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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.
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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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This makes it easier to get started in Python. IDLE features include the Python shell window(interactive interpreter), auto-completion, syntax highlighting, smart indentation, and a basic integrated debugger. It is however not suitable for the completion of larger projects and best suitable for educational purposes only.  Pros A cross-platform where a developer can search within any window, search through multiple files and replace within the windows editor  Supports syntax highlighting, auto code completion, smart indentation and editable configurations Includes Python shell with highlighter Powerful Integrated Debugger with continuous breakpoints, global view, and local spaces Improves the performance  Call stack visibility Increases the flexibility for developers Cons Used for programming just for beginners Limited to handle normal usage issues. Supports basic design  Large software development cannot be handled  Sublime text Key Features: It is a source code editor, supported on all platforms. It is a very popular cross-platform  and a better text editor. It possesses a built-in support for Python for code editing and packages to extend the syntax and editing features. All Sublime Text packages are written in Python and also a Python API. Installation of the packages often requires you to execute scripts directly in Sublime Text. it is designed to support huge programming and markup languages. Additional functions can be applied by the user with the help of plugins.  Pros More reliable for developers and is cross-platform Supports GOTO anything to access files  Generates wide index of each method, class, and function. AllowsUser interface toolkit Easy navigation to words or symbols Multiple selections to change things at one time Offers command palette to sort, edit and modify the syntax and maintain the indentation.  Offers powerful API and package ecosystem Great performance Highly customizable Allows split editing and instant project switch  Better compatibility with language grammar Custom selection on specific projects Cons Not free Installation of extensions is quite tricky Does not support for direct executing or debugging code from within the editor Less active GIT plugin AtomKey Features: It is an open source code editor developed by Github. It is supported on all platforms. It has features similar to that of Python. It has a framework based on atom shells which help to achieve cross platform functionality. With a sleek interface, file system browser, and marketplace for extensions, it offers a framework for creating desktop applications using JavaScript, HTML, CSS . Extensions can be installed when Atom is running.It enables support for third party packages. Its major feature is that although it is a code editor,it can also be used as an IDE. It is also used for educational purposes. Atom is being improvised day by day, striving to make the user experience rewarding and not remain confined to beginners use only.  Pros Cross-platform  Smooth editing Improves performance of its users Offers built-in package manager and file system browser Faster scripting  Offers smart auto-completion  Smart and flexible Supports multiple pane features Easy navigation across an application Simple to use Allows user interface customization Full support from GitHub Quick access to data and information Cons For beginners only Tedious for sorting configurations and plugins Clumsy tabs reduce performance  Slow loading Runs on JavaScript process  Built on Electron, does not run as a native application VimKey Features: Categorized as a stable open source code editor, VI and VIM are modal editors. As it is supported on almost every platform such as: Windows, LINUX, MAC OS, IOS, Android, UNIX, AmigaOS, MorphOS etc. it is highly configurable. Because of its modal mode of operation, it differs from most other text editors. It possesses three basic modes: insert mode, normal or command mode and command line mode. It is easily customized by the addition of extensions and configuration which makes it easily adaptable for Python development.  Pros Free and easily accessible Customizable and persistent  Has a multi-level undo tree  Extensions are added manually Configuration file is modified Multi-buffers support simultaneous file editing Automated indentation  Good user interface Recognition and conversion of file formats Exclusive libraries including wide range of languages Comes with own scripting language with powerful integration, search and replace functionality Extensive system of plugins Allows debugging and refactoring  Provides two different modes to work: normal and editing mode Strings in VIM can be saved and reused  Cons Used as a text editor only No different color for the pop-up option Not good for beginners PyDev Key Features: It is also categorized as an open source IDE mainly written with JAVA.Since it is an eclipse plugin, the Java IDE is transformed into Python IDE. Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. Pros Free open source Robust IDE feature set Auto-completion of codes and analysis Smart indentation Interactive console shortcuts Integrated with Django configuration  Platform independent Cons: User interface is not great  Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. Pros Easy and less tedious installation for development purposes Cons Spacious files  Not supported on Linux Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  Pros Free and available on every platform  Small, light-weight but highly extensible Huge compatibility Has a powerful code management system Enables debugging from the editor Multi-language support  Extensive libraries Smart user interface and an acceptable layout Cons Slow search engine Tedious launch time Not a native app just like Atom WingKey Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  Pros Open source Find and go-to definition Customizable and extensible Auto-code completion Quick Troubleshoot  Source browser shows all the variables used in the script Powerful debugger  Good refactoring  Cons Not capable of supporting dark themes Wing interface is quite intimidating Commercial version is expensive Python-specific IDEs and Editors Anaconda - Jupyter NotebooksKey Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. Pros Free Open source  Good user interface Server-client structure Educational tool- Data science, Machine learning  Supports numerical simulation  Enables to create, write, edit and insert images Combines code, text and images Integrated libraries - Matplotlib, NumPy, Pandas Multi-language support Auto code completion Cons Sometimes slow loading is experienced Google Colaboratory Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   Pros Available to all Code can be run without any interruption Highly user interactive No heavy file downloads Integrated libraries Multi-language support Updated in google drive Update the Python package for execution  Runs on cloud Comments can be added in cells Can import Jupiter or IPython notebooks Cons  All colaboratory files are to be stored in google drive Install all specific libraries No access to unsaved files once the session is over Pycharm Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. Pros Great supportive community Brilliant performance. Amazing editing tools Robust debugging tool Smart code navigation Quick and safe refactoring  Built in developer tools Error detection and fix up suggestions Customizable interface Available in free and paid version Cons Slow loading  Installation is quite difficult and may hang up in between SpyderKey Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. Pros Free open source IDE Quick troubleshoot Active framework Smart editing and debugging Syntax is automatically highlighted Auto completion of codes Good for data science and machine learning Structured framework Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib Finds and eliminates bottlenecks Explores and edits variables directly from GUI  Performs well in multi-language editor and auto completion mode Cons Spyder is not capable to configure a specific warning Too many plugins degrades its performance ThonnyKey Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  Pros Simple Graphical user interface.  Free open source IDE Best for beginners Simple debugger with F5, F6, F7 Keys Tackles issues with Python interpreters Highlights syntax error Auto-completion of code Good representation of function calls User can change reference mode easily Step through expression evaluation Reply and resolve to comments Cons Interface is not that good for developers Confined to text editing No template support Slow plugin creation Too basic IDE for software development Which Python IDE is right for you? Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. For data science learners Jupyter Notebooks and Google Colaboratory is preferred. Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. Conclusion Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . 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. 
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Top 10 Python IDEs and Code Editors

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Scala Vs Kotlin

Ever-changing requirements in coding have always been happening, ones that cause programmers to change their minds about using the appropriate programming language and tools to code. Java has been there for a long time, a really long time, 24 years ago. It is relatively easy to use, write, compile, debug, and learn than other programming languages. However, its certain inhibitions like slow performance, unavailability of any support for low-level programming, possessing poor features in GUI 4, and having no control over garbage collection is putting Java developers in a dilemma on choosing an alternative to Java, such as JetBrains’ programming language, Kotlin, presently an officially supported language for Android development or Scala, an all-purpose programming language supporting functional programming and a strong static type system. Today, we will discuss how developers can decide to choose Scala or Kotlin as an alternative to Java. We will briefly talk about Scala and Kotlin separately and talk about their application before moving forward to looking at the differences, advantages, and disadvantages of both and finally have you decide which one of these two suits your requirements. User’s requirement Before we begin, here is a question for the readers, ‘What are you looking for in the next programming language that you will use?’ It is an obvious question because the programming purposes drive the actual basis and need of developing a language. Do you need a language that strives to better Java or use a language that lets you do things that aren’t possible in Java? If it is the first reason, then Scala might be the best one for you, otherwise, it is a simplified programming language like Kotlin. Now let us first briefly discuss Scala and Kotlin individually. ScalaDeveloped by Martin Odersky, the first version of Scala was launched in the year 2003 and is a classic example of a  general-purpose, object-oriented computer language, offering a wide range of functional programming language features and a strong static type system. Inspired from Java itself, Scala, as the name suggests, is highly scalable and this very feature sets Scala apart from other programming languages. When we say that Scala is inspired from Java, that means developers can code Scala in the same way they do for Java. Additionally, Scala makes it possible to use numerous Java and libraries within itself as well. It is designed to be able to use an elegant, concise and type-safe method to express common programming patterns. Scala is a very popular programming language amongst developers and rising up its ranks in the world of technology. Although Scala comes with a number of plus points, there are some which make it a bit ineffective. Here are the strengths and weaknesses of Scala. Strengths: Full Support for Pattern Matching, Macros, and Higher-Kinded Types Has a very flexible code syntax Gets a bigger Community Support Enables overloading operators Weaknesses: Slow in compilation Challenging Binary Compilation Not so proficient in the Management of Null SafetyKotlin Developed by JetBrains, Kotlin was released on February 2012 as an open-source language. Until now, there have been two released versions with the latest one being Kotlin 1.2, the most stable version that was released on November 28, 2017. Since Kotlin is extremely compatible with Java 6 the latest version of Java on Android, it has gained critical acclaim on Android worldwide and additionally, it offers various key features that are prepared only for Java 8 and not even Java 6 developers have access to that. Kotlin provides seamless and flawless interoperability with Java. That means, developers can easily call Java codes from Kotlin and same goes the other way around. The built-in null safety feature avoids showing the NullPointerException (NPE) that makes developing android apps easy and joyful, something every android programmer wants. Below mentioned are the key pointers on the strengths and weaknesses of Kotlin. Strengths Takes a Functional Programming Approach and Object-Oriented Programming style(OOP) Style  Has Higher-Order Functions Short, Neat, and Verbose-Free Expression  Supported by JetBrains and Google. Weaknesses: More limited Pattern Matching Additional Runtime Size Initial Readability of Code Shortage of Official Support Smaller Support Community. Ease of learning: Scala vs Kotlin Scala is a powerful programming language packed with superior features and possesses a flexible syntax. It is not an easy language to learn and is a nightmare for newcomers. Kotlin, on the other hand, has been reported to have been an easy-to-learn language for many Java developers as getting started with Kotlin is relatively easy and so is writing codes. Even though it is a comparatively easier language to learn and code with, Kotlin lacks the solid set of features that is common in Scala. It might take less time to learn a programming language, but the most important thing to look for is a comprehensive array of features. Scala, even though a very difficult language to learn, is cherished by the developers as it lets them do things that cannot be done in Kotlin Here are the major differences between Scala and Kotlin: ScalaKotlinType inferenceEfficientImmutabilityExtension FunctionsSingleton objectMassive InteroperabilityConcurrency controlLessens Crashes at RuntimeString interpolationSmart Cast FunctionHigher-order functionSafe and ReliableCase classes and Pattern matching Lazy computationLow adoption costRich collection setMaking the appropriate choice of languageNow, whether you may like a programming language or not, if that very language helps you get the best out of your job, then you will have to live with it. These are the facts about getting the best results. The outcome is the main factor in you deciding the appropriate language for your job. Kotlin is the only option for Android development as Android doesn’t use JVM, so any old JVM-compatible language will not work in Android. Kotlin