python

Top 5 Python backend frameworks you must know

Posted on November 4, 2022November 4, 2022 by shreena morjaria

As we all know, Python is a language that makes coding a fun part of life. You might have read that Python is an absolute beginner-friendly language. Second cool feature is that it is completely an Open-Source language. I love to read code of some open source projects. If you want to know how such a wonderful language is built, go to GitHub and read the code.

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!

Python or Java: Which is superior?

Posted on November 3, 2022November 4, 2022 by nemo job

In the 21st century, programming is the backbone of technology. Programming language encompasses a collection of instructions, commands, and codebase to design an application or software program.

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.

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.

Parameters	Python	Java
Syntax	The learning curve is simple	Complex understanding. Takes time to adapt
Performance	Comparatively slower than Java	Considerably fast since it is a compiled programming language
Backend frameworks	Django (feature-rich) and Flask (micro-framework)	Spring
Game Development Engines	Cocos, Panda3D	MonkeyEngine
Cross-Platform	Yes	Yes, supported by JVM
Industry Examples	Quora, Instagram, Meta, Pinterest, Google	Uber, Spotify, Netflix, Minecraft
Machine learning libraries	Tensorflow, Pytorch	Weka, MOA, Mallet,
History	Developed by Guido Van Rossum in the late 1980s, Python1.0 was released in 1994.	In 1991, initially started as a project called ‘Oak’ by James Gosling. 1995 witnessed the first implementation of Java 1.0.
Syntax Style- Semicolon	No semicolons needed	A semicolon must be present at the end of the line.
Indentation	Extremely significant. The program won’t compile without accurate indentation.	Indentation does not influence the compilation of programs.
Practical Applications	Used in applications including data analysis, scientific computing, big data, implementation of machine learning, and artificial intelligence.	Ideal for developing e-commerce platforms, android applications, network security, financial software products, fraud detection, and desktop applications.
Code Readability	Focuses on code-readability, thus maintaining a clean and concise code base.	Requires efforts to focus on better code readability.

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.

Java v/s Python: Key differences

Posted on November 3, 2022November 7, 2022 by harsha satyakam

Programming is the pinnacle of technology, and every company invests in software solutions. There has been no better time to become a developer, but which programming language to choose? Java and Python are popular programming languages with a wide range of applications. While Java is the faster language between the two, it can be challenging to learn for a beginner. Python’s simple learning advantage is shadowed by its slow performance.

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.

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4 effective tips and tricks to write better Python code

Posted on September 28, 2022 by

Python is one of the most discussed programming languages among tech professionals at present. Not only for software development but also for machine learning and data science, Python is the preferred choice. Due to its fairly easy learning curve and mature and supportive community, new learners can get started easily. Also, hundreds of open-source libraries and frameworks, and the versatility and efficiency of Python make it a valuable programming language to add to your skills.

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:

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:

{‘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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6 useful Python tools for developers in 2022

Posted on September 28, 2022 by

Developers prefer Python because of its versatility, usage, and ease of access. In the software industry, it is one of the frequently used programming languages across several disciplines, such as data science, artificial intelligence, machine learning, and mathematical computation.

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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What makes Python a poor choice for large-scale full-stack development?

Posted on September 19, 2022 by

Millions of developers prefer Python as a programming language; thus, it is one of the most loved open-source programming languages with a strong developer community. Still, some developers view Python as one of the anomalies of software development. They believe Python is strictly a “glue” scripting language more suitable for routine system automation or connecting two applications. That’s not true; Python as a programming language is powerful enough for industrial-strength software development.

When we look under the hood, Python has every quality that makes it suitable for several software development projects. It follows an object-oriented approach and is open-source. Also, hundreds of thousands of modules and libraries further extend the scope of what can be achieved with Python.

However, Python still fails to penetrate one computing segment, and that’s enterprise development. Python is still viewed as just a scripting language for enterprise-level software development.

This article assesses the shortcomings of Python that make it a poor choice for large-scale full-stack development.

1. Absence of GUI and teamwork tools

The primary reason Python may not be fit for full-stack development at an enterprise level is that it’s unnecessarily complex to create graphical user interfaces in Python. For GUI, Python is shipped with Tkinter, which is most often used by Python developers to create interfaces but is not capable enough to meet the requirements of large-scale projects fully. Modern software development is highly graphic, and lacking a proper GUI tool in Python puts it at a disadvantage.

