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Insertion sort is a simple sorting algorithm and it is used to sort an array by iteratively inserting elements into a sorted subarray that results in a final sorted array. Insertion sort starts iteration from the second element of the array and compares each element with the ones before it. If the element being compared is smaller than the one before it, then the algorithm swaps the two elements (and keeps swapping with the next element before it after the last swap) until the smaller element is in its correct sorted position. Given below is the step by step explanation of how insertion sort works Start with the second element of the array, and compare it with the first element. If the second element is smaller, swap the two elements. After that, move to the third element and compare the third element with the second element. If the third element is smaller than the second element, then swap it with the second element, and then compare the new second element with the first element. If the second element is smaller than the first, swap them. Continue this process for all remaining elements in the array, comparing each element with the ones before it and swapping them if necessary to keep the array sorted. Once all elements have been sorted, the array becomes sorted in ascending order. For better clarity, let us look at the below array and see how the insertion sort algorithm would sort the array, Example array: , and the steps to sort are as below – [5, 2, 4, 6, 1, 3] Step 1: [2, 5, 4, 6, 1, 3]
Step 2: [2, 4, 5, 6, 1, 3]
Step 3: [2, 4, 5, 6, 1, 3]
Step 4: [1, 2, 4, 5, 6, 3]
Step 5: [1, 2, 3, 4, 5, 6] In this example, the algorithm first compares with and swaps both since 2 is less than 5, this results in a partially sorted array that looks like . Algorithm then compares with , and since 4 is smaller, it swaps 4 with 5, resulting further partially sorted array . The algorithm continues in this way and keeps on swapping elements till the time array is fully sorted. 2 5 [2, 5, 4, 6, 1, 3] 4 5 [2, 4, 5, 6, 1, 3] Given below is the pseudo-code for insertion sort, you can pick it up and implement in any programming language – for j = 2 to n:
    key = A[j]
    i = j - 1
    while i > 0 and A[i] > key:
        A[i+1] = A[i]
        i = i - 1
    A[i+1] = key In the above pseudocode, is the array that we want to sort with insertion sort, is the length of the array, and “ is the current element (temporary variable to store current element), and is the variable that holds the index of the element before the current element gets sorted. A n key" i The outer for loop in the above pseudo-code iterates over every element in the array, starting from the second element. The inner loop compares the current element with the ones before it and swaps them if necessary to keep the array sorted. Th variable key keeps track of the current element being sorted, and the variable i keeps track of the index of the element before it. e Stability: Is Insertion Sort Stable? Insertion sort is a stable sorting algorithm. Well, what do we really mean by an algorithm being stable? Okay, in short, a sorting algorithm is called a stable algorithm if it preserves the relative order of equal elements in the input array. Example: If 5 appears twice in the array, then left most 5 in the sorted array should be the 5 that occurred first in the original array. In insertion sort, when two elements have the same value, the algorithm always chooses the one that comes first in the input array and moves the other one to a position after it. Therefore, the relative order of equal elements is preserved, and insertion sort is a stable sorting algorithm. This property of stability is particularly useful in cases where the input data has some inherent order, and we want to sort it based on some other criteria while maintaining the original order of equal elements. For example, when sorting a list of students by their grades, if two or more students have the same grade, we might want to maintain the order in which they appear in the original list. In such cases, we can use a stable sorting algorithm like insertion sort to achieve the desired result. The time complexity of Insertion Sort We typically look at time complexity in best, worse, and average cases. when it comes to insertion sort, the time complexity of it is O(n^2) in the worst and average cases, where n is the number of elements in the array. This essentially means that the time it takes for the algorithm to sort the array increases quadratically with the size of the array. The worst case for insertion sort occurs when the starting array is in reverse order. This is because each element in the leftover unsorted array is required to be compared to every element in the already sorted array to find the correct position for the element. This results in n comparisons for the first element, n-1 for the second element, n-2 for the third element, and so on, for a total of (n*(n-1))/2 comparisons, which is O(n^2). But why is the average case time complexity the same as the worst case? The average case time complexity of insertion sort is also O(n^2) because the algorithm has to make the same number of comparisons regardless of the initial order of the array. When compared to other algorithms like bubble sort and selection sort that have the same worst-case time complexity, insertion sort can end up being faster since it is more efficient when the input array is already partially sorted. In addition, insertion sort has a small constant factor, which means it can be faster than other algorithms for small input sizes. In general, insertion sort is a simple and efficient algorithm for sorting small arrays, but it becomes less practical for larger arrays because its time complexity grows quickly with the size of the array. For larger arrays, more advanced algorithms, such as merge sort or quicksort, are typically used because they have better worst-case and average-case time complexity. You should also look at binary insertion sort to improve the insertion sort algorithm time complexity further. Space Complexity of Insertion Sort Insertion sort space complexity is O(1), which essentially means that the extra memory needed to accomplish the sorting doesn’t depend on the size of the original input array. The only extra space used by the algorithm is for temporary variables, such as the “ variable used in the above pseudo-code example. However, these temporary variables like “key” have a constant size that does not depend on the size of the input array, so the additional space consumption to the overall space complexity of the algorithm is negligible. key" Given the above, insertion sort becomes a good option for sorting arrays where memory usage is a critical factor. The situations, where memory is not a problem, and array size is huge, then you should opt for other algorithms like merge sort or quicksort – they would behave much better on time complexity. Insertion Sort program in C If you want to write a program to implement insertion sort in C programming language, below is a quick reference code for you. void insertionSort(int arr[], int n) {
    int i, key, j;
    for (i = 1; i < n; i++) {
        key = arr[i];
        j = i - 1;

        // Move elements of arr[0..i-1], that are
        // greater than key, to one position ahead
        // of their current position
        while (j >= 0 && arr[j] > key) {
            arr[j + 1] = arr[j];
            j = j - 1;
        }
        arr[j + 1] = key;
    }
} In the above code, the function takes an array of integers and the size of array n as two arguments. The algorithm code then iterates through the array from index 1 to n-1, also and for each element at index i, it compares it with the previous elements in the array (from index i-1 down to 0) and moves them one position to the right until the correct position for the current element is found. insertionSort arr The variable named “key” is used to temporarily store the current element and the variable “j” is used to keep track of the position in the sorted portion of the array where the current element should be inserted. At the end of each iteration, the current element is inserted into the correct position in the sorted portion of the array, by placing it after the last element that is smaller than it. The loop terminates when the correct position for the current element is found, or when the beginning of the array is reached (j < 0). After the loop completes, the array is sorted in ascending order. Insertion Sort program in Java If you want to write a program to implement insertion sort in Java programming language, below is a quick reference for you. public static void insertionSort(int[] arr) {
    int n = arr.length;
    for (int i = 1; i < n; i++) {
        int key = arr[i];
        int j = i - 1;
        while (j >= 0 && arr[j] > key) {
            arr[j + 1] = arr[j];
            j--;
        }
        arr[j + 1] = key;
    }
} Insertion Sort program in Python If you want to write a program to implement insertion sort in Python programming language, below is a quick reference for you. def insertion_sort(arr):
    n = len(arr)
    for i in range(1, n):
        key = arr[i]
        j = i - 1
        while j >= 0 and arr[j] > key:
            arr[j + 1] = arr[j]
            j -= 1
        arr[j + 1] = key Insertion Sort program in Javascript If you want to write a program to implement insertion sort in JavaScript, below is a quick reference for you. function insertionSort(arr) {
  const n = arr.length;
  for (let i = 1; i < n; i++) {
    const key = arr[i];
    let j = i - 1;
    while (j >= 0 && arr[j] > key) {
      arr[j + 1] = arr[j];
      j--;
    }
    arr[j + 1] = key;
  }
} Quick Recap of Insertion Sort: Key Features This brings us to the end of this tutorial, but before we move on, given below is a quick recap of everything you need to know about insertion sort. Insertion sort doesn’t require additional space, and for that reason, it is considered to be an in-place sorting algorithm. Simple and intuitive to understand, even for beginners. Works great on small and partially sorted arrays, with worst-case time complexity of O(n^2). Stable algorithm – Preserves relative order of equal elements in the input array. You can further improve the performance of insertion sort by implementing binary search to efficiently find the correct position of the current element. Insertion sort is not suited for large arrays or arrays which are totally random in order. If your array continuously keeps getting additional elements in the end (live feed), insertion sort is a good option since it picks elements left to right one by one. Hope you enjoyed the article, if so, please do share it with your friends in the University and over social media. Also published here.

Intro to Insertion Sort Algorithm With Code Examples

Python is among the most used and most loved programming languages of recent times. It is used across many technology areas, including web development, GUI development, data science, AI, ML, and scientific computing. Python is also used widely in universities and colleges to teach programming to students. The simplicity of python has made even young kids fall in love with programming. Python is simple, expressive, concise, and offers English-like syntax, but it is interpreted language and hence a bit slow. To overcome the slowness of python, developers have created multiple different compilers to compile python either to other programming languages, to machine code, or to do Just in Time compilation. Some of the popular python compilers include Cython, Nuitka, Brython, PyPy, and Iron Python. Alright, so where are we headed towards with so many python compilers out there in the wild? Let us dive deep and try to understand what these compilers are trying to achieve. Let us start with the very basics! Python is a programming language. It is not a compiler or interpreter in itself. Any improvements in Python language specifications or new features would result in nothing till the time those are incorporated in the interpreters or compilers. CPython Talking about CPython, it is the default implementation of Python specifications. It comes with the python distribution itself and interprets the python programs for the machine. Given that it is the default implementation of Python, whenever Python is upgraded with new features, language developers upgrade the CPython version as well and include it in the distribution that you download to set up Python on your machine. So, Python and CPython are different yet can be used together. They can work hand in hand and are upgraded together. What is Cython? is a separate compiler that can understand both Python and C specifications. It is more like a superset of Python compiler and allows you to work with Python and C together in a single project. Cython How does this help? Alright, so, Cython compiles the hybrid python and C code into a very efficient C program which in turn can be compiled to the machine code using C compilers. Given the speed gains we get using Cython it is used heavily to develop python extension modules. Many popular python modules like SciPy are written in Cython itself. Just to clarify a bit more, the mixed programs that you write in C and Python can be termed as a new programming language itself. That language is built using very popular Pyrex specifications. So, while Cython is a compiler in these terms, it has its own language specifications as well that, of course, comply with the Python specifications. Cython vs. Python debate gets to the next level because of speed gains too. It is also worth noting that the speed gains from Cython programs are up to 15x compared to the raw python code interpreted using CPython (the default interpreter). What is PyPy? works on the compilation principle. Like interpreters, JIT compilers also pick up the raw code but turns the code into efficient machine code just before the execution. PyPy Just in Time There are a lot of optimization techniques involved in JIT compilers and that makes JIT compilers work much faster than the raw interpreters. PyPy also consumes lesser memory at run time and hence has an advantage over raw Python interpreters. You might want to understand the fact that while PyPy gives performance gains and memory advantages, it is not always true. JIT compilers have overheads as the compilers need to load at the run time, and for that reason, if your python execution processes are very small as compared to the JIT compiler overheads, the gains might not be evident. However, for long-running processes, PyPy speed gains generally overweigh the initial overheads. Developers claim to get speed gains of around 5x any many benchmarking experiments. Closing Thoughts While there are multiple compilers, they all try to solve different problems and work differently. Looking at the statistics and usage, Cython seems to be the leader in the game and provides the fastest execution speeds, has a great community, good documentation, and is a potential candidate to get included in the default python distribution itself. PyPy is a . It will have its own space, improving over the period of time, and for sure here to stay. You might want to do some experiments with PyPy and see if it fits your use case. JIT compiler I hope you liked the article, do let me know your experience working with various Python compilers. Cheers!!

Analyzing Python Compilers: CPython Vs. Cython Vs. PyPy

Preparing for an interview, exam, or just curious to learn about what compilers and interpreters do? In a very basic sense, a compiler compiles the entire code altogether for later use whereas an interpreter reads the code line by line at run time. However, to understand the depths of how modern-day compilers and interpreters work for various programming languages, we need to go through a lot more details. Given below are the topics we will cover to settle the compiler vs interpreter debate – What is a Compiler The historical definition of a compiler defines it as software that converts the source code of a computer program to machine instructions or machine code. Source code is the code that developers write whereas machine code is all 0s and 1s, and consists of instructions for the computer CPU to perform tasks. The above definition essentially means that a compiler must understand the mechanics of the programming language in which source code is written, and for that reason, compilers are language-specific. Example: One of the C compilers is GCC and the java bytecode compiler is Javac. The process of compiling source code to machine-executable requires many in-between steps. The below diagram illustrates the typical flow of converting a human-readable program to the computer-executable code – Also note that the output in the form of machine code/executable is not 100% generic, it includes processor-specific instructions. AMD for example may not understand the binary/machine code generated for the Intel processors. So, compilers need to be platform-specific too. What about a transpiler… Nowadays, the term compiler is used to cover many other use cases too. It is also used for the software that translates source code from one format to another. The more appropriate term for this kind of software is transpiler but it is all blurry. The input to the transpiler doesn’t necessarily have to be a programming language source code and the output doesn’t necessarily have to be the machine code. Take an example of a Java compiler that translates “.Java” files to .class files. “.Class” file is not the final machine code, it is the intermediate bytecode, which needs further translation or interpretation for the machine. But still, we call the software that converts “.Java” to “.Class” a compiler. You can also find an alternate compiler for Java that translates Java code to C code by converting Java files to C language files. C language code then can further be compiled to machine code by the C compiler. Many would call this type of software a language source code converter (transpiler) rather than a compiler. What is an Interpreter? Again, if we look at the historical definition of an interpreter, an interpreter is software that reads the source code line by line and generates machine instructions at run time. So, it doesn’t pre-compile anything but interprets the provided input, on the fly, to instruct the CPU for performing tasks in sequence. The below diagram illustrates the simplistic flow of how the interpreter works- Like compilers, interpreters are not universal either and are designed to read specific input formats. For example, an interpreter can be designed to interpret JavaScript source code or Java bytecode or for that matter any other input format. Also, note that different programming languages implement interpreters differently, we will see a few in the subsequent sections of this article. Wait, What about JIT (Just in Time Compiler) Just in Time compiler is another variation of the compilers that you encounter in today’s world. JIT compiler typically reads pre-compiled bytecode generated by the compiler and translates it into the machine code, on the fly, during run time. Let us extend our understanding of the java compiler. Java compiler converts .java to .class. “.Class: contains the bytecode which runs on the Java virtual machine – JVM. But what does it have to do with JIT? JVMs’ earlier implementations used to read bytecode line by line and generate machine instructions on the fly, more like an interpreter. Soon after, JVMs started to implement JIT as well, to convert the entire bytecode into machine code just before the execution, in the memory. Why JIT? To improve execution performance! Pre-compiled machine code is optimized and the CPU performs faster as compared to an interpreter which executes bytecode line by line. JIT itself brings some overhead in terms of memory consumption, but the benefits are generally more than the overhead. Compiler vs Interpreter: Few Examples Let us see how the source code of various programming languages ultimately translates to machine instructions – C Language (Machine Code by Compiler) This is a straightforward use case where a C compiler translates C code to the machine instructions. There are multiple tasks performed by the compiler in between though – Preprocessing – Expand macros and include files. Also, remove the comments, etc. to generate one clean set of code.
Compiling – Convert source code to assembly
Assembly – Convert assembly code to CPU instructions
Linking – Put together all the modules either beforehand (Static Linking) or at run time (Dynamic Linking). Once developer-written source code is compiled, it is no longer needed for program execution. All that a CPU need is the final compiled code. Java (Compiler and JVM Interpreter) Java is clearly a two-step process. The first step is to generate platform-independent bytecode in the form of class files from java files. This is what makes Java a platform-independent language too from a developer perspective since developers just need to worry about generating standard class files. If classes are platform-specific then what about the different architecture of processors. Well, that is taken care of by the JVMs, you have platform-specific JVMs (Interpreters) that produce the output as per the instructional pattern required by the specific platform. You may want to read about Online Java Compilers Python (Compiler vs Interpreter) Python is more like Java from a life cycle perspective. There is a minor difference though, developers do not need to compile the code, python implementation takes care of it and converts the source code in .py files to compiled code in .pyc files behind the scene. .pyc files are then interpreted by the PVM – Python Virtual Machine, at runtime, similar to how Java bytecode is interpreted by the JVM. From a developer’s perspective, Python looks like an interpreted language but in practice, it is a compiled language and the code is actually pre-compiled. Furthermore, the Python ecosystem also has something called Jython that converts .py code to the bytecode that can run on JVM itself instead of PVM. Not only that, but it also has IronPython which makes python code run on .Net environments. You may also like to read: Python to Javascript converter
Compiling Python with C Closing Remarks Compiler vs interpreter is more like a scholarly discussion these days and brings in differing views without concrete definitions. The ultimate goal is to get to the machine code, whether you do it in one way or the other, and using one tool or multiple is purely dependent on the use cases. Furthermore, the journey of the source code is not as simple as it might seem, there are multiple steps in between including code cleanup, removing comments, the inclusion of referred files, pre-compiling, assembly of the code, modules linking, language conversion in some cases, bytecode generation in few others and whatnot. Also Published here Preparing for an interview, exam, or just curious to learn about what compilers and interpreters do? In a very basic sense, a compiler compiles the entire code altogether for later use whereas an interpreter reads the code line by line at run time. However, to understand the depths of how modern-day compilers and interpreters work for various programming languages, we need to go through a lot more details. Given below are the topics we will cover to settle the compiler vs interpreter debate – compiler vs interpreter What is a Compiler The historical definition of a compiler defines it as software that converts the source code of a computer program to machine instructions or machine code. Source code is the code that developers write whereas machine code is all 0s and 1s, and consists of instructions for the computer CPU to perform tasks. compiler The above definition essentially means that a compiler must understand the mechanics of the programming language in which source code is written, and for that reason, compilers are language-specific. Example: One of the C compilers is GCC and the java bytecode compiler is Javac. The process of compiling source code to machine-executable requires many in-between steps. The below diagram illustrates the typical flow of converting a human-readable program to the computer-executable code – Also note that the output in the form of machine code/executable is not 100% generic, it includes processor-specific instructions. AMD for example may not understand the binary/machine code generated for the Intel processors. So, compilers need to be platform-specific too. What about a transpiler… Nowadays, the term compiler is used to cover many other use cases too. It is also used for the software that translates source code from one format to another. The more appropriate term for this kind of software is transpiler but it is all blurry. The input to the transpiler doesn’t necessarily have to be a programming language source code and the output doesn’t necessarily have to be the machine code. Take an example of a Java compiler that translates “.Java” files to .class files. “.Class” file is not the final machine code, it is the intermediate bytecode, which needs further translation or interpretation for the machine. But still, we call the software that converts “.Java” to “.Class” a compiler. You can also find an alternate compiler for Java that translates Java code to C code by converting Java files to C language files. C language code then can further be compiled to machine code by the C compiler. Many would call this type of software a language source code converter (transpiler) rather than a compiler. What is an Interpreter? Again, if we look at the historical definition of an interpreter , an interpreter is software that reads the source code line by line and generates machine instructions at run time. interpreter So, it doesn’t pre-compile anything but interprets the provided input, on the fly, to instruct the CPU for performing tasks in sequence. The below diagram illustrates the simplistic flow of how the interpreter works- Like compilers, interpreters are not universal either and are designed to read specific input formats. For example, an interpreter can be designed to interpret JavaScript source code or Java bytecode or for that matter any other input format. Also, note that different programming languages implement interpreters differently, we will see a few in the subsequent sections of this article. Wait, What about JIT (Just in Time Compiler) Just in Time compiler is another variation of the compilers that you encounter in today’s world. JIT compiler typically reads pre-compiled bytecode generated by the compiler and translates it into the machine code, on the fly, during run time. JIT compiler Let us extend our understanding of the java compiler. Java compiler converts .java to .class. “.Class: contains the bytecode which runs on the Java virtual machine – JVM. But what does it have to do with JIT? JVMs’ earlier implementations used to read bytecode line by line and generate machine instructions on the fly, more like an interpreter. Soon after, JVMs started to implement JIT as well, to convert the entire bytecode into machine code just before the execution, in the memory. Why JIT? To improve execution performance! Pre-compiled machine code is optimized and the CPU performs faster as compared to an interpreter which executes bytecode line by line. JIT itself brings some overhead in terms of memory consumption, but the benefits are generally more than the overhead. Compiler vs Interpreter: Few Examples Let us see how the source code of various programming languages ultimately translates to machine instructions – C Language (Machine Code by Compiler) This is a straightforward use case where a C compiler translates C code to the machine instructions. There are multiple tasks performed by the compiler in between though – Preprocessing – Expand macros and include files. Also, remove the comments, etc. to generate one clean set of code. Compiling – Convert source code to assembly Assembly – Convert assembly code to CPU instructions Linking – Put together all the modules either beforehand (Static Linking) or at run time (Dynamic Linking). Preprocessing – Expand macros and include files. Also, remove the comments, etc. to generate one clean set of code. Compiling – Convert source code to assembly Assembly – Convert assembly code to CPU instructions Linking – Put together all the modules either beforehand (Static Linking) or at run time (Dynamic Linking). Once developer-written source code is compiled, it is no longer needed for program execution. All that a CPU need is the final compiled code. Java (Compiler and JVM Interpreter) Java is clearly a two-step process. The first step is to generate platform-independent bytecode in the form of class files from java files. This is what makes Java a platform-independent language too from a developer perspective since developers just need to worry about generating standard class files. If classes are platform-specific then what about the different architecture of processors. Well, that is taken care of by the JVMs, you have platform-specific JVMs (Interpreters) that produce the output as per the instructional pattern required by the specific platform. You may want to read about Online Java Compilers Online Java Compilers Python (Compiler vs Interpreter) Python is more like Java from a life cycle perspective. There is a minor difference though, developers do not need to compile the code, python implementation takes care of it and converts the source code in .py files to compiled code in .pyc files behind the scene. .pyc files are then interpreted by the PVM – Python Virtual Machine, at runtime, similar to how Java bytecode is interpreted by the JVM. From a developer’s perspective, Python looks like an interpreted language but in practice, it is a compiled language and the code is actually pre-compiled. Furthermore, the Python ecosystem also has something called Jython that converts .py code to the bytecode that can run on JVM itself instead of PVM. Not only that, but it also has IronPython which makes python code run on .Net environments. You may also like to read: Python to Javascript converter Compiling Python with C Python to Javascript converter Python to Javascript converter Compiling Python with C Compiling Python with C Closing Remarks Compiler vs interpreter is more like a scholarly discussion these days and brings in differing views without concrete definitions. The ultimate goal is to get to the machine code, whether you do it in one way or the other, and using one tool or multiple is purely dependent on the use cases. Furthermore, the journey of the source code is not as simple as it might seem, there are multiple steps in between including code cleanup, removing comments, the inclusion of referred files, pre-compiling, assembly of the code, modules linking, language conversion in some cases, bytecode generation in few others and whatnot. Also Published here Also Published here here

How Compilers Differ From Interpreters

Both Django and Flask are used by enterprises to develop websites, single-page applications, app backends, microservices, and APIs in general. One can use either Django or Flask in a web development project but there are many differences due to which developers choose one over the other in different situations. Django is a full-stack framework and provides almost everything out of the box to create a full-featured web application. Flask is a microframework with a small core and is used mainly for microservices and APIs development. In this article, we will cover key differences, similarities, fitment of each framework for different use cases, scalability, performance, learning curve, community support, and much more. Let us start off with a brief introduction to Flask and Django. What is Django? Django was created at newspaper by Simon Willison and Adrian Holovaty back in 2005. As of today, it is maintained by Django Software Foundation and available for free public use under the BSD license. Lawrence Journal-World Django is popular among developers for rapid applications development since it comes packed with many ready-to-use components to achieve common programming tasks. With Django, the key concern of developers generally shrinks down to the business logic and the rest is taken care of by the framework itself. It wouldn’t be surprising to say that one can put together a fully functional web application in a matter of hours. Is Creating an Application in a Matter of Hours Possible? If we park business logic and creativity aside, most web applications offer similar kinds of features, some offer more features than the others though. Keeping that in mind, Django developers have done a fantastic job and created components to serve almost all features required to build a full-stack web application, including the admin interface. Talking a bit more about this, Django auto generates an admin interface based on the model of your web application. The generated interface mostly provides all features required for CRUD operations and can be further customized as per the project needs. Other common website features like RSS feeds, user authentication & authorization, site maps, page cache, etc. are also generated without writing much code. Talking about automation around the application model, Django provides an object-relational mapper which is used to define the database layout in a very simple Python code syntax. The layout can be installed in one click to give you the database schema and rich Python APIs to access the data. Django is also known for the security features it offers. You get out-of-the-box protection against vulnerabilities like SQL injection, clickjacking, and CSRF among others. Who Uses Django? Speed development and reduced time to market make Django a framework of choice for enterprises. It is not surprising to see many successful content management systems, high-traffic websites, administrative tools, social networks, computing platform backbends, and scalable open APIs are built using Django. The popularity of Django can be gauzed from the fact that applications like Instagram, Spotify, Pinterest, Mozilla, Netflix, and Disqus use Django in the overall tech stack to achieve various programming tasks. As we continue Flask vs Django debate, you will notice that while both are used in web development, there are major differences in terms of how developers and enterprises utilize these frameworks. Read more about Django here – . Find tutorials and courses to learn Django: Django Official Django Tutorials. What is Flask Framework? Flask is also a python-based framework, like the mighty Django, and is used heavily in web applications development. It was created by Armin Ronacher back in 2010 and is currently available under the BSD license for free use. Flask is a microframework and started off as a wrapper around and libraries. Developers have improved and added many more features to Flask over the last decade. WerZeug is a utility library and provides web server interface gateway capabilities whereas Jinja is a templates engine. Werkzeug Jinja Flask is best used for developing microservices and comes with a very small core. Though it allows developers to use the extension modules and scale the application as per the requirements. A small core with additional extensions can be used to build a full-stack application like with Django though it would take much more effort as compared to Django. So, you need to be careful about making Flask vs Django comparisons up front. Who Uses Flask? Flask came into existence five years after Django but is fast becoming the framework of choice for Python web developers. If GitHub stars give you any indication, as of writing, Flask has 56k stars vs 58k stars for Django. Given the five-year gap, Flask seems to be impressing developers more than Django. Flask is also used by enterprises of all sizes. The key reason for higher Flask penetration is its easy fitment in the existing development stack. Many developers choose Flask only to solve part of the puzzle rather than building the entire full stack with Flask. Airbnb for example has used Flask to build an admin library for the Airflow project. Twilio on the other hand, uses Flask for its backend APIs. Other organizations using Flask include the likes of Uber, Pinterest, and Netflix. Read more about Flask here: . Find tutorials here: . Flask Official Flask Tutorials Flask vs Django: Opinionated or Not? This is one of the key questions every developer must ponder before finalizing the framework. One calls a framework opinionated if it enforces ways of programming or uses a defined set of components to achieve various programming tasks. The enforced components or ways are generally perceived as best or right, at least per the framework developers. Unopinionated frameworks, on the other hand, provide a lot of abstraction and do not enforce specific ways to achieve programming tasks. You might still get suggestions or prescriptions from the framework developers, but the abstract nature of the framework allows you to deviate easily. Django Modular is the word that best describes the Django framework. Its architecture is such that components are decoupled from each other and allows for plug and play to a great extent. The above statement can be misguiding though and might make Django look like an unopinionated framework, but that is not completely true. You would encounter many areas in Django when touching the ways of it is not possible and you have to follow the prescribed approach only. There are also areas where you can make a choice among the various prescribed options to achieve the given programming task, but you still cannot go beyond the boundaries of Django. As a side note, my personal take is that being opinionated or not doesn’t greatly impact Flask vs Django decision in a project but continue reading to make your own opinion. Over the last decade, Django has evolved and brought in more flexibility, and I am tempted to categorize it as a “somewhat opinionated” framework rather than calling it fully opinionated. While the best way to speed development is moving ahead with opinionated mode but experienced developers tend to deviate keeping in mind overall project needs and scalability. Flask You will find Flask completely different from Django in this regard. Flask developers created Flask at the time when Django was at its peak and already ruling the world of web development. So, what was the need for Flask? Developers needed a more flexible framework to fit into the existing stack and solve part of the puzzle with ease. Flask is minimalist and doesn’t enforce on you what extensions or third-party modules you should choose to scale. Developers can pick and choose various third-party extensions to create a full-stack web application. For example, one can choose SQLAlchemy to connect to the databases or may go with other less popular options. With Flask, everything is very loosely coupled, highly unopinionated, flexible, and left to the hands of developers. This also happens to be the key difference between Flask and Django among many others. Learning Curve: Flask vs Django Python Framework Flask and Django both inherit the ease of programming that Python offers. Both frameworks can utilize various modules and components offered by the Python ecosystem, Flask is much more flexible though! Developers use Flask to achieve specific tasks like microservices behind mobile, web, and desktop apps. On the other hand, developers use Django to create full-featured large-scale applications. It might become cumbersome to put together a full-stack application that provides an admin interface, front end, RSS feeds and page caching, etc. in Flask. Furthermore, Django is huge and developers need to understand all that to get full control of the application. For that reason, if we keep aside the size and complexity of the application, Flask is much easier to learn. Scalability and Application Performance Scalability depends on the underlying programming language, application construct/architecture, and hardware configuration. Given that both frameworks are based on Python and considering we intend to provide similar hardware configuration, our discussion shrinks mostly to the application structure and architecture. Furthermore, when we talk scalability, we mean ease of scaling application features by adding more code. Scalability also means ease of adding more developers as well as the capability to handle concurrent user requests. Keeping it short, Flask is more flexible and lets developers make choices, so the scalability and performance would get down to the choices made by developers. If you are working on a huge application in Flask, you must make all the right choices to create a scalable application. Django on the other hand gives you consistent application performance and scalability if you stick to the Django prescription and do not deviate much. Having said all this, you wouldn’t be able to mark Django as a clear winner over Flask or the other way round. Conclusion Django is the big brother in both size and age and for that reason has a bigger footprint overall. However, Flask is fast growing in popularity and the rate of growth of Flask is much faster than Django. With the current trend, Flask can overtake Django in terms of popularity, number of applications, jobs, and salary in the next couple of years. This brings us to the end of this comparison. We would be happy to read your opinion on the key differences between Flask and Django. So, please do comment and share the knowledge with others via social media channels. Thank you for reading!! You might also like – Django Framework Tutorials and Courses Flask Tutorials and Courses

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