Multi-database support.
• Easy to access command-line tools.
• Built-in MochiKit JavaScript library.
• Simplified coordination of frontend design and server deployment powered by ToscaWidgets 

Flask

Flask is one of the most eminent, lightweight WSGI micro-framework supporting full-stack web development in Python programming. 

The learning curve is simple. And the developers can create efficient and highly scalable web applications quickly. Apart from that, Flask also has efficient features for integrated unit testing and secured cookies support. 

The best part? Flask framework isn’t restricted to a particular library or tool extension. You get the flexibility to utilize any Python libraries and tools.

Eminent Features Of Flask Framework

• Google App Engine compatible
• Integrated debugger 
• In-built Unit testing
• Client-Side work cookie support
• Unicode based
• jinja2 template and Werkzeug WSGI toolkit
• HTTP request handling 

Cherry Py

This 10-year-old Python API framework has emerged as one of the quickest and most stable web development frameworks. CherryPy is an open-source Python web development framework embedded with its own multi-hung server. Therefore, it can be easily used on any functional framework that supports Python.

Since CherryPy supports web development similar to object-oriented Python programming, smaller source development can be done in less time.

Eminent features of CherryPy

• Integrated tools support static content, encoding, caching, authentication, etc
• Easily supports versatile HTTP servers at once
• Integrated support for profiling, coverage, and testing.
• Runs efficiently on various Python versions, including 2.7+, 3.5+, PyPy, Jython and Android
• A robust configuration framework for both developers and deployers 

Grok

Grok is an open-source framework supported by Zope toolkit technology. Developers can enjoy an agile development experience. The framework focuses on two fundamental principles: 

• Convention over configuration 
• DRY (Don’t Repeat Yourself). 

Grok enables rapid application development processes.

Eminent features of Grok framework:

• Provides a robust foundation to developers for creating powerful, highly-functional and extensible web applications
• A powerful object database for storage
• Web developers can leverage the powerful benefits of Zope 3
• Integrated security allows only secured users to access your application 
• With Grok component architecture, developers can minimize the unpredictability of web development
• Powers custom business web application development using various building blocks and other essential assets. 

Web2py 

Web2py represents another highly-functional, open-source and scalable full-stack Python development API framework.

This API Framework supports almost every operating system, including mobile interfaces. This web-based, feature-enriched framework encompasses powerful functionalities such as a code editor, single-click deployment and much more. 

The only drawback of this API framework is that it doesn’t support Python 3 version.

Based on model view controller design, Web2py facilitates rapid development. 

Eminent features of Web2py include:

• Enables readability of multiple protocols
• Web-based structure and platform independent framework
• Backward compatibility
• Enables role-based access controls
• Installation or configuration don’t require any pre-requirements. 

Falcon

This micro-framework is popular in the industry for developing Web APIs. Developers can design minimal, easy and simple designs for HTTP and REST architectures using Falcon. Falcon powers some prominent industry leaders, including LinkedIn and OpenStack. 

Eminent features of the Falcon framework include:

• WSGI Support
• Complete code coverage
• Developers can easily access headers and bodies without any inconvenience. 
• Python support with impeccable speed
• Upfront exception handling 
• Extensible and highly-optimized codebase 

Tornado

Tornado is a Python API development framework presenting an offbeat framework library. Given the non-blocking framework I/O and powerful capability to deal with 10,000+ simultaneous connections, it has become an extraordinary tool for developing web applications that need a huge number of simultaneous clients.

Eminent Features of Tornado include:

• Enables easy implementation of 3rd-party authentication and authorization.
• It offers superior-quality output.
• User authentication support and Web templates.

Wrapping Up

APIs are rapidly becoming popular in the development field. They have now become the cornerstone of modem web development. Choose the best Python API framework for your business, depending on your unique development project requirements.

You can also learn creating your own APIs here.

The post Top 10 Python APIs and How to Create Your Own in 2023 appeared first on Talent500 blog.

In this article, we will discuss the most widely used Python backend libraries. So, without further ado, let us get started.

Fun Fact 1: Python is an ocean of libraries.

Yes, Python has excellent libraries that you can use in your software development journey. Python has a great community that constantly develops libraries and makes changes to them. Libraries with a specific focus are also available like nltk for natural language processing, scikit-learn for machine learning applications, or Django for backend applications.

Let’s discuss the top 5 Python frameworks to go for.

Fun Fact 2: Being open-source is awesome. Python libraries listed in this article are completely open-source. It means you can go through their code on GitHub to understand how these libraries are built!

• 
  

  Django

  
  

Django is truly the best Python library for development. It’s written purely in Python and it’s fast and secure. You can create web applications within a matter of a few hours. It’s fully loaded with all the required tools to create websites. 

