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append() vs extend() in a nutshell

In Python, the difference between the append() and extend() method is that:

The append() method adds a single element to the end of a list.

The extend() method adds multiple items.

Here is a quick cheat sheet that describes the differences.

extend() append()

Adds multiple elements to a list. Adds the input as a single element to a list.

Length increases by the number of elements added. Length increases by 1.

Time complexity is O(n), with n being a number of list elements. Time complexity O(1) or constant.

In this comprehensive guide, you are going to take a detailed look at the differences between append() and extend().

append() vs extend(): Overview

In Python, a list is a mutable data type used for storing elements. Lists support easy access to the elements. You can add, update, and delete items.

In Python, you need to deal with lists a lot.

When working with a list you commonly want to add elements to it after creation.

For example, if your list represents a queue for a lunch, you want to be able to add/remove participants at any time.

In this guide, we are going to focus on two ways of adding elements to the end (right-hand side) of a list:

The append() method.

The extend() method.

These methods are built-in into the list data type.

Let’s start adding elements to a list with the append() method.


A Python list type supports the append() method. This method adds an element to the end (right-hand side) of an existing list.

The syntax for the append method is:



list is the existing list to which we want to add an element.

element is the element to be added to the end of list.

This method only accepts one argument at a time. In other words, you cannot add multiple elements at the same time.

Perhaps the most common way to use the append() method is by adding a single value to the end of a list.

Example 1

For example, let’s add a single number to the end of a list of numbers:

a = [1, 2, 3] a.append(4) print(a)


[1, 2, 3, 4]

As you can see, the original list of numbers was updated and now there is an extra number at the end of it.

Example 2

As another example, let’s try to add multiple numbers into the list of numbers using the append() method.

a = [1, 2, 3] a.append(4, 5) print(a)

As mentioned, the append() method does not take more than one argument. Thus, doing this results in an error:

TypeError: append() takes exactly one argument (2 given)

At this point, you might consider a workaround of adding multiple numbers as a list.

a = [1, 2, 3] a.append([4, 5]) print(a)


[1, 2, 3, [4, 5]]

But as you can see, this does not work as you would like it to.

Instead of adding the elements separately, the append() method just adds the whole list to the end of the other list as a single element.

So now the list of numbers consists of three numbers and one list of numbers.

The append() method treats a list of numbers as a single object.

If you want to add multiple elements to a list using the append() method, you need to create a loop.

Example 3

Let’s add multiple elements to the end of a list with a for loop and the append() method:

a = [1, 2, 3] for number in [4, 5]: a.append(number) print(a)


[1, 2, 3, 4, 5]

Now the numbers are added to the end of the list the way you wanted.

However, because adding multiple elements in one go is such a common thing to do, there is a separate method for doing that.

To add multiple elements to the end of a list, use the extend() method.


In Python, you can use the extend() method to add multiple elements to the end of a list. Similar to the append() method, the extend() method modifies the original list.

The syntax of the extend() method is:



list is the name of the list you want to add elements to.

elements is an iterable, such as a list, that has the elements you want to add to the end of list.

Behind the scenes, the extend() method loops through the elements provided as arguments and adds them to the end of the list.

The time complexity of the extend() method depends on the number of elements added to the end of the list. Given n added elements, the time complexity is O(n).

A common use case for the extend() method is to add a number of elements to an existing list.

Example 1

For example:

a = [1, 2, 3] a.extend([4, 5]) print(a)


[1, 2, 3, 4, 5]

Notice how this is much neater than using a for loop + the append() method you saw in the previous chapter.

By the way, the extend() function argument does not need to be a list. As I mentioned, it can be any iterable type in Python.

In Python, strings and tuples are also iterables.

If you are unfamiliar with iterables, please read this article.

Anyway, let’s see some examples of using other types of iterables with the extend() method.

Example 2

For instance, instead of specifying the numbers as a list, you could use a tuple:

a = [1, 2, 3] a.extend((4, 5)) print(a)


[1, 2, 3, 4, 5] Example 3

As another example, let’s pass a string argument to the extend() method:

a = [1, 2, 3] a.extend("45") print(a)


[1, 2, 3, '4', '5']

This works because a string is iterable. In other words, one string is a sequence of characters.

In the above code, the extend() method loops through the string character by character and adds each character to the end of the list.

Notice that you cannot extend a single element with the extend() method. This is because the type of argument needs to be iterable, that is, something that can be looped through

Example 4

For example, an integer cannot be looped through. Thus, adding a single integer using the extend() method fails.

Here is an illustration:

To add a single element with the extend() method, you need to place the element into an iterable.