has it all what it takes to compile, debug, and run the software on Android because of which it is in-built into Android Studio. However, Kotlin is not so usable outside Android development. If you are one of the developers who like working with Eclipse for your IDE, then Scala IDE is better than the Kotlin Plugin even if you can make Eclipse work with both the languages with limitations. Scala IDE is more advanced than the Kotlin plugin and is easier to set up. Some developers found it quite difficult to make the Kotlin plugin work. This case is quite the same with NetBeans. Kotlin is still getting there but is already popular amongst Java developers as it offers an easier transition than Scala. Kotlin is still maturing, but many Java people find adopting it is an easier transition than Scala is.  Scala, however, is for developers who are focused more on discovering new ideas while Kotlin is for those who want to get results. Kotlin stresses fast compilation but is more restrictive while Scala gives a lot of flexibility. Go for Scala if you breathe functional programming! It has more appropriate features for this type of programming than Kotlin does. Scala supports currying and partial application, the methods of breaking down functions requiring multiple arguments offering more flexibility. Go for the one that is the most appropriate one for your work, style of working and what you are aiming at. Think before you leap. The Outcome At the end of the day, all that matters is what you want to use the language for. While Scala goes well for the projects that require a combination of functional, OOP style programming languages, and where programmers need to handle lots of data or complex modelling, Kotlin becomes the best choice when you want something less frustrating than Java while developing apps because using Kotlin makes app development less cumbersome and a great thing to work on. It is just like a better-looking version of Java with less lengthy codes. 
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Scala Vs Kotlin

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Xcode vs Swift

Xcode and Swift are two different products developed by Apple for macOS, iOS, iPadOS, watchOS, and tvOS. While Xcode is an integrated development environment (IDE) for macOS containing a suite of software development tools to develop software for macOS, iOS, iPadOS, watchOS, and tvOS, Swift is a general-purpose, multi-paradigm, compiled programming language developed iOS, macOS, watchOS, tvOS, Linux, and z/OS. So it is clear that they can not be compared with each other. On the contrary, Swift is compatible with Xcode as Swift v 5.1, the default version of Swift is included in Xcode v 11. In this article, we will go through what Xcode and Swift are in general and cover their features strengths and weaknesses followed by how Swift is compatible with Xcode. XcodeIt was first released in 2003 as version 1 with the latest stable one being version 10.2.1 released on 17 April 2019. It can be downloaded from the Mac App Store and is free to use for macOS Mojave users. Registered developers may download the preview releases and previous versions of the suite using via the Apple Developer website.  Overview of the major featuresSupport: Programming languages such as C, C++, Objective-C, Objective-C++, Java, AppleScript, Python, Ruby, ResEdit (Rez), and Swift are supported by Xcode with source code along with support for a variety of programming models including Cocoa, Carbo, and Java. Not only that, there is additional support via third parties for GNU Pascal, Free Pascal, Ada, C#, Perl, and D Capability: Xcode can build fat binary files that include the code for various architectures in the Mach-O executable format. Known as universal binary files, these allow the application to run on both PowerPC and Intel-based (x86) platforms including both 32-bit and 64-bit codes Compiling and debugging: Xcode uses the iOS SDK to compile and debug applications for iOS that run on ARM architecture processors GUI tool: Xcode comprises of the GUI tool, Instruments that runs dynamic tracing framework on the top of DTrace, a dynamic tracing framework designed by Sun Microsystems and released as a part of OpenSolaris. Advantages and disadvantages of Xcode: Xcode is designed by Apple and will only work with Apple operating systems: macOS, iOS, iPadOS, watchOS, and tvOS. Since its release in 2003, Xcode has made significant improvements and the latest version, Xcode 10.2.1 has all the features that are needed to perform continuous integration. Let us have a look at the pros of using Xcode: Equipped with a well designed and easy to use UI creator Excellent for code completion Using Xcode, a developer can learn profiling and heap analysis in a natural way Xcode’s simulator lets you easily test your app while you build it in an environment that simulates your iPhone The app store has a wide range of audience who are willing to pay for apps. Now, the cons: Clunky and outdated Objective C makes it more frustrating if you are habituated to use a modern language No support for tabbed work environments makes it difficult