Django follows the Model View Template Architecture. Django is based on the DRY (Don’t Repeat Yourself) Principle. It’s always better to follow modular design patterns to develop scalable applications.

The Django framework officially supports four databases, namely PostgreSQL, MySQL, SQLite, and Oracle. YouTube, DropBox, Instagram, and Pinterest all these tech giants rely on Django. Django is mainly known to reuse lots of common code and design patterns. 

Django has also improved with each release with new functionalities and bug fixes. 

GitHub : https://github.com/django/django

Django GitHub page has earned 66.4k stars till now and has 2,272 contributors. Read the official document of Django here.

• 
  

  Scrapy

  
  

Scrapy is a free and open-source web crawling framework written in Python. It was originally designed to perform web scraping but can also be used for extracting the data using APIs. 

Scrapy can handle bigger tasks with ease when it comes to downloading webpages or scraping groups of URLs within a minute. Scrapy supports concurrency as it is asynchronous so it can load multiple websites in parallel. It supports different data formats such as CSV, JSON, and XML.

GitHub :  https://github.com/scrapy/scrapy

An open-source project with 44.6k stars and is used by 31.2k+ users. Check out the Scrapy homepage for more information.

• 
  

  Flask

  
  

Flask is a lightweight web application framework written in Python that provides different tools that make creating web apps easier. Flask is known as the WGI framework which stands for Web Server Gateway Interface. This is a way for web servers to pass requests to web applications or frameworks. Flask has strong community support from collaborators and learners. And it is open-source as well.

Can you imagine that you can build a website using a single Python file? It is as simple as that! You just need to learn how to use their tools to make web apps quickly.

GitHub:  https://github.com/pallets/flask

A cool open-source library with 60.6k stars and strong support of 1.2M+ users, 681+ contributors trying to make it more usable. To explore their documentation, click here.

• 
  

  Web2Py

  
  

Web2Py is a scalable, open-source, and full-stack Python framework. Web2Py comes with its own web-based IDE. Other things include a separate debugger, and one-click deployment. It has the ability to run on different platforms, i.e it’s a cross-platform framework. 

It also provides us with data security that prevents vulnerabilities like cross-site scripting, injection flaws, and malicious file execution on devices. Web2Py has its own Error Tracking System. It gives us Role Based Access Control.

GitHub:  https://github.com/web2py/web2py

Visit this link to check out Web2Py documentation. 

• 
  

  CherryPy

  
  

CherryPy is an open-source minimalist web framework that makes Python web applications no different than building any other object-oriented program. CherryPy can handle sessions, cookies, file uploads, statics, or any other typical web app feature. It also supports cross-platform functionality. 

CherryPy can run multiple HTTP web servers at a time. It consists of tools for authentication, sessions, caching, and encoding. It has built-in support for coverage and testing. 

GitHub: https://github.com/cherrypy/cherrypy

Follow this link to get started with CherryPy.

So these are some powerful and widely used Python Frameworks by Web Developers to make their life easy. 

Fun Fact 3: Wanna be an open-source contributor by helping the community? Yes, you can!

As I mentioned, all these libraries are open-source. You can read the code and if you want to contribute to the above libraries, make sure to join their communication channels and go through their READ MEs. Happy learning!

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Programming languages are a vital component of application development. Just like a carpenter, mechanic, architect, etc., have their own specialized toolset, programming languages are essential tool sets for software developers. 

However, with advancements in the software development paradigm, several programming languages are becoming popular. Which programming language should you choose? Answering this question can be an overwhelming task. 

This blog will discuss the two most popular programming languages: Python and Java. 

The Programming Languages Wearing The Crown

In the software development world, the scope, importance, and scalability of Java and Python require no formal introduction. 

However, there is an ongoing battle between Python and Java to understand which one is superior. While Python is relatively new in the application engineering domain, it has made rapid progress in the past few years. On the other hand, Java is an old player and retains its glorious position.  

To give a quick introduction, Java is a compiled language. Therefore it is a relatively faster and more efficient programming language when compared to Python. Python’s USP is that it has simpler, more concise, and easy-to-understand syntax than Java. It helps developers to design the same functionality using fewer lines of code than Java. 

Java vs. Python: A Comparative Analysis

Performance

Bytecode compilation is a similarity between the two programming languages : Java and Python. Therefore, both languages support cross-platform development without any difference between operating systems. 

Although both programming languages are used for cross-platform development, there are certain differences between the performance of these two programming languages. 

• Java features a static-typing syntax. Static typing syntax enables a simpler and faster compilation when compared to dynamically typed syntax. Therefore the developed software product has less susceptibility to bugs or errors and delivers smooth performance. 
• Another winning point for Java is that it is integrated with a Just-in-time (JIT) compiler. With the support of this compiler, the bytecode is directly bundled into the native machine, allowing direct calling of the compiled code.  
• Such advanced functionality is the primary reason for the agility and efficiency of this programming language.