Another reason why Python is not usually preferred for large-scale full-stack development is the lack of tools for teams to collaborate. In enterprise software development, this shortcoming is unacceptable and viewed as a near-fatal flaw. Programming languages like Java and C++ are preferred for enterprise-level full-stack development because they have dedicated tools to help teams collaborate while working on large-scale projects. It can be risky when working with multiple programmers and using a language with no such support tool. While Python addresses this issue using modules and namespace resolution to ensure no conflict within a multi-programmer environment, it is still not convincing enough for teams to include it in large-scale projects.

2. Database access layer limitations

Large-scale software development projects have substantial data requirements. Modern applications produce or consume data by terabytes, and such data management requires established technologies such as ODBC and JDBC. Unfortunately, Python’s database access layer is less robust than these technologies. It might not be understated to categorize Python’s DB layer as primitive and underdeveloped. This is again a significant flaw that makes Python unfit for any full-stack developer to use for enterprise-level applications.

Modern software development is highly agile, and organizations look for programming languages that can interact immediately and smoothly with complex legacy data or adopt robust and rapid SQL database access. Python doesn’t comply with these requirements, and as there are no words on it in the near term, it is not suitable for large-scale development stacks.

3. Python is slow at runtime

Another primary reason to avoid Python in your stack is slower runtime compared to other programming languages like PHP, JavaScript, C++, or Java. While it might not be a problem when you have a few hundred lines of code, it becomes a primary concern when a full-stack developer has to write giant programs with hundreds of thousands of lines of code.

Python is not very sensitive to hardware resources as a high-level programming language. Python code requires an interpreter instead of a compiler. As an interpreter compiles the code line by line, it is not difficult to assume how it can be a problem when you’re working with a giant code base. Another reason why Python is slow at runtime is that it’s a dynamically typed language. In dynamically typed languages, variable data types are not specified when we use any variable. The data type is assigned at the runtime. As a result, the interpreter checks the data type every time a variable is read, written, or referenced to allocate memory accordingly. It slows down Python code at runtime.

4. Inadequate documentation

Suppose you have any experience working on large-scale projects. In that case, you understand how important it is to have comprehensive documentation available on technology before you decide to include it in your development stack. As compared to its rival programming languages, such as Java, Perl, and PHP, Python severely suffers from the lack of extensive documentation. Furthermore, there are not as many books available on Python. For comparison, PHP has twice as many titles as compared to Python on the market. While the online Python documentation is largely well organized and a good starting point, it only acts as sparse reference material. If your team does not have experienced coders, you can be stuck for a long time without enough learning resources for Python developers to help you fix issues.

Conclusion

While a powerful high-level programming language, Python has its limitations with large-scale full-stack development. Although the highly active Python community and dedicated development team across the globe are working on rectifying the limitations of Python to make it suitable for enterprise-level development, at present, it is better suited for small-scale software development projects.

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4 reasons why Python’s popularity is still soaring

Posted on September 19, 2022September 19, 2022 by

Python is one of the fastest-growing programming languages that continue to soar in popularity. According to Tiobe, a software quality assurance company that indexes the popularity of programming languages every month, the popularity of Python is at an all-time high.

The programming language yet again topped in its August 2022 rankings. When so many new technologies are emerging every day in the software development industry, one might wonder why the popularity of Python is still soaring.

According to the Tiobe index report, Python seems unstoppable because it’s hard to find a field of programming in which the programming language is not used extensively nowadays. From web development to data science, it has applications in every domain.

First developed by Guido van Rossum in the 1980s, Python started seeing a significant uptick in its adoption when Python 2.0 was released in 2000. It had many important new features such as cycle-detecting garbage collector, memory management, and support for Unicode. The most recent Python 3.0 was released as a backward-incompatible update in 2008. The release corrected some fundamental design flaws in the language and made it much more robust and fit for modern software development.

In this post, we look closely at why Python continues to grow in popularity.

1. Simplicity

One distinctive feature of Python from other programming languages is its simplicity. The ease of learning, understanding, and use make it easy to adopt. People with little or no software development experience can use Python to build applications. It is one of the most studied programming languages in the world, as we discovered by JetBrains’ State of Developer Ecosystem research. The simplicity of adoption is a major driving force behind the increasing popularity of Python. Universities, online schools, and companies are using Python as the first programming language to teach the fundamental coding principles to students and employees. Anything can be achieved using Python, whether it’s web development, writing scripts to automate tasks, or analyzing vast amounts of data. Given its scope of application, Python keeps attracting newcomers.