Example 5

Let’s use the extend() method to add a single element to the end of a list. To do this, the element needs to be wrapped inside of an iterable.

a = [1, 2, 3] a.extend([4]) print(a)


[1, 2, 3, 4]

However, instead of doing it this way, use the append() method as it is semantically more appropriate.


At this point, you should have a good understanding of how the append() and extend() methods work in Python.

To recap, the difference between append() and extend() methods is:

The append() method adds a single element to a list.

The extend() method adds multiple elements to a list.

Next, let’s take a look at the performance of these methods by running simple experiments.

Performance: append() vs extend()

Based on the previous example, you can do the same things using append() and extend().

To add multiple elements, you can use:

extend() by giving it an iterable argument.

append() by looping through an iterable and appending each element separately.

Likewise, to add a single element to the end of a list, you can use:

append() method by passing the single element as an argument.

extend() method by wrapping the single element into an iterable and passing it as an argument.

This might make you wonder how these approaches compare to one another when it comes to performance.

Let’s compare the performances of append() and extend() methods by running simple experiments.

Adding Multiple Elements: append() vs extend()

Let’s run an experiment of adding multiple elements to a list.

We are going to compare append() with for loop approach to extend() method.

Here is the code:

import timeit def append(l, iterable): for item in iterable: l.append(item) def extend(l, iterable): l.extend(iterable) t_append = min(timeit.repeat(lambda: append([], "0123456789876543210123456789"), number=100_000)) t_extend = min(timeit.repeat(lambda: extend([], "0123456789876543210123456789"), number=100_000)) print(f"Appending took {t_append}snExtending took {t_extend}s")


Appending took 0.16888665399892488s Extending took 0.034480054999221466s

As you can see, appending took ~5 times longer than extending.

So performance-wise, use the extend() method when adding multiple elements to a list.

Adding a Single Element: append() vs extend()

As another example, let’s compare the performance of adding a single element to the end of a list by:

Using the append() method by passing a single element as an argument.

Using the extend() method by wrapping the element into an iterable.

Here is the code:

import timeit def append_one(l, elem): l.append(elem) def extend_one(l, elem): l.extend([elem]) t_append = min(timeit.repeat(lambda: append_one([], 0), number=2_000_000)) t_extend = min(timeit.repeat(lambda: extend_one([], 0), number=2_000_000)) print(f"Appending took {t_append}snExtending took {t_extend}s")


Appending took 0.38043899900003453s Extending took 0.49051314600001206s

As you can see, extending wastes a bit more time than appending.

However, you should not pay too much attention to the timings of these approaches. Instead, you should use an approach that is semantically correct. In other words, instead of looking at the performance numbers, use code that accurately describes what you are trying to accomplish.

Anyway, this completes our guide on append() vs extend() in Python.

To finish off, let’s take a look at two alternative approaches to adding elements to lists.

Other Similar Methods

In Python, append() and extend() methods are not the only ways to add elements to the end of a list.

In addition to these, there are two other ways you can use:

The insert() method.

The + operator.

Let’s briefly introduce these methods.


In Python, the insert() method can be used to insert an element at a specific index in a list.

The syntax is as follows:

list.insert(index, element)


index is the index at which you want to add the element.

element is the element to be inserted in the list.

This means you can use insert() to add an element to the end of a list too.

For instance, let’s insert a number at the end of a list:

a = [1, 2, 3] a.insert(3, 4) print(a)


[1, 2, 3, 4]

This method only allows you to insert a single element at a time.

If you try to add multiple elements as a list to the end of a list, you get a similar result when using append() this way.

For instance:

a = [1, 2, 3] a.insert(3, [4, 5, 6]) print(a)


[1, 2, 3, [4, 5, 6]] The + Operator

In Python, you can also add multiple elements to the end of a list using the addition operator (+).

Notice that this does not modify the original list. Instead, it creates a new list with the added elements.

For example:

a = [1, 2, 3] a = a + [4, 5] print(a)


[1, 2, 3, 4, 5] Conclusion

Today you learned what is the difference between the append() and extend() methods in Python lists.

To recap, both methods can be used to add elements to the end of a list.

The append() can be used to add one element at a time.

The extend() method can be used to add multiple elements at once.

Thanks for reading.

Happy coding!

Further Reading

Best Programming Sites

50 Python Interview Questions

You're reading Python Append() Vs Extend(): What Is The Difference

What Is Exception In Python?

An exception is an event, which occurs during the execution of a program that disrupts the normal flow of the program’s instructions. In general, when a Python script encounters a situation that it cannot cope with, it raises an exception. An exception is a Python object that represents an error.

