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How To Use Python Lambda Functions

Modern-day programming languages like Python and other programming languages like C, C#, and Java have added lambda functions to their syntax to increase the functionality of the languages.Python Lambdas are anonymous functions which are small and restricted and come without an identifier.Lambda Calculus is a universal model of computation which was formalized by Alonzo Church in the 1930s. In simple terms, it is a formal system in mathematical logic that is based on pure abstraction and can encode any Turing Machine.The two models of computations— lambda calculus and Turing machines can be translated into one another. This equivalence is known as the Church-Turing Hypothesis. However, the lambda calculus is Turing complete and does not keep any state, unlike the Turing machine.The Turing machine has led to the discovery of the imperative style of programming. This type of approach requires managing states and comprises of programming with statements which drive the program in steps, using a comprehensive and detailed instruction format. Languages like C, Fortran, or Python uses this type of programming technique.On the other hand, the lambda philosophy focuses on data abstraction, transformation, and purity. Their approach is declarative in nature and functional languages like Haskell, Lisp, or Erlang directly inherit them.However, functional features are also a part of the imperative programming style, especially with the implementation of lambda functions in Python or Java.Although Python is not a functional language, some functional features like map(), filter(), reduce(), and the lambda operator were added to Python in the year 1994. To learn more about such features, join our Python certification course.What are Lambdas in Python?Lambda expressions or lambda forms are used to create anonymous functions. Let us first see an identity function. It is an identity relation or identity map that returns the same argument that was originally provided as input. This is a standard Python definition defined using the def keyword:>>> def identity(z): ...    return zOn the other hand, a Python lambda function can have any number of parameters and is written in a single line of code. However, the body of the function can have only one expression.An example of a Python lambda construct:>>> lambda z: zThe expression above comprises of three essential parts:The lambda keyword.The parameters.The function body.Another example of a lambda function with an argument:>>> (lambda z: z + 1)(5) 6Here, the argument and the function are surrounded by parentheses. The function adds 1 to the argument.Since lambda function is an expression, you are allowed to name it whatever you want. Let us perform reduction to compute the value of an expression by naming the lambda function:>>> add = lambda x: x + 1 >>> add(5) 6We can also declare multi-argument function by listing arguments and separating them with commas ( , )  but without using parentheses:>>> name = lambda first_name, last_name: f'The full name is {first.title()} {last.title()}' >>> name('Alex', 'Pandian') 'The full name is Alex Pandian'Here, the function add takes two arguments and returns a string by interpolating the two parameters first_name and last_name.What are Anonymous Functions in Python?A function without a name is known as an anonymous function in Python. It is created with the help of the keyword lambda.An example of an anonymous function with two arguments:>>> lambda a, b: a + bHere, the function has two arguments x and y and is defined using lambda and is not bound to any variable. The function accepts the two arguments and returns their sum.However, you can invoke the same function using an interactive interpreter-only feature of Python via the underscore(_):>>> _(4, 5) 9In the interactive interpreter, the single underscore(_) is bound to the last expression evaluated which means it points to the last lambda function.The functions which take one or more arguments and returns the result are known as higher-order functions.A lambda function can be a higher-order function:>>> higher_order_function = lambda x, f1: x + f1(x) >>> higher_order_function(3, lambda x: x * x) 12 >>> higher_order_function(3, lambda x: x + 3) 9The higher-order functions are considered as built-in functions in the Python Standard Library. Some of the examples are filter(), map(), reduce() or key functions like min(), max(), sort() or sorted().Python Lambdas vs Regular FunctionsA Lambda function might be considered a function with a syntax within a programming language which makes the language “sweeter” for human use. It is created to make things easier to read and express.However, there are a number of precise differences between the regular functions and Python lambdas. The following section highlights them all.A. Difference in FunctionsIn fundamental terms, there is almost no difference between a regular function with a single return line and a lambda function. However, let us check what difference it would make while we write it in code:>>> import dis >>> sub = lambda a, b: a + b >>> type(sub) <class 'function'> >>> dis.dis(sub)   1           0 LOAD_FAST                0 (x)               2 LOAD_FAST                1 (y)               4 BINARY_ADD               6 RETURN_VALUE >>> sub <function <lambda> at 0x000001E47F3EC268>The dis module subjects a readable version of the Python bytecode generated by the compiler, which in turn displays the low-level instructions used by the Python interpreter.Let us try the same with a regular function:>>> import dis >>> def sub(x,y): return a + b >>> type(sub) <class 'function'> >>> dis.dis(sub)   1           0 LOAD_FAST                0 (x)               2 LOAD_FAST                1 (y)               4 BINARY_ADD               6 RETURN_VALUE >>> sub <function <lambda> at 0x000001E47F3EC268>You can see the Python bytecode is similar in both cases. However, what differs is the style of naming. With def, the function is named as sub whereas lambda is used in case of Python built-in function.B. Difference in TracebackAn example of exception raising using lambda:>>> divide_by_zero = lambda a: a / 0 >>> divide_by_zero(2) Traceback (most recent call last):     File "<stdin>", line 1, in <module>     File "<stdin>", line 1, in <lambda> ZeroDivisionError: division by zeroWhen you’re using a lambda expression, the traceback of an exception identifies only the function, resulting in the exception as <lambda>.On the other hand, when you’re using a regular function, the traceback provides the name of the function in particular:>>> def divide_by_zero(a): return a / 0 >>> divide_by_zero(2) Traceback (most recent call last):     File "<stdin>", line 1, in <module>     File "<stdin>", line 1, in divide_by_zero ZeroDivisionError: division by zeroThe traceback returns the function name divide_by_zero.C. Difference in SyntaxA lambda expression differs in syntax from a normal function in a number of ways: Expressions and StatementsA lambda function can contain only expressions and cannot include any statements. It will raise a SyntaxError upon using statements like return, assert or raise.Let us see an example of using an assert statement in a lambda function:>>> (lambda a: assert a == )(2) SyntaxError: invalid syntaxThe interpreter raises an error while parsing the code since assert is not a valid keyword in a lambda expression. Single ExpressionIn a Python lambda expression, you can distribute the expression across several lines using parentheses or a multi-line string. But the function will still remain as a single expression:>>> (lambda a: ... (a % 2 and 'odd' or 'even'))(5) 'odd'The lambda expression above spreads over two lines but still remains a single expression which evaluates a number and results into odd or even depending upon the argument. AnnotationsYou can use Python type hints and type checking when you’re using normal functions:def name(first_name: str, last_name: str) -> str:     return f'{first_name.title()} {last_name.title()}'If there are any errors with the function name, it can be caught using packages like mypy or pyre. On the other hand, if you’re using a lambda expression, a SyntaxError will be raised:>>> lambda first_name: str, last_name: str: first_name.title() + " " + last_name.title() -> str  File "<stdin>", line 1     lambda first: str, last: str: first.title() + " " + last.title() -> str SyntaxError: invalid syntax IIFEImmediately Invoked Function Execution or IIFE is a JavaScript function that gets executed as soon as it is defined. However, you cannot use it outside the Python Interpreter.An example of such is:>>> (lambda a, b: a + b)(4,6) 10The IIFE is mainly used to pass the definition of a Python function into a higher-order function like map(), filter() or reduce().D. Difference in ArgumentsPython supports different ways of passing arguments in a lambda expression just like regular functions. Some of which are as follows:Named arguments.Variable list of arguments.Positional arguments.Variable list of keyword arguments.Keyword only arguments.An example to illustrate different ways of passing arguments in a lambda function:>>> (lambda a, b, c: a + b + c)(1, 2, 3) 6 >>> (lambda a, b, c=3: a + b + c)(1, 2) 6 >>> (lambda a, b, c=3: a + b +c)(1, b=2) 6 >>> (lambda *arg: sum(arg))(1,2,3) 6 >>> (lambda **kwarg: sum(kwarg.values()))(number_one=1, number_two=2, number_three=3) 6 >>> (lambda a, *, b=0, c=0: a + b + c)(1, b=2, c=3) 6E. Difference in DecoratorsA decorator is a function that accepts another function and allows adding a behavior to a function or class. Its syntax is @decorator which is written before a function.An example of a decorator is:def decorator(a):     def wrap(*arguments):         print(f"Calling function '{a.__name__}'")         return a(arguments)     return wrap @decorator def decorated_func(x):     print(f"With argument '{x}'")If we invoke decorated_func(“Hello World”), the output will be as follows:Calling function 'decorated_func' With argument 'Hello World'The function decorated_func() prints With argument 'Hello World', but the decorator adds a behavior and also prints  Calling function 'decorated_func'An example of applying decorator in lambda function is mentioned below:# Defining a decorator def trace(a):   def wrap(*arg, **kwarg): print(f"[TRACE] func: {f.__name__}, args: {arg}, kwarg: {kwarg}") return a(*arg, **kwarg)   return wrap # Applying decorator to the function @trace def add(t):     return t + 2 # Calling the decorated function add(3) # Applying decorator to a lambda function print((trace(lambda y: y ** 2))(3))If we execute the following code, we’ll get the following output:[TRACE] func: add, arg: (3,), kwarg: {} [TRACE] func: <lambda>, arg: (3,), kwarg: {} 9The function add is decorated with @trace and invoked. The lambda function gets implemented to the decorator. The lambda function name is <lambda> and the regular function is add.F. Difference in ClosuresA closure is a function object that remembers values and is bound to a specified value in the enclosing scope of that function. In simple words, closure is a record that stores a function and defines the environment in which they run.Both lambda functions and regular functions can be implemented as closures.An example to show a closure construct with a regular function:def outer_function(a):     b = 4     def inner_function(c):         print(f"a = {a}, b = {b}, c = {c}")         return a + b + c     return inner_function     for i in range(5):     closure = outer_function(i)     print(f"closure({i+5}) = {closure(i+5)}")inner_function() is a nested function that is being returned by outer_function(). The nested function evaluates the sum of three arguments. The outer_function() is invoked 5 times in a for loop resulting in the output:x = 0, y = 4, z = 5 closure(5) = 9 x = 1, y = 4, z = 6 closure(6) = 11 x = 2, y = 4, z = 7 closure(7) = 13 x = 3, y = 4, z = 8 closure(5) = 15 x = 4, y = 4, z = 4 closure(5) = 017An example to show closure using a Python lambda function:def outer_function(a):     b = 4     return lambda c: a + b + c   for i in range(5):     closure = outer_function(i)     print(f"closure({i+5}) = {closure(i+5)}")The output of the above code will be:closure(5) = 9 closure(6) = 11 closure(7) = 13 closure(7) = 15 closure(7) = 17In both situations, the normal function and the lambda function behaves precisely similar.G. Difference in Evaluation TimeA lambda function’s behavior as a closure may be counter-intuitive, especially while working with loops.  Using a regular function:>>> def wrap(x): ...    def f(): ...        print(x) ...    return f ... >>> numbers = 'one', 'two', 'three' >>> f1 = [] >>> for x in numbers: ...    f1.append(wrap(x)) ... >>> for f in f1: ...    f() ... one two threeWhen the function f1 gets appended to the list, x is evaluated at definition time.Let us implement the same using lambda function:>>> numbers = 'one', 'two', 'three' >>> f1 = [] >>> for x in numbers: ...    