However, Java has one drawback. The codes are generally long, therefore, sometimes tough to understand. 

• Python uses dynamic type syntax, which isn’t as compatible to align with various platforms. 
• Additionally, Python supports runtime testing of the codebase. Any complication with the program or codebase may require holding the entire application. 
• All of these variables in the Python programming language lead to reduced speed and efficiency. 

According to the TIOBE Index, August 2021, Java was stealing the show as one of the top-performing programming languages. And Python was in 3rd position. However, in 2022, Python gained immense popularity and jumped to the first position in the TIOBE Index, 2022. 

Python now enjoys the highest market share with a greater than 15.42 % rating. Java, on the other hand, moved to third position after C and received a rating of 12.40 %. The popularity of Python is mainly because it requires much less coding to perform the same function as Java. 

Syntax

• As discussed previously, Python is a dynamically-typed software development language. It doesn’t require stating specific variable types because checks are made during the runtime. This leads to a simpler syntax with a close resemblance to the English language. 
• Additionally, you don’t need to use enclosing braces in Python. The language strictly follows the indentation rules. Therefore the overall code becomes easy to read and understand and beginner-friendly.
• With Java, you’ll need to comply with strict syntax rules. The language is statically-typed. Here declaring the variable types is specifically necessary. In the case of any abnormality, the code will not compile.
• Although the statically-typed language is not beginner-friendly, many developers enjoy the comfort of clarity. It can be difficult to follow indentation rules in Python for large codebases. 

Machine Learning

With the advancement in technology, machine learning has become an essential element of programming languages. With machine learning, developers and analysts derive important insights through predictions using appropriate data sets. 

Implementing machine learning algorithms can sometimes be tricky and challenging because it requires certain practical and theoretical expertise. 

• As we have mentioned before, from the syntax perspective, Python is relatively easy. Therefore developers from various backgrounds choose Python as their preferred machine learning implementation language. 
• Python gives the flexibility to experiment with machine learning and utilize it in various application development specialties/domains. The features of Python-powered adaptation and implementation of machine learning open a wide array of opportunities for organizations. 
• The Python Library is vast and allows easy access for further development. 
• However, Java has its unique set of capabilities for supporting machine learning implementation. Easy debugging and usage makes Java suitable for large-scale operations and developing enterprise-level software. 
• Some common libraries used by developers in Java include Weka, Mallet, Deeplearning4j, and MOA.

When it comes to machine learning implementation, some software engineering individuals consider it outdated. This is mainly because of its complex syntax. However, it is still widely popular as compared to other programming languages. 

From the machine learning perspective, Python offers greater functionality to the developers and therefore enjoys more credibility. 

Web Development

Backend web development is concerned with writing server-side code. Developing your own backend technology is not just extremely challenging but also time-consuming. You need to focus on every aspect, starting from security to reliability and performance. 

To make backend development easy, developers crafted frameworks that enable backend web development without reinventing the wheel.

• In Python, the popular frameworks for backend development include Django and Flask.
• Flask is a simple micro web framework. It offers you the fundamental functionalities you’d require for software development, such as routing requests, without much overhead. 
• Django, on the other hand, is a more feature-enriched framework. It allows the development of a powerful backend web application while capitalizing on product efficiency and security.
• Django features a powerful ORM layer that enables smooth interactions with databases and performing various operations on the data.

Spring is one of the widely-popular backend web development frameworks in Java. The best part is that it’s supported by an incredible ecosystem and a massive community. Leading enterprises such as Dell, GE, etc., leverage Spring framework for Java-based backend web development. 

While Spring might not be as popular as Django in the current market, it is still widely used for developing enterprise-level applications. 

Use Cases

Both Python and Java are used widely for developing various types of software and applications. When should you use each of these programming languages for different programs or product development?

When To Use Java?

Java is suitable for the following development: 

• Highly functional and efficient mobile applications 
• Desktop GUI Apps
• Middleware Products 
• Enterprise Software Solutions and Embedded Systems 

When To Use Python?

Python, on the other hand, offers an exceptional solution for the following developments:

• Game development
• Language development 
• Prototyping  
• Image processing and creative or graphic designing 
• Machine Learning Applications
• Operating System frameworks

Benefits: Python Vs. Java

We have compiled a concise list of benefits for Java programming language and Python.