When a Python script raises an exception, it must either handle the exception immediately otherwise it terminates and quits.

Handling an exception

If you have some suspicious code that may raise an exception, you can defend your program by placing the suspicious code in a try: block. After the try: block, include an except: statement, followed by a block of code which handles the problem as elegantly as possible.


Here is simple syntaxof try….except…else blocks −

try:    You do your operations here;    ...................... except ExceptionI:    If there is ExceptionI, then execute this block. except ExceptionII:    If there is ExceptionII, then execute this block.    ...................... else:    If there is no exception then execute this block.

Here are few important points about the above-mentioned syntax −

A single try statement can have multiple except statements. This is useful when the try block contains statements that may throw different types of exceptions.

You can also provide a generic except clause, which handles any exception.

After the except clause(s), you can include an else-clause. The code in the else-block executes if the code in the try: block does not raise an exception.

The else-block is a good place for code that does not need the try: block’s protection.


This example opens a file, writes content in the, file and comes out gracefully because there is no problem at all −

 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    fh.write("This is my test file for exception handling!!") except IOError:    print "Error: can't find file or read data" else:    print "Written content in the file successfully"    fh.close() Output

This produces the following result −

Written content in the file successfully Example

This example tries to open a file where you do not have write permission, so it raises an exception −

 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "r")    fh.write("This is my test file for exception handling!!") except IOError:    print "Error: can't find file or read data" else:    print "Written content in the file successfully" Output

This produces the following result −

Error: can't find file or read data The except Clause with No Exceptions

You can also use the except statement with no exceptions defined as follows −

try:    You do your operations here;    ...................... except:    If there is any exception, then execute this block.    ...................... else:    If there is no exception then execute this block.

This kind of a try-except statement catches all the exceptions that occur. Using this kind of try-except statement is not considered a good programming practice though, because it catches all exceptions but does not make the programmer identify the root cause of the problem that may occur.

The except Clause with Multiple Exceptions

You can also use the same except statement to handle multiple exceptions as follows −

try:    You do your operations here;    ...................... except(Exception1[, Exception2[,...ExceptionN]]]):    If there is any exception from the given exception list,    then execute this block.    ...................... else:    If there is no exception then execute this block. The try-finally Clause

You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute, whether the try-block raised an exception or not. The syntax of the try-finally statement is this −

try:    You do your operations here;    ......................    Due to any exception, this may be skipped. finally:    This would always be executed.    ......................

You cannot use else clause as well along with a finally clause.


 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    fh.write("This is my test file for exception handling!!") finally:    print "Error: can't find file or read data" Output

If you do not have permission to open the file in writing mode, then this will produce the following result −

Error: can't find file or read data

Same example can be written more cleanly as follows −


 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    try:       fh.write("This is my test file for exception handling!!")    finally:       print "Going to close the file"       fh.close() except IOError:    print "Error: can't find file or read data"

When an exception is thrown in the try block, the execution immediately passes to the finally block. After all the statements in the finally block are executed, the exception is raised again and is handled in the except statements if present in the next higher layer of the try-except statement.

Huawei P40 Vs Pro Vs Pro+: What’s The Difference?

P40 series of smartphones, but with three different models to choose from at different prices, specs and features it’s probably hard to know which is the best one for you.

Well don’t worry because here we’re going to compare the three P40 phones across key areas highlighting the differences so you can buy the right one. We won’t be going into every tiny bit of detail to avoid this turning into a lengthy essay, but rather looking at what will be most important when deciding on a P40.

We’ve been briefed ahead of the launch but Huawei likes to keep things like the prices until the presentation so we’ll add this when we can. For now let’s look at how the P40, P40 Pro and P40+ compare when it comes to things like cameras, screens and more. 

See what we think in our Huawei P40 review and how the

P40 Cameras

Let’s kick off with the most important comparison, since the camera tech you get is different across all three phones.

There’s one camera that all three P40 phones get which is 50Mp ‘Ultra Vision Wide Lens’. The P40 then comes with a 16Mp ultra wide angle camera and an 8Mp telephoto camera with 3x optical zoom.

The Pro Pro swaps those two additional cameras for a 40Mp ultra wide angle ‘cine lens’ and a 12Mp 5x Optical Periscope Telephoto lens. Then there’s a time of flight (ToF) sensor, too.

The Huawei P40 Pro+ camera module

Finally, the P40 Pro+ has the same 50- and 40Mp cameras, joined by an 8Mp 3x Optical Telephoto, 8Mp 10x Optical Super Periscope Telephoto and the ToF sensor.