Here are some quality Python learning resources.

2. Versatility

If you are an experienced programmer, you will appreciate not only Python’s fully featured object-oriented approach but also its versatility and cross-platform compatibility. Not many languages can combine OOPS and cross-platform compatibility like Python. Most experienced programmers use Python to write admin scripts or “duct tape” code that can bind applications together in a toolchain. This feature is important as it allows freedom to test code without building an environment from scratch to run the code. Unsurprisingly, Python has many applications in DevOps and testing automation.

Not only on-premise environments but cloud platforms also support Python, given its incredible applications. You can deploy Python on any operating system, including Windows, Linux, Mac, or any cloud platform. Even some of the popular cloud platforms offer Python as a cloud-based “function as a service,” for instance, AWS Lambda, Azure Functions, and Google Cloud Functions.

3. Open-source and extensible

Python is an open-source programming language. It implies it has zero start-up costs and no barriers to entry. Anyone can download the interpreter and development tools to start building Python applications. It is another reason why Python continues to grow in popularity even when premium alternatives are available. Another benefit that works in its favor is how extensible Python is. Hundreds of thousands of freely available open-source packages further extend Python’s core functionality for application development. With a simple “pip install,” you can import and use any Python library or package. Due to its vast community support and growing popularity, Python has dethroned JavaScript as the most discussed programming language on the popular developer community platform, Stack Overflow.

4. Data Science applications

Modern websites and applications produce a humongous amount of data. This is why data has become the single most valuable commodity today. But raw data is not helpful, and Python offers a wide array of options to collect, analyze, and represent data in meaningful information.

There are several Python libraries dedicated to data science and numeric computation. NumPy, TensorFlow, Keras, PyTorch, Pandas, Scikit-learn, and PySpark are the Python libraries used for data analysis, machine learning, significant data transformations, and neural networks. The JetBrains survey we mentioned above found that 49% of the respondents use Python for data analytics and 42% for machine learning applications. Due to its scope of use in modern AI and machine learning applications, Python developers are in high demand and will continue to be so for the foreseeable future.

Conclusion

These are some of the significant reasons why Python is growing at such a fast pace. Its incredible applications in modern software development and AI and machine learning further boost its popularity among beginners and experienced developers. As a Python developer, you can explore almost any domain of software development.

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Python developer toolkit: Industry-specific skills, learning resources, interview prep & more

Posted on May 2, 2022 by

Python is a popular language for web and application development. According to the 2021 Stack Overflow Developer Survey, approximately 68% of software developers who have worked with Python for a project want to continue with it as their preferred language for project development. The same survey found that Python is also the most-wanted coding language among the developers who are not currently using it and want to learn.

If you use Python for backend development, you have chosen a language that offers versatility and will continue to be in high demand in the years to come.

This toolkit aims at helping you become a top-tier Python developer. We have taken out all the guesswork and handpicked the resources to help you write high-quality Python code.

Django and Flask: Core Python concepts must be clear to you to write unique code, but you must also understand the web frameworks for web projects. Django and Flask are the two most popular Python web frameworks that you can use to deploy web projects faster.

Django is a high-level web framework that allows Python developers to create high-quality web projects with clean code and pragmatic design. One can import several libraries into Django to improve its functionality.

Flask is a micro web framework that is considered a template engine. It allows writing web applications without writing any low-level code. However, it has no database abstraction layer, form validation, or support for third-party libraries.

Object Relational Mapping: ORM is a technique to convert data between two incompatible types using any object-oriented programming language. Python developers should understand ORMs to create virtual object databases without using SQL for data manipulation in their applications.

Frontend technologies: Python developers should know how the front end of web applications works. Python can efficiently work with the three frontend markup languages: HTML5, CSS3, and JavaScript. To generate usable and compatible web applications, you need to learn how Python causes these three markup languages using parsers, transpilers, and compilers.

Git: It is the most popular version control system to iterate your code and keep track of changes you made to the code. As a python developer, you must learn the ins and outs of using Git.

Technical Interview Practice with Python

10 Python Interview Questions With Example Answers

A guide to executing Linear Regression in Python

Backend developer: interview preparation resources

In-demand skills for a backend developer

How to land a high paying backend developer job

Whether you are an experienced Python developer or a backend developer exploring Python for the first time, here are the resources to speed up.