Java

• Safe and secured: Java doesn’t rely on explicit references. It operates in a virtual server known as a ‘sandbox.’ The virtual server leverages a bytecode verifier to detect unlawful programs. It offers additional security to the entire Java package. 
• Object-oriented: Java supports a more logical approach for large, enterprise-level project development. Using Java, you can develop categories that encompass data, entities, and methods. Apart from that, it also allows code reusability, easy adaptability, and simple troubleshooting. 
• Multi-threading: Java allows the simultaneous execution of several threads. To enhance efficiency and speed during multiple-thread execution, they function on shared memory space.

Python

• Empowers advancements: The dynamic-typing functionality makes Python a user-friendly programming language. Besides, the parallel programming enables independent code operation to address issues and bottlenecks more seamlessly.
• Greater Flexibility: Python supports a wide array of programming concepts, allowing a more flexible and efficient strategy for code development and application engineering. This makes it ideal for start-ups and small enterprises that might require to quickly switch strategies for scalability. It is also great for engineers who want flexibility.
• Enhanced efficiency: The dynamic typing and simple syntax make Python more productive. The easy access system and control functionalities are added benefits. 
• Comprehensive libraries: The comprehensive libraries are capable of handling numerous common operations and commands. Plus, Python has code for performing innumerable tasks, such as developing documentation and CGI.

Java Vs. Python: Comparison At A Glance

The following table summarizes the key differences between Java and Python.

Frequently Asked Questions

Which is Superior- Java or Python?

Both programming languages have their unique set of benefits and disadvantages. Both have different practical applications. So it ultimately depends on what you desire to develop and your development goals.

Is Python Easier than Java?

Python is definitely beginner-friendly since it has a simpler codebase. One of the vital reasons that make Python easy is the readable, English-like syntax. Java, on the other hand, has a steeper learning curve.

Which is faster- Java or Python?

Java is powered by a JIT compiler giving it better performance than Python. 

Should I Choose Python or Java for Backend Development?

Python can be learned and understood easily. Therefore, you can get up and running on backend web development comparatively quickly. However, if you’re looking for long-term performance in complex applications, Java is the ideal choice. 

Which Pays More- Java or Python?

The median salary range for both Python and Java developers is approximately the same. Python developers’ average salary is approximately a median of $97,000, while Java developers get a median of $96,000 in the US.

Should I switch from Java to Python or vice-versa?

Currently, various opportunities are available for both Java and Python developers. Unless you have a specific goal or interest, switching isn’t necessary. 

Wrapping Up

Java and Python are both incredibly capable and extremely popular languages. Therefore, you will never find a lack of resources once you choose a particular language and embark on your development endeavor. Defining your goals and understanding what you want to achieve can help you select a particular language. Share your thoughts in the comment section. 

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Each programming language is well-established, platform-independent, and has a large supportive community. However, the similarities end here, and each language has its different use. This article covers the Python versus Java differences in detail. Let’s take a closer look at each language’s pros, cons, and scope.

Java: Introduction

Java has been around since 1995 and is one of the most popular programming languages for enterprise software development. As a general-purpose, object-oriented programming language, Java is compiled, unlike Python, which is why it’s faster.

Java code gets directly converted into machine code without needing a compiler, making it quick and efficient. It also gives better control over hardware aspects like CPU usage. For years, Java has been widely used for big data, web development, and android app development, but now it is also gaining traction in the cloud and the Internet of Things development. NASA, Google, and Facebook are some companies that build their products using Java.

Python: Introduction 

Guido van Rossum created Python in 1991. His focus was on creating a more readable programming language. This simplicity of learning is one of the driving forces behind the popularity of Python. Due to its more concise syntax, it is easier to code and test programs in Python than in Java. It supports a variety of programming styles giving developers the freedom to follow their own choice. This is why Python is suitable not only for object-oriented programming but also for reflective and functional programming.

Python is highly popular among backend and app developers and is the preferred choice for data science and machine learning applications. The availability of extensive libraries like Scikit-learn and Pandas is why it is used in some of the most significant applications such as Google Search and YouTube.

Key Differences between Java and Python 

1. Speed

Speed and efficiency are the two biggest reasons to use Java for application development. As many of the processes of Java run automatically, you don’t have to micromanage how everything works. It is a high-level language and uses automatic memory allocation.

Because of its dynamically typed and interpreted nature, Python executes the code line by line, making it slow. The extra work this programming language does when running code makes it less suitable for applications requiring speed and performance.

2. Versatility 

It is challenging to match Python’s versatility for Java. Java indeed finds more adoption for enterprise application development, but Python offers so much flexibility that it encourages open experimentation. Programmers love Python because it allows them the freedom to build more versatile applications. Python is the most suitable language for you if you want to do many things without worrying about speed. According to Computer Weekly, “Although there might be a better solution for any given problem, Python will always get the job done well.”