Here’s a more detailed look at each phone’s rear camera array


50Mp Ultra Vision Wide Lens, 23mm, f/1.9, OIS

16Mp Ultra Wide Angle Lens, 17mm, f/2.2

8Mp 3x Optical Telephoto Lens, 80mm, f/2.4, OIS

P40 Pro

50Mp Ultra Vision Wide Lens, 23mm, f/1.9, OIS

40Mp Ultra Wide Angle CineLens, 18mm, f/1.8

12Mp 5x Optical Periscope Telephoto Lens, 125mm, f/3.4, OIS

Huawei Time of Flight

P40 Pro+

50Mp Ultra Vision Wide Lens, 23mm, f/1.9, OIS

40Mp Ultra Wide Angle CineLens, 18mm, f/1.8

8Mp 3x Optical Telephoto Lens, 240mm, f/2.4, OIS

8Mp 10x Optical SuperPeriscope Telephoto Lens, 125mm, f/4.4, OIS

Huawei Time of Flight

When it comes to the front facing camera, all the P40 models have a 32Mp sensor but the Pro models have autofocus and Depth IR, in part for improved facial recognition.

We think photographers should buy the P40 Pro+. Here are some more photography details for the P40 phones:

Audio Zoom (Pro and Pro+)

Steady Telephoto

ISO 51200

Ultra Slow Motion 7680fps


4K @60fps

4K Time Lapse

Pro Mode

4K Selfie Video

P40 Screens

As is typical, the regular P40 has a smaller screen at 6.1in. It also has a slightly shorter aspect ratio and resolution at 19.5:9 2340×1080.

Getting a Pro or Pro+ gets you a larger 6.58in display and a taller 19.8:9 aspect ratio. The resolution is then higher at 2640×1200.

The regular Huawei P40 screen

All three phones have OLED technology with a punch-hole camera arrangement at the top-left but the ‘overflow display’ – where it curves over the edges – is limited to the Pro models.

Then there’s also the fact that Pro/+ gets you a 90Hz refresh rate where the regular P40 is lower at 60Hz. The Pros certainly have the better display then if you are ok with the larger size.

 P40P40 ProP40 Pro+Size6.1in6.58in6.58inResolution




Aspect ratio




Refresh rate60Hz90Hz90Hz

P40 Battery life & Charging

This is another area where the regular P40 lags behind the Pro models as it has a smaller 3800mAh battery and both wired charging is limited to 22.5W. The phone doesn’t have wireless charging.

Meanwhile, getting either the Pro or the Pro+ bumps you to 4200mAh and both wired and wireless charging is able to supply up to 40W and the adapter is supplied in the box. Note that the Pro is limited to 27W on wireless.


P40 Pro

P40 Pro+

Battery capacity3800mAh4200mAh4200MahWired charging22.5W40W40WWireless charging–27W40W

P40 Colours & Waterproofing

This might not be as important as the things above but will still be critical to some people’s buying decision.

The P40 comes in a range of colours, depending on where you live, including Ice White, Black, DeepSea Blue, Silver Frost and Blush Gold.

Although most flagship phones come with top waterproofing, the P40 is a little lower at IP53 (dust protected but not tight, and able to cope with spraying water). Meanwhile the Pro models are fully dust and waterproof at IP68.

P40 Common Specs

To finish things off, here’s a list of specs that all the P40 models share:

Kirin 990 processor

Wi-Fi 6





In-screen fingerprint scanner


P40 Price & Availability

The P40 and P40 Pro will be available from 7 April, while the Pro+ will be released later in June. Here are the details for the prices along with the memory and storage you get.


£699 or 799 Euro


View it at Huawei

P40 Pro

£899 or 999 Euro


View it at Huawei

P40 Pro+

1399 Euro


Which P40 should I buy?

Taking an objective look at the phones, the regular P40 isn’t overly attractive due to all the specs and features that it doesn’t get.

Namely a fancier screen, additional photography skills, wireless charging and faster wired charging. However, those wanting a smaller size and wanting to spend less will need to opt for it.

Chossing between the Pro models is harder since they are pretty similar across the board. We’d say the P40 Pro is the sweet spot unless you really want the ceramic finish of the Pro+, extra storage and will make use of its superior cameras.

Related stories for further reading

Java Vs. Python: Which Language Is Right For You?


Programming languages are a core component of computer science and are essential for creating various applications. In recent years, Java and Python have become the most popular programming languages. Java has been around for a while and is, therefore, more popular than Python. Nevertheless, Python has now started to gain popularity due to its simplicity.

Both languages are developers’ favorites, providing a wide variety of libraries.