f1.append(lambda: print(x)) ... >>> for f in f1: ...    f() ... three three threeSince n is a free variable bound at runtime  when we invoke the function f, an unexpected error occurs printing three all the time.However, to get rid of this situation, you can assign the free variable during definition time:>>> numbers = 'one', 'two', 'three' >>> f1 = [] >>> for x in numbers: ...    f1.append(lambda x=x: print(x)) ...  >>> for f in f1: ...    f() ... one two threeThe lambda argument is initialized with a default value. So, when the function is invoked, the default value x set at definition time is used.Testing LambdasYou can test Python lambdas similarly like regular functions with the help of two modules— unittest and doctest.unittestTesting Python lambda function using unittest:import unittest add = lambda x: x + 2 class Lambda_Testing(unittest.TestCase):     def test_add1(self):         self.assertEqual(add(2), 4)     def test_add2(self):         self.assertEqual(add(2.2), 4.2)     def test_add3(self):         # Should fail         self.assertEqual(add(3), 6) if __name__ == '__main__':     unittest.main(verbosity=2)The function Lambda_Testing tests three methods, each implemented as a lambda function. After execution, the Python file produces the following output:$ python lambda_unittest.py test_add3 (__main__.Lambda_Testing) ... FAIL test_add1 (__main__.Lambda_Testing) ... ok test_add2 (__main__.Lambda_Testing) ... ok ====================================================================== FAIL: test_add_three (__main__.Lambda.Testing) ---------------------------------------------------------------------- Traceback (most recent call last):   File "lambda_unittest.py", line 18, in test_add_three     self.assertEqual(add3(3), 6) AssertionError: 5 != 6 ---------------------------------------------------------------------- Ran 3 tests in 0.001s FAILED (failures=1)There are two successful test cases and one failure for test_add3. However, if we change the expected result from 6 to 5, the Lambda_Testing will be satisfied for all tests.doctestThe doctest module tests Python lambdas by extracting Python code from docstring:add = lambda a: a + 2 add.__doc__ = """Add 2 to a number.     >>> add(2)     4     >>> add(2.2)     4.2     >>> add(3) # Should fail     6     """ if __name__ == '__main__':     import doctest     doctest.testmod(verbose=True)You can execute the code above using doctest.testmod(), the output will be:$ python lambda_doctest.py Trying:     add(2) Expecting:     4 ok Trying:     add(2.2) Expecting:     4.2 ok Trying:     add(3) # Should fail Expecting:     6 ********************************************************************** File "lambda_doctest.py", line 16, in __main__.add Failed example:     add(3) # Should fail Expected:     6 Got:     5 1 items had no tests:     __main__ ********************************************************************** 1 items had failures:    1 of   3 in __main__.add 3 tests in 2 items. 2 passed and 1 failed. ***Test Failed*** 1 failures.Here, the failure of the test result is also because of the same test failures explained above in the execution of test cases. Although  it is possible to use docstrings for lambda functions, Python recommends using docstrings for regular functions rather than lambda functions.Where should you not use Lambda Expressions?You can simply say that if there is a situation where lambda expressions are not supported, normal functions would be more suitable in such places.However, there might be situations when you need to avoid lambda expressions in your code:When it doesn’t follow the Python Style Guide ( PEP 8 ).When it is difficult to read and interpret.When it doesn’t support readability.Exception Raising An example of raising an exception in a Python lambda:>>> def throw_except(excpt): raise excpt >>> (lambda: throw_except(Exception('Exception is raised')))() Traceback (most recent call last):     File "<stdin>", line 1, in <module>     File "<stdin>", line 1, in <lambda>     File "<stdin>", line 1, in throw Exception: Something bad happenedThough there are some really clever ways of raising an exception in Python lambda, it is always suggested to use a normal function. This is because a statement is never correct in syntax in the body of a Python lambda function.Cryptic StyleLambda functions are brief in nature, because of which it might be difficult to read. An example of a bad writing style lambda  expression:>>> (lambda _: list(map(lambda _: _ // 2, _)))([1,2,3,4,5,6,7,8,9,10]) [0, 1, 1, 2, 2, 3, 3, 4, 4, 5]Let us modify this lambda code by naming the variables:>>> (lambda some_list: list(map(lambda a: a // 2, list)))([1,2,3,4,5,6,7,8,9,10]) [0, 1, 1, 2, 2, 3, 3, 4, 4, 5]The code is still difficult to read and understand. However, if we execute the same code using a regular function by distributing the logic across several lines, it would greatly impact the readability of the code:>>> def divide(list):       divide = lambda a: a // 2       return map(divide, list) >>> list(divide([1,2,3,4,5,6,7,8,9,10]))) [0, 1, 1, 2, 2, 3, 3, 4, 4, 5]Though this method is not the best, it surely enhances the readability of the code in comparison to the lambda expressions.Python ClassAn example of a Python class using lambda:class Car:     """Car with methods as lambda functions."""     def __init__(s, brand, year):         s.brand = brand         s.year = year     brand = property(lambda s: getattr(s, '_brand'),                     lambda s, value: setattr(s, '_brand', value))     year = property(lambda s: getattr(s, '_year'),                     lambda s, value: setattr(s, '_year', value))     __str__ = lambda s: f'{s.brand} {s.year}'  # 1: error E731     honk = lambda s: print('Honk!')     # 2: error E731If you use a style guide tool like flake8 for checking the code above, it will result in following errors for __str__ and honk:E731 do not assign a lambda expression, use a defSince multiple strings like '_brand and'_year' are used, the properties that are difficult to read are inclined to errors.Ultimately, considering a general rule, you should have a preference for regular functions instead of lambda functions in the context of Python programming.Where should you use lambda expressions?Lambdas provide a shortened syntax for writing functions, which return a single expression. Since Python supports functional programming, lambdas are one of the most common use cases in such a paradigm of programming. Lambdas allow a fine way to pass functions as arguments to another function by creating a one-time function and passing it as a parameter.Let us discuss some situations where using lambdas are very appropriate and also suggested by Python.Functional Constructs Built-in functions like map(), filter(), and functools.reduce(), found in the functools module, are often used along with lambda expressions.Three examples of those functions along with lambda expressions:>>> list(map(lambda x: x.upper(), ['alex', 'bob', 'charles'])) ['ALEX', 'BOB', 'CHARLES'] >>> list(filter(lambda x: 'o' in x, ['alex', 'bob', 'charles'])) ['BOB'] >>> from functools import reduce >>> reduce(lambda acc, x: f'{acc} | {x}', ['alex', 'bob', 'charles']) 'alex | bob | charles'There are other functional constructs which are alternatives to these constructs and are considered more Pythonic in nature.Key FunctionsPython key functions are higher-order functions that directly determine the algorithm driven by the key function. They accept a named parameter known as key, which in turn takes a lambda function.Some of the most commonly used key functions are sort(), sorted, min(), max(), nlargest() and nsmallest().Let us sort a list of IDs using sorted(), in which each ID is a concatenated string of string id and a  number. To sort the list of IDs with the help of the corresponding number associated, we’ll assign lambda to the key argument:>>> ids = ['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200'] >>> print(sorted(ids))#Sorting ['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200'] >>> sorted_ids = sorted(ids, key=lambda x: int(x[2:]))#Integer sorting >>> print(sorted_ids) ['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200']UI FrameworksSome of the UI frameworks that use lambda expressions for their UI events are Tkinter, wxPython or .NET Windows Forms.An example of a Tkinter program using lambda:import tkinter as t import sys window = t.Tk() window.grid_columnconfigure(0, weight=1) window.title("Lambda Calculus") window.geometry("500x200") label = t.Label(window, text="HELLO WORLD") label.grid(column=0, row=0) button = t.Button(     window,     text="Reverse the string",     command=lambda: label.configure(text=label.cget("text")[::-1]), ) button.grid(column=0, row=1) window.mainloop()When the Reverse button is clicked, an event gets fired that triggers the lambda, which in turn changes the label from HELLO WORLD  to DLROW OLLEH.Though lambda can handle firing events, it is always recommended to use a normal function instead as the lambda ends up being self-contained in a shortcode structure.The Python InterpreterPython lambdas are considered as bliss when you use it inside the Python Interpreter. In comparison to regular functions, creating a single-line lambda function to look into some code is much easier. The lambdas in interpreter are just like scrap paper that is useless after being used once and are later thrown away.The timeit moduletimeit is a Python module that is used to time small fragments of Python code. In simple words, it measures the evaluation time of small code snippets.An example of timeit.timeit() being called directly:>>> from timeit import timeit >>> timeit("factorial(100)", "from math import factorial", number = 5) 1.799199999652501e-05A NameError exception would be raised if the full context of the statement is not passed as a string. So the environment should be set up that is required by the main function to be timed.However a  cleaner and more readable solution to the above code is by using a lambda function:>>> from math import factorial >>> timeit(lambda: factorial(100), number=5) 1.799199999652501e-05Although the lambda version takes less execution time, the string version has a benefit of executing the functions again.What are the alternatives of lambda expressions?The higher-order functions like map(), filter(), and functools.reduce() can be used in place of lambdas by changing the form with lists and generators.Using map() map() takes two arguments— a function and an iterable like string, lists or tuples. It executes the function object for each element and returns a modified list of elements.An example to illustrate map() with lambda by transforming a string of integers into its capitalized form:>>> list(map(lambda x: x.capitalize(), ['alex', 'bob', 'charles'])) ['Alex', 'Bob', 'Charles']list() should be invoked so that the iterator gets converted into the modified list which can be shown as output in the Python Interpreter.Using filter()filter() accepts two arguments— a boolean-valued function and an iterable. It is an excellent way to filter out all elements of a sequence for only those which returns True.An example of filter() with lambda which returns all odd numbers from a list:my_list = [5, 10, 21, 95, 51, 69, 72, 22, 70, 99] final_list = list(filter(lambda x: (x%2 != 0) , my_list)) print(final_list) [5, 21, 95, 51, 69, 99]The filter() function returns the list of elements, just like map().Using reduce()reduce() is a functools module function that takes two arguments just like map() and filter(). However, it might also take an initializer as a third argument as the initial value of the accumulator.An example of reduce() with lambda to calculate the sum of a list:from functools import reduce my_list = [5, 8, 12, 25, 50, 500] sum = reduce((lambda x, y: x + y), my_list) print(sum) 600The Pythonic Nature of Lambda ExpressionsThe style guide of Python, PEP 8 discourages lambdas and recommends using normal functions since it is more beneficial. On the other hand, though lambda functions have some good uses in Python, they have a lot of limitations.Lambdas are meant to be used as anonymous functions that will be used especially with functions like map(), filter(), and reduce(). So, you can consider them to be Pythonic since they are quick and elegant in their use and they depend on how you use them.In simple terms, lambdas are considered Pythonic if there are no Pythonic elements available other than that.SummaryLet us sum up what we’ve learned in this article so far:Lambdas and Anonymous Functions.Where should we use a lambda.When should we use a  regular function.Lambda expression abuses.Appropriate uses of Lambdas.Lambda alternatives.Pythonic nature of Lambda.Python lambdas are considered as salt. If used in small amounts, it will enhance the flavor but if used more, the dish will be spoiled. If you are intending to know more about Lambda and Lambda Calculus, you can refer to the following links—Lambda CalculusLambdasTo gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course.