3. Syntax

Java is a strictly typed language. Developers have to explicitly declare variable types, which is not easy for beginners. The statically typed nature of Java makes it difficult to learn. Here is an example of Java code syntax:

public class Fruit {

 String name;

 String color;

 public Fruit(String name, String color){

 this.color=color;

 this.name=name;

 }

 public void myfunction()

 {

 System.out.println(“Hello I’m a :” +name);

 }

 public void mycolor()

 {

 System.out.println(“Hello my color is:” + color);

 }

On the other hand, Python is a dynamically typed language that does not require explicitly declaring variables. The variables are interpreted at the run time. This freedom makes Python syntax more like the English language. Here is the example of Python syntax for the same class we defined above in Java:

class Fruit:

 def_init_(mysillyobject, name, color);

 mysillyobject.name=name

 mysillyobject.color=color

 def myfunction(abc) :

 print(“hello I’m a “+ abc.name)

 def mycolor(abc) :

 print(“hello my Color is ” + abc.color)

p1 = Fruit (“Apple”, “red”)

p1.myfunction()

Conclusion 

Java and Python are capable and popular programming languages. Whichever programming language you choose, there are enough tools and community support available to help you along your journey.

As a beginner, your focus should be on learning and building things as soon as possible. For this, Python is the most suitable programming language because of its easy-to-follow syntax and availability of extensive libraries. However, if your end goal is to build enterprise-level applications, Java offers scalability, security, and speed.

Are you a Python or Java developer looking for career-redefining job opportunities? Talent500 is a remote team-building platform for start-ups and Fortune 500 companies. Join our elite pool of talent and get discovered by the best employers. Sign up here.

The post Java v/s Python: Key differences appeared first on Talent500 blog.

This article will list the essential backend developer skills that will be valuable in 2023.

Let’s get started.

1. Pick the correct backend programming language 

The web development domain is highly dynamic, with new technology introduced frequently. But, instead of getting distracted by the noise, focus on the programming languages with a solid presence. Java, Python, and PHP are three programming languages preferred for backend development.

Java is most widely used for building robust systems. It is a strictly typed programming language that’s scalable and general-purpose. A backend developer must be skilled in Java to build a solid infrastructure. As for PHP, it is the most commonly used server-side scripting language. As an open-source language, it is one of the most cost-effective technologies that most companies want to use for their projects. Python is another essential programming language for backend developers. It is a premier programming language that is simple to learn but robust enough for any complex project.

2. Knowledge of databases 

As proficient as you have to be with the programming languages for backend development, you also need strong database skills. A database is critical for storing data and content in any web application. Your ability to recover, arrange, alter and store information efficiently defines the performance of your application. Any backend developer should have extensive knowledge of various DBMS technologies.

In 2022, the two most commonly used databases are SQL and NoSQL. The SQL database is one in which all data is mapped on a table, and each entry in the table is linked to another entry through recursive methods. While SQL databases are suitable for storing queries and producing results, speed becomes a problem when creating highly scalable applications. The NoSQL database offers the solution as there is no need to structure the data beforehand. Such databases work on JSON (JavaScript Object Notation) and XML (Extensible Markup Language) and are extremely fast.

3. Knowledge of APIs

API stands for Application Programming Interface. It is a medium through which software can communicate and share data. In modern web applications, API plays a crucial role in driving mass adoption as the application can be used for multiple purposes.

Simply put, API is a set of rules and definitions that software, clients, and services follow to communicate with each other over the Internet. The server and client are the two receiving ends, while the API connects them both. For a backend developer, it is essential to understand the HTTP methods of API development. The most commonly used API commands are:

• GET: to retrieve a resource
• DELETE: to delete a resource
• PUT: to replace an existing resource
• POST: to create a new resource

Most employers expect backend developers to have extensive knowledge of APIs. To connect your backend with other services, you should know how to create an API in various programming languages such as Python, NodeJS, PHP, etc. JSON and XML are the two most recognized formats of API information generation.

4. Server infrastructure knowledge 

Server infrastructure is changing day by day. Today, cloud-native servers are a standard, and businesses no longer want to store and operate their applications through a central location. Since backend developers are working behind the scenes, they must understand various web servers and their technologies. The most commonly used servers are Apache, Nginx, IIS servers, and Microsoft IIS.

Some of the essential backend skills expected from a developer include the ability to manage a hosting environment. As a backend developer, you must be able to scale the applications according to changing demands and configure servers to handle the increased traffic. Accessibility and security compliance related to servers is also essential information. You should know version control among other technologies, such as Git and GitHub.

Another essential skill here is to familiarize yourself with cloud server infrastructures such as AWS, Google Cloud Platform, and Heroku. These leaders in the cloud server market are often used to deploy container-based services such as IaaS (Infrastructure as a Service), PaaS, and SaaS (Software as a Service).