If you need help deciding which programming language would be a better fit for your project, Java or Python, read this article to better your understanding of both. Here, we shall look at the differences between them and see which suits your project.

Table of Contents Difference Between Java and Python

Here is a table that briefs you about the primary differences between Java and Python. Have a look!

Java Python

Java is a compiled language. Any platform with a Java Virtual Machine (JVM) can run the compiled code because it is converted into bytecode. A Python interpreter can run programs written in the interpreted computer language Python.

Java was created for class-based and object-oriented paradigms. Python supports the functional, procedural, object-oriented, and imperative paradigms.

More time and effort are required for the developers to keep understandable Java code. Python keeps the code brief, organized, and readable.

Java is a language with strong typing. It prevents the compiler from changing a variable’s data type unless the variable is type-casted. Since Python is a dynamically typed language, declaring variables is unnecessary. The interpreter can recognize and modify the variable’s data type without declaring it.

Java is faster as all the types are assigned during compilation. Python is slower because it uses an interpreter and assumes the types as it runs.

Source: Scand

You may be wondering which is better, Java or Python? The following section covers different areas of dissimilarities between the two.

Language Features – Java and Python Syntax and Readability

Java: Machine learning is a statically typed language with strict syntax rules. It requires semicolons (;) to separate statements and curly braces ({}) to define blocks of code. This can make the code more verbose, but it also creates a more structured approach. If you have enough coding knowledge and experience, feel free to work with Java.

Source: Cuelogic

Type System

Java: Java has a static type system, where the type of a value is known at compile time. This helps prevent type-related errors before the program is executed but may require more upfront planning.

Python: Python has a dynamic type system, where the type of a value is determined at runtime. This can make writing code faster and more flexible, although it can also lead to runtime errors that might have been caught with static typing.

Object-Oriented Programming

Java: Java is strictly an object-oriented programming (OOP) language, where everything revolves around the concept of objects and their interactions. Inheritance, polymorphism, and encapsulation are the three main OOP features supported in Java.

Python: Python supports multiple programming paradigms, including OOP. Python’s OOP capabilities include support for inheritance, polymorphism, and encapsulation but may be less enforced than in Java. You can write object-oriented code in Python if you prefer, but you aren’t required to do so.


Java: Java provides built-in support for multithreading and concurrency through the java.util.concurrent package, along with powerful synchronization primitives like monitors and locks. Java supports both parallelism (simultaneous execution) and concurrency (shared resource management).

Python: Python has limited native support for concurrency due to the Global Interpreter Lock (GIL), which allows only one thread to execute at a time. Although, the issue can be resolved by using more than one interpreter. Nevertheless, Python supports concurrency libraries like asyncio and multiprocessing, which help circumvent the GIL for specific use cases.

Error Handling

Java: Java handles errors through a robust exception-handling mechanism, which includes checked (compile-time errors) and unchecked exceptions (runtime errors). Checked exceptions must be explicitly caught or declared by the developers to be thrown or instantly fixed, enforcing good error-handling practices.

Performance and Scalability Speed and Efficiency

Java is faster than Python as it is a compiled language, while Python is an interpreted language. Java code must be compiled before running, ensuring that errors are detected before the code is executed. Whereas Python code is directly executed without prior compilation, which can cause issues during runtime.

Memory Management

Java is better than Python in memory management. Java has a garbage collector that automatically removes objects that are no longer needed or have no references. Python also has a garbage collector but is not as efficient as Java’s.

Parallel Processing

Python is better than Java in parallel processing. Python has a module called “multiprocessing” that allows for parallel processing, while Java’s parallel processing capabilities are complex and require more lines of code.

Handling Large Datasets

Python is better than Java at handling large datasets. Python has libraries like NumPy and Pandas specifically designed for handling large datasets, while Java does not have a similar library.

Java is better suited for complex and large-scale applications, while Python is better for data analysis, scientific computing, and machine learning.

Use Cases and Industry Applications Web Development

Java: Java is a popular choice for web development due to its extensive library support, scalability, and performance. It is used with frameworks like Servlet API, JDBC (Java Database Connectivity) API, and Java EE (Enterprise Edition) for building robust web applications.

Python: Python has gained popularity in web development because of its simplicity and the availability of frameworks like Django and Flask. These frameworks allow developers to create web applications quickly and efficiently.

Data Science and Machine Learning

Java: Java has several libraries available, such as Weka 3, Deeplearning4j, and MOA, JSAT, which can be utilized for data science and machine learning tasks. In fact, the Java Virtual Machine is one of the most widely used ML platforms as it enables developers to work with identical code across multiple platforms.