to work with multiple windows Hardly any information can be found online to solve problems due to a previous Apple NDA on Xcode development It is a complicated process to export your app onto a device Will only work with Apple operating systems The App Store approval process can be annoyingly lengthy.SwiftSwift was launched at Apple's 2014 Worldwide Developers Conference as a general-purpose, multi-paradigm, compiled programming language for iOS, macOS, watchOS, tvOS, Linux, and z/OS Being a new entry these operating systems, Swift accelerates on the best parts of C and Objective C without being held back by its compatibility. It utilises safe patterns for programming, adding more features to it, thus making programming easier and more flexible. By developing their existing debugger, compiler and framework infrastructure, it took quite some time to create the base for Swift. Furthermore, Automatic Reference Counting was used to simplify the memory management part. The framework stack which was once built upon a solid framework of Cocoa and Foundation has undergone significant changes and is now completely regulated and refurbished. Developers who have worked with Objective-C do find Swift quite similar. Objective-C’s dynamic object model and its comprehensively named parameters provide a lot of control to Swift.  Developers can use Swift to have access to the existing Cocoa framework in addition to the mix and match interoperability with an objective C code. Swift uses this common rule to offer multiple new features in combination with object-oriented and procedural portions of the language. The idea is to create the best possible language for a wide range of uses, varying from desktop and mobile apps, systems programming, and scaling up to cloud services. The designing of Swift was done to make sure that developers find it easy to maintain and write correct programs. Coding done in Xcode is safe, fast and expressive. Swift offers a host of features that give developers the control needed to make the code easy to read and write. Furthermore, Apple made Swift to be easily understandable to help developers avoid making mistakes while coding and make the code look organised, along with the modules that give namespaces and eliminate headers. Since Swift uses some features present in other languages, one of them being named parameters written with clean syntax that makes the APIs much easier to maintain and read. Here are some of the additional features of Swift: Multiple return values and Tuples Generics Short and quick iterations over a collection or range Structs that support extensions, methods and protocols Functional programming patterns Advanced control flow Powerful error handling. These features are systematically designed to make them work together resulting in creating a powerful but fun-to-use language. Advantages and disadvantages of Swift: Pros of using the Swift Programming language: Easy to read and maintain: The Swift program codes are based on natural English as it has borrowed syntaxes from other programming languages. This makes the language more expressive Scalable: Users can add more features to Swift, making it a scalable programming language. In the future, Swift is what Apple is relying on and not Objective C Concise: Swift does not include long lines of code and that favours the developers who want a concise syntax, thus increasing the development and testing rate of the program Safety and improved performance: It is almost 40% better than the Objective-C when speed and performance are taken into consideration as it is easy to tackle the bugs which lead to safer programming Cross-device support: This language is capable of handling a wide range of Apple platforms such as iOS, iOS X, macOS, tvOS, and watchOS. Automatic Memory Management: This feature present in Swift prevents memory leaks and helps in optimizing the application’s performance that is done by using Automatic Reference Counting. Cons of Swift: Compatibility issues: The updated versions Swift is found to a bit unstable with the newer versions of Apple leading to a few issues. Switching to a newer version of Swift is the fix but that is costly Speed Issues: This is relevant to the earlier versions of the Swift programming language Less in number: The number of Swift developers is limited as Swift is a new programming language Delay in uploading apps: Developers will be facing delays over their apps written in Swift to be uploaded to the App Store only after iOS 8 and Xcode 6 are released. The estimated time for release is reported to be September-October, 2014. Conclusion So as we discussed both Xcode and Swift, it is clear that they cannot be compared to each other. In fact, they both complement each other to deliver impressive results without any headaches. Apple relies on both quite a lot and it is certain to have Swift and Xcode the perfect combination of a robust application and a user-friendly programming language.
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Xcode vs Swift

Xcode and Swift are two different products develop... Read More

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