Conclusion

Backend developers are in hot demand. To land your dream job in 2022, you must master the above-listed backend skills. Apart from these, you should also have strong computer science skills, such as data structures, algorithms, and programming patterns.

Talent 500 is a global remote team-building platform that Fortune 500 companies and fast-growing start-ups use to recruit backend developers. Join our elite talent pool to get discovered by some of the largest global companies. Sign up here.

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This article shares some tips and tricks for beginners and experienced programmers to write better Python code. The tips will help write more readable and faster code quickly.

1. Start using list comprehension instead of for loops 

For loops in Python code can cause unnecessary nesting that results in hard-to-read and maintain code. You can make your Python code much simpler with a list comprehension. It is a more straightforward and elegant way to create lists from existing lists. Using brackets, you can repeatedly execute each iteration with a single line of code. You can make your code time and space efficient by replacing loops and iterative statements with a list comprehension.

Here’s the usual syntax for list comprehension:

newList = [ expression(element) for element in oldList if condition ]

In practical applications, it is used like this:

# Loop iteration using list comprehension

List = [character for character in ‘HackerNoon’]

 # Displaying list

print(List)

# Output

# [‘H’, ‘a’, ‘c’, ‘k’, ‘e’, ‘r’, ‘N’, ‘o’, ‘o’, ‘n’]”

2. Using NotImplementedError for offensive programming

Python comes with an in-built NotImplementedError exception, a valuable feature for offensive programming. In offensive programming, developers avoid defensive principles when dealing with software bugs.

One example is when we control the input to align it with the format accepted by our code. It is not easy to sanitize every input, and it can be an inconvenience for future development.

Have a look at the code below in which we define a registering decorator and some functions:

import math

REGISTRY = {}

def register(name):

   def _decorator(fn):

     REGISTRY[name] = fn

     return fn

   return _decorator

@register(“relu”)

def rectified(x):

   return x if x > 0 else 0

@register(“sigmoid”)

def sigmoid(x):

   return 1/(1 + math.exp(-x))

def activate(x, funcname):

   if funcname not in REGISTRY:

     raise NotImplementedError(f”Function {funcname} is not implemented”)

   else:

     func = REGISTRY[funcname]

     return func(x)

print(activate(1.23, “relu”))

print(activate(1.23, “sigmoid”))

print(activate(1.23, “tanh”))

Here we used the NotImplementedError with a custom error message for the function activate(). The output of the code will be:

1.23

0.7738185742694538

Traceback (most recent call last):

  File “/Users/MLM/offensive.py”, line 28, in <module>  

print(activate(1.23, “tanh”))

  File “/Users/MLM/offensive.py”, line 21, in activate

   raise NotImplementedError(f”Function {funcname} is not implemented”)

NotImplementedError: Function tanh is not implemented

The result of the first two functions is printed, but the third function fails as we haven’t defined the tanh function yet.

This is an example of how you can use the NotImplementedError: in places in your code that still don’t meet the validity of the conditions but later on will have validation as the project proceeds.

3. Use generators to save memory 

In Python, the basic function of a generator is to evaluate the elements of a code on demand. As the syntax for list comprehension, here you use parentheses instead of square brackets. 

Here’s a code to generate the square of all even numbers in a list using generators: 

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print(“The given list is:”, myList)

mygen = (element ** 2 for element in myList if element % 2 == 0)

print(“Elements obtained from the generator are:”)

for ele in mygen:

   print(ele)

The output:

The given list is: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Elements obtained from the generator are:

4

16

36

64

100

Beginners might find it hard to decide how the list comprehension differs from generators, given that their syntax is almost identical. 

Unlike list or set comprehension, generator comprehension does not initialize any objects. This is how using a generator comprehension lowers the memory requirement for a program. 

4. Define default values in Dictionaries with .get() and .setdefault()

Beginners often struggle with providing a default value for the Dictionaries function in their Python code. You can forget about assignment default value with the use of .setdefault() and .get().

With the .setdefault() method, you can set dict[key]=default if there is no key for the dict function already. You set the .setdefault() like this:

dict.setdefault(key, default=None)

Example code:

a_dictionary = {“a”: 1, “b”: 2, “d”: 4}

a_dictionary.setdefault(“c”, 3)

print(a_dictionary)

The output of the above code would look like this:

{‘a’: 1, ‘b’: 2, ‘d’: 4, ‘c’: 3}

The same functionality can be achieved using the .get() method, but you pass a default value for the key here. Here’s how it works:

a_dictionary = {“a”: 1, “b”: 2, “d”: 4}

print(a_dictionary.get(“c”, 3))

print(a_dictionary)

The output of the above code:

3

{‘a’: 1, ‘b’: 2, ‘d’: 4}

Conclusion 

Python has scope in multiple fields, and as you learn the scope of the language, you will know more tricks to optimize your code.