Python: Python is the go-to language for data science and machine learning due to its extensive ecosystem of libraries, like Pandas, OpenNN, NumPy, and Scikit-learn, as well as deep learning libraries, like TensorFlow and Keras. The programming language is widely used in building blockchain-based applications like smart contracts, cryptography, etc.

Mobile Application Development

Java: Java is used for multi-platform mobile app development; as most developers say, “With Java, write once, run everywhere.” Nevertheless, it is significantly used for Android app development due to its official support by Google in Android Studio, based on IntelliJ IDEA software.

Python: In comparison, Python is less widely used for mobile app development as it does not have pre-built mobile development capabilities; however, frameworks like Kivy, PyQt, and Beeware’s Toga library exist to create cross-platform mobile apps with Python.

Game Development

Java: Java is often used in game development for Android devices or games built on platforms like PC or consoles using engines such as libGDX, Litiengine, GreenLightning, Jbox2D, and JMonkeyEngine. Surprisingly, one of the most popular games, Minecraft, was written in Java!

Python: Python is used for game development, although its use is not as prominent as C++ (with DirectX and OpenGL) and Java. However, many game developers have successfully developed games using Python using libraries like Pygame, Pyglet, Kivy, Panda3D, and many more.

Enterprise Software Development

Java: Java has long been a common choice for enterprise software development due to its stability, scalability, security features, and ability to support large-scale systems. Especially with Java EE (Enterprise Edition), the complexity of building large-scale software applications is significantly reduced. The platform allows for easy, universal software deployment for multi-tier, server-centric applications.

Python: Python is increasingly adopted in enterprise contexts, particularly for smaller projects for task automation, data analysis, and rapid prototyping. However, Java remains the more established language for large-scale enterprise software development.

Integrated Development Environments (IDEs)

Java and Python both have a wide range of IDEs to choose from. For Java, the most popular IDEs are Eclipse, IntelliJ IDEA, JDeveloper, and BlueJ. On the other hand, Python is often associated with IDLE, but it also has many other popular IDEs, such as Pycharm, Spyder, Visual Studio, and Jupyter.


Source: JetBrains

Libraries and Frameworks

Java has many libraries and frameworks available for developers. Some of the most popular include Spring, Hibernate, Struts, and Spark. Python also has a strong set of libraries and frameworks, with some of the most popular being NumPy, Pandas, TensorFlow, and Django.

Community Support and Resources

Both Java and Python have large and active communities of developers. Java has been around longer, so it has a larger community overall. However, Python has been growing rapidly in recent years and has become very popular for data analysis and scientific computing, so its community is also very active and growing quickly. Plenty of resources are available for both languages, with numerous blogs, forums, and online courses available for both.

Job Market and Career Prospects

Now that you have read about how each language works, what the syntax is, which language is more suited for mobile development, etc., the logical question is Java or Python, which is better for future development?

Demand for Java vs. Python developers

Java: Java has consistently been a popular programming language, and its demand continues to be strong in the software development industry. Many large-scale applications, like web applications and Android app development, utilize Java for its stability and scalability. Enterprises still prefer Java because of its vast ecosystem, making it a top business choice.

Python: Python has grown significantly in popularity over the last few years, primarily due to its simplicity, readability, and versatility. It is widely used in industries like web development, data science, machine learning, artificial intelligence, and more. Python’s demand stems from these emerging fields and the fact that it’s an excellent language for beginners.

Salary and Career Growth Potential

In India, the starting salary of a Java developer with no experience is around INR 2,000,000 per annum. With skills and experience, it can go as high as INR10,00,000 per annum.

Python: Python developers also enjoy competitive salaries, especially if they specialize in more in-demand fields like machine learning or data science. The career growth potential for Python developers is substantial since Python’s applicability spans numerous industries. Many roles focus on specialization or diversity involving other programming languages.

The starting salary of a Python developer with zero experience is somewhere around INR1,90,000 annually. With time and experience (4-9 years), it can go as high as INR 9,00,000 annually.

Future Trends and Predictions

Possible improvements to Java’s performance may arise from innovations in Project Panama (connecting JVM with native code for other APIs written in C++/C, etc.) and Loom initiatives.

Source: Vaadin

Source: TechnoBrains


Both programming languages have their benefits and drawbacks. For starters, Python is straightforward to learn, easy to code, and has a large library. On the other hand, Java is more compatible and excels at creating games and apps for mobile devices. They are both powerful, widely used programming languages that can evolve to accommodate cutting-edge technologies.