How To Use Python Lambda Functions

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How To Use Python Lambda Functions

Modern-day programming languages like Python and other programming languages like C, C#, and Java have added lambda functions to their syntax to increase the functionality of the languages.

Python Lambdas are anonymous functions which are small and restricted and come without an identifier.

Lambda Calculus is a universal model of computation which was formalized by Alonzo Church in the 1930s. In simple terms, it is a formal system in mathematical logic that is based on pure abstraction and can encode any Turing Machine.

The two models of computations— lambda calculus and Turing machines can be translated into one another. This equivalence is known as the Church-Turing Hypothesis. However, the lambda calculus is Turing complete and does not keep any state, unlike the Turing machine.

The Turing machine has led to the discovery of the imperative style of programming. This type of approach requires managing states and comprises of programming with statements which drive the program in steps, using a comprehensive and detailed instruction format. Languages like C, Fortran, or Python uses this type of programming technique.

On the other hand, the lambda philosophy focuses on data abstraction, transformation, and purity. Their approach is declarative in nature and functional languages like Haskell, Lisp, or Erlang directly inherit them.

However, functional features are also a part of the imperative programming style, especially with the implementation of lambda functions in Python or Java.

Although Python is not a functional language, some functional features like map(), filter(), reduce(), and the lambda operator were added to Python in the year 1994. To learn more about such features, join our Python certification course.

What are Lambdas in Python?

Lambda expressions or lambda forms are used to create anonymous functions. Let us first see an identity function. It is an identity relation or identity map that returns the same argument that was originally provided as input. This is a standard Python definition defined using the def keyword:

>>> def identity(z):
...    return z

On the other hand, a Python lambda function can have any number of parameters and is written in a single line of code. However, the body of the function can have only one expression.

An example of a Python lambda construct:

>>> lambda z: z

The expression above comprises of three essential parts:

  • The lambda keyword.
  • The parameters.
  • The function body.

Another example of a lambda function with an argument:

>>> (lambda z: z + 1)(5)
6

Here, the argument and the function are surrounded by parentheses. The function adds 1 to the argument.

Since lambda function is an expression, you are allowed to name it whatever you want. Let us perform reduction to compute the value of an expression by naming the lambda function:

>>> add = lambda x: x + 1
>>> add(5)
6

We can also declare multi-argument function by listing arguments and separating them with commas ( , )  but without using parentheses:

>>> name = lambda first_name, last_name: f'The full name is {first.title()} {last.title()}'
>>> name('Alex', 'Pandian')
'The full name is Alex Pandian'

Here, the function add takes two arguments and returns a string by interpolating the two parameters first_name and last_name.

What are Anonymous Functions in Python?

A function without a name is known as an anonymous function in Python. It is created with the help of the keyword lambda.

An example of an anonymous function with two arguments:

>>> lambda a, b: a + b

Here, the function has two arguments x and y and is defined using lambda and is not bound to any variable. The function accepts the two arguments and returns their sum.

However, you can invoke the same function using an interactive interpreter-only feature of Python via the underscore(_):

>>> _(4, 5)
9

In the interactive interpreter, the single underscore(_) is bound to the last expression evaluated which means it points to the last lambda function.