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Companies offer incredible perks and benefits for senior Python developers who can showcase their skills.

This article will explore the senior Python developer career landscape which will help you to understand the role and requirements

Career prospects: Demand, Job Openings, and Salary 

Senior Python developers can find some of the best-paid jobs. Apart from development jobs, Python developers are in huge demand for roles that involve data analysis, data manipulation, and data visualization. There is much scope for senior Python developers in data analytics and data science. According to Statista, Python is the fourth most widely-used programming language for development. It is becoming increasingly popular for machine learning and artificial intelligence applications.

Some recommended backend developer resources:

In-demand skills for a back-end developer

How to land a high paying back-end developer job

We believe the best indicator of demand for a language is the number of job postings. We analyzed current job postings for senior Python developers on Indeed, Glassdoor, and Naukri. Over 70,000 job openings for the role on , Naukri followed by 5000+ requirements for seniors Python developers on Indeed, and over 3400 job openings on Glassdoor for the same position. There has been a substantial increase in demand for Python developers in the last couple of years.

Some of the biggest names in the software development industry, such as Google, Dropbox, Netflix, Facebook, and Spotify, use Python for their applications. As a senior Python developer, you have the opportunity to work with one of your dream companies.
According to Payscale.com, the average salary for Python developers in India is INR 5,83,060. This is the median salary, with experienced senior Python developers earning more than INR 1 million a year.

What do companies look for in Python developers during interviews?

From senior Python developers, just as with other developers, companies expect them to have some skills in addition to the technical qualifications. As a senior developer, you should portray a willingness to learn and a passion for the job.

For a senior position, companies will want a solid background in computer science. Your understanding of basic algorithmic concepts such as how to sort, search, and find through various data structures is tested. Most Python developers work on the back-end, so they should understand databases more comprehensively.

Understanding important Python libraries relevant to the role, such as Pandas, Apache Spark, and PyTorch. Companies prefer Python developers who are active on Github and contribute to open-source software projects.

It would help if you tried to tell the interviewers how you stay up to date with the latest developments. You can tell them about your favorite technical books or which blogs interest you.

As a senior Python developer, you should demonstrate impeccable technical skills and come across as a committed and inquisitive candidate to stand out.
We have created an exclusive Python Developer Toolkit to help developers prepare for interviews.

Essential Interview Questions and Answers

What is slicing in Python?

Slicing in Python is used to access parts of sequences like strings, lists, and tuples. The syntax for using slicing is [start:end:step]. We can make the ”step” and just [start:end] to return all the sequence elements.

Here is a subclass of a dictionary:

class DefaultDict(dict):
def missing(self, key):
return []
d = DefaultDict()
d[‘florp’] = 127

Will the following code work? Why or why not?

The code will work because of the standard dictionary object in Python 2 or 3. The use of a subclass dictionary is unnecessary.

However, the subclass won’t work with the code in any case because __missing__ returns a value but does not change the dict itself:

d = DefaultDict()
print d
{}
print d[‘foo’]
[]
print d
{}

As a result, the code will work because it won’t produce any errors. However, it won’t function as it is intended to.

How is memory managed in Python?

There are several methods for memory management in Python:

Python private heap space: All the objects and data structures in Python are in a private heap. Developers do not have access to this private heap. Instead, the Python interpreter takes care of the heap.

Python memory manager: The Python memory manager allocates the heap space for Python objects. The programmer can access some functionalities of the memory manager.

Inbuilt garbage collector: Like Java and C, Python also has an inbuilt garbage collector that recycles all the unused memory to make it available for the heap space.

What will be the output of this code snippet? Explain your answer. 

class Parent(object):
x = 1
class Child1(Parent):
pass
class Child2(Parent):
pass
print Parent.x, Child1.x, Child2.x
Child1.x = 2
print Parent.x, Child1.x, Child2.x
Parent.x = 3
print Parent.x, Child1.x, Child2.x

The output of the code will be:
1 1 1
1 2 1
3 2 3

Most Python developers are confused by the last line of the output. They assume the outcome will be 3 2 1 rather than 3 2 3.

This is because most developers miss that this code changes the value of Parent.x and Child2.x, but not Child1.x. In Python, class variables are internally handled as dictionaries. If a variable name is not in the dictionary of the current class, then the interpreter searches the class hierarchy until the referenced variable name is there.

What is PYTHONPATH?

PYTHONPATH is an environment variable when a module gets imported. It is used to look up the presence of imported modules in various directories.

What are decorators in Python?

Decorators are the functions to add functionality to an existing process without making any changes to the structure of that function. 