Hence when we look at Java vs. Python, one must consider various factors such as their project requirements, ease of learning, performance, scalability, libraries, and community support. While Java boasts strong performance and scalability, making it well-suited for large-scale systems and web applications, Python’s simplicity and versatile library collection make it an excellent choice for beginners and projects focused on data analysis or machine learning. Ultimately, the decision is contextual and should align with your needs and goals.

Visit Analytics Vidhya (AV) to learn more about Java and Python and explore their application in data science, machine learning, and other related fields like artificial intelligence. AV is a top-notch educational portal that is a one-stop destination for all learning materials and expert ML, AI, and data science guidance. They offer a vast selection of blogs, courses, and a community of experts to help you prepare for a technology-dominated future.


Router Mode Vs Bridge Mode – What’s The Difference

You’ve probably seen the setting that lets you choose whether to run the device as a bridge or a router while navigating through the network configurations. The purpose for both of these modes is the same, i.e, to offer network connectivity over a larger coverage area. 

However, some minor technical intricacies make these two modes totally different in how they operate. In this context, we’ll compare these two networking modes today and recommend the best option to get the most out of your internet experience.

In router mode, IPs of devices connected are stored in tables and most viable route is selected to transfer data. The IPs can be given dynamically by the device or defined by user.

A router is involved in receiving the packets with the destination address attached to them and, forwards the packets to the receiver in the most convenient route. This process of forwarding the data is called dynamic routing.

It connects the LAN with the WAN. The router Converts an IP from one class to another using a process called Network Address Translation (NAT). 

Router Mode is just to make the networking device function as a Router. Generally, the Modem/Router combo offered by the ISPs is, by default, set to the Router mode. However, if you use multiple routers for Wi-Fi range expansion, a private set of IPs created by the router generates conflict and can cause issues on the network. This is what is known as the Double NAT.

Router Mode

The router mode also supports multiple authentication methods, making it a more flexible option while setting up a connection. 


Ideal for connecting  Remote Networks

Brings the benefits of NAT

Enables more security Layers

Supports Multiple connection types (Dynamic IP/Static IP/PPPoE)


Dynamic Routing can consume Bandwidth

Double NAT becomes imminent when multiple devices are connected in router mode

Bridges use source and destination MAC before forwarding packets, contributing to less network congestion. Bridging essentially is the process of connecting two networks and making them function as one.

Now, getting into Bridge mode, is a setting that can be configured to make networking devices work simultaneously and extend the port access to a wider area. This configuration will disable the Network Access Translation (NAT) on one of the routers and turn it into a layer 2 device (On OSI) and extends the LAN. 

Bridge Mode

With the IP assigning or NAT turned off, the router will not work as a DHCP server, which reduces the IP address conversion time. This can also be said as the latency of transferring the data between two points is relatively faster because of reduced data hops. 

Traceroute Command

Enabling the bridge mode on a primary networking device will take away the routing capability from the device, if present. Configuring a device to work on the bridge mode renders the WAN port useless since the device is typically non-existent apart from extending the primary connection.


Extends network coverage over a larger area

Negates the possibility of Double NAT

Can work Between Firewalls

Offers high network reliability


Service providers might not support the device

Cannot Configure Source or Destination NAT 

Limited IP pools and devices

From an OSI model perspective, the router is a Level three device that performs the routing of traffic in an extended network. Routers are referred to as the network’s gateway as they forward the packets from one network to another. This is the normal modus operandi when a networking device is set to run on a Router mode. 

Router With OSI Layer

A bridge is a network framework segregating the LAN into segments and dividing the traffic into those segments. On an OSI Model, it works on the Data Link layer. Bridges are used to overcome the limitations of the bus topology by reducing network traffic and limiting bandwidth usage. Thus, enabling the Bridge mode does this function by converting the networking device into a bridge. 

Bridge with OSI Layer

Getting into other differences between these two modes, we can find some major divergences in their performance, security, usage, and specialization. 

When it comes to the packet transfer process, a router mode works efficiently by directing them to just the necessary destination. This helps in reducing traffic and boosts the overall performance of the network. However, Bridge mode provides flexibility to connect many devices, extend the range of the Network and improve the network speed. 

When two routers are connected to each other, there is the possibility of Double NAT in the routing mode. The issue can become prominent while using VoIP service or port forwarding rules. 

The main purpose of the bridge mode is not to function as a security layer  but to reduce traffic using segmentation. To make things simple, if you are planning to use bridge mode for a home network, it provides equal amount of security as a router.

On the other hand, since routers are primarily used to connect two remote devices or networks, routers can help to create a secure network. Under router mode, different security protocols are adopted, including encryption, to secure the data. This makes the router safer than using it as a bridge.

If a networking device is operating on a bridge mode, it will not have the routing capabilities and can be used to extend the range of  LANs. And hence, the bridge mode is seen as easier to operate for LANs. 

Router mode is required when you need to move back and forth between multiple networks. For instance, between your ISP and your home network. Also, a router mode enabled in a networking device can distribute a single wired Internet connection to multiple clients. These are the reasons for the router mode being suitable for both LAN and WAN environments. 

Enabling bridge mode can be useful in those places where the number of devices trying to connect to the network is large and network coverage needs expansion. 

The Routers support a limited number of devices connected at a time and also do not cater to the requirement of wider network coverage. Thus, router mode is suitable for home use. 

Except for the fact that both are router/modem configurations, there are only a few similarities between Bridge mode and Router mode. However, when viewed closely, both of these modes of networking can be used to extend network connectivity. 

Users can extend the existing network without using the NAT feature while using Bridge mode. Network range extensions are possible while in router mode, but IP address conflicts also can arise with it.

Bridge Mode helps you to utilize the networking devices you have to extend the network coverage across a larger area. If you are setting a Mesh system, you must operate your primary networking device on bridge mode.

As already discussed, having the bridge mode on eliminates the possibility of Double NAT. If any network-based activity you are performing is getting affected by Double NAT, you can try using this mode on your router. Operating on Bridge mode also can be a good option if the device provided by your ISP lacks wireless strength. 

For an average home user, running on the Router mode is relatively easy and caters all the home requirements. If you have multiple networks, you can use the router mode to separate them by creating multiple SSIDs. On a home network, you can also operate the primary networking device on a Router mode and connect a spare router as an extender. 

After all of the discussion, here is a quick summary of the major differences between Router Mode and Bridge Mode. 

Netflix Usa Vs Netflix India – What’s The Difference?

When this content streaming giant started out in the 90s, it has a simple and effective selling point to deliver premium entertainment directly to home users. Thanks to the infinitely rapid spread of the internet in the past decade, Netflix was able to take its model not just online across the United States, but open its wings and go international. Spanning across the entire world except for Syria, North Korea, and Crimea due to US sanctions, Netflix has become synonymous with premium online entertainment.

Despite the service being a worldwide phenomenon, the amount of content available across the service is region-specific. You’ve already seen true Netflix fans discuss the possibility of unlocking Netflix USA in India, and there’s a good reason to do so. Let’s find out what makes Netflix USA and Netflix India so different in the first place.

Related: Netflix vs Hotstar vs Amazon Prime

While it is almost impossible to put the content of Netflix under a microscope with thousands of movies and TV shows, there is some number-crunching we can do. To start off with movie titles, the Netflix movies library in the United States consists of 4500+ movies at an average, with the number growing and even declining sometimes.

The catalog of Netflix movies for the Indian region is restricted to just 500+ movies in comparison to the massive catalog of Netflix USA. There are multiple reasons for this, which includes copyright and language restrictions, content exclusivity and others. But you have to take into account what Netflix India offers that Netflix USA simply doesn’t.

Best streaming services in India

Thanks to its in-house production of Netflix Original content, the entertainment streaming service rules the chart with 1150+ TV shows on the Netflix USA roster at the moment. In comparison, you would expect an equal amount of TV shows to be available on Netflix India, since there aren’t too many regional TV shows available, with the exception of some Pakistani soap dramas of course.

However, there is also a stark difference in the number of Netflix USA TV shows that are available for viewing in India, with the figure being just around 530+ at the moment. This means that as a Netflix India user, you miss out on some of the best TV shows of all time like F.R.I.E.N.D.S, Modern Family, The Flash and many others.

Price: Netflix USA = Netflix India

Considering the massive currency divide between the two nations, you’d expect the streaming service subscription to be cheaper in India, especially when you look at the difference in the content library. With the Netflix USA Standard subscription costing $7.99 and the same on the Indian Netflix platform costing 500 INR (approximately $7.70), there isn’t much of a price gap between the two.

Winner: Netflix USA

As a Netflix user in India, users hoped to enjoy the best of western entertainment with top Netflix Original movies and TV shows at a fairly premium price. While it is quite pleasant that Netflix is keen on offering regional-specific content to woo the local viewership, it comes as a sacrifice of thousands of movies and TV shows that are offered on Netflix USA. Though, there is a trick to let you watch Neflix USA content from India.

On average, Netflix India has less than 12% of the streaming content available on Netflix USA, despite costing almost the same. Since this ratio isn’t going to change anytime soon, it might be time to take things into your hands and fire up a VPN to unblock Netflix USA through your Netflix India subscription.

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