The functions which take one or more arguments and returns the result are known as higher-order functions.

A lambda function can be a higher-order function:

>>> higher_order_function = lambda x, f1: x + f1(x)
>>> higher_order_function(3, lambda x: x * x)
12
>>> higher_order_function(3, lambda x: x + 3)
9

The higher-order functions are considered as built-in functions in the Python Standard Library. Some of the examples are filter(), map(), reduce() or key functions like min(), max(), sort() or sorted().

Python Lambdas vs Regular Functions

Python Lambdas vs Regular Functions| Differences between Lambda functions and regular functions in Python

A Lambda function might be considered a function with a syntax within a programming language which makes the language “sweeter” for human use. It is created to make things easier to read and express.

However, there are a number of precise differences between the regular functions and Python lambdas. The following section highlights them all.

A. Difference in Functions

In fundamental terms, there is almost no difference between a regular function with a single return line and a lambda function. However, let us check what difference it would make while we write it in code:

>>> import dis
>>> sub = lambda a, b: a + b
>>> type(sub)
<class 'function'>
>>> dis.dis(sub)
  1           0 LOAD_FAST                0 (x)
              2 LOAD_FAST                1 (y)
              4 BINARY_ADD
              6 RETURN_VALUE
>>> sub
<function <lambda> at 0x000001E47F3EC268>

The dis module subjects a readable version of the Python bytecode generated by the compiler, which in turn displays the low-level instructions used by the Python interpreter.

Let us try the same with a regular function:

>>> import dis
>>> def sub(x,y): return a + b
>>> type(sub)
<class 'function'>
>>> dis.dis(sub)
  1           0 LOAD_FAST                0 (x)
              2 LOAD_FAST                1 (y)
              4 BINARY_ADD
              6 RETURN_VALUE
>>> sub
<function <lambda> at 0x000001E47F3EC268>

You can see the Python bytecode is similar in both cases. However, what differs is the style of naming. With def, the function is named as sub whereas lambda is used in case of Python built-in function.

B. Difference in Traceback

An example of exception raising using lambda:

>>> divide_by_zero = lambda a: a / 0
>>> divide_by_zero(2)
Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "<stdin>", line 1, in <lambda>
ZeroDivisionError: division by zero

When you’re using a lambda expression, the traceback of an exception identifies only the function, resulting in the exception as <lambda>.

On the other hand, when you’re using a regular function, the traceback provides the name of the function in particular:

>>> def divide_by_zero(a): return a / 0
>>> divide_by_zero(2)
Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "<stdin>", line 1, in divide_by_zero
ZeroDivisionError: division by zero

The traceback returns the function name divide_by_zero.

C. Difference in Syntax

A lambda expression differs in syntax from a normal function in a number of ways:

  1.  Expressions and Statements

A lambda function can contain only expressions and cannot include any statements. It will raise a SyntaxError upon using statements like return, assert or raise.

Let us see an example of using an assert statement in a lambda function:

>>> (lambda a: assert a == )(2)
SyntaxError: invalid syntax

The interpreter raises an error while parsing the code since assert is not a valid keyword in a lambda expression.

  1.  Single Expression

In a Python lambda expression, you can distribute the expression across several lines using parentheses or a multi-line string. But the function will still remain as a single expression:

>>> (lambda a:
... (a % 2 and 'odd' or 'even'))(5)
'odd'

The lambda expression above spreads over two lines but still remains a single expression which evaluates a number and results into odd or even depending upon the argument.

  1.  Annotations

You can use Python type hints and type checking when you’re using normal functions:

def name(first_name: str, last_name: str) -> str:
    return f'{first_name.title()} {last_name.title()}'

If there are any errors with the function name, it can be caught using packages like mypy or pyre. On the other hand, if you’re using a lambda expression, a SyntaxError will be raised:

>>> lambda first_name: str, last_name: str: first_name.title() + " " + last_name.title() -> str
 File "<stdin>", line 1
    lambda first: str, last: str: first.title() + " " + last.title() -> str
SyntaxError: invalid syntax
  1.  IIFE

Immediately Invoked Function Execution or IIFE is a JavaScript function that gets executed as soon as it is defined. However, you cannot use it outside the Python Interpreter.

An example of such is:

>>> (lambda a, b: a + b)(4,6)
10

The IIFE is mainly used to pass the definition of a Python function into a higher-order function like map(), filter() or reduce().

D. Difference in Arguments

Python supports different ways of passing arguments in a lambda expression just like regular functions. Some of which are as follows:

  • Named arguments.
  • Variable list of arguments.
  • Positional arguments.
  • Variable list of keyword arguments.
  • Keyword only arguments.

An example to illustrate different ways of passing arguments in a lambda function:

>>> (lambda a, b, c: a + b + c)(1, 2, 3)
6
>>> (lambda a, b, c=3: a + b + c)(1, 2)
6
>>> (lambda a, b, c=3: a + b +c)(1, b=2)
6
>>> (lambda *arg: sum(arg))(1,2,3)
6
>>> (lambda **kwarg: sum(kwarg.values()))(number_one=1, number_two=2, number_three=3)
6
>>> (lambda a, *, b=0, c=0: a + b + c)(1, b=2, c=3)
6

E. Difference in Decorators

A decorator is a function that accepts another function and allows adding a behavior to a function or class. Its syntax is @decorator which is written before a function.

An example of a decorator is:

def decorator(a):
    def wrap(*arguments):
        print(f"Calling function '{a.__name__}'")
        return a(arguments)
    return wrap
@decorator
def decorated_func(x):
    print(f"With argument '{x}'")

If we invoke decorated_func(“Hello World”), the output will be as follows:

Calling function 'decorated_func'
With argument 'Hello World'

The function decorated_func() prints With argument 'Hello World', but the decorator adds a behavior and also prints  Calling function 'decorated_func'

An example of applying decorator in lambda function is mentioned below:

# Defining a decorator
def trace(a):
  def wrap(*arg, **kwarg):
print(f"[TRACE] func: {f.__name__}, args: {arg}, kwarg: {kwarg}")
return a(*arg, **kwarg)
  return wrap
# Applying decorator to the function
@trace
def add(t):
    return t + 2
# Calling the decorated function
add(3)
# Applying decorator to a lambda function
print((trace(lambda y: y ** 2))(3))

If we execute the following code, we’ll get the following output:

[TRACE] func: add, arg: (3,), kwarg: {}
[TRACE] func: <lambda>, arg: (3,), kwarg: {}
9

The function add is decorated with @trace and invoked. The lambda function gets implemented to the decorator. The lambda function name is <lambda> and the regular function is add.

F. Difference in Closures

A closure is a function object that remembers values and is bound to a specified value in the enclosing scope of that function. In simple words, closure is a record that stores a function and defines the environment in which they run.

Both lambda functions and regular functions can be implemented as closures.

An example to show a closure construct with a regular function:

def outer_function(a):
    b = 4
    def inner_function(c):
        print(f"a = {a}, b = {b}, c = {c}")
        return a + b + c
    return inner_function
 
  for i in range(5):
    closure = outer_function(i)
    print(f"closure({i+5}) = {closure(i+5)}")

inner_function() is a nested function that is being returned by outer_function(). The nested function evaluates the sum of three arguments. The outer_function() is invoked 5 times in a for loop resulting in the output:

x = 0, y = 4, z = 5
closure(5) = 9
x = 1, y = 4, z = 6
closure(6) = 11
x = 2, y = 4, z = 7
closure(7) = 13
x = 3, y = 4, z = 8
closure(5) = 15
x = 4, y = 4, z = 4
closure(5) = 017

An example to show closure using a Python lambda function:

def outer_function(a):
    b = 4
    return lambda c: a + b + c
 
for i in range(5):
    closure = outer_function(i)
    print(f"closure({i+5}) = {closure(i+5)}")

The output of the above code will be:

closure(5) = 9
closure(6) = 11
closure(7) = 13
closure(7) = 15
closure(7) = 17

In both situations, the normal function and the lambda function behaves precisely similar.

G. Difference in Evaluation Time

A lambda function’s behavior as a closure may be counter-intuitive, especially while working with loops.  

Using a regular function:

>>> def wrap(x):
...    def f():
...        print(x)
...    return f
...
>>> numbers = 'one', 'two', 'three'
>>> f1 = []
>>> for x in numbers:
...    f1.append(wrap(x))
...
>>> for f in f1:
...    f()
...
one
two
three

When the function f1 gets appended to the list, is evaluated at definition time.