In Python, decorators are declared with @decorator_name and called in a bottom-up format.

Here’s an example code snippet:

def decorator_lowercase(function): # defining python decorator
def wrapper():
func = function()
input_lowercase = func.lower()
return input_lowercase
return wrapper
@decorator_lowercase ##calling decoractor
def intro(): #Normal function
return ‘Hello, World!’
hello()
Output: ”hello, world!”

Explain split(), sub(), and subn() methods in Python?

These are the methods that belong to the Python RegEx or ”re” module. They are used to modify strings.

split(): It splits a given string into a list. 

sub(): It is used for finding a substring for a given regex pattern, and then replacing the matched string with a different string.

subn(): A method similar to the sub() method is to return the new string and the number of replacements.

Free Online Assessments for Python Developers 

Here are some resources for Python developers to practice their skills:

Testdome

Mettl Python Programming Assessment

RealPython Quizzes

Takeaway

The prospects of Python developers are promising as big data and machine learning become essential. As Python has applications in machine learning, AI, data science, and analytics, with the right skills and experience, developers can land jobs with the best perks.
Talent500 is the platform where start-ups and Fortune 500 companies build their remote development teams. Join the elite pool of Python developers to get the best career redefining opportunities quickly.

The post Questions & answers for senior Python developer interviews in 2022 appeared first on Talent500 blog.

To get the most out of this programming language, developers must know the most helpful Python tools. We are listing handpicked tools for Python developers that can enhance their software development lifecycle skills.

1. PyCharm IDE

Every developer has their favorite Integrated Development Environment or IDE. However, if you are writing code in Python, you will be most productive when using PyCharm IDE. It is developed by JetBrains which is one of the best companies in creating IDEs for many different programming languages and not just Python.

This Python IDE is essentially the best code editor for Python developers out there. It lets you code much more efficiently due to its autocomplete feature that suggests all available keywords in Python. It also highlights any typo or missing syntax or keyword within your code. Also, it is effortless to install Python libraries in PyCharm, further extending its scope.

2. Pip Package

The robustness of Python is due to the availability of hundreds of open-source packages that you can use to customize its application for a particular field. Installing the Python packages can be tedious, which is why you can take the help of an intelligent tool called Pip.

Pip is one of the most widely used Python tools that allows developers to install any Python package with a single command line. It is highly beneficial when you are working on a project and want to import a package while writing code; you can easily do it using Pip. Just type the command – Pip package name to install the package without ever leaving the IDE. It helps to keep the productivity of Python developers high.

3. Scikit-Learn

To use Python for data science or machine learning applications, you must familiarize yourself with the Scikit-Learn tool. It is an open-source Python tool that can perform data mining and analysis at speed. Most developers and data scientists use Scikit-Learn to benchmark sample datasets, given its efficiency, accuracy, and speed. You can install this Python library in the PyCharm IDE. Some of the essential features included in Scikit-Learn are classification, clustering, regression, dimensionality reduction, model selection, and preprocessing.

4. Sphinx

The open-source community greatly influences Python and the developers often create their Python packages and let other developers use them through GitHub or other code-sharing platforms. One of the requirements of making your packages is to document the code such that others can use it. Python has a solution for this too. Sphinx is a Python tool that can generate documentation for your code automatically.

Sphinx can generate program documentation through your terminal or Windows CMD. It will ask you questions about the code, what it does, the release date, etc., and based on the information, it will create documentation. You can publish the document as an HTML file or can download its PDF file.

5. Selenium

Most developers and testers use Selenium. It is one of the best Python tools for automation testing, which is used in the software industry for all kinds of software development projects. Selenium is an open-source Python framework that you must learn to automate web applications through scripts. This tool can run scripts in several programming languages, including Python, Java, Ruby, Perl, C#, and .Net.

Every developer must have this Python tool on their list. With Selenium, you can automate web applications for any browser (Chrome, Firefox, Safari, Opera, or Internet Explorer). It also works on all major operating systems and can be integrated with testing tools like TestNG and JUnit.

6. LXML

If you are working with a C project for machine learning, then you can use this Python tool to work with libxml2 and libxslt libraries. It is one of the most feature-rich Python tools designed for C developers that provides them easy access to the functionalities of the libxml2 and libxslt libraries through the ElementTree API. At the same time, several other tools offer the same functionality, LXML, distinct because it combines the XML capabilities with the speed and simplicity of the native Python API. This allows developers to code faster by extending the scope of the ElementTree API by adding support for XPath, RelaxNG, C14N, XML Schema, and XSLT.

Conclusion 

You must be familiar with these Python tools if you are a beginner or intermediate Python developer. These tools can help you write Python code more efficiently and save time by installing and updating packages.

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