Official resources

Official Docs Tutorial

Official Guide for Programmers

Online courses & tutorials

Learn Python in y minutes: An excellent guide for experienced backend developers to quickly grasp how Python is structured.

Developing a Real-Time Taxi App with Django Channels and Angular: Learn the real-life application of Python by building an app using Python tools such as Django, Angular, WebSockets, and Redis.

Authentication with Flask, React, and Docker: An advanced tutorial to learn how to build intermediate-to-advanced web applications in Python using technologies like Flask, React, Docker and Heroku.

Python 3 Course: A course offered by Codeacademy for learning Python 3 for application development.

DataCamp Python Programming Course: This course teaches how to use Python for Data Science applications.

Google Python Class: Google has created this course for beginners to help them learn through an industry-based curriculum.
How to Develop Quality Python Code: It is a reliable resource for developers to learn about Python development environments, project structure, and application dependencies.

Books for Python developers

Fluent Python: Clear, Concise, and Effective Programming

Programming Python: Powerful Object-Oriented Programming

Introduction to Machine Learning with Python: A Guide for Data Scientists

Automate The Boring Stuff With Python, 2Nd Edition: Practical Programming For Total Beginners

Learning Python 5ed: Powerful Object-Oriented Programming

Online communities for Python developers

Pyslackers: An open community for Python programming enthusiasts.

Full Stack Python: A community teaching how to build, deploy and operate Python applications.

Podcasts for Python developers

Talk Python to Me: A podcast focusing on the people and organizations coding on Python.

Django Riffs: A web development podcast for web developers using Python and Django for projects.

The Real Python Podcast: A Python community weekly podcast that shares interviews and coding tips.
Podcast.__init__: A dedicated Python podcast that shares stories on the industry and interviews “with the people who make it great.”

Soft skills for Python developers

While we have listed all the resources that will enable you to skill your technical learnings as a Python developer, to land a job, you have to nurture some essential soft skills as well.

Communication: For software developers, communication is an important skill. You must be able to express your ideas and thought process to stakeholders. Also, active listening is something a developer should emphasize. As a part of a development team, you will have to listen to your colleagues, users, and customers.

Open-mindedness and adaptability: The software industry keeps evolving. Once, the waterfall development model was the industry standard, but agile and lean approaches soon took over. This is why developers should be open-minded to adapt as new technologies and paradigms emerge. Open-mindedness also improves your code by discovering your weaknesses, accepting them, and improving.

Critical thinking, creativity, and problem-solving: Being a developer is a challenging role. Sure, the scope and complexity of the problems will vary; at the core, a developer has to be a critical thinker and creative. It would help if you always kept learning and innovating to solve complex problems.

Here are some additional soft skill improvement resources for Python developers:

Six soft skills that still impress employers in 2022

7 Most valuable soft skills of exceptional software engineers

Upskilling: a complete guide for professionals

Conclusion

Python is an excellent language for backend developers to master. Its applications are broad, and companies of all sizes use the language for their web and software projects. We hope this toolkit will prove to be a one-stop guide to learning everything essential skills, interview insights, and more.

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A guide to executing Linear Regression in Python

Posted on December 4, 2020December 4, 2020 by akhyansh

Regression and classification are two commonly used supervised learning algorithms and while classification is used for discrete values (e.g. email spam detection), regression is employed when the values are continuous (e.g. weather forecasting). Breaking down the term linear regression is important: ‘regression’ means that there is a causal or ‘cause and effect’ relationship between two or more variables and ‘linear’ implies that this correlation is linear, that is, if you plot it in a 2D space, you’d get a straight line.

Linear regression has a wide range of real-life applications from assessing the risks that insurance providers take to vendors adjusting their prices based on income statistics. Modelling such data in Python is fairly easy too. In this guide you’ll learn some basic linear regression theory and how to execute linear regression in Python.

What is simple linear regression?

In linear regression, you map dependent variables with independent variables. The dependent features are also known as outputs or responses and the independent features are called inputs or predictors. Here is some theory based on insights from Mirko Stojiljković’s article on Real Python.

A linear regression model can be defined by the equation:

? = ?₀ + ?₁?₁ + ⋯ + ?_n?_n + ?

? is the dependent feature

x = (?₁, … ?_n) are the independent features

?₀…?_n are the regression coefficients or model parameters

? is the error of estimation, which can be averaged out to be zero

In case of simple linear regression, there is only one independent feature.

So, the regression model narrows down to:

? = ?₀ + ?₁?₁

In carrying out linear regression you are arriving at estimators for ?₀…?_n_,the regression coefficients.

The estimated regression function then is:

?(x) = ?₀ + ?₁?₁ + ⋯ + ?_n?_n

At any given point, the difference between the actual and predicted response would be the residual (R).

For ? = 1, …, m,

R_i = ?_i – ?(?_ᵢ)

To arrive at the best set of coefficients, the goal is to minimize the SSR, sum of squared residuals.

SSR = Σᵢ(?ᵢ – ?(?ᵢ))²

How to execute simple linear regression in Python?

To implement simple linear regression in Python you can use the machine learning package Scikit-Learn. You can try out the code on Jupyter.

Import libraries

Pick the dataset

Consider this simple dataset provided by T McKetterick on Kaggle which provides, from a small sample of American women aged 30-39, average mass as a function of height.

Now, importing the CSV using pandas:

To get a glimpse of the dataset, execute:

The output is:

(15, 2)

And to view some of the data run:

The output is:

To “see” if the data would be good for linear regression, it makes sense to plot it.

The result is:

As you can see, this is very linear in form.

Provide the data

Here, you are to divide the data into independent and dependent variables:

Create a linear regression model:
Now, on to creating a linear regression model with Scikit-Learn and fitting it with the dataset.

To train the model use:

The output is:

LinearRegression()

To predict features run:

Evaluate the model

Root mean square error is a metric we want to minimise. So, let’s see how our model has performed:

View the results:

You get:
Root mean squared error: 0.49937056025884025
R2 score: 0.9891969224457968

Both these are very good values.

Also, take a look at the coefficients.

You get:

Slope: [61.27218654]
Intercept: -39.06195591884392

Having ?₀ = -39 makes sense, as a person shouldn’t have positive weight for zero height (x).

Take a look at the line predicted by the model:

Now that you have a working model, you can use it to predict dependent variables for a new dataset, say, for a larger group of women of a similar age group and demographic.

So, you could get weight values for random heights, such as, 1.3, 1.5, 1.2, 2.4, 1:

Have a look at x and y_pred:

You get:

[[1.3]
[1.5]
[1.2]
[1.4]
[2.4]
[1. ]]

And similarly,

To get:

[ 40.59188659 52.84632389 34.46466793 46.71910524 107.99129178
22.21023062]

How to execute multiple linear regression in Python?

As an example of multiple linear regression consider data on cars from CarDekho, available at Kaggle.

Import Libraries

Provide the dataset

To view the first 5 rows of data:

You get:

Prepare the data
Now, for the purpose of evaluation, consider that the present price depends on only 3 of the above factors: years, selling price, and kilometers driven.

This time around, try dividing the dataset itself, into training and testing sets. You could keep a ratio of 80:20.

You can view the dimensions of the matrices:

You get:
(240, 3)
(61, 3)
(240,)
(61,)

Train and test

Once again, use Scikit-Learn library to train and test:

The output is:

LinearRegression()

In multiple linear regression, the model will try to arrive at optimal values for all coefficients.

You can view these now:

You get:

That is, if the present price increases by ‘1 unit’, the selling price should increase by 1.87 units. (e^-1 ~ 3.68). Or if the kilometers driven increase by 1 unit, the selling price drops by 3.14 units.

Prediction and evaluation
You can now make predictions on the dataset:

To view actual vs precited values,

This is what you get:

You can now evaluate RMSE and R2 score for training and test data:

You get,

RMSE = 2.040579021493682
R2 score = 0.8384238519780597

And for the test data:

You get:

RMSE = 1.6867312004140655
R2 score = 0.887446046362064

So, what do you think?

Are these metrics a good starting point?

Or would it help to carry out some exploratory analysis first, and have some other variables play a factor in deciding the selling price?

In the field of data science, being inquisitive helps arrive at the right answers.

It also helps to work on projects with some of the best minds in the industry, and for that, you can simply sign up with Talent500. Our dynamic skill assessment algorithms align your skills to premium job openings at Fortune 500 companies. This way you’ll put what you learn in data science to practice and get hands-on experience with top real-world problems!

*You can gain complimentary insight from the following blogs, which also served as resource material for this post:

https://realpython.com/linear-regression-in-python/#polynomial-regression

https://stackabuse.com/linear-regression-in-python-with-scikit-learn/

https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155

Python vs. Node.js: Choosing The Right Technology for Backend Development

Posted on October 30, 2020 by akhyansh

Choosing the right programming language when taking up a new project is perhaps one of the toughest decisions for the programmers. The reason is simple. Every project has some unique specifications and problems, and there exists no such software in the programming world that can be used for everything.

Different programming languages have different strengths and weaknesses, and, therefore, specific use cases. Thus, it becomes imperative to identify the right programming language for any project at the outset to minimise any hiccups in the later stages. In this write-up, we will compare two popular programming languages, Python and Node.js, to identify which works better in what situation.

Understanding Python and Node.js

Python is one of the most popular languages for machine learning. It is a general-purpose programming language that is object-oriented, dynamically typed, and supports multiple programming use cases. You can use Python for developing robust applications for web, mobile, and desktop with seamless options for back-end development that makes it extremely popular with programmers across the world.

Node.js is also mainly used as a back-end framework. It is a JavaScript runtime environment that has been created on Google Chrome’s V8 JavaScript engine. Node.js can be used for both front-end and back-end development and is useful for building efficient and scalable JavaScript-driven web applications. Node.js is mainly used for web-based applications, but the use cases for Python are much more versatile.

The convenient nature of Python certainly gives it an edge over Node.js, but picking a clear winner isn’t that simple. Below, we have shared a comparison between Python and Node.js on some pre-defined parameters to help you make the right choice for your next development project.

	Python	Node.js
Use cases	Used for developing data science apps, voice and face recognition software and 3D modelling software. Generally, it is one of the best options for developing larger projects.	Preferred for asynchronous programming and developing real-time web applications like chatbots and streaming applications. It is preferred for smaller projects with lesser memory.
Universality	Web, desktop, mobile back-end and front-end.	Web, desktop and mobile back-end only.
Speed	Slow speed due to slower due to its synchronous nature.	Much of the Node.js core API is built around an idiomatic asynchronous event–driven architecture which makes it faster.
Syntax	Simple and compact syntax that lets you achieve more with fewer lines of code.	Mostly similar to the browser’s JavaScript syntax. Prior experience with JavaScript will make it easier to work with Node.js.
Scalability	Python lacks proper scalability support for several reasons. It is a slower programming language that doesn’t support multithreading, which prevents it from running multiple tasks, simultaneously. However, developers often use Python implementations like CPython and load balancing mechanisms to overcome this issue.	Node.js is built into the runtime environment and comes with a cluster module to handle multi-tasking. Node.js also allows for both vertical and horizontal scaling of web applications, clearly scoring over Python in this regard.
Extensibility	Python is highly extensible through frameworks like Django and Flask for both web-only and full-stack development. There’s also Jython, the implementation of Python in Java, which simplifies scripting and allows rapid application development.	Node.js comes with a pool of frameworks like DerbyJS ad Koa.js to extend its features, making it a more versatile framework for development.
Libraries	Python Packages and libraries are handled by pip, which is the default package installer for Python. Talking about the number of packages, Python offers more than 220 thousand packages across numerous categories like image processing, data science, etc.	The packages in Node.js are handled by node.js npm, which is also called the Node Package Manager. In numbers, it contains over 1.3 million packages, beating pip hollow in terms of extensibility with the right package.
Community	Python is an old and popular language, which also means it has a large and helpful community of users to find support and resources online. Developers are free to contribute to Python and its packages through the communities.	The Node.js community is also beaming with talented developers, eager to help and contribute to the development of the Node.js framework.
Popular Framework	Django, Flask, Falcon & Bottle	Express.js., Hapi.js, Sail.js & Koa.js

Conclusion

Both Python and Node.js are potent options for programmers to develop web and other applications. However, to choose between the two, you need to consider two factors – the purpose of the project and your skillset as a developer. While both the languages have significant advantages over the other, it is the purpose of the project and your comfort level, as well as that of your team, with either of the technologies, that would direct your choice.

Overall, both the languages are equally preferred, and software engineers well-versed with Python and Node.js are in high demand across the globe. If you are planning a job change or looking at climbing the next rung in your career, you may sign up on Talent500 to empower your job search for key roles in Fortune500 companies.