Let us implement the same using lambda function:

>>> numbers = 'one', 'two', 'three'
>>> f1 = []
>>> for x in numbers:
...    f1.append(lambda: print(x))
...
>>> for f in f1:
...    f()
...
three
three
three

Since n is a free variable bound at runtime  when we invoke the function f, an unexpected error occurs printing three all the time.

However, to get rid of this situation, you can assign the free variable during definition time:

>>> numbers = 'one', 'two', 'three'
>>> f1 = []
>>> for x in numbers:
...    f1.append(lambda x=x: print(x))
... 
>>> for f in f1:
...    f()
...
one
two
three

The lambda argument is initialized with a default value. So, when the function is invoked, the default value x set at definition time is used.

Testing Lambdas

You can test Python lambdas similarly like regular functions with the help of two modules— unittest and doctest.

unittest

Testing Python lambda function using unittest:

import unittest
add = lambda x: x + 2
class Lambda_Testing(unittest.TestCase):
    def test_add1(self):
        self.assertEqual(add(2), 4)
    def test_add2(self):
        self.assertEqual(add(2.2), 4.2)
    def test_add3(self):
        # Should fail
        self.assertEqual(add(3), 6)
if __name__ == '__main__':
    unittest.main(verbosity=2)

The function Lambda_Testing tests three methods, each implemented as a lambda function. After execution, the Python file produces the following output:

$ python lambda_unittest.py
test_add3 (__main__.Lambda_Testing) ... FAIL
test_add1 (__main__.Lambda_Testing) ... ok
test_add2 (__main__.Lambda_Testing) ... ok
======================================================================
FAIL: test_add_three (__main__.Lambda.Testing)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "lambda_unittest.py", line 18, in test_add_three
    self.assertEqual(add3(3), 6)
AssertionError: 5 != 6
----------------------------------------------------------------------
Ran 3 tests in 0.001s
FAILED (failures=1)

There are two successful test cases and one failure for test_add3. However, if we change the expected result from 6 to 5, the Lambda_Testing will be satisfied for all tests.

doctest

The doctest module tests Python lambdas by extracting Python code from docstring:

add = lambda a: a + 2
add.__doc__ = """Add 2 to a number.
    >>> add(2)
    4
    >>> add(2.2)
    4.2
    >>> add(3) # Should fail
    6
    """

if __name__ == '__main__':
    import doctest
    doctest.testmod(verbose=True)

You can execute the code above using doctest.testmod(), the output will be:

$ python lambda_doctest.py
Trying:
    add(2)
Expecting:
    4
ok
Trying:
    add(2.2)
Expecting:
    4.2
ok
Trying:
    add(3) # Should fail
Expecting:
    6
**********************************************************************
File "lambda_doctest.py", line 16, in __main__.add
Failed example:
    add(3) # Should fail
Expected:
    6
Got:
    5
1 items had no tests:
    __main__
**********************************************************************
1 items had failures:
   1 of   3 in __main__.add
3 tests in 2 items.
2 passed and 1 failed.
***Test Failed*** 1 failures.

Here, the failure of the test result is also because of the same test failures explained above in the execution of test cases. Although  it is possible to use docstrings for lambda functions, Python recommends using docstrings for regular functions rather than lambda functions.

Where should you not use Lambda Expressions?

You can simply say that if there is a situation where lambda expressions are not supported, normal functions would be more suitable in such places.

However, there might be situations when you need to avoid lambda expressions in your code:

  • When it doesn’t follow the Python Style Guide ( PEP 8 ).
  • When it is difficult to read and interpret.
  • When it doesn’t support readability.

Exception Raising 

An example of raising an exception in a Python lambda:

>>> def throw_except(excpt): raise excpt
>>> (lambda: throw_except(Exception('Exception is raised')))()
Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "<stdin>", line 1, in <lambda>
    File "<stdin>", line 1, in throw
Exception: Something bad happened

Though there are some really clever ways of raising an exception in Python lambda, it is always suggested to use a normal function. This is because a statement is never correct in syntax in the body of a Python lambda function.

Cryptic Style

Lambda functions are brief in nature, because of which it might be difficult to read. 

An example of a bad writing style lambda  expression:

>>> (lambda _: list(map(lambda _: _ // 2, _)))([1,2,3,4,5,6,7,8,9,10])
[0, 1, 1, 2, 2, 3, 3, 4, 4, 5]

Let us modify this lambda code by naming the variables:

>>> (lambda some_list: list(map(lambda a: a // 2,
list)))([1,2,3,4,5,6,7,8,9,10])
[0, 1, 1, 2, 2, 3, 3, 4, 4, 5]

The code is still difficult to read and understand. However, if we execute the same code using a regular function by distributing the logic across several lines, it would greatly impact the readability of the code:

>>> def divide(list):
      divide = lambda a: a // 2
      return map(divide, list)
>>> list(divide([1,2,3,4,5,6,7,8,9,10])))
[0, 1, 1, 2, 2, 3, 3, 4, 4, 5]

Though this method is not the best, it surely enhances the readability of the code in comparison to the lambda expressions.

Python Class

An example of a Python class using lambda:

class Car:
    """Car with methods as lambda functions."""
    def __init__(s, brand, year):
        s.brand = brand
        s.year = year
    brand = property(lambda s: getattr(s, '_brand'),
                    lambda s, value: setattr(s, '_brand', value))
    year = property(lambda s: getattr(s, '_year'),
                    lambda s, value: setattr(s, '_year', value))
    __str__ = lambda s: f'{s.brand} {s.year}'  # 1: error E731
    honk = lambda s: print('Honk!')     # 2: error E731

If you use a style guide tool like flake8 for checking the code above, it will result in following errors for __str__ and honk:

E731 do not assign a lambda expression, use a def

Since multiple strings like '_brand and'_year' are used, the properties that are difficult to read are inclined to errors.

Ultimately, considering a general rule, you should have a preference for regular functions instead of lambda functions in the context of Python programming.

Where should you use lambda expressions?

Lambdas provide a shortened syntax for writing functions, which return a single expression. Since Python supports functional programming, lambdas are one of the most common use cases in such a paradigm of programming. Lambdas allow a fine way to pass functions as arguments to another function by creating a one-time function and passing it as a parameter.

Let us discuss some situations where using lambdas are very appropriate and also suggested by Python.

Functional Constructs 

Built-in functions like map(), filter(), and functools.reduce(), found in the functools module, are often used along with lambda expressions.

Three examples of those functions along with lambda expressions:

>>> list(map(lambda x: x.upper(), ['alex', 'bob', 'charles']))
['ALEX', 'BOB', 'CHARLES']
>>> list(filter(lambda x: 'o' in x, ['alex', 'bob', 'charles']))
['BOB']
>>> from functools import reduce
>>> reduce(lambda acc, x: f'{acc} | {x}', ['alex', 'bob', 'charles'])
'alex | bob | charles'

There are other functional constructs which are alternatives to these constructs and are considered more Pythonic in nature.

Key Functions

Python key functions are higher-order functions that directly determine the algorithm driven by the key function. They accept a named parameter known as key, which in turn takes a lambda function.

Some of the most commonly used key functions are sort(), sorted, min(), max(), nlargest() and nsmallest().

Let us sort a list of IDs using sorted(), in which each ID is a concatenated string of string id and a  number. To sort the list of IDs with the help of the corresponding number associated, we’ll assign lambda to the key argument:

>>> ids = ['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200']
>>> print(sorted(ids))#Sorting
['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200']
>>> sorted_ids = sorted(ids, key=lambda x: int(x[2:]))#Integer sorting
>>> print(sorted_ids)
['ID1', 'ID9', 'ID11', 'ID3', 'ID25', 'ID200']

UI Frameworks

Some of the UI frameworks that use lambda expressions for their UI events are Tkinter, wxPython or .NET Windows Forms.

An example of a Tkinter program using lambda:

import tkinter as t
import sys
window = t.Tk()
window.grid_columnconfigure(0, weight=1)
window.title("Lambda Calculus")
window.geometry("500x200")
label = t.Label(window, text="HELLO WORLD")
label.grid(column=0, row=0)
button = t.Button(
    window,
    text="Reverse the string",
    command=lambda: label.configure(text=label.cget("text")[::-1]),
)
button.grid(column=0, row=1)
window.mainloop()

When the Reverse button is clicked, an event gets fired that triggers the lambda, which in turn changes the label from HELLO WORLD  to DLROW OLLEH.

Though lambda can handle firing events, it is always recommended to use a normal function instead as the lambda ends up being self-contained in a shortcode structure.

The Python Interpreter

Python lambdas are considered as bliss when you use it inside the Python Interpreter. In comparison to regular functions, creating a single-line lambda function to look into some code is much easier. The lambdas in interpreter are just like scrap paper that is useless after being used once and are later thrown away.

The timeit module

timeit is a Python module that is used to time small fragments of Python code. In simple words, it measures the evaluation time of small code snippets.

An example of timeit.timeit() being called directly:

>>> from timeit import timeit
>>> timeit("factorial(100)", "from math import factorial", number = 5)
1.799199999652501e-05

A NameError exception would be raised if the full context of the statement is not passed as a string. So the environment should be set up that is required by the main function to be timed.

However a  cleaner and more readable solution to the above code is by using a lambda function:

>>> from math import factorial
>>> timeit(lambda: factorial(100), number=5)
1.799199999652501e-05

Although the lambda version takes less execution time, the string version has a benefit of executing the functions again.

What are the alternatives of lambda expressions?

The higher-order functions like map(), filter(), and functools.reduce() can be used in place of lambdas by changing the form with lists and generators.

Using map() 

map() takes two arguments— a function and an iterable like string, lists or tuples. It executes the function object for each element and returns a modified list of elements.

An example to illustrate map() with lambda by transforming a string of integers into its capitalized form:

>>> list(map(lambda x: x.capitalize(), ['alex', 'bob', 'charles']))
['Alex', 'Bob', 'Charles']

list() should be invoked so that the iterator gets converted into the modified list which can be shown as output in the Python Interpreter.

Using filter()

filter() accepts two arguments— a boolean-valued function and an iterable. It is an excellent way to filter out all elements of a sequence for only those which returns True.

An example of filter() with lambda which returns all odd numbers from a list:

my_list = [5, 10, 21, 95, 51, 69, 72, 22, 70, 99]
final_list = list(filter(lambda x: (x%2 != 0) , my_list))
print(final_list)
[5, 21, 95, 51, 69, 99]

The filter() function returns the list of elements, just like map().

Using reduce()

reduce() is a functools module function that takes two arguments just like map() and filter(). However, it might also take an initializer as a third argument as the initial value of the accumulator.

An example of reduce() with lambda to calculate the sum of a list:

from functools import reduce
my_list = [5, 8, 12, 25, 50, 500]
sum = reduce((lambda x, y: x + y), my_list)
print(sum)
600

The Pythonic Nature of Lambda Expressions

The style guide of Python, PEP 8 discourages lambdas and recommends using normal functions since it is more beneficial. On the other hand, though lambda functions have some good uses in Python, they have a lot of limitations.

Lambdas are meant to be used as anonymous functions that will be used especially with functions like map(), filter(), and reduce(). So, you can consider them to be Pythonic since they are quick and elegant in their use and they depend on how you use them.

In simple terms, lambdas are considered Pythonic if there are no Pythonic elements available other than that.

Summary

Let us sum up what we’ve learned in this article so far:

  • Lambdas and Anonymous Functions.
  • Where should we use a lambda.
  • When should we use a  regular function.
  • Lambda expression abuses.
  • Appropriate uses of Lambdas.
  • Lambda alternatives.
  • Pythonic nature of Lambda.

Python lambdas are considered as salt. If used in small amounts, it will enhance the flavor but if used more, the dish will be spoiled. If you are intending to know more about Lambda and Lambda Calculus, you can refer to the following links—

To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course.

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  AdvantagesIt is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. DisadvantagesScikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.AdvantagesVery simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. DisadvantagesIt is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. AdvantagesThe power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. AdvantagesIt is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. AdvantagesIt is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages Proper documentation is not available. There is inadequate community support. ConclusionPython, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool. 
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Top 12 Python Packages for Machine Learning

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Top-Paying Programming Certifications for 2021

Programming is at the core of software development, which is why there is a huge demand for programmers—a demand that is growing exponentially and is expected to rise at a steady rate even in the future. In today’s world, just about everything is getting automated and digitization has become the new normal.Recruiters are on the lookout for professionals who have solid programming and full-stack development skills. Every recruiting agency and organizational HR recruiting team has put in place a thorough screening process, and this active hiring in startups, SMEs, and multinational companies has raised the bar for many aspiring programmers. Having a software development, web development, or programming certification will give you an upper hand at the time of recruitment. A certification from a reputed accreditation body will validate your skills and make you stand out among your peers.Having an extra certification apart from your UG or PG degree makes you a better fit for the job role in which you have an interest. But before you opt for any certification, you need to understand which programming language will take you where; and the potential benefits of pursuing a certification course of that particular programming language.In this article, you will get to know about the top programming certifications of 2021 and how to achieve them.What are Programming certifications?Programming certifications are exam-oriented, and verify your skill and expertise in that field. Different organizations provide different programming certification exams that define your level, skills, and abilities vis `a vis that programming language. Having a programming certification will give you an edge over other peers and will highlight your coding skills.Most Popular Programming CertificationsC & C++ CertificationsOracle Certified Associate Java Programmer OCAJPCertified Associate in Python Programming (PCAP)MongoDB Certified Developer Associate ExamR Programming CertificationOracle MySQL Database Administration Training and Certification (CMDBA)CCA Spark and Hadoop Developer1. C & C++ CertificationsRegardless of your specialization in your UG or PG courses, clearing a developer-rated certification will not only make your resume stand out from others but also enhance your skills and boost your confidence. We have curated the top-most and popularly available certifications with descriptions that can help you decide which one is appropriate for your career path.C & C++ Certifications: C and C++ are often called the mother of Procedure-oriented and Object-oriented programming languages, which is absolutely true. These two programming languages have been around for many decades. Colleges and universities all over the world teach these as the base language. To get global recognition for the C and C++ certification, C++ Institute and Pearson VUE decided to carve a niche in this part of the certification landscape by offering the world's first international C/C++ certifications.Aspirants and professionals can choose either C or C++ as a career option/path and climb the certification ladder from associate to professional to senior. The C Programming Language Certified Associate (CLA) and C++ Certified Associate Programmer (CPA) are the core and first-level C and C++ certifications.CLA comprises of topics likeIntroduction to compiling and software development;Basic scalar data types and their operators;Flow control;Complex data types: arrays, structures and pointers;Memory management;Files and streams;Structuring the code: functions and modules;Preprocessor directives and complex declarations.CPA comprises of topics likeIntroduction to compiling and software development;Basic scalar data types, operators, flow control, streamed input/output, conversions;Declaring, defining and invoking functions, function overloading;Data aggregates;String processing, exceptions handling, dealing with namespaces;Object-oriented approach and its vocabulary;Dealing with classes and objects, class hierarchy and inheritance;Defining overloaded operators, user-defined operators, exceptions;Demand and Benefits: Having a CLA certification verifies that the programmer or the aspirant has an understanding of all the necessary and essential universal concepts of computer programming and developer tools. The course also covers all the syntax and semantics of different C constructs plus the data types offered by the language. This course brings crisp knowledge on writing programs using standard language infrastructure regardless of the hardware or software platform.A C++ Certified Associate Programmer (CPA) certification will give you an upper hand because it comprises syntax and semantics of the C++ language plus basic C++ data types. Apart from that, it contains principles of the object-oriented model and C++ implementation. Also, you will get to know about the various C++ standard libraries through this certification process. The average entry-level salary of a C/C++ developer with this certification will be $ 7,415 per annum. With two to three years of experience, the average salary hikes to $ 10,593 annually.Top companies and industries hiring CLA and CPA are Philips, Calsoft Pvt. Ltd., Cognizant, Synopsys Inc., private universities, Mphasis, etc.Where to take Training for Certification: CPP Institute has all the study resources you need to prepare for this examination. Apart from that, you can study from YouTube free resources.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to expand their knowledge of C/C++ or start their career as a C/C++ programmer or developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:CLA Certification: $ 147.50 (50% discount voucher)CPA Certification: $ 147.50 (50% discount voucher)Exam fee for certification:CLA Certification: $ 295CPA Certification: $ 295Retake fee for certification: Aspirants who have paid the complete exam price (USD 295) or have completed a course aligned with certification in the self-study mode (50% discount voucher) can have a free retake of the CPA or CLA exam. There is no limit to the number of times a candidate may retake the exam. You must wait 15 days before being allowed to re-sit that exam.2. Oracle Certified Associate Java Programmer OCAJPThis is a Java programming certification provided by Oracle. Java is among the most popular programming languages. James Gosling is the creator of Java which was earlier named Oak. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). Java consistently tops the 'most used programming languages’ list and is one of the most extensively used software development platforms. If you have the plan to get a proper training course online before appearing for the certification exam, KnowledgeHut (https://www.knowledgehut.com/programming/java-training) has that for you.It is the preliminary and most basic certification provided by Oracle for Java. It helps gain fundamental understanding of Java programming and builds a foundation in Java and other general programming concepts. The certification encompasses two subcategories –OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11)It comprises of topics likeJava BasicsWorking with Java Data TypesUsing Operators and Decision ConstructsCreating and Using ArraysUsing Loop ConstructsWorking with Methods and EncapsulationWorking with InheritanceHandling ExceptionsClass Methods and EncapsulationDescribing and Using Objects and ClassesHandling ExceptionsJava Technology and the Java Development EnvironmentInheritance and InterfacesUnderstanding ModulesUsing Operators and Decision ConstructsWorking with Java ArraysWorking with Selected classes, Java Primitive Data Types and String APIsDemand and Benefits: Having an OCAJP certification verifies that the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., private universities and many others.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Java programming. It has workshops with hands-on learning and 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to settle as a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fee for Certification: $ 245Application fee for certification:OCAJP8: $ 245OCAJP11: $ 249Exam fee for certification:OCAJP8: $ 245OCAJP11: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Certified Associate in Python Programming (PCAP)Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Python released in 1991 and within 5 to 6 years, this programming language become the most popular and widely used programming language in various disciplines. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc. PCAP is a professional Python certification credential that measures your competency in using the Python language to create code and your fundamental understanding of object-oriented programming.It comprises of topics likeBasic concepts of PythonOperators & data typesControl and EvaluationsModules and PackagesData AggregatesException HandlingStringsFunctions and ModulesObject-Oriented ProgrammingList Comprehensions, Lambdas, Closures, and I/O OperationsClasses, Objects, and ExceptionsDemand and Benefits: Having a Python certification verifies that the programmer or the aspirant has all the necessary and essential skills needed to become an expert Python developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The average entry-level salary of a Python developer starts at around $100k per annum. With a few years of experience, the average salary hikes to $ 105k annually.Top companies and organizations hiring certified Python programmers are Bank of America, Atlassian, Google, Adobe, Apple, Cisco Systems, Intel, Lyft, IBM, etc.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Python programming. It has hands-on learning with 24 hours of instructor-led online lectures. Apart from that, the course has 100 hours of MCQs and three live projects.Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, or computer science aspirant - who wants to pursue a career as a Python developer or  Python programmer can opt for this certification training. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 295Exam fee for certification: $ 295Retake fee for certification: If a candidate fails the exam, he/she has to wait for 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam.4. MongoDB Certified Developer Associate ExamMongoDB is a NoSQL, document-based high-volume heterogeneous database system. Instead of having tables with rows and columns, MongoDB uses a collection of documents. It is a database development system that provides scalability and flexibility as per query requirements. Its document models are easy to implement for developers and can meet complex demands at scale.MongoDB created this MongoDB Certified Developer Associate Exam for individuals who require to verify their knowledge on fundamentals of designing and building applications using MongoDB. They recommend this certification for those who want to become software engineers and have a solid understanding of core MongoDB along with professional experience.It comprises of topics likeMongoDB BasicsCRUDIndexing and PerformanceThe MongoDB Aggregation FrameworkBasic Cluster AdministrationAggregation & ReplicationShardingMongoDB Performance  MongoDB for Python DevelopersMongoDB for Java Developers or MongoDB for JavaScript DevelopersData ModelingDemand and Benefits: Having a MongoDB Certified Developer Associate Exam certification verifies that the programmer or the aspirant has all the necessary and essential skills to become a NoSQL database expert. The MongoDB certification is inexpensive and in demand. The average salary for a software developer with MongoDB skills starts from $ 8200 per annum.Top companies and organizations hiring certified MongoDB developers are Accenture, Collabera, Leoforce LLC., Adobe, Trigent Software, Lyft, etc.Where to take Training for Certification: KnowledgeHut has a comprehensive course structure for those who want to learn MongoDB & Mongodb Administrator. It has 24+ hours of instructor-led online lectures and 80+ hours of hands-on with cloud labs. This self-paced course also includes capstone projects to give participants a feel of real world working.  Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to embark on a career as a MongoDB developer or start his/her career as a NoSQL database expert or do better in their current role as a MongoDB developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 150Exam fee for certification: $ 150Retake fee for certification: MongoDB University is no longer allowing a free retake with the exam fee. The candidate has to pay an additional $10 to reschedule or retake the exam.5. R Programming CertificationIt is a part of the data science specialization from Johns Hopkins University under Coursera. This course teaches R programming for efficient data analysis. It covers different R programming concepts like building blocks of R, datatypes, reading data into R from external files, accessing packages, writing functions, debugging techniques, profiling R code, and performing analysis.It comprises of topics like:Basic building blocks in RData types in RControl StructuresScoping Rules - OptimizationCoding StandardsDates and TimesFunctionsLoopingDebugging toolsSimulating data in RR ProfilerDemand and Benefits: Having an R Programming certification verifies that the programmer or the aspirant has all the necessary and essential skills require to get a job role as data analyst. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified R programmer with this certification is ₹ 508,224 per annum.Top companies and industries hiring certified R programmers are Technovatrix, CGI Group Inc., Amazon, Sparx IT Solutions, Accenture, Uber, etc.Where to take Training for Certification: KnowledgeHut has a fascinating training course for those who wants to become a R programmer. It has 22+ hours of instructor-led live training and three self-paced live projects.Who should take the Training (roles) for Certification: Any data analyst, graduate, post graduate student, experienced data analyst or computer science aspirant - who wants to settle as a R programmer or data analyst can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: FreeFee for certification: $ 60 (Coursera Plus Monthly)Retake fee for certification: Free6. Oracle MySQL Database Administration Training and Certification (CMDBA)It is another course offered by Oracle for SQL developers. Oracle University designed this course for database administrators who want to validate their skills with developing performance, blending business processes, and accomplishing data processing work. Structured Query Language (SQL) is one of the top database management query languages that allows us to access and manipulate databases. If you want to verify your database skills during a job interview or impress your peers at your workplace then this certification is worth getting. This certification path includes Professional, Specialist, and Developer levels. The candidate should pass the MySQL Database Administrator Certified Professional Exam Part 1 & Part 2 to earn the certification.It comprises of topics likeInstalling MySQLMySQL ArchitectureConfiguring MySQLUser ManagementMySQL SecurityMaintaining a Stable SystemOptimizing Query PerformanceBackup StrategiesConfiguring a Replication TopologyDemand and Benefits: Having an CMDBA certification verifies that the programmer or the aspirant has all the necessary and essential skills required to get a job role as SQL developer. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Top-Paying Programming Certifications for 2021

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Top IT Certifications for Java Developers in 2021

Programming languages are at the heart of computer science and software development. They help developers write efficient code for developing digital solutions through applications and websites. Programming helps in automating, maintaining, assembling, and measuring the processed data.  Java is one such popular programming language. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). James Gosling is the creator of Java which was earlier named Oak. Java ranks high in the top programming languages list and is one of the most extensively used software development platforms. It is well suited to developing software solutions and other innovative projects and simulations.  Since Oracle acquired Sun Microsystems in January 2010, they have been responsible for the further development of the Java platform. All the mentioned top Java certifications verify a specific expertise level and knowledge of the Java platform highlighting particular domains. Without further due, let us now dig into the top 5 Java certifications and their details. About Oracle’s Java CertificationsOrganizations and industries consider certifications as proof of knowledge, especially when the certifications are from a recognized body or firm. Aspirants and professionals looking for possibilities in the Java development domain can avail of a plethora of benefits through the certifications mentioned in this article. There are six levels of Oracle Java Certification based on job roles, skills, and responsibilities: Oracle Certified Junior Associate (OCJA) Oracle Certified Associate (OCA) Oracle Certified Professional (OCP) Oracle Certified Specialist (OCS) Oracle Certified Expert (OCE) Oracle Certified Master (OCM) Among them, the top five Java certifications that are in demand for the year 2021 are – 1. Oracle Certified Associate Java Programmer OCAJPIt is the preliminary and most basic certification provided by Oracle for Java. It helps you gain fundamental understanding of Java programming and build a foundation in Java and other general programming concepts. There are two subcategories in this certification – OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11) OCAJP8 comprises of topics like  Creating and Using Arrays Handling Exceptions Java Basics Using Loop Constructs Using Operators and Decision Constructs Working with Inheritance Working with Java Data Types Working with Methods and Encapsulation Working with Selected classes from the Java API OCAJP11 comprises of topics like Applying Encapsulation Creating and Using Methods Creating Simple Java Programs Describing and Using Objects and Classes Handling Exceptions Java Technology and the Java Development Environment Programming Abstractly Through Interfaces Reusing Implementations Through Inheritance Understanding Modules Using Operators and Decision Constructs Working with Java Arrays Working with Java Primitive Data Types and String APIs Demand and Benefits: Having an OCAJP certification verifies that the programmer or the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.   (Source: Glassdoor) Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., and more. Where to take Training for Certification: KnowledgeHut has a fascinating course, designed for beginners in Java programming. It offers hands-on learning with 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to be a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 2) Oracle Certified Professional Java Programmer OCPJPIt is a professional-level certification program provided by Oracle for Java developers. It verifies the candidates' knowledge and professional expertise. Using this certification, aspirants and other hard-core Java programmers can distinguish themselves from those Java professionals who are not certified. It comes in the second level of Oracle's Java Certification list. There are two subcategories of this certification – OCPJP Java Standard Edition 8 (OCPJP 8) and  OCPJP Java Standard Edition 11 (OCPJP 11) This certification is preferable if someone has professional experience with Java or has already worked for some years in Java technology.  OCPJP8 comprises of topics like: Advanced Class Design Building Database Applications with JDBC Concurrency Exceptions and Assertions Generics and Collections Java Class Design Java File I/O (NIO.2) Java I/O Fundamentals Java Stream API Lambda Built-in Functional Interfaces Localization Use Java SE 8 Date/Time API OCPJP11 comprises of topics like: Annotations Built-in Functional Interfaces Concurrency Database Applications with JDBC Exception Handling and Assertions Functional Interface and Lambda Expressions Generics and Collections I/O (Fundamentals and NIO.2) Java Fundamentals Java Interfaces Java Stream API Lambda Operations on Streams Localization Migration to a Modular Application Parallel Systems Secure Coding in Java SE Application Services in a Modular ApplicationDemand and Benefits: Once you are a certified Professional Java Programmer (OCPJP), you can switch to better salary slabs and organizations that hire senior Java developers. This certification also helps in getting internal promotions as Java developers in different organizations and firms. The average salary of a certified professional Java developer is $ 5300 - $ 8610 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, Morgan Stanley, Chetu, Mphasis, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java developers and professionals for learning intermediate Java topics. It has hands-on learning with 32 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any Java programmer who wants to apply for a senior Java developer's role or start his/her career as a Java programmer can opt for this professional certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Oracle Certified Expert - Web Component Developer OCEWCDIt is an intermediate-level course offered by Oracle for Java web developers. The Oracle Certified Expert Web Component Developer is for web developers who want to write web applications using Java. Through this course, they can prove their expertise in developing web apps using JSP and Servlet technologies. It verifies your expertise in Servlet 3.0 and helps in creating dynamic Web content and Web services.  It comprises of topics like Understanding Java EE Architecture Managing Persistence using JPA entities and Bean Validation Implementing business logic using EJBs Using Java Message Service API Implement SOAP Services using JAX-WS and JAXB APIs Creating Java Web Applications using Servlets and JSPs Implementing REST Services using JAX-RS API Creating Java Applications using WebSockets Developing Web Applications using JSFs Securing Java EE 7 Applications Using CDI Beans Demand and Benefits: You can opt for this course once you are a certified Professional Java Programmer (OCPJP) or certified associated Java programmer. This certification course will help you get a job in organizations having rigorous work in Servlet, Java Server Page, JSF, and web microservices. The average salary of a certified professional Java developer is $ 8,850 - $ 11,930 per annum. Top companies and industries hiring Oracle Certified Web Component Developers are Amdocs, IBM, Oracle, Capgemini, SAP, Shine, Byjus, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java web developers (. It has hands-on learning with instructor-led online lectures and live projects. Apart from this, you can get online training from Oracle University as wellWho should take the Training (roles) for Certification: Any programmer or computer science aspirant who wants to settle as a Java web developer or start his/her career as a Java web content and web service developer can opt for this certification course. As a prerequisite, you have to pass the OCPJP to opt for this certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 4. Oracle Certified Professional Java Application Developer (OCPJAD)It is an advanced-level course offered by Oracle for Java application developers. The Oracle Certified Professional Java Application Developer (OCPJAD) is for software developers who want to write different applications and automation tools using Java. Through this course, developers can prove their expertise and abilities to develop and deploy applications through Java Enterprise Edition 7. OCPJAD is ideal for desktop application developers, frontend + backend app developers, software engineers, and application architects. It comprises of topics like Creating Batch API Developing CDI Beans Concepts of Concurrency Creating Java Applications with Web-Sockets Creating Java Web Applications with JSPs Developing Java Web Applications with Servlets Developing Web Applications with JSFs Implementing Business Logic with EJBs Performing REST Services with JAX-RS API Implementing SOAP Services with JAX-WS and JAXB APIs Java EE 7 system architecture Java EE 7 Security Techniques Java Message Service API Managing Persistence with JPA Entities and Bean-ValidationDemand and Benefits: Once you pass the Certified Professional Java Application Developer (OCPJAD), you can seek employment in organizations that work on critical application development and command higher salaries. This professional certification will give you exposure to develop APIs, implementing business logic using EJBs, create message services, and apply security systems. The average salary of a certified professional application developer is $ 9,800 - $ 13,910 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, NetSuite Inc., SAP, Cognizant, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity with hands-on learning exposure and live projects. Apart from this, you can get online training from Oracle University as well. Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to become a certified Java application developer or move to the specialized sector of API development using REST, security architect or software engineer can opt for this certification course. As a prerequisite, you should have passed the OCAJP certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.5. Oracle Certified Master Java Enterprise Architect (OCMJEA)Large-scale development and service firms have different critical applications and systems to develop, manage, and maintain. Such systems require full-stack developers and specialized professionals with proven skills. Such organizations and MNCs hire only highly experienced professionals and specialists who can supervise the extensive operation, architect the defects, and define & develop systems as per requirements. The Oracle Certified Master Java Enterprise Architect (OCMJEA) is one of the most prestigious Java certifications a Java developer can achieve.  It comprises of topics like Architect Enterprise Applications through Java EE Developing Applications for the Java EE 6 Developing Applications for the Java EE 7 Developing Applications with Java EE 6 on WebLogic Server 12c Java Design Patterns Java EE 6: Develop Business Components with JMS & EJBs Java EE 6: Develop Database Applications with JPA Java EE 6: Develop Web Services with JAX-WS & JAX-RS Java EE 7: New Features Java SE 7: Develop Rich Client Applications Java SE 8: Programming Java SE 8 Fundamentals Object-Oriented Analysis and Design Using UML, etc. Demand and Benefits: Once you pass the Certified Master Java Enterprise Architect course, you get the essential skills and understanding of how to execute application development on an enterprise level. Such an experienced professional gains full-stack Java development skills. They get hired with the responsibility of undertaking Java projects from the very start to their final delivery. Many Certified Master Java Enterprise Architects work as managers or senior managerial roles in industries and firms. The average salary of a certified professional application developer is $ 14,000 - $ 19,210 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are IBM, Oracle, Microsoft, HCL, Capgemini, NetSuite Inc., SAP, Cognizant, Atlassian, etc. Where to take Training for Certification: KnowledgeHut has a fascinating Java course  with hands-on learning exposure and a live project. Apart from that, a professional can train himself through ILT (Instructor-Led-in-Class), Learning Subscription, TOD (Training on Demand), LVC (Live Virtual Class), or classes delivered by Oracle Authorized Education Center . Other Oracle Authorized Partner Oracle Academy, Oracle University Training Center, or Oracle Workforce Development Program can also benefit and train you in this course.  Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to move to a senior role in the enterprise-level or want to become a manager or team lead can opt for this certification course. As a prerequisite, you need to have passed the OCPJP certification.  Course fees for Certification:  $248 Application fee for certification: $ 248 Exam fee for certification: $ 248 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. Java is an evergreen programming language and is here to stay, at least for the next couple of decades. A vast community of professionals and entry-level aspirants enjoy the benefit of this pure object-oriented, class-based, multi-paradigm, high-level programming language. Java Certification requires proper training.KnowledgeHut has the required infrastructure and quality education faculty, both online and offline, to train aspirants for these Oracle Certifications. It caters to well-structured, industry-oriented Java certification training, explicitly designed to serve the candidates according to the latest industry needs. Getting proper training from KnowledgeHut will help aspirants master core knowledge of Java plus equip themselves with the industry standards to manage large projects. 
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Top IT Certifications for Java Developers